CAT IN THE CLASSROOM: UNDERSTANDING INSTRUCTOR BEHAVIOR

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Theses and Dissertations--Communication Communication

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CAT IN THE CLASSROOM: UNDERSTANDING INSTRUCTOR CAT IN THE CLASSROOM: UNDERSTANDING INSTRUCTOR

BEHAVIOR AND STUDENT PERCEPTIONS THROUGH BEHAVIOR AND STUDENT PERCEPTIONS THROUGH

COMMUNICATION ACCOMMODATION THEORY COMMUNICATION ACCOMMODATION THEORY

Terrell Kody Frey University of Kentucky, [email protected] Author ORCID Identifier:

https://orcid.org/0000-0003-1001-8821 Digital Object Identifier: https://doi.org/10.13023/etd.2019.408

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REVIEW, APPROVAL AND ACCEPTANCE REVIEW, APPROVAL AND ACCEPTANCE

The document mentioned above has been reviewed and accepted by the student’s advisor, on

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Terrell Kody Frey, Student

Dr. Derek R. Lane, Major Professor

Dr. Anthony Limperos, Director of Graduate Studies

CAT IN THE CLASSROOM:

UNDERSTANDING INSTRUCTOR BEHAVIOR AND STUDENT

PERCEPTIONS THROUGH COMMUNICATION ACCOMMODATION THEORY

________________________________________

DISSERTATION

________________________________________

A dissertation submitted in partial fulfillment of the

requirements for the degree of Doctor of Philosophy in the

College of Communication and Information

at the University of Kentucky

By

Terrell Kody Frey

Lexington, Kentucky

Director: Dr. Derek Lane, Professor of Communication

Lexington, Kentucky

2019

Copyright © Terrell Kody Frey 2019

https://orcid.org/0000-0003-1001-8821

ABSTRACT OF DISSERTATION




Adjusting one’s communication is a fundamental requirement for human

interaction (Gasiorek, 2016a). Individuals adapt communication behavior according to

the circumstances surrounding the situation, resulting in different patterns and forms of

speech relative to spouses, family members, coworkers, or friends. Yet, researchers in

instructional communication have not yet substantially applied adjustment as a theoretical

lens for understanding instructor-student classroom interactions (Gasiorek & Giles, 2012;

Soliz & Giles, 2014; Soliz & Bergquist, 2016). Apart from overlooking this useful

theoretical approach, instructional communication scholarship can also be improved by

accounting for 1) shifting group identities in higher education that change how instructors

and students communicate, 2) incomplete conceptualizations of student perceptions in

existing research, and 3) a consistent lack of concern for the hierarchical structure of

educational data. This dissertation seeks to resolve these limitations through an

application of one of the most prominent theories of adjustment: communication

accommodation theory (CAT; Giles, 1973; Giles, Willemyns, Gallois, & Anderson,

2007a). The research specifically extends the CAT framework to an instructional setting by

investigating how student perceptions of instructor nonaccommodation across several modes

of communication (i.e., nonverbal, linguistic/verbal, content, support) influence information

processing ability, relationships with instructors, and beliefs about instructors. Data were

collected from 573 undergraduate students across 38 sections of a basic communication

course (BCC). Students completed an online questionnaire assessing perceptions of the

appropriateness of their instructor’s behavior (i.e., nonaccommodation), extraneous load,

communication satisfaction, instructor-student rapport, instructor credibility, and

instructor communication competence. The results first forward a nuanced measure for

assessing nonaccommodation in a manner consistent with the theoretical propositions of

CAT. Second, a series of analyses using hierarchical linear modeling (HLM; Raudenbush

& Bryk, 2002) showed significant associations between perceptions of

nonaccommodation across modes and students’ reported classroom outcomes.

Interestingly, several of the individual, direct relationships disappeared when multiple

modes of nonaccommodation were considered simultaneously, introducing the possibility

that individuals may prioritize the appropriateness of certain behaviors within context.

The data hierarchy (i.e., students enrolled in course sections) did exert some influence on

the relationships between variables, yet the majority of variance accounted for across

models occurred at the student level. Implications of the results related to both theory and

practice within the basic communication course are presented in the discussion.

KEYWORDS: Instructional Communication, Communication Accommodation Theory;

Hierarchical Linear Modeling, Instructor-Student Relationship, Cognitive Load, Basic

Communication Course

Terrell Kody Frey

(Name of Student)

10/25/2019

Date




By

Terrell Kody Frey

Derek R. Lane, Ph.D.

Director of Dissertation

Anthony Limperos, Ph.D.

Director of Graduate Studies

10/25/2019

Date

DEDICATION

To Grandma and SuperDon. Thank you for always believing in me. Love you and miss

you every day.

iii

ACKNOWLEDGMENTS

This dissertation could not have been realized without the help and guidance of

several people. First, I would like to thank my wife, Olivia. Your kindness, support, and

patience throughout this entire process has taught me more about what it means to be a

friend and spouse than I could have imagined. We talked about how we felt like life was

being put on hold until I finished my time as a student – I cannot wait for our new chapter

to begin together. Cooking became my dissertation ‘distraction’ when I did not want to

write, and I promise to keep making dinner even though it will no longer be the same

writing distraction it once was. I love you!!

Next, my dissertation chair, Dr. Derek Lane, inspired me to be a better friend,

mentor, and person. I will never forget receiving feedback on this work from him at 3:00

am on a weeknight and wondering why in the world he would be up that late reading

through my literature review. Between his sabbatical and subsequent year as Interim Dean,

I was truly amazed at how he balanced his passion for students with his skills as a teacher,

researcher, and administrator. For the lessons, lunches, and resources you provided me, I

am forever grateful! I can guarantee my future students will get the same level of

dedication when it comes to their work that you have provided me.

To my dissertation committee – Dr. Brandi Frisby, Dr. Marko Dragojevic, Dr. Xin

Ma, and Dr. Renee Kaufmann – thank you for supporting me in this endeavor. I always

wanted this process to be challenging, and I hope that pushing my boundaries in some way

allowed you to become better scholars as well. Your feedback and insights helped me to

reach a level that I did not think was possible. I cannot express enough gratitude for the

time you spent helping make this project a realization. Thank you!

iv

Third, I want to thank AJR. I have probably listened to Neotheater and The Click

over 500 times since this process started – it became the official anthem of my project and

really the last few years of my life. You somehow captured every emotion – hope, fear,

sadness, anxiety, joy, wonder – that I felt while writing. Thank you for inspiring me with

your word and music. In particular, shout out to the tracks Next Up Forever, Karma, Dear

Winter, The Good Part, Come Hang Out, and Normal. Cannot wait to say thanks in person

at a show one day!

Fourth, thank you to the staff at the Starbucks at 1869 Plaudit Place in Hamburg.

You noticed I was spending all of my time there, you asked about me and my work, and

you showed me that it was possible to care about a stranger. You may not have realized it,

but your support, even if it was in the form of a coffee refill, helped to make this

dissertation possible.

Finally, thank you to all of my friends, family, and students who helped along the

way by reading various drafts, commiserating with me when times were tough, and getting

high-quality jobs that inspired me to finish as fast as possible (looking at you, Casey).

There’s no way I can name you all, but you know who you are. I am thankful for the best

friends in the world! Now can we skip to the good part?

v

TABLE OF CONTENTS

ACKNOWLEDGMENTS ................................................................................................. iii

LIST OF TABLES ............................................................................................................ vii

CHAPTER 1. RATIONALE AND STATEMENT OF THE PROBLEM ......................... 1

Changing Student Dispositions in Higher Education ..................................................... 2

Instructional Communication ......................................................................................... 4

Organization .................................................................................................................... 8

CHAPTER 2. REVIEW OF LITERATURE .................................................................... 10

Communication Accommodation Theory .................................................................... 10

Defining Nonaccommodation: Appropriateness .......................................................... 12

Using Nonaccommodation to Rethink Student Perceptions ......................................... 14

Outcomes of Perceived Nonaccommodation ................................................................ 19

Information Processing: Cognitive Load .................................................................. 20

Instructor-Student Relational Quality: Rapport and Satisfaction ............................. 24

Instructor Perceptions: Communication Competence and Credibility ..................... 27

Course Section as a Second Level of Analysis ............................................................. 33

CHAPTER 3. METHODS ................................................................................................ 37

Research Design and Protocol ...................................................................................... 37

Level-One Data: Student Perceptions ....................................................................... 37

Level-Two Data: Course Section .............................................................................. 40

Research Participants .................................................................................................... 42

Scale Development: Perceptions of Instructor Nonaccommodation Scale .................. 44

Phase One.................................................................................................................. 44

Phase Two ................................................................................................................. 46

Instrumentation ............................................................................................................. 49

Perceptions of Instructor Nonaccommodation.......................................................... 49

Extraneous Load ....................................................................................................... 50

Communication Satisfaction ..................................................................................... 51

Instructor-Student Rapport........................................................................................ 51

Instructor Credibility ................................................................................................. 53

Instructor Communication Competence ................................................................... 54

Data Analysis ................................................................................................................ 54

Hierarchical Linear Modeling ................................................................................... 54

vi

Specifying the Models .............................................................................................. 55

Null Models with Classroom Variables as Outcomes .............................................. 56

Parsimonious Models with Classroom Variables as Outcomes ................................ 57

Proportion of Variance Explained ............................................................................ 58

CHAPTER 4. RESULTS .................................................................................................. 60

Hypothesis One: Nonaccommodation and Extraneous Load ....................................... 60

Hypothesis Two: Nonaccommodation and Communication Satisfaction .................... 65

Hypothesis Three: Nonaccommodation and Instructor-Student Rapport ..................... 70

Hypothesis Four: Nonaccommodation and Instructor Trustworthiness ....................... 75

Hypothesis Five: Nonaccommodation and Instructor Caring ....................................... 79

Research Question One: Nonaccommodation and Instructor Competence .................. 83

Hypothesis Six: Accommodation and Instructor Communication Competence .......... 87

Research Question Two: Outcome Variability Across Course Sections ...................... 92

Research Question Three: Relationship Differences Across Course Sections ............. 94

CHAPTER 5. DISCUSSION ............................................................................................ 96

Operationalizing Students’ Perceptions of Nonaccommodation .................................. 97

Perceptions of Nonaccommodation on Classroom Outcomes .................................... 102

Perceived Nonaccommodation and Information Processing .................................. 103

Perceived Nonaccommodation and the Instructor-Student Relationship ............... 107

Perceived Nonaccommodation and Student Impressions of Instructors................. 111

Influence of the Data Hierarchy .................................................................................. 116

Implications for Instructor Content Behavior ......................................................... 119

Limitations .................................................................................................................. 122

Future Directions ........................................................................................................ 128

APPENDIX ..................................................................................................................... 136

Survey Questions ........................................................................................................ 136

Perceptions of Instructor Nonaccommodation Scale .............................................. 136

Cognitive Load........................................................................................................ 138

Communication Satisfaction ................................................................................... 140

Instructor-Student Rapport...................................................................................... 141

Instructor Credibility ............................................................................................... 143

Communication Competence .................................................................................. 145

REFERENCES ............................................................................................................... 147

VITA ............................................................................................................................... 167

vii

LIST OF TABLES

Table 3.1 Descriptive Information for Level-Two Data Structure (Course Sections) ...... 40

Table 3.2 Final Item Pool.................................................................................................. 47

Table 3.3 Means, Standard Deviations, and Zero-Order Correlations for Variables in Full

Sample (N = 573) .............................................................................................................. 52

Table 4.1 Statistical Results of the Null Model of Course Section Effects on Extraneous

Load .................................................................................................................................. 62

Table 4.2 Parsimonious Model for Extraneous Load with Coefficient (Slopes) of

Nonaccommodation (Absolute Effects) ............................................................................ 63

Table 4.3 Statistical Results for Full Student Level Model (Relative Effects) for

Extraneous Load ............................................................................................................... 64

Table 4.4 Statistical Results of the Null Model of Course Section Effects on

Communication Satisfaction ............................................................................................. 67

Table 4.5 Student Level Model for Communication Satisfaction with Coefficient (Slopes)

of Nonaccommodation (Absolute Effects) ....................................................................... 68


Communication Satisfaction ............................................................................................. 69

Table 4.7 Statistical Results of the Null Model of Course Section Effects on Instructor-

Student Rapport ................................................................................................................ 72

Table 4.8 Student Level Model for Instructor-Student Rapport with Coefficient (Slopes)



Instructor-Student Rapport................................................................................................ 74

Table 4.10 Statistical Results of the Null Model of Course Section Effects on Instructor

Trustworthiness ................................................................................................................. 76

Table 4.11 Student Level Model for Instructor Trustworthiness with Coefficient (Slopes)



Instructor Trustworthiness ................................................................................................ 78


Caring ................................................................................................................................ 80

Table 4.14 Student Level Model for Instructor Caring with Coefficient (Slopes) of



Instructor Caring ............................................................................................................... 82


Competence....................................................................................................................... 84

viii

Table 4.17 Student Level Model for Instructor Competence with Coefficient (Slopes) of



Instructor Competence ...................................................................................................... 86


Communication Competence ............................................................................................ 89

Table 4.20 Student Level Model for Instructor Communication Competence with

Coefficient (Slopes) of Nonaccommodation (Absolute Effects) ...................................... 90


Instructor Communication Competence ........................................................................... 91

Table 4.22 Summary of Intraclass Correlation Coefficients for Each Null Model .......... 93

1

CHAPTER 1. RATIONALE AND STATEMENT OF THE PROBLEM

Across a wide variety of backgrounds and disciplines, including communication,

scholars identify communicative adjustment, or adapting one’s verbal or nonverbal

behavior in interaction (Gasiorek, 2016a), as a fundamental component of human

interaction. The ability to subjectively interpret others’ intentions, motives, and beliefs as

a prerequisite for one’s own behavior constitutes a uniquely human quality that provides

advantages to cultural or social groups. In other words, the capability to symbolically

produce meaning for others by strategically, consciously, and sometimes unconsciously

adjusting behavior in a way that optimizes conditions for understanding is a deeply

engrained component of human life (Enfield & Levinson, 2006).

Despite the prominence and utility of adjustment as a theoretical approach to

social behavior, however, limited lines of communication research have incorporated this

framework as an explanatory vehicle for classroom behavior and message reception (see

Conley & Ah Yun, 2017; Gasiorek & Giles, 2012; Soliz & Bergquist, 2016; Soliz &

Giles, 2014). Few would disagree that the premise of adjustment extends to

communicative encounters between instructors and students, who alter their behavior in

situations like providing feedback (Kerssen-Griep, 2001; Trees, Kerssen-Griep, & Hess,

2009), conversing about bad grades (Henningsen, Valde, Russell, & Russell, 2011;

Wright, 2012), or disclosing private information (Hosek & Thompson. 2009). Thus, a

logical extension of this reasoning suggests that instructors utilize adjustment to create

conditions conducive to achieving shared meaning with students; an instructor who

adjusts appropriately and in line with expectations for behavior (e.g., Chory & Offstein,

2017; 2018) is likely to be seen as more effective. Likewise, students who interpret an

2

instructor as meeting their unique needs through communication are likely to have better

classroom experiences. Exploring this process through an adjustment lens would allow

investigators to better characterize the mechanisms by which this process occurs.

Consequently, researchers can use adjustment as a theoretical framework for

investigating how instructors behave relative to students’ needs to create classroom

conditions ideal for maximizing learning outcomes (i.e., optimal classroom conditions).

This call for research grounded in adjustment becomes especially necessary when

considering the changing nature of student demographics that define higher education.

Individual needs that are changing due to shifting identities and cultural expectations

influence whether adjustment is enacted (or received) appropriately or inappropriately.

The next section outlines these demographic shifts in detail.

Changing Student Dispositions in Higher Education

Researchers, along with the general public, commonly allude to the notion that

differences exist between the values, preferences, and worldviews of generational groups,

and data tend to support this perspective. Deane, Duggan, and Morin (2016) reported that

millennial students identified the death of Bin Laden, the Boston Marathon bombing, and

the Sandy Hook shooting as a few of the top historic events to define their respective

lifetimes. Simultaneously, none of these events were identified by generation X, baby

boomers, or silent generation participants in the same study. These differences in ideals

tend to influence the way generations communicate, as well as the way in which others

communication with them (Hicks, Riedy, & Waltz, 2018).

For example, in 2016, Communication Education published a series of stimulus

essays in which authors were challenged to identify the ways that the unique needs of

3

today’s millennial students change the nature of the classroom environment (Buckner &

Strawser, 2016). Responses ranged across a variety of ideas, including increased

hovering, sheltering behavior from parents (Frey & Tatum, 2016), shifting attitudes

towards academic entitlement (Goldman & Martin, 2016), and growing beliefs about

society as a participatory culture lacking independence (McAllum, 2016). Collectively,

this work suggests that today’s students have diverse interactional needs that shape their

expectations for classroom communication.

Moreover, such changes are undoubtedly influencing how instructors and students

interact, where they communicate with one another, and what types of content they

discuss (Anderson & Carter-falsa, 2002; Asikainen, Blomster, & Virtanen, 2018).

Scholars must work to better understand how communication functions within this

interaction; instructor-student interactions and relationships in higher education “can be

regarded as a precondition of successful learning for all students” (Hagenauer & Volet,

2014, p. 379). Thus, researchers can better reflect the educational landscape by also

considering how adjustment is influenced by various identities.

Fortunately, instructional communication scholars are in position to meet this call

(see Hosek & Soliz, 2016). Instructional scholarship seeks to understand how classroom

interactions, as well as the larger context in which they occur, shape and influence

learning conditions, climates, and outcomes. For example, Hosek (2015) concluded that

students’ shared identity with instructors was associated with increases in both

satisfaction and affect. Relatedly, Hendrix (1997) reported that students relied on the

same communicative cues to evaluate the credibility of black and white professors, yet

they simultaneously sought more evidence for the academic credentials of the black

4

professors. This research has clearly begun to articulate the influence of identity on

classroom communication processes; however, this line of thinking can be enhanced by

addressing several critiques related to the conceptualization and operationalization of

student perceptions. The next section provides an overview of instructional

communication and specifically outlines the critiques that should be addressed to move

research forward.

Instructional Communication

Staton (1989) argued that instructional communication concerns “the study of the

human communication process as it occurs in instructional contexts – across subject

matter, grade levels, and types of settings” (p. 365). Implied within this definition is an

inherent understanding of the human communication process, whereby instructors and

students co-create meaning for one another through verbal and nonverbal messages

(Mottet & Beebe, 2006). That is, communication is the vehicle through which instructors

and learners directly influence the meaning-making process (Sprague, 2002). The

interactions that occur between instructors and students in pursuit of shared meaning are

shaped by the dispositions of each individual, as well as the larger context in which they

occur.

Nyquist and Booth (1977) defined the boundaries of an instructional context by

identifying 4 distinguishing characteristics: (1) a context in which the only goal is the

improvement of some competency, (2) roles within the environment are determined by an

individual’s expertise in the subject matter, (3) the climate produces continual evaluation

of development, and (4) evaluations occur in a busy system where communication

overload is common. Student-instructor interactions are directly shaped as a result of

5

these characteristics. Moreover, implicit in this characterization is the presence of a goal

of changing one’s capability in a particular area. Thus, instructional communication is

concerned with the development of learning across settings and goals. In any

instructional situation, learning, or the development of conditions that set up students to

learn the most (i.e., optimal conditions), is at the core of interactions that occur between

instructors and students.

For example, there is overwhelming empirical evidence that instructional

messages affect students’ ability to process material (e.g., cognitive load; Sweller, 1988,

1989), their relationships with instructors (e.g., rapport; Frisby & Martin, 2010; and

communication satisfaction; Goodboy, Martin, & Bolkan, 2009), and their beliefs about

instructors (e.g., credibility; Finn et al., 2009; and communication competence;

Titsworth, Quinlan, & Mazer, 2010). Although none of these outcomes represent

learning as a desired outcome, they do reflect important conditions that instructional

communication has shown puts students in position to learn. This dissertation posits that

these conditions for learning are largely dependent on students’ individual perceptions of

an instructor’s behavior.

Yet, before this claim can be effectively evaluated, instructional communication

research must overcome two flaws in the present conceptualization of student

perceptions. First, instructional communication research often fails to theoretically

acknowledge the belief that traditionally positive behavior can produce negative effects

when it occurs too frequently relative to one’s expectations. To illustrate, Comstock,

Rowell, and Bowers (1995) claimed that students reported greater learning and

motivation at moderately high levels of nonverbal immediacy (i.e., behavior implemented

6

to reduce psychological closeness; eye contact, gestures, physical closeness) compared to

low and high levels. Their work proposes that despite positive benefits linked to increases

in instructor immediacy in prior research (see Christensen & Menzel, 1998), it “seems

that where teacher nonverbal immediacy is concerned, students can get either too little or

too much of a good thing” (Comstock et al., 1995, p. 262). Instructional scholarship can

be improved by theoretically acknowledging the possibility that students might

experience negative effects from traditionally positive instructor behaviors when students

feel they occur too often.

Second, research involving student perceptions often fails to acknowledge and

control for the confounding influence of nested student observations. Most educational

systems, including those at the collegiate level, have hierarchical structures (i.e., students

nested within schools, students nested within instructors, students nested within course

sections) that influence assumptions about relationships between variables (Bryk &

Raudenbush, 1992). These nested structures (i.e., levels) suggest that observations (i.e.,

student perceptions) are not truly independent of one another, resulting in applications of

linear regression that suffer from aggregation bias. Instructional researchers subsequently

jeopardize findings from inflated estimates through correlated errors terms when multiple

individuals nested within similar data structures are used to estimate effects (Raudenbush

& Bryk, 2002). Thus, researchers can more precisely answer questions about the fidelity

of instructor communication and student reception by turning to tools like hierarchical

linear modeling (HLM) that model simultaneous regression equations at more than one

level of analysis (i.e., multilevel; Luke, 2004).

Taken together, this dissertation seeks to address four distinct problems present

7

within existing instructional communication scholarship: (1) a lack of a theoretical

grounding in communication adjustment, (2) a failure to theoretically account for

students’ various identity factors, and (3) a mischaracterization of student perceptions

caused by overlooking behavior perceived to occur too frequently and (4) inattention to

the inherently nested structure of educational data. Accordingly, communication scholars

outside of the realm of instruction, particularly those who study social influence, identity,

language, and adjustment, routinely acknowledge the importance of these factors and

present a theoretical approach to rethinking student perceptions that might holistically

explain how instructor-student interactions impact the creation of learning conditions.

Specifically, Gasiorek (2016a) stated that one of the most comprehensive

frameworks for studying adjustment is communication accommodation theory (CAT;

Giles, 1973). CAT explains how and why communicators adjust behavior in

communicative interactions, as well as the consequences of doing so, from a theoretical

grounding in identity (Giles & Gasiorek, 2013). Essentially, CAT argues that speakers

adjust communication behavior to achieve certain goals in context. However, listeners

often perceive attempts to adjust behavior as inappropriate or unsuccessful when they fail

to meet or potentially exceed individual expectations (Coupland, Coupland, Giles, &

Henwood, 1988). As a result, communication may fail, listeners may perceive a speaker

negatively, or comprehension may be lessened. CAT’s propositions offer a robust

framework for understanding how students can perceive the same instructor behavior in

multiple ways, including how perceptions of instructor behavior that is not adjusted

appropriately (i.e., nonaccommodation) affect the creation of classroom conditions

conducive to learning.

8

Presently, applications of CAT to instructor-student interactions are sparse (see

Mazer & Hunt, 2008), and much of the relevant literature that has been conducted has

investigated interactions occurring outside of the immediate classroom situation (Jones,

Gallois, Callan, & Barker, 1995; Jones, Gallois, Callan, & Barker, 1999). Yet, scholars

have noted the potential of CAT as a theoretical framework for understanding how

instructor-student interactions shape affect and understanding (Soliz & Giles, 2014), and,

as will be demonstrated in chapter two, the theoretical propositions of CAT make sense

for rethinking student perceptions in the instructional communication discipline.

Consequently, the primary purpose of this dissertation is to apply CAT in a way

that conventionally explains how a) students’ perceptions of their instructors’

inappropriate communicative behaviors and b) multiple levels of analysis (i.e., student

observations within and between course sections) play a role in influencing students’

beliefs about important outcomes known to impact reports of learning in an instructional

setting: cognitive load, satisfaction, instructor-student rapport, instructor credibility, and

instructor communication competence. This purpose addresses each of the

aforementioned challenges to improve instructional communication research and reflects

a manifestation of trends of changing student expectations and shifting sociocultural

factors among higher-education institutions.

Organization

This dissertation is organized into five chapters. Chapter one overviewed existing

problems in instructional communication research and introduced communication

adjustment, specifically CAT, as a lens for better understanding classroom interactions

and outcomes. Additionally, this chapter established the rationale for conducting the

9

current research. Chapter two grounds the study within the context of existing literature

by overviewing the theoretical propositions of CAT, detailing how CAT will resolve

theoretical discrepancies in student perceptions and synthesizing present knowledge into

a series of hypotheses and research questions. Chapter three offers a thorough overview

of the procedures and methods used to conduct the research, and chapter four provides

the results of the analysis. Finally, chapter five outlines the theoretical and practical

implications of the study while suggesting directions for future research in this area.

10

CHAPTER 2. REVIEW OF LITERATURE

The rationale in chapter one is based on the belief that CAT as a theoretical

framework will coherently explain how student and instructor interactions create

classroom conditions ideal for facilitating understanding, comprehension, and affect.

Specifically, this dissertation examines how a) students’ perceptions of their instructor’s

behavior and b) the contextual level of analysis (i.e., within and across course sections)

impact classroom outcomes.

This chapter reviews the extant literature surrounding communication

accommodation theory (CAT), student perceptions, and outcomes important to classroom

success. The chapter first provides an overview of the theoretical propositions of CAT.

Second, an argument is made regarding why CAT as a theoretical framework for

understanding instructor-student interactions makes conceptual sense. Third, the chapter

concludes by synthesizing existing knowledge in the form of several hypotheses and

research questions that demonstrate how CAT models relationships between instructor

communication behavior, student perceptions, and relevant outcomes. Results will then

be used to provide a theoretical understanding of how student perceptions and contextual

levels concurrently influence the creation of ideal learning conditions. The dissertation

first turns to an overview of the theoretical foundations of CAT.

Communication Accommodation Theory

CAT is an interpersonal and intergroup theory of communication; it describes

communication between interactants as a result of the direct enactment of individuals’

personal or social identities (Dragojevic & Giles, 2014; Fox & Giles, 1996; Giles &

Ogay, 2007; Harwood, Giles, & Palomares, 2005). The theory posits the various

11

mechanisms for how, when, and why individuals adjust communication behavior to one

another as a means of facilitating understanding (i.e. cognitive function) and managing

social distance (i.e., affective function; Street & Giles, 1982). Although originally

concerned with tangible, observational shifts in micro-level language (Giles, 1973), CAT

has grown significantly to account for and explain adjustment as it occurs across a variety

of objective language features and subjective interpretations and evaluations (Giles et al.,

2007a). Particularly, what constitutes behavior as accommodative or nonaccommodative

largely depends on the epistemological perspective taken by the researcher or theorist.

Many communication researchers conceptualize behavior as accommodative or

nonaccommodative depending on a speaker’s objective behavior. Their research focuses

on concrete, discernible shifts in communicative behavior as a means of becoming more

similar to or different from a partner (e.g., “measurable changes in volume, pitch, speech

rate;” Gasiorek & Giles, 2012, p. 277). The theoretical classifications for these

adjustments are convergence (accentuating verbal and nonverbal similarities), divergence

(accentuating verbal and nonverbal differences), and maintenance (the absence of

adjustment to or from an interlocutor) behaviors. Accommodative and

nonaccommodative behavior can also be defined in terms of a speaker’s intentions;

individuals might psychologically accommodate to their partners by adjusting towards

perceived characteristics. That is, speakers might not converge or diverge to the actual

speech of their interactional partner but adjust to or away from their stereotyped

perceptions of another’s speech (see Thakerar, Giles, & Cheshire, 1982). The third

approach to CAT frames adjustment from the perspective of the listener. CAT

researchers have recognized over time that individuals respond and react differently to

12

various forms of communication behavior in context. Researchers have moved beyond

the concepts of objective and psychological adjustment to define experiences based on

subjective, perceptual evaluations attributed to listeners who may interpret similar

communication behaviors in a variety of ways (i.e., perceived nonaccommodation; Giles,

2016; Giles & Gasiorek, 2013; Gasiorek & Giles, 2015). To address the existing

problems within instructional contexts outlined in chapter one, this dissertation aligns

with this perspective to emphasize the importance of subjective perceptions and

individual listener experiences.

Defining Nonaccommodation: Appropriateness

Instead of observing communicative patterns where interlocutors adjust speech

(objectively or psychologically) to become more similar or different from one another,

considering adjustment from the perspective of the listener introduces a greater focus on

the perceived appropriateness of the behavior in question. In this regard, “communication

is considered accommodative when it is perceived to be appropriate and facilitating

interaction in a desirable way” (Gasiorek & Dragojevic, 2017, p. 278). Simultaneously,

when a listener feels behavior does not meet these standards, it is considered

nonaccommodative (Gasiorek, 2016b). Nonaccommodation is more than the absence of

adjustment; it usually involves some form of perceived dissimilarity or disassociation that

occurs as a result of another’s behavior (Giles & Gasiorek, 2013). Ultimately,

nonaccommodation occurs when a listener feels a speaker has adjusted in a way that does

not meet his or her respective needs (see Coupland et al., 1988).

Coupland et al. (1988) further characterized this process by separating

nonaccommodation into two distinct components: overaccommodation and

13

underaccommodation. Overaccommodation refers to communication behavior perceived

to exceed the necessary threshold for successful interaction. Underaccommodation refers

to communication behavior that is perceived to fall short of the level required for

successful interaction (Gasiorek, 2016b). As suggested by Gasiorek and Dragojevic

(2017), this implies that individuals hold a desired (i.e., optimal) level of adjustment for

interactions with specific contexts (p. 278). How nonaccommodation is perceived

depends on individual evaluations of behavior relative to this expectation. Accordingly,

nonaccommodation is entirely subjective and strictly contextualized (Giles & Gasiorek,

2013). What might be considered nonaccommodative in some situations may be

considered situationally appropriate within others. Regardless of the respective qualities

of the behavior observed, a message recipient’s perception of a behavior dictates whether

the message exceeds the expected level of appropriateness of the interaction (i.e.,

overaccommodation) or is not adjusted enough to meet the needs of the recipient (i.e.

underaccommodation; Thakerar et al., 1982).

For example, perhaps a student feels that an instructor underaccommodates when

he or she acts dismissive or provides unclear explanations. Contrarily, a student may feel

that an instructor overaccommodates when he or she is overly helpful, speaks

exceptionally slowly, or provides exceedingly simplistic explanations (Jones, Gallois,

Barker, & Callan, 1994). Within each example, the same instructional behavior, clarity, is

framed to either exceed or not meet students’ expectations. Thus, CAT presents

researchers with an opportunity to expand theoretical explanations related to how

instruction occurs. Particularly, framing classroom experiences in terms of listeners’

feelings of appropriateness (i.e., nonaccommodation) makes sense for rethinking how

14

instructional communication scholars approach student perceptions for several reasons.

Using Nonaccommodation to Rethink Student Perceptions

As noted, CAT highlights the perspective of the listener (i.e., student) in the

communicative process. Instructional communication scholars have long acted under the

assumptions of the process-product paradigm, which assumes that teacher behavior (i.e.,

process) precedes and is primarily responsible for student learning (i.e., product)

(Friedrich, 2002; Staton-Spicer & Wulff, 1984; Waldeck, Kearney, & Plax, 2001;

Waldeck, Plax, & Kearney, 2010). Yet, this paradigmatic view is too linear to

acknowledge how meaning is co-created transactionally by instructors and students.

Although an instructor’s objective behavior is important, CAT suggests that the creation

of ideal classroom outcomes might instead depend largely on a students’ interpretation of

behavior. This is consistent with the belief that much of what scholars know about

effective instruction stems from students’ individual interpretations of behavior

(Nussbaum, 1992).

Importantly, Hosek and Soliz (2016) argued that student perceptions of instructor

behavior in classroom contexts are influenced by their respective positions within a larger

social hierarchy. This hierarchy draws upon group-based scripts, stereotypes, and

expectations to directly influence the enactment of communication. CAT introduces the

possibility that students’ individual identities influence whether the adjustment made by

an instructor is perceived as appropriate (i.e., accommodation) or inappropriate (i.e.,

nonaccommodation; Giles & Ogay, 2007). It is vital for scholars to know and understand

what instructors think they are communicating to students, but students’ and instructors’

personal and social identities may influence messages to the point where the intended

15

message is not actually being received. Although an instructor might intend to

accommodate to a student, there is no guarantee that the student will identify a behavior

as such (Thakerar et al., 1982).

Second, CAT offers a theoretical rationale that defends scholars’ use of student

perceptions to best understand instructor behavior. Critics of instructional communication

often lament the discipline’s tendency to revisit constructs over and over in greater detail

each time (Conley & Ah Yun, 2017; Sprague 2002), as well as its consistent reliance on

theory developed outside of instructional settings (Johnson, Labelle, & Waldeck, 2017;

Waldeck et al., 2001; Waldeck et al., 2010). However, CAT transcends these critiques by

advancing unique ways of conceptualizing instructor-student interactions. As noted, CAT

began as a theory concentrating on the effects of observable, measurable behaviors by

message senders, but recent iterations highlight subjective perceptions and evaluations of

communicative behavior as integral constructs for assessing outcomes (Gasiorek, 2016a).

To illustrate, consider the concepts of over and underaccommodation (Coupland

et al., 1988), in which a speaker fails to meet or far exceeds the level of adjustment

required for successful interaction, respectively. These nonaccommodative perceptions

may not represent the most accurate conditions for producing understanding.

Subsequently, comprehension, affect, and potentially learning might be lessened

(Dragojevic et al., 2016a). More research is needed to explore this possibility, and Soliz

and Giles (2014) specifically referenced the potential for CAT to facilitate this type of

understanding. They stated that “in the instructional context, CAT could be used to

examine the motivation and relational or instructional outcomes (affect for learning,

cognitive learning) associated with teacher-student (non)accommodation in and outside

16

of the classroom” (p. 26).

Moreover, one might consider common instructional scenarios in which behaviors

that have been identified as interfering with the learning process (e.g., instructor

misbehaviors; Goodboy & Myers, 2015) function to enhance interactions with students.

For example, some researchers have suggested that a strategic lack of clarity might

achieve positive learning outcomes (Klyukovski & Medlock-Klyukovski, 2016). Students

are likely to interpret the same instructor behavior in different ways (Schrodt & Finn,

2011), and it follows that CAT’s foundation in identity might explain how or why these

perceptions occur in such a manner.

Third, the subjective, listener perspective offered by CAT may resolve incomplete

knowledge claims from instructional communication scholars about what constitutes

effective classroom communication. This is based in the idea that CAT allows

researchers to conceptually reframe traditionally linear relationships between instructor

behavior and outcomes in a way not typically acknowledged by instructional

communication scholars. Many scholars believe that instructor behaviors are positively

related to student outcomes, such that instructors who use more of a particular behavior

will always see increases in student outcomes (Christensen & Menzel, 1998).

Yet, CAT introduces the proposition that messages can become ineffective when

they surpass a listener’s expectations. For example, Richmond, Gorham, and McCroskey

(1987) first proposed the possibility that moderate levels of immediacy may have the

most significant impact on student learning. Essentially, the authors speculated that

instructor immediacy may reach a point whereby it no longer increases perceptions of

learning; immediacy may have a curvilinear effect on learning where increased levels of

17

instructor eye contact, touch, smiling, or other attempts to decrease psychological

distance produce negative outcomes (see also Comstock et al., 1995; Houser, 2005). CAT

theoretically explains this proposition by suggesting that instructors’ immediacy

behaviors become negative because students feel those behaviors have surpassed their

needs (Jones et al., 1994).

Finally, accommodation can occur across specific modes of communication.

Objectively, accommodation can occur when a communicator shifts on one dimension

(i.e., unimodal) or multiple dimensions at once (i.e., multimodal) (Dragojevic et al.,

2016a); communicators might be moving toward an individual in one respect while

concurrently moving away in another (Dragojevic et al., 2016b). Logically, this means

message receivers might perceive accommodation to occur across several dimensions

(Gnisci, 2005). This provides opportunities for researchers to investigate whether claims

about the advantages of moderate levels of behavior forwarded by researchers like

Comstock et al. (1995) and Richmond et al. (1987) occur for communicative behaviors

outside of nonverbal immediacy. To illustrate, Simonds, Meyer, Quinlan, and Hunt

(2006) argued that there is no difference between instructors who speak quickly and

instructors who speak moderately fast during lectures. CAT may reveal new insights to

explain these findings by evaluating the claim directly from the listener’s perspective of

what constitutes moderate and fast speaking behavior rather than objectively

manipulating the concept.

However, this task becomes increasingly complex when one considers the

multitude of names, conceptualizations, and approaches used to understand the actions

instructors take in the classroom (Sprague, 1992). A steady emphasis on the instructor’s

18

importance in facilitating positive classroom experiences has resulted in a body of

research examining a range of behaviors including speech characteristics (e.g., rate, tone),

self-disclosure, humor, swearing, confirmation, nonverbal immediacy, content relevance,

technology use, slang, and clarity, among many others (see Mazer, 2018). McCroskey,

Valencic, and Richmond (2004) attempted to simplify the spectrum of instructor

behaviors by collapsing them together, suggesting that instructor behavior can be

understood as comprising observable nonverbal and verbal behaviors.

However, classroom behaviors can also be categorized according to the larger

function which they might serve for instructors or students (e.g., rhetorical and relational

goals theory; Mottet, Frymier, & Beebe, 2006). For example, instructors often spend

valuable classroom time trying to increase content understanding and providing academic

or social support to students. Therefore, many of the nonverbal or verbal behaviors

referenced by McCroskey et al. (2004) may be utilized for specific reasons. Considering

these two approaches together, it becomes clear that an exhaustive typology of instructor

behaviors across modes may not be feasible. Instead, the dissertation provides an initial

exploration of instructor nonaccommodation across various modes of communication by

selecting a series of relevant, tangible behaviors instructors frequently utilize.

Specifically, this study argues that most instructor behaviors conceptually fall under the

categories of (1) Nonverbal, (2) Linguistic/Verbal, (3) Content, and (4) Support behavior.

Although this is not intended to be a comprehensive typology of instructor

communication behavior, this categorization provides a comprehensible, yet inclusive,

system for investigating instructor adjustment in various forms. Following the

establishment of how nonaccommodation is enacted, as well as why it makes sense to

19

conceptualize instruction in this manner, CAT also articulates what the likely outcomes

of nonaccommodation will be across a variety of different contexts (Dragojevic et al.,

2016a).

Outcomes of Perceived Nonaccommodation

For message receivers, CAT posits that outcomes are based on the level of

appropriate adjustment perceived to occur in an interaction. In understanding these

judgments, CAT research has introduced a host of correlates and outcomes stemming

from individual perceptions of behavior, including perceptions of the speaker (e.g.,

credibility; Aune & Kikuchi, 1993), the quality of the communication (Watson & Gallois,

1999), power (Jones et al., 1995), liking and closeness (Harwood, 2000), individual

health (Cai, Giles, & Noels, 1998), and compliance with requests (Barker et al., 2008).

Typically, research supports the premise that perceived accommodative behaviors are

related to positively oriented outcomes while nonaccommodative behaviors are inversely

related to these same outcomes. For example, Gasiorek and Giles (2012) manipulated an

interaction between a student and a teaching assistant (TA) providing clarity on a topic.

Evaluations of vignettes then demonstrated that nonaccommodation resulted in negative

perceptions of the encounter and of the speaker. Thus, nonaccommodative TAs were seen

as unhelpful and as having low credibility. In a follow up study, students’ open-ended

responses showed that encounters of nonaccommodation with instructors also resulted in

difficulty in student content comprehension.

Moreover, CAT clearly specifies that instructors will adjust as a result of both

cognitive and affective motivations. This reasoning leads to the idea that perceptions of

nonaccommodation should influence students’ ability to process material (e.g., cognitive

20

load), their feelings about their relationship with the instructor (e.g., rapport and

communication satisfaction), and their feelings about the instructor in general (e.g.,

instructor communication competence and credibility). Each outcome represents an

important element in the creation of positive classroom conditions conducive to learning

and reflect the various functional motivations (i.e., affect and cognition) why someone

would choose to accommodate with an interactional partner. The next portion of this

review provides a more detailed overview of these theoretically linked variables.

Information Processing: Cognitive Load

Researchers in educational psychology and communication have conceptualized

students’ information processing abilities through Cognitive Load Theory (CLT). CLT

addresses how message senders implement instructional methods to increase the

cognitive resources available for learners. Essentially, the theory explores the factors that

influence students’ information processing capabilities (Sweller, 1988, 1989; van

Merrienboer & Sweller, 2005). Researchers interested in cognitive load concentrate on

the way students focus their limited cognitive resources within instructional environments

(Chandler & Sweller, 1991). A student’s capability to store information within his or her

working memory is dependent on the level of cognitive load that results from the

difficulty of the content, the way the content is presented to the student, and the student’s

own individual effort to process the information (Paas, Renkl, & Sweller, 2003). Since

nonaccommodation can lead to difficulty in comprehension, students who perceive

communication as inappropriately adjusted may experience increased cognitive load that

mitigates their ability to internalize information into their working memories.

Researchers have identified three dimensions of cognitive load that reflect

21

increased mental effort to process information (i.e., negative load) and a student’s

intentional effort to process information (i.e., positive load): intrinsic load, extraneous

load, and germane load (DeLeeuw & Mayer, 2008; Paas et al., 2003). Intrinsic load refers

to the inherent difficulty of the material being presented, as well as the level of prior

knowledge a learner brings to a task or situation (Jong, 2010). Importantly, van

Merrienboer & Sweller (2005) stated that intrinsic load cannot be altered by instruction

because it involves an interaction between the nature of the material and the learner’s

experience with it. Second, extraneous load stems from the instructor’s presentation of

the material or pedagogical strategy (Jong, 2010). Contrary to intrinsic load, extraneous

load involves constructions of cognitive schema that are not necessary for learning;

students who use their cognitive resources to process extraneous information limit the

amount of resources available for processes related directly to the content or task. As

such, extraneous load can be directly influenced by the instructor (e.g., distractions;

Frisby, Sexton, Buckner, Beck, & Kaufmann, 2018). Third, whereas intrinsic and

extraneous load are considered detrimental to student learning (Paas et al., 2003),

effective instruction enhances germane load for learners. Germane load relates to the load

remaining in working memory to invest in new cognitive schemas after accounting for

extraneous and intrinsic load (Sweller, van Merrienboer, & Paas, 1998). Instructors

should seek to increase germane load so that learners have the capacity to deeply and

effectively process information after accounting for the negative effects of intrinsic and

extraneous load. For example, Paas and van Gog (2006) argued that instructors can

increase germane load for students by providing a variety of worked examples in which

solutions are delineated in a series of clear and comprehensible steps.

22

However, the present integration of CAT differs from research concerning CLT in

that the focus is solely on the influence of the instructor’s behavior. Thus, relative to the

current research, it seems that perceptions of nonaccommodation should only influence

extraneous load (i.e., load influenced by the instructor’s presentation) rather than each

dimension separately. Nonaccommodation presents a process whereby students’ mental

processing capability is split; their focus shifts between superfluous factors like an

instructor’s accent or the instructional system and the actual content. When an instructor

behaves in a manner that is deemed inappropriate, the theory would suggest that students

subsequently build schemas for content unrelated to the learning task. For instance, it

seems reasonable that students would spend additional mental load processing language

by an instructor they do not understand rather than efficiently following directions or

internalizing new content. This load then interferes with the remaining germane load that

students may expend processing information in a deep and meaningful way.

CLT research often concentrates on the ways in which instructional designers

may strategically implement pedagogical systems that will enhance information

processing capabilities (Jong, 2010). This is similar to the way in which many CAT

researchers frame accommodation from the perspective of the message speaker. CAT

proposes that speakers assess the interactional needs of their respective partners and

organize their output as a means of increasing communication efficiency (Coupland,

1984). That is, speakers accommodate to become more predictable, understandable, and

intelligible to the interactant (Dragojevic et al., 2016a, Gallois, Ogay, & Giles, 2005). For

example, Bourhis, Roth, and MacQueen (1989) highlighted doctor-patient

communication as an area in which patients expected their health professionals to speak

23

in native, everyday language that they could understand rather than their highly

specialized medical jargon. Thus, most participants felt that doctor convergence to the

perceived intelligibility of the patient would enhance communicative efficiency. Mazer

and Hunt (2008) found that convergence by instructors through slang increased students’

overall ability to process information; it is possible that slang may have made content

relevant in a way that reduced the amount of extraneous processing performed by

students (see also, Sexton, 2017). This suggests that communicative behavior can

decrease load by increasing the similarity between the message intended and the message

received (Gallois et al., 2005), which subsequently allows students to use cognitive

resources to build schemas necessary for the relevant learning task.

Ultimately, when defined from a listener’s perspective, nonaccommodation can

lead to breakdowns in communication, misunderstanding, or potentially poor

performances from students (Gasiorek, 2015). Students may feel that an instructor’s

attempts to diversify vocabulary, increase volume, or reduce jargon may not be

accommodative enough for their respective needs. Gill (1994) reported that North

American students recalled more from North American instructors compared to British or

Malaysian instructors; North American accents appeared to help North American

students recall information. Preliminary focus groups conducted for this dissertation also

revealed that students reacted strongly and negatively to instructors who incorporated

increased amounts of jargon into their lectures. It seems that instructors who

accommodate to students’ needs organize information in a way that makes processing

less effortful, while nonaccommodation should lead students to invoke more of their

cognitive resources to comprehend an accent, understand material, or process

24

information.

Collectively, research dictates that the increasing presence of factors unessential

to the learning task or objective (i.e., nonaccommodation) will enhance the amount of

extraneous load experienced by students, while communication perceived as appropriate

(i.e., accommodation) will mitigate students’ reports of extraneous load. This leads to the

following hypotheses:

H1a-d: Students’ perceptions of instructors’ a) nonverbal, b) verbal/linguistic, c)

content, and d) support nonaccommodation will be positively related to

extraneous load.

Instructor-Student Relational Quality: Rapport and Satisfaction

Instructor behaviors also have clear implications for affective outcomes that

nurture instructor-student relational development (Frymier & Houser, 2000). In fact,

Street and Giles (1982) label the affective function for adjustment as the core of the

theory, and multiple years of CAT-related research has supported the notion that

researchers often focus their efforts on understanding affective outcomes following

adjustment or perceived adjustment (Soliz & Giles, 2014). As stated by Lane, Frey, and

Tatum (2018), the conceptualization of affect in an instructional setting “is primarily

concerned with the more fleeting experiences of liking and satisfaction,” including

perceptions of the overall communicative encounter and the general relationship (p. 224).

Consequently, it makes sense that students’ perceptions of nonaccommodation should be

related to reports of communication satisfaction and instructor-student rapport.

Hecht (1978) conceptualized communication satisfaction as an internal, affective

state occurring in response to the feedback experienced in the accomplishment of a

25

communicative goal. Students have affective expectations that must be met in order to

facilitate a positive classroom experience, and instructors who communicate in

appropriate ways should reinforce this feeling (Goodboy et al., 2009). Literature also

shows that specific instructor behaviors like humor, immediacy, and confirmation have a

significant impact on the satisfaction reported by students (Myers, Goodboy, & Members

of COMM 600, 2014). Giles et al. (2007a) reflect this thinking when articulating their

key principles of CAT, positing that message senders will “increasingly non-

accommodate (e.g., diverge from) the communicative patterns believed characteristic of

their interactants, the more they wish to signal (or promote) . . . relational dissatisfaction

or disaffection with and disrespect for the others’ traits, demeanor, actions, or social

identities” (p. 148).

From a listener perspective, Gasiorek (2015) proposed that the same typical

relationships should hold; nonaccommodation generally results in negative consequences.

Student perceptions of instructor behavior as accommodative suggests that the behaviors

were performed appropriately relative to the students’ needs. This will likely lead

students to feel greater satisfaction with the instructor following their perceived attempts

to take their needs into account. Contrarily, nonaccommodation suggests that the

behavior occurred too infrequently or too frequently relative to those same needs.

Students are likely to feel less satisfaction with an instructor who tries too little or too

much to reduce the social distance between them. Collectively, this leads to the following

hypothesis:


content, and d) support nonaccommodation will be negatively related to

26

communication satisfaction.

Relatedly, instructional communication researchers have spent considerable time

and effort understanding how rapport functions in a classroom setting as an indicator of

instructor-student relational quality. Frisby and Martin (2010) defined rapport from an

instructional perspective as “an overall feeling between two people encompassing a

mutual, trusting, and pro-social bond” (p. 147). The construct comprises two dimensions:

enjoyable interaction (i.e., positively perceiving one’s communication with another

individual) and personal connection (i.e., the extent that the affiliation is characterized by

understanding or caring; Gremler & Gwinner, 2000). Essentially, instructor-student

rapport reflects students’ perceptions of the quality of the connection they have with an

instructor, and students’ reports of rapport are likely to vary depending on those

individual connections (Frisby & Buckner, 2018). Researchers have indicated that

increased rapport is positively linked to important outcomes like student participation

(Frisby, Berger, Burchett, Herovic, & Strawser, 2014; Frisby & Myers, 2008), justice

(Young, Horan, & Frisby, 2013), and teacher efficacy (Frisby, Beck, Smith-Bachman,

Byars, Lamberth, & Thompson, 2016).

Although research on the behavioral establishment of students’ perceptions of

rapport is limited, Frisby and Buckner (2018) reasoned that rapport encompasses a

variety of prosocial behaviors while excluding antisocial behaviors. Prosocial behaviors

might include encouraging questions from students, encouraging students to succeed, or

spending additional time explaining concepts for students. Antisocial behaviors are

characterized by disinterest, disrespect, or unfairness (Wilson, Ryan, & Pugh, 2010).

These potential rapport-building behaviors conceptually mirror the accommodative and

27

nonaccommodative stances articulated by Jones et al. (1994). In their study, students

described accommodative instructors as those who treated the student as an equal, were

prepared to help, or showed that they were willing to listen. Students described

nonaccommodative instructors as arrogant, unhelp, belittling, or overly concerned.

Furthermore, there is evidence that perceptions of accommodative behavior are

linked to rapport between message senders and receivers (Crook & Booth, 1997). The

listener perspective to CAT proposes that rapport should be influenced by the

appropriateness of the behavior in question as it relates to beliefs about the connection a

student builds with his or her instructor; “rapport is a perceived outcome based on

students’ observed instructor communication” (Frisby et al., 2014, p. 110). CAT suggests

that perceived convergence towards one’s idealization of an intergroup member would

enhance perceptions of similarity between communicators, which would then lead to

more positive evaluations and greater relational quality. At the same time, perceived

divergence or maintenance may reiterate status or group differences that prevent

individuals from forming a perception of closeness. Consequently, if students feel an

instructor has behaved inappropriately by communicating in a way that does not meet

their needs, it is reasonable to expect that students will report less rapport than when an

instructor is perceived to behave appropriately. This leads to the following hypothesis:


content, and d) support nonaccommodation will be negatively related to

instructor-student rapport.

Instructor Perceptions: Communication Competence and Credibility

Dragojevic et al (2016a) suggested that most CAT-focused researcher has

28

explored general evaluations of a speaker following a communicative interaction. For

instructional communication researchers, one of the most significant variables, based in

evaluations of a speaker, that is known to influence classroom processes is instructor

credibility (see Finn et al., 2009). Instructor credibility refers to students’ perceptions

about the believability of an instructor; it reflects the image of the instructor that students

hold in their minds. McCroskey and Teven (1999) argued that instructor credibility

consists of three dimensions related to the source: competence, trustworthiness, and

goodwill. That is, the construction of a student’s image of an instructor is based in his or

her beliefs about the instructor’s knowledge of subject matter, the degree to which they

believe he or she possesses integrity, and how concerned they believe he or she is about

their welfare (McCroskey & Teven, 1999). Several studies have found support for the

function of instructor credibility as a mediating outcome between instructor

communication and classroom outcomes (see McCroskey et al., 2004; Schrodt et al.,

2009), and other theories outside of instruction also provide support for perceptions of a

message source as key cogs in the relationships between language, attitudes, and behavior

(for an example, see psychological reactance theory; Brehm & Brehm, 1981). The tenets

of CAT generally support an inverse relationship between nonaccommodation and

students’ reports of goodwill (i.e., caring) and trustworthiness (see Giles et al., 2007a),

yet the influence of these perceptions on source competence is unclear.

First, there is a wealth of empirical support provided for the relationship between

perceptions of nonaccommodation and trust in other contexts (Giles et al., 2007a). For

example, Barker, Choi, Giles, and Hajek (2008-2009) and Barker et al. (2008) provided

support across national samples (i.e., America, Mongolia, Korea, Japan, Guam, and

29

Canada) that civilians’ perceptions of police officers’ accommodative behavior predicted

their trust in the police. Consequently, CAT research suggests that trust is an integral

element in determining whether individuals will comply with officers’ requests following

their feelings about the appropriateness of the officer’s behavior. Although clear, explicit

differences exist between police-civilian and instructor-student contexts, the extensive

literature concerning students’ perceptions of instructor behavior and trustworthiness

suggest that these results are likely to hold in an instructional environment as well; there

should be a direct, negative relationship between perceptions of nonaccommodation and

trustworthiness even in a classroom setting (Giles et al., 2007a). For example, Myers and

Bryant (2004) found that instructors convey perceptions of their character (i.e.,

trustworthiness) through their flexibility with students. This could be interpreted as a

form of adjustment based on students’ needs. Thus:


content, and d) support nonaccommodation will be negatively related to instructor

trustworthiness.

Second, Finn et al. (2009) argued that goodwill, or caring, is the driving

dimension of perceptions of instructor credibility. Instructor caring encompasses

empathy, understanding, and responsiveness, each of which communicates to students

that an instructor is concerned about their individual well-being (McCroskey, 1992).

Straits (2007) proposed that students are more likely to perceive an instructor as caring

when he or she remains available, welcomes questions, wants students to learn or

succeed, or gets to know students on a personal level. When asked to provide their

thoughts on what constitutes an appropriately behaving (i.e., accommodating) instructor,

30

students reported many of these same communication behaviors (Jones et al., 1994). CAT

propositionally explains this relationship due to an increase in in-group similarity

between partners that decreases feeling of social distance, directly suggesting that

increases in perceived accommodation should typically result in increased understanding

and empathy and perceptions of nonaccommodation should result in more negative

reactions.



caring.

These relationships are grounded in the CAT premise that individuals are likely to

have more favorable evaluations of others who behave in a manner consistent with salient

in-group identities. However, instructional communication scholars typically treat

competence as an indicator of an individual’s knowledge of subject matter. For example,

McCroskey and Teven’s (1999) 18-tem ethos-credibility scale assess the competence

subdimension using the following 6 items: intelligent/unintelligent, untrained/trained,

inexpert, expert, informed, uninformed, incompetent/competent, and bright/stupid.

Presently, existing research does not clearly demonstrate how or why perceptions of

accommodation, which ultimately decrease social distance, or nonaccommodation, which

generally has the opposite effect, might lead a student to evaluate an instructor as more or

less knowledgeable. Perhaps even when an instructor communicates inappropriately

relative to students’ needs, perceptions of competence are unaffected due to the salient

role of an instructor as inherently possessing more relevant knowledge that must be

transmitted to students as the goal of instructor (Jones et al., 1995). Or, perhaps

31

nonaccommodation influences perceptions of source expertise through its influence on

cognitive processes; students might find instructors who inappropriately adjust to them as

an unclear or ineffective teacher (Gasiorek & Giles, 2012). Thus, the relationship

between students’ perceptions of instructor nonaccommodation and competence is not

concrete. Collectively:

RQ1: What is the relationship between student perceptions of instructor a)

nonverbal, b) verbal/linguistic, c) content, and d) support accommodation and

instructor competence?

Beyond credibility, CAT suggests that perceptions of nonaccommodation will

influence beliefs about a speaker’s communicative effectiveness. Gallois and Giles

(1998) viewed communicative effectiveness a process of using encoding and decoding to

increase the congruence between the message intended to be sent by a speaker and the

message received by another interactant. Said differently, CAT suggests that an

individual’s ability to adjust his or her communication should function as a general

indicator of their communicative competence (Gallois et al., 2005). In fact, recent

theoretical developments in CAT call for increased attention to the function of

accommodation as a form of appropriate and effective communication (Gallois, Gasiorek,

Giles, & Soliz, 2016; Pitts & Harwood, 2015). Many instructional programs in

communication competence, including those related to CAT (see Parcha, 2014),

emphasize the interpersonal dimensions of communication and fail to account for group

dynamics. CAT allows for more nuanced approaches to understanding the influence of

group identities in determining what constitutes communication as effective and

appropriate in context: “in some cases…people’s goals are not cooperative, and can even

32

be anti-social: people may, for example, want to win a zero-sum conflict, or to depreciate

another group” (Gallois et al., 2016, p. 201). Thus, applying CAT to an instructional

setting positions the construct as sensitive to individual expectations about what

constitutes competent levels of instruction.

Communication competence involves an individual’s impression of his/her own

or another’s communication appropriateness and effectiveness within a given context

(Spitzberg, 1983; Spitzberg & Cupach, 1984). Given this conceptualization, Spitzberg

and Cupach (1984) noted that one of the most fundamental considerations for

understanding one’s competence comes from their ability to adapt (i.e., adjust) to the

surrounding environment. Duran and Spitzberg (1995) articulated this idea one-step

further: “adaptability is accomplished by perceiving contextual parameters and enacting

communication appropriate to the setting” (p. 260). Adaptability can also be understood

as a result of one’s perceived ability to encode or decode messages in an appropriate or

effective manner (Monge, Backman, Dillard, & Eisenberg, 1981). Essentially, one of the

driving forces behind this conceptualization of competence stems directly from

perceptions of an individual’s ability to appropriately adjust to the features of the context

and the interactant. Thus, instructors viewed as the most competent by students know

when and how to implement various behaviors effectively in order to meet student needs.

Contrarily, students are likely to view instructors who do not adjust to meet their needs as

less communicatively competent. Therefore:



caring.

33

Course Section as a Second Level of Analysis

Finally, beyond the problems posed by an inadequate use of student perceptions

to understand instructor behavior, instructional communication research also suffers from

a lack of methodological acknowledgment of the inherently hierarchical structure of

many educational systems (i.e., the various levels of analysis). For example, researchers

interested in the basic communication course (BCC) routinely ask questions about course

standardization, student progress, and pedagogy as they occur across sections of the same

general course. BCCs often rely on lecturers or teaching assistants to instruct multiple

sections, at multiple times, with multiple students. Course sections also vary in length

(e.g., 50 minutes taught 3 times per week; 75 minutes taught twice per week), format

(e.g., standard face-to-face; hybrid; online), composition (e.g., engineers-only, nurses-

only, combined section) or class ranking (e.g., traditionally all first-year students, upper-

class students, non-traditional).

For this reason, perhaps students who are enrolled in the same course section,

learn from the same instructor, or provide multiple data points for analysis, are influenced

by these characteristics to the point where their observations are not fully independent of

one another (Hayes, 2006; Luke, 2004). This reasoning is also reflected in the logic of

nonaccommodation; students enrolled in classes or course sections together must report

on the same instructor in order to uncover how, why, and what happens when they form

different perceptions of the same individual based on behavior. Therefore, regression

assumptions for data in two course sections with similar features (e.g., time, enrollment

restrictions, format) may not be truly independent. It also seems logical that instructors

employ different pedagogical methods across sections, the time of day could influence

student engagement, or instructors could present themselves differently to meet the needs

34

of different groups of students. Researchers can provide better, more precise answers to

questions surrounding these processes by incorporating methods that effectively account

for a structure of students nested within course sections.

Further, the potential differing effects between first level (i.e., student

perceptions) variables across levels of analysis (i.e., course section) for students can be

attributed to the way CAT theoretically models contextual influence. Coupland et al.

(1988) argued that perceptions of accommodation and nonaccommodation are

increasingly influenced by context. Yet, despite some researchers’ attempts to

decontextualize the theory for generalizability (Atkinson & Coupland, 1988), most

researchers appear to recognize that adjustment processes do not occur in a vacuum. For

example, Brown, Giles, and Thakerar (1985) found that respondents’ perceptions of

certain communication elements (i.e., speech rate in their study) interacted with effects

exhibited by the context. Furthermore, the context studied in this case was instructional.

Respondents provided less-negative evaluations of a slow speaker after being told the

situation involved a psychologist explaining complex principles to a group with less

experience. In the case of many higher education institutions, the immediate course

section as a second-level grouping may a have similar contextual or climatic influence on

students’ experiences.

Fundamentally, instructional researchers, basic course directors, or administrators

can begin to investigate how multiple levels of analysis influence outcomes, experiences,

and ultimately, student progress. Such research may begin to model the influence of

contextual and climatic variables on classroom and school conditions seen in educational

research on adolescent school systems at a secondary educational level (see Ma, Ma, &

35

Bradley, 2008). Such groupings have clear theoretical relevance to CAT while

simultaneously presenting the possibility to control and adjust for overarching contexts

that shape practical applications. If the relationships between perceptions of

nonaccommodation and the relevant outcomes differ across course sections, researchers

and basic course administrators can use this information to begin to make changes,

implement training, and investigate specific reasons for these differences. In sum, first-

level relationships between perceptions of instructor nonaccommodation and outcomes

may differ depending on the contextual influence of second-level groupings. Thus, the

level one units are students and the level two units are course sections. Several research

questions are present as an introductory exploration of this theoretical and practical

concept:

RQ2: How much do specific course sections vary in terms of students’ reports of

a) extraneous load, b) communication satisfaction, c) instructor-student rapport, d)

instructor credibility, and e) instructor communication competence?

RQ3: How does the strength of association between perceptions of

accommodation and student reports of a) cognitive load, b) communication

satisfaction, c) instructor-student rapport, d) instructor credibility, and e)

instructor communication competence vary across course sections?

Collectively, this dissertation contributes to claims associated with perceptions of

instructor communication behavior by addressing problems related to a) a failure by

communication scholars to effectively operationalize student perceptions of behavior

through individualized expectations and b) a lack of acknowledgment of the hierarchical

classroom structure. This chapter presented CAT – specifically the concept of perceived

36

nonaccommodation – as a framework that resolves these discrepancies and provides a

rationale for relationships between students’ perceptions of instructor behavior and the

outcomes necessary for fulfilling classroom experiences. To do so, the chapter first

outlined the theoretical propositions of CAT relative to message receivers. Second, the

review articulated how adjustment occurs with a specific focus on using the theory to

conceptualize student perceptions. Third, a series of hypotheses and research questions

were presented to assess what happens in the aftermath of perceived nonaccommodation

in an instructional setting. Furthermore, two research questions were included that

evaluate the variability between course sections on the associated outcomes and whether

the strength of association between perceptions of nonaccommodation and outcomes

differs among course sections. Chapter three provides a detailed overview of the methods

used to test the hypotheses and answer the research questions.

37

CHAPTER 3. METHODS

The purpose of this study was to examine how student perceptions of instructor

nonaccommodation across levels of analysis influence students’ information processing

(e.g., extraneous load), relationships with the instructor (e.g., communication satisfaction

and rapport), and beliefs about the instructor (e.g., credibility and instructor

communication competence). The hypotheses and research questions were investigated

using a post-test only, online questionnaire. This chapter presents details related to a)

research procedures, b) participants, c) instrumentation, and d) data analysis strategies.

Research Design and Protocol

Level-One Data: Student Perceptions

Students were recruited to complete the survey as part of a programmatic

assessment of the basic communication course (BCC) at a large Southeastern university.

At the institution where data was collected, students choose one of three possible course

sequences to fulfill the composition and communication requirement of the general

education curriculum; the BCC represents one alternative. Each semester, the BCC

course directors and departmental assessment coordinator implement a pre/post-test

survey design to track the growth, development, and fulfillment of course objectives

across the semester. Students complete both questionnaires (for course credit) through

Qualtrics, an online survey engine, during the first two weeks of the semester (pre-test)

and again during the final two weeks of the semester (post-test). Completion of the both

the pre-test and the post-test is integrated into the course as an assignment worth 2% of

the final course grade. Thus, the entire population of students (N = 935) enrolled in the

course is required to participate in the research at both time points in order to receive full

38

course credit. This procedure is blanketed by an approved IRB protocol (IRB Protocol

Number 12-0687), and the current research was integrated as a part of this ongoing

assessment. However, two specific modifications were made to this existing protocol to

secure the approval of the data collection for this dissertation: a) the addition of the

primary researcher to the existing protocol and b) the inclusion of the new survey

research instruments (detailed later in this chapter). Following this approval, students

completed the survey during the final two weeks of the semester.

Specifically, during the final two weeks of the course, students were contacted

through email with access to the course assessment procedures. The email contained

instructions for completing the post-test questionnaire directly from the director of

assessment for the college. Data collection during the last two weeks ensures that

students will be familiar with their instructor’s communicative behavior and will have

developed reflections about the nature of the course. Yet, there are limitations to

collecting student-level data during the final two weeks. At this time, students are

completing final projects, studying for exams, or experiencing apathy due to the

impending end of the semester; it is important to recognize the potential influence of

these factors on data quality. Nonetheless, the overarching assessment is critically

important to the success of the BCC and the future development, modification, and

progression of the program. Thus, the necessity of collecting data from students at this

time outweighed the problems posed by the collection time period.

Data collection ranged across a period of 10 days, beginning on April 17, 2019

and concluding on April 26, 2019. As approved within the existing IRB protocol,

students completed both the pre-test and the post-test for a grade (20 points, or 2% of the

39

total course grade). Instructors were individually responsible for informing students of

the survey, and students took responsibility for completing the survey outside of

instructional time. Students received a score of 0 for completing only the pre-test or post-

test, as well as for not completing either survey. Students were informed at both time

points that they would not earn the 20 points if they declined to participate in the survey.

Importantly, once students accessed the questionnaire, they were immediately directed to

an informed consent statement that detailed the rights they have as research participants.

Students were then given the option to consent to having their data included as a part of

the project. Students who declined consent were still allowed to complete the assignment

to obtain credit. Individual instructors were informed with the names of the students who

completed the assignment rather than the names of those who consented to having their

data included so they could appropriately assess student completion.

The questions relative to this dissertation were directly integrated as a part of the

ongoing assessment survey (see Appendix). Each semester, the assessment coordinator

determines the outcomes of interest assessed in the survey. The series of questions posed

for this research were located at the end of the traditional assessment survey, following

the coordinator’s predetermined survey items. Thus, students first answered the questions

posed by the coordinator, followed by the questions unique to this dissertation, in order to

receive credit for the assignment; there was no explicit differentiation between sets of

questions asked for this dissertation and questions asked by the assessment coordinator.

The questions asked by the assessment coordinator evaluated student reports of public

speaking and writing self-efficacy, as well as levels of apprehension across oral and

group communication settings. Consequently, the assessment coordinator has access to

40

all data associated with the online survey.

Participants were assured that identifying information would be confidential and

would not be distributed or linked to the data analysis. The survey began with a series of

demographic questions. Next, students completed questions about their experience in the

BCC while referencing their specific instructor. This method nests students within course

sections to account and control for differences between and across course sections. The

questionnaire assessed students’ perceptions of instructors’ nonaccommodative behavior

as independent variables, followed by extraneous load, communication satisfaction,

instructor-student rapport, instructor trustworthiness, instructor caring, instructor

competence, and instructor communication competence, as outcome variables.

Level-Two Data: Course Section

The data hierarchy was constructed by using the course section in which a student

was enrolled as the level-two structure. No level-two predictors were included in the

analysis. Table 3.1 provides descriptive information that identifies the course section

code, the cluster size for that section, the number of course sections taught by the

instructor of that section, the average age for the section, and the mean expected grade for

the section (1 = A, 2 = B, 3 = C, 4 = D). Section numbers were recoded in order to protect

the identification of respective students and instructors. The following section provides

descriptions of the participants and questionnaire instruments included in the study.

Table 3.1 Descriptive Information for Level-Two Data Structure (Course Sections)

Course Section

Number Cluster Size

Number of

course sections

taught by

instructor

Average Age Mean Expected

Grade

Section 1 19 3 19 1.11

41

Table 3.1 (continued)

Section 2 15 4 19.33 1

Section 3 16 2 20.44 1.69

Section 4 11 2 18.55 1.64

Section 5 13 4 18.84 1.46

Section 6 16 2 19 1.44

Section 7 15 2 18 1.27

Section 8 18 3 18.67 1.44

Section 9 18 3 19.25 1.25

Section 10 18 3 19.17 1.11

Section 11 12 4 19.25 1.58

Section 12 15 4 18.67 1

Section 13 14 4 18.77 1.21

Section 14 14 2 18.92 1.15

Section 15 20 4 18.55 1.05

Section 16 8 4 20.75 1

Section 17 7 4 18.71 1

Section 18 15 2 18.87 1.4

Section 19 12 2 19.58 1.08

Section 20 12 4 18.42 1.25

Section 21 13 2 19.08 1.38

Section 22 19 2 18.79 1.11

Section 23 21 3 18.76 1.33

Section 24 19 2 22.79 1.39

Section 25 19 2 18.89 1.05

Section 26 13 2 19.08 1.83

Section 27 12 2 18.75 1

Section 28 20 1 19.7 1.4

Section 29 12 1 18.83 1.67

42


Section 30 22 4 19.05 1.14

Section 31 23 4 18.91 1.3

Section 32 10 4 18.5 1.1

Section 33 13 1 18.77 1.15

Section 34 11 2 19 1.1

Section 35 15 2 19.6 1.07

Section 36 16 2 18.88 1

Section 37 15 2 18.87 1.2

Section 38 17 3 18.76 1.35

Research Participants

An a priori power analysis was conducted prior to data collection to evaluate the

sample size necessary to detect significant effects in the proposed models. The analysis

was conducted using Raudenbush’s (1997) Optimal Design software (see also Spybrook,

Raudenbush, Congdon, & Martinez, 2011) for a cluster randomized trial with person-

level outcomes and the treatment implemented at level 2 (i.e., class sections randomly

assigned to students). In addition, this software requires estimates for several relevant

research parameters, including the significance level of the test, total number of level-two

units, standardized effect size, expected intraclass correlation (i.e., the percentage of

variance in each student outcome – cognitive load, satisfaction, instructor-student

rapport, credibility, communication competence - accounted for by differences between

course sections on their average of the respective outcome), and expected explained

proportion of variance by level-two covariates (α = .05, J = 38, δ = .30, ρ = .10, R2 = .10,

respectively). Under these conditions, the power analysis suggested that a total sample

size of 874 students, with an average of 23 students per cluster, would be necessary to

43

detect an effect size of .30 with 70% power.

Data cleaning procedures adhered to guidelines that attempted to preserve the

integrity of the data and account for student apathy, attrition, or a lack of variation in

response answers. This process reduced the number of participants who completed the

survey (N = 877) to a final sample (N = 573). First, participants who completed the

survey outside of a range of expectations were removed (n = 227). In the past, surveys

conducted by the departmental assessment coordinator ranged from 20-25 minutes to

complete. With the addition of the new questions, participants took an average of 28.72

minutes to complete the survey. Based on this average, it was determined that students

who completed the survey in less than 10 minutes or more than 8 hours (i.e., longer than

one sitting) would be removed. Notably, these cutoff points were also selected in

response to the wide range of completion times recorded across the entirety of the data

set; most students completed the survey extremely quickly (i.e., under 15 minutes), which

would have necessitated eliminating approximately half of the data set.

Second, the data were checked for completeness. Participants with no recorded

responses (e.g., students attempting to receive credit by selecting the link but not

completing the survey; n = 8) and participants who completed the survey more than once

(e.g., students who did not receive completion confirmation and wanted to ensure credit;

n = 13) were removed. Third, students were removed if they did not consent to their work

being included in the assessment (n = 51). Fourth, multivariate outliers were identified

and removed (n = 5) by calculating Mahalanobis distance and comparing chi-square

critical values. The participants identified were excluded from all analyses (Tabachnick

& Fidell, 2013). Collectively, 573 participants were retained for analyses.

44

Further, as detailed in the procedures above, the number of students given the

opportunity to participate in the research exceeded the amount required for sufficient

power (N = 935), yet the number who actually did complete the survey (N = 877)

combined with the data cleaning procedures resulted in a final sample of respondents that

falls short of this criterion (N = 573). Thus, the sample may be underpowered.

Across both levels of data, participants consisted of 573 undergraduate students

(199 men, 371 women, 2 preferred not to mention, 1 did not report) enrolled in 38

sections of the basic communication course (BCC) at a large southeastern university.

Participants’ ages ranged from 18 to 55 (M = 19.25, SD = 2.80). Students were first years

(440; 76.8%), sophomores (103; 18.0%), juniors (14; 2.4%), seniors (14; 2.4%), and

unsure (1; 0.20%), with 1 unreported (0.20%). They reported their ethnicity as

White/Caucasian (465; 81.2%), Black/African-American (44; 7.7%), Asian/Asian-

American (25; 4.4%), Bi/Multi-Racial (23; 4.0%) Native American (2; 0.30%), and Other

(12; 2.1%), with 2 students unreported (2; 0.30%).

Scale Development: Perceptions of Instructor Nonaccommodation Scale

To answer the proposed hypotheses and research questions, this dissertation

sought to first build a measure capable of accurately capturing student perceptions of

instructor nonaccommodation. To do so, the research followed procedures for scale

development recommended by Slavek and Drnovsek (2012).

Phase One

Phase one was concerned with establishing theoretical importance and

justification for a scale’s existence. Essentially, phase one demonstrated the need for a

new measure of nonaccommodation through practices centered on the construction of

45

content validity. Content validity refers to the extent that a measure acceptably and

accurately represents a sample of the universe of items that measure a construct

(Kimberlin & Winterstein, 2008). Evidence for content validity is provided through an

extensive synthesis of both CAT and instructional literature concerning behaviors

potentially operationalized as nonaccommodative (Clark & Watson, 1995; DeVellis,

2016).

First, an initial item pool of behaviors was established by (1) consulting existing

typologies of accommodative and nonaccommodative behavior, (2) referencing the small

sample of works employing CAT for instructor-student communication, and (3)

synthesizing this information alongside well-known instructional communication

message variables (Mazer, 2018). According to Hinkin (1998), this deductive process is

appropriate when the researcher has enough of a theoretical understanding to determine

what items will be relevant to the construct of interests.

This process resulted in the identification of several behaviors established within

CAT research that have relevance for typical instructor-student communication patterns.

Items in the literature representing accommodative behavior included statements such as

“my instructor is supportive” and “my instructor gives me useful advice” (Williams et al.,

1997). Nonaccommodative behaviors included “my instructor makes angry complaints”

(Harwood, 2000) and “my instructor intrudes on my privacy” (Speer, Giles, & Denes,

2013). Jones et al. (1994) also asked students to describe behaviors associated with

several confederate instructors who were portraying different accommodative or

nonaccommodative stances during an out-of-class interaction.

The researchers provided rich descriptions of the behaviors associated with these

46

instructors (e.g., “my instructor tries to understand my point of view;” “my instructor

treats me as inferior” Jones et al., 1994). Next, various forms of objective speaker-

accommodation goals outlined within the larger CAT literature (i.e., approximation,

interpretability, discourse management, control, emotional expression; Giles et al.,

2007a) were referenced to understand the strategic behaviors that instructors might

employ to accommodate to students. According to the theory, speakers achieve their

conversational goals by adjusting these behaviors based on their interactant’s

characteristics, so it makes theoretical sense to draw from these characterizations as the

basis of behaviors that may be perceived as accommodative by students. For example,

Mazer and Hunt (2008) demonstrated that slang functions as both an approximation and

interpretability behavior, which students subsequently believe influences their

comprehension and affect when used appropriately. Thus, the initial item pool comprised

behaviors overlapping from both CAT and instructional communication bodies of

research.

The pool of behaviors was then synthesized to represent the larger categories of

instructor message behaviors established in chapter two (Nonverbal, Verbal/Linguistic,

Content, and Support). Furthermore, considering the breadth and scope of both CAT and

instructor message variables, many initial scale items were retained within each category.

Phase Two

The second phase in the scale development process established the

representativeness and appropriateness of the items generated in phase one (Slavek &

Drnovsek, 2012). Specifically, decisions were made concerning the wording of items,

sampling procedures, and preparation of data for analysis. First, items were written so

47

that students could reflect on the past behavior of their instructor. Moreover, the items

were written as low-inference, objective behaviors so that respondents could reasonably

make inferences about his or her instructor’s level of nonaccommodation (“My instructor

used eye contact”). Second, five items were created for each of the nonverbal,

linguistic/verbal, content, and support categories to broadly capture the wide range of

identified instructor behaviors. This resulted in a final, 20 item initial item pool. The

item-selection decisions were triangulated through conversations with an instructional

communication expert. The decision to rely on subsets of instructor behaviors in each

category was also grounded in a) the need to reduce testing fatigue and attrition that

might result from an exceptionally long initial item pool and b) the comprehensiveness of

the selected categories in portraying instructor behavior (see p. 22). Five items were

included to form the Nonverbal subscale, and this item-selection process was repeated

with items from the Linguistic/Verbal, Content, and Support categories, respectively (see

Table 3.2 for a list of items). This extensive process resulted in a finalized, 20 item pool

explicitly designed to accurately captures students’ perceptions of their instructor’s

nonaccommodative behavior.

Table 3.2 Final Item Pool

Nonverbal

Made eye contact with me

Smiled at me

Showed enthusiasm

Used gestures to emphasize points

Moved around the classroom when speaking

Linguistic/Verbal

Used slang that I would use

Concentrated on articulating words for clarity

Tried to use simple language

Used jargon that was tough to understand

48


Made an effort to pronounce words correctly

Content

Provided feedback to me

Incorporated examples to make course content relevant

Explained course content thoroughly

Simplified course content for me

Repeated his/her ideas to help me understand

Support

Provided emotional support

Made me feel comfortable

Was concerned about my success in the class

Was responsive to my needs

Empathized with me

The last step in phase two involved determining the most effective rating format

for gauging responses to each item. This necessitated a return to theory to maximize

consistency between the conceptualization of nonaccommodation and subsequent

operationalization. Specifically, the current approach advances stronger alignment

between nonaccommodation as defined by listeners and a bipolar rating format.

CAT argues that listeners determine if behavior is nonaccommodative based on

whether that behavior does not meet, meets, or exceeds their expectations; “the same

communication behavior could also be interpreted as differentially accommodative or

nonaccommodative by different listeners” (Gasiorek, 2016b, p. 90). Research also

suggests that a bipolar rating format, compared to traditional Likert methods, may allow

survey respondents to more precisely and intuitively rate behavior in a manner consistent

with this concept (Vergauwe, Wille, Hofmans, Kaiser, & De Fruyt, 2017). The key

component of the bipolar format is the inclusion of a midpoint (labeled 0) that identifies

whether a participant feels a behavior has occurred an “appropriate amount” for their

needs. Contrarily, respondents can also rate whether behavior has occurred too little for

49

their needs on the left side of the scale or too much for their needs on the right side.

Therefore, the format accounts for the possibility that some individuals hold different

expectations for the frequency with which specific behaviors should occur. Moreover,

responses are rooted in individual expectations of appropriateness rather than relying on a

standard, uniform expectation of appropriateness for all. This logic informed the creation

of the Perceptions of Instructor Nonaccommodation Scale (PINS) detailed below.

Instrumentation

The following measures were used to operationalize the independent and

dependent measures as a means of testing the proposed hypotheses and research

questions. The scale items were presented to participants in the respective order listed

(see Appendix for full survey). Descriptive statistics and bivariate correlations for the full

sample (N = 573) are reported in Table 3.3.

Perceptions of Instructor Nonaccommodation

Students’ perceptions of their instructor’s nonaccommodative behavior were

operationalized using the Perceptions of Instructor Nonaccommodation scale (PINS)

created for this study (see Appendix, p. 137). The 20-item scale asks participants to rate

the appropriateness of their instructor’s nonverbal (n=5; e.g., My instructor used gestures

for emphasis), verbal/linguistic (n=5; e.g., My instructor used simple language), content

(n=5; e.g., My instructor used examples to make course content relevant), and support

(n=5; e.g., My instructor was concerned about my success in the class). Responses

assessed the perceived frequency of behavior through a bipolar rating format ranging

from too little (-4) to just the right amount (0) to too much (+4). Responses were then

linearly transformed by calculating the absolute value of each individual item, producing

50

a scale ranging from appropriate frequency (0) to inappropriate frequency (+4).

Following this procedure, responses were reverse coded so that higher scores indicated

perceptions of less inappropriate behavior (i.e., accommodation) and lower scores

represented more inappropriately perceived behavior (i.e., nonaccommodation). Thus,

following these procedures, the scale assesses the degree of nonaccommodation of the

target behavior.

Using the transformed absolute values, the Nonverbal, Content, and Support

subscales demonstrated strong reliability: Nonverbal (α = .87), Content (α = .91), and

Support (α = .92). Three separate principal components analyses (PCA) with varimax

rotation also revealed each subscale to be unidimensional, accounting for 67.77%,

73.70%, and 77.10% of the total variance, respectively. However, analyses revealed that

the reliability of the Verbal/Linguistic dimension could be substantially improved by

removing two items demonstrating low correlations with other variables (i.e., Used slang

that I would use; Used jargon that was tough to understand). Additionally, a PCA using

varimax rotation indicated that the removal of these items could improve the total

variance accounted for by the measure. Thus, these two items were removed. The

revised, 3 item Verbal/Linguistic dimension (α =.82) was unidimensional, accounting for

73.79% of the variance.

Extraneous Load

Extraneous load was operationalized using a modified version of the Cognitive

Load Questionnaire (CLQ) developed by Leppink, Paas, Van Gog, van der Vleuten, and

van Merrienboer’s (2014). Specifically, this dissertation utilized the 4 items comprised by

the extraneous load subdimension (“The explanations and instructions in the course are

51

very unclear”). Responses were collected using a 10-point Likert scale ranging from Not

at all the case (0) to Completely the case (9) (see Appendix, p. 139). Higher scores

indicate that students experienced greater extraneous load. Alpha reliability for the

instrument was acceptable: α = .90. A PCA with varimax rotation demonstrated that the

measure was unidimensional accounting for 77.52% of the total variance.

Communication Satisfaction

The abbreviated version of Goodboy et al.’s (2009) Communication Satisfaction

Scale was used to measure students’ perceptions of their overall communicative

satisfaction with their instructor. The abbreviated version consists of 8-items and has

demonstrated good reliability across a range of studies. Participants were asked to reflect

on their communication with the instructor using a Likert-type scale ranging from

Strongly Disagree (1) to Strongly Agree (7) (see Appendix, p. 141). Alpha reliability for

the instrument was acceptable: α = .92. A PCA with varimax rotation demonstrated that

the measure was unidimensional accounting for 65.93% of the total variance.

Instructor-Student Rapport

Instructor-student rapport was operationalized using Frisby and Myers’ (2008)

adapted version of Gremler and Gwinner’s (2000) measure of rapport. The instrument

consists of 11 items measuring students’ enjoyable interaction (six items; “My instructor

relates well to me”) and personal connection (five items; e.g., “My instructor has taken a

personal interest in me) with their instructor. Participants’ responses were collected using

a 7-point Likert scale from Strongly Disagree (1) to Strongly Agree (5) (see Appendix, p.

142). Alpha reliability for the instrument was acceptable: α = .95. A PCA with varimax

rotation demonstrated that the measure was multidimensional, with two factors

52

Table 3.3 Means, Standard Deviations, and Zero-Order Correlations for Variables in Full Sample (N = 573)

Variable M SD 1 2 3 4 5 6 7 8 9 10

1. Nonverbal 3.58 .59 --

2. Verbal 3.73 .49 .65** --

3. Content 3.64 .57 .67** .64** --

4. Support 3.68 .57 .64** .50** .68** --

5. ExLoad 2.88 1.88 -.17** -.09* -.22** -.14** --

6. CommSat 6.05 .86 .32** .14** .29** .31** -.21** --

7. Rapport 3.94 .77 .27** .16** .26** .27** -.17** .73** --

8. Comp 6.49 .70 .27** .15** .28** .23** -.30** .60** .53** --

9. Caring 6.14 .93 .32** .22** .30** .32** -.17** .67** .68** .72** --

10. Trust 6.49 .73 .30** .17** .29** .27** -.26** .62** .57** .87** .80** --

11. CC 6.22 .78 .28** .17** .32** .29** -.27** .70** .62** .59** .59** .58**

Note. Nonverbal = nonverbal nonaccommodation, Verbal = verbal/linguistic nonaccommodation, Content = Content

nonaccommodation, Support = support nonaccommodation, ExLoad = extraneous load, CommSat = communication

satisfaction, Rapport = instructor-student rapport, Comp = instructor competence, Caring = instructor caring, Trust = instructor

trustworthiness, CC = instructor communication competence. ** p < .01 (1-tailed). * p < .05 (1-tailed).

53

corresponding with the enjoyable interaction and personal connection dimensions

collectively accounting for 80.64% of the total variance. However, existing research

demonstrates an analytical precedent of treating this measure as unidimensional. Thus,

the same approach was adopted for this dissertation.

Instructor Credibility

Instructor credibility was operationalized using McCroskey and Teven’s (1999)

ethos/credibility scale. This 18-item instrument asks participants to report perceptions of

another individual’s credibility using semantic differentials on a 7-point scale. Six of the

items are related to perceptions of competence, (i.e. “Informed/Uninformed”), six of the

items are related to perceptions of trustworthiness (i.e. “Honest/Dishonest”), and six of

the items are related to perceptions of caring (i.e. “Concerned with me/Not concerned

with me”) (see Appendix, p. 144). The Competence subdimension demonstrated good

reliability (α = .90). A PCA with varimax rotation also revealed a unidimensional

structure accounting for 67.71% of the total variance. Likewise, the Trustworthiness

subdimensions demonstrated good reliability (α = .91). A second PCA with varimax

rotation revealed a unidimensional structure accounting for 70.31% of total variance.

However, analyses indicated that the reliability of the Caring dimension could be

improved with the removal of one item demonstrating low correlation with other items

(i.e., Self-centered/Not self-centered). A PCA with varimax rotation revealed that this

same item did not meet the .60/.40 factor loading criterion; therefore, the item was

removed. The revised, 5 item measure demonstrated consistent reliability (α = .87). A

PCA with varimax rotation revealed a unidimensional factor structure accounting for

67.42% of the total variance.

54

Instructor Communication Competence

Instructor communication competence was measured using the Communicator

Competence Questionnaire (CCQ) from Monge et al. (1981). The scale has been used

successfully in evaluating perceptions of instructor-student relationships by Titsworth et

al. (2010), who claimed that it “was designed to focus on encoding and decoding skills

that facilitate interaction between people in role positions similar to the teacher-student

relationship” (p. 437). The CCQ consists of seven items related to encoding skills (e.g.,

“My instructor can deal with others effectively”) and five items related to decoding skills

(e.g., “My instructor pays attention to what other people say to him/her”) adapted to fit

the instructional setting. Responses were elicited using a Likert-type scale ranging from

Strongly Agree (1) to Strongly Disagree (7) (see Appendix, p. 146). Items were coded so

that higher scores indicated greater perceptions of instructor communication competence.

Reliability analysis also indicated that alpha could be improved by eliminating two

reverse-coded items that did not significantly correlate with other items. Moreover, a

PCA revealed a multidimensional structure with the two-reverse coded items loading on

their own factor. After removing these items, the alpha reliability for the revised, 10-item

measure was acceptable (α = .96). A PCA with varimax rotation also revealed a

unidimensional structure accounting for 74.05% of the total variance.

Data Analysis

Hierarchical Linear Modeling

The hypotheses and research questions were tested using hierarchical linear

modeling (HLM; Raudenbush & Bryk, 2002). HLM is a large sample procedure based on

the relevant number of level-one and level-two units (Hayes, 2006; Luke, 2004).

55

Importantly, several researchers have recommended that the number of groups (i.e.,

level-two units) are more important than the number of individuals per group (i.e.,

students) to ensure accuracy and power of the statistical test (Hox, 2002; Maas & Hox,

2005; Snijders, 2005). Generally, this research recommends collecting a sample size of at

least 50 units at level-two to ensure accuracy in the data analysis procedures; data from

38 course sections, or level-two groupings, are available for the current study. Following

data cleaning procedures, seven separate data sets were constructed for the analysis

representing one for each of the seven dependent variables. From here, any remaining

missing data on the level-one dependent variables was removed through listwise deletion

(Hox, Moerbeek, & Van de Schoot, 2018).

Specifying the Models

This dissertation incorporated a two-level HLM model (Luke, 2004; Raudenbush

& Bryk, 2002) to examine the effects of student perceptions of nonaccommodation (i.e.,

level-one predictors) nested within course sections (i.e., level-two) on extraneous load,

communication satisfaction, instructor-student rapport, instructor competence, instructor

caring, instructor trustworthiness, and instructor communication competence. Separate

analyses were conducted for each dependent variable using Hierarchical Linear Models

software (HLM7; Raudenbush, Bryk, & Congdon, 2011) and following procedures

outlined by Ma et al. (2008). Separate regression models were fitted for the individual

course sections. This procedure produced mean scores adjusted for student perceptions of

each mode of nonaccommodation (i.e., nonverbal, linguistic/verbal, content, support) for

each section. These mean scores then served as independent variables in a series of

models seeking to explain variation among students enrolled in different course sections.

56

Null Models with Classroom Variables as Outcomes

The first phase in evaluating the multilevel framework is the construction of the

null model. Seven separate null models were constructed for each of the dependent

variables. The null model contains no predictor variables at either the first or second level

and is used for two purposes: 1) to estimate the grand mean of each outcome variable

with adjustment for groupings of students within class sections and for different sample

sizes across sections and 2) to partition variance between levels (Ma et al., 2008). This

model illustrates whether level-two units (i.e., 38 course sections) differ on the average

value of the outcome (i.e., intraclass correlation coefficient; ICC). A high ICC produced

by the null model indicates the degree to which the multilevel data structure may impact

the outcome(s) of interest. In other words, the ICC informs the researcher whether HLM

is appropriate by indicating the level of nonindependence across groups. The model

similar is to a random-effects ANOVA model, whereby the researcher is left with a

measure of the variance both within and between instructors for each outcome variable.

To illustrate, Equation 3.1 demonstrates the null model for extraneous load

(EXLOADij) with no level-one predictors (i.e., student perceptions) entered in the model.

This model assumes that extraneous load for student i in course section j is related to the

intercept (β0j or the average extraneous load for course section j), the difference between

course section j average and extraneous load by student i (unique student-level error; εij),

and the difference between course section j and the grand mean (unique course section-

level error; U0j). The average level of extraneous load across course sections is represent

by ϕ00.

EXLOADij = β0j + εij

57

β0j = ϕ00 + U0j (3.1)

or EXLOADij = ϕ00 + U0j + εij

Parsimonious Models with Classroom Variables as Outcomes

The second phase involved building a model that examined whether the

relationship between student perceptions of instructor nonaccommodation and outcomes

differed across course sections (i.e., model with level-one predictors). This process was

completed in two steps. First, predictor variables, all grand-mean centered (see Kreft, de

Leeuw, & Aiken, 1995), were tested one at a time in four sequential models with random

effects (i.e., assuming effects vary across sections). This data-driven process individually

revealed which variables to treat as fixed, treat as random, or remove from the model; it

examined effects on the outcome independent of other variables (i.e., absolute effects).

Variables kept as random vary significantly across course sections, and variables changed

to fixed means that the slope does not vary significantly across course sections. Equation

3.2 demonstrates an example of the process of parsimonious model development, using

perceptions of nonverbal nonaccommodation, with the addition of several new

parameters:

β1j = slope for perception of nonaccommodation

ϕ10 = the average effect of perception of nonaccommodation

U1j = unique error for course-section j nonaccommodation-load slope

Thus, 3.2 illustrates nonverbal nonaccommodation treated as a random effect

influencing extraneous load.

EXLOADij = β0j + β1jNV_NONACCij + εij

β0j = ϕ00 + U0j (3.2)

58

β1j = ϕ10 + U1j

or EXLOADij = ϕ00 + ϕ10NV_NONACCij + U0j + U1jNV_ACCij + εij

Second, the significant level-one variables were collectively added to the model,

with the appropriate fixed or random effects, to evaluate whether effects persisted in the

presence of all predictors (i.e., relative effects); the relative effect of the independent

variable was adjusted for the shared effect of all variables (see Ma et al., 2008). Non-

significant random effects with the largest p-values were removed first. If the random

effects were significant, then the fixed effect with the largest p-value was removed. The

model was run a second time, and the next independent variable with the largest p value

was removed, if present. This process continued until all the remaining level-one

independent variables were significant. After the full-model has been finalized,

meaningful effects can be interpreted; significant intercepts and level-one variables with

random errors suggest variability across course sections.

Proportion of Variance Explained

The final step in the data analysis plan involved calculating the proportion of

variance explained at level one (student level) and level two (course section level). This

process combines the variance explained by both the null and full models. The null

represents the amount of variance that could be explained, and the variance from the full

model suggests the amount of variance that the researcher has not yet explained. Stated

differently, the proportion of variance for the full-model, where only intercepts vary

randomly, represents the variance not accounted for by significant student-level variables.

The proportion of variance explained at each level and overall was calculated using the

formula in Equation 3.3 below.

59

R2Lv1 = (σ2

null– σ2full) / σ

2null

R2Lv2 = (τ2

null – τ2full) / τ

2null (3.3)

In models where level-one predictors vary randomly in addition to the intercepts,

the proportion of variance cannot be calculated; the reliability is provided instead to

indicate the ability of sample means in the model to estimate the parameters. The

presence of random effects in a model generates variance estimates for each slope, such

that the explained proportion differs from the null model.

This chapter detailed the specific participants, procedures, instruments, and data

analysis plans that were used to test the hypotheses and answer the research questions

identified in chapter two. The information presented in this chapter will inform the

empirical results presented in chapter four.

60

CHAPTER 4. RESULTS

The purpose of this dissertation was to explain how students’ perceptions of

instructor nonaccommodation across multiple levels of analysis influence relevant

theoretical outcomes. This chapter presents a series of HLM analyses for each outcome

variable (i.e., null, parsimonious, and full models) that collectively test the hypotheses

and answer the research questions.

Hypothesis One: Nonaccommodation and Extraneous Load

H1 predicted that students’ perceptions of their instructors’ a) nonverbal, b)

verbal/linguistic, c) content, and d) support nonaccommodation would be positively

related to extraneous load. Perceptions of nonaccommodation served as independent

variables, and extraneous load was the outcome variable. Each independent variable was

grand-mean centered; raw scores were transformed by subtracting the sample mean. This

aids in the interpretability of HLM parameters by rescaling the independent variable so β

represents the average change in the outcome variable when the independent variable

increases by one unit. Moreover, the intercept becomes the mean outcome for a

participant with perceived nonaccommodation equal to the sample average.

The null model revealed a grand mean of 2.90 (p < .001), suggesting that the

average extraneous load reported by students within course sections was relatively low

(see Table 4.1, p. 62). Variance components for the null model were 3.29 for students and

0.23 for course section (p < .001). Thus, the proportion of variance attributable to

students was 93.48% and the proportion attributed to course section was 6.52%. Student-

level variance accounted for a large proportion of the variance in extraneous load

compared to course section.

61

The absolute effects of student-level variables are presented in the parsimonious

model (Table 4.2 p. 63). At the student-level, nonverbal, verbal/linguistic, content, and

support nonaccommodation had significant absolute effects on extraneous load across

course sections. Recall that perceived nonaccommodation was scaled so 0 was equal to

the most inappropriate behavior and 4 was equal to the most appropriate behavior (i.e.,

accommodation). For this analysis, students who perceived an instructor as less

nonaccommodative reported less extraneous load. Specifically, a one-unit increase in

perceived nonverbal nonaccommodation (i.e., becoming less nonaccommodative; moving

closer to a perception of appropriate behavior) was associated with a -0.52 decrease in

extraneous load. Second, a one-unit increase in perceived verbal/linguistic

nonaccommodation was associated with a -0.29 decrease in extraneous load. Third, a

one-unit increase in perceived content nonaccommodation was associated with a -0.75

decrease in extraneous load. Fourth, a one-unit increase in perceived support

nonaccommodation was associated with a -0.45 decrease in extraneous load. H1 was

supported.

The relative effects of the student-level model are presented in Table 4.3 (p. 64);

relative effects are controlled for other variables in the model (Ma & Klinger, 2000; Ma

et al., 2008). Importantly, the data from the previous step indicated that none of the

accommodation slopes differed between course sections. Therefore, in the construction of

the full model, each of the variables was fixed and no random effects were included. The

analysis showed that only the effect of content nonaccommodation remained significant,

and its relative effect was similar to its absolute effect. However, the nonverbal,

verbal/linguistic, and support effects disappeared when controlling for content

62

nonaccommodation. Together, the significant variables reduced the variance at both

student and course section levels. When compared to the null model, the full model

explained 11% of variance at the course section level and 7% of variance at the student

level.

Table 4.1 Statistical Results of the Null Model of Course Section Effects on Extraneous

Load

Extraneous Load

Fixed Effects

Coefficient SE t-ratio p

Intercept (Extraneous Load)

γ00 2.90 0.11 26.85 <0.001

Random Effects

Variance df Chi-square p

Between-course section

variability (Intercept) 0.23 37 76.02 <0.001

Within-course section

variability 3.29

Reliability (Intercept) 0.50

Intraclass Correlation 0.07

63

Table 4.2 Parsimonious Model for Extraneous Load with Coefficient (Slopes) of

Nonaccommodation (Absolute Effects)

Extraneous Load

Fixed Effects


Student Level Variables

Nonverbal Acc. γ10 -0.52 0.14 -3.84 <0.001■

Verbal/Linguistic Acc. γ10 -0.29 0.14 -2.03 0.050■

Content Acc. γ10 -0.75 0.15 -5.09 <0.001■

Support Acc. γ10 -0.45 0.13 -3.34 0.002■

Random Effects


Between-course section variability

Nonverbal Acc. Slope 0.03 37 39.54 0.357■

Verbal/Linguistic Acc.

Slope 0.01 36 33.89 >0.500■

Content Acc. Slope 0.13 37 50.94 0.063■

Support Acc. Slope 0.05 37 31.85 >0.500■

Note. Symbols denote treatment of variable in full model based on absolute effects. ■ = Fixed Effect ┼ = Random Effect ▲= Removal

64


Extraneous Load

Extraneous Load

Fixed Effects


Intercept (Extraneous Load)

γ00 2.88 0.10 27.75 <0.001


Content Acc. γ10 -0.71 0.14 -4.94 <0.001

Random Effects





variability 3.07

Proportion of Variance Explained

At course section level

(between sections) 0.11

At student level (within

sections) 0.07

65

Hypothesis Two: Nonaccommodation and Communication Satisfaction


verbal/linguistic, c) content, and d) support nonaccommodation would be negatively

related to communication satisfaction. Student perceptions of nonaccommodation served

as independent variables, and communication satisfaction as the outcome variable. The

null model revealed a grand mean of 6.03 (p < .001), suggesting that students were

generally very satisfied with their communicative interactions with instructors within

course sections (see Table 4.4, p. 67). Variance components for the null model were 0.63

for students and 0.13 for course section (p < .001). Thus, the proportion of variance

attributable to students was 83.14% and the proportion attributed to course section was

16.86%. Student-level variance accounted for a larger proportion of the variance in

communication satisfaction compared to course section.


model (Table 4.5, p. 68). At the student-level, all nonaccommodation variables had a

significant absolute effect on communication satisfaction across course sections.

Moreover, the absolute effects for nonaccommodation were similar for nonverbal,

content, and support behavior, while the absolute effect for verbal/linguistic

nonaccommodation was considerably lower. These results indicate that students who

perceived instructors as less nonaccommodative reported greater communication

satisfaction. Specifically, a one-unit increase in perceived nonverbal nonaccommodation

(i.e., becoming less nonaccommodative) was associated with a 0.45 increase in

communication satisfaction. Second, a one-unit increase in perceived verbal/linguistic

nonaccommodation was associated with a 0.24 increase in communication satisfaction.

Third, a one-unit increase in perceived content nonaccommodation was associated with a

66

0.41 increase in communication satisfaction. Fourth, a one-unit increase in perceived

support nonaccommodation was associated with a 0.45 increase in communication

satisfaction. H2 was supported.

The relative effects of the student-level model are presented in Table 4.6 (p. 69).

The data from the previous step indicated that the slopes for nonverbal and support

nonaccommodation differed between course sections. Therefore, in the construction of

the full model, nonverbal and support nonaccommodation were treated as random while

the remaining variables were treated as fixed. Following the analyses, content and

support nonaccommodation remained significant when controlling for other variables,

although the magnitude of the relative effects decreased slightly. Additionally, nonverbal

and verbal/linguistic nonaccommodation effects disappeared when controlling for other

perceptions. The significance of the random slope for support accommodation suggests

that the relationship between the support nonaccommodation and communication

satisfaction differs across course sections when controlling for other variables in the

model.

67

Table 4.4 Statistical Results of the Null Model of Course Section Effects on



Fixed Effects


Intercept (Communication

Satisfaction) γ00 6.03 0.07 90.97 <0.001

Random Effects





variability 0.63



68

Table 4.5 Student Level Model for Communication Satisfaction with Coefficient (Slopes)

of Nonaccommodation (Absolute Effects)


Fixed Effects



Nonverbal Acc. γ10 0.45 0.07 6.16 <0.001┼

Verbal/Linguistic Acc. γ10 0.24 0.08 2.88 0.007■

Content Acc. γ10 0.41 0.07 5.60 <0.001■

Support Acc. γ10 0.45 0.10 4.57 <0.001┼

Random Effects



Nonverbal Acc. Slope 0.06 37 53.11 0.042┼


Slope 0.04 36 33.47 >.500■


Support Acc. Slope 0.19 37 89.05 <0.001┼


69




Fixed Effects


Intercept (Communication

Satisfaction) γ00 6.03 0.05 110.06 <0.001


Content Acc. γ10 0.30 0.09 3.48 <0.001

Support Acc. γ20 0.29 0.12 2.45 0.019

Random Effects





variability (Support Acc.

Slope)

0.23 36 95.39 <0.001


variability 0.52

Reliability

Intercept (random at the

course section level) 0.61

Support Acc. (random at the


70

Hypothesis Three: Nonaccommodation and Instructor-Student Rapport



related to instructor-student rapport. Student perceptions of nonaccommodation served as

independent variables, and instructor-student rapport as the outcome variable. The null

model revealed a grand mean of 3.92 (p < .001), suggesting that students generally

indicated positive rapport with instructors within course sections (see Table 4.7, p. 72).

Variance components for the null model were 0.50 for students and 0.10 for course

section (p < .001). Thus, the proportion of variance attributable to students was 83.89%

and the proportion attributed to course section was 16.11%. Student-level variance

accounted for a large proportion of the variance in instructor-student rapport compared to

course section.


model (Table 4.8, p. 73). At the student-level, each nonaccommodation variable had a

significant absolute effect on instructor-student rapport across course sections. Moreover,

the absolute effect for each nonaccommodation variable was similar in magnitude.

Students who perceived instructors as less nonaccommodative reported greater instructor-

student rapport. Specifically, a one-unit increase in perceived nonverbal

nonaccommodation (i.e., becoming less nonaccommodative) was associated with a 0.30

increase in instructor-student rapport. Second, a one-unit increase in perceived

verbal/linguistic nonaccommodation was associated with 0.21 increase in instructor-

student rapport. Third, a one-unit increase in perceived content nonaccommodation was

associated with 0.32 increase in instructor-student rapport.. Fourth, a one-unit increase in

71

perceived support nonaccommodation was associated with 0.33 increase in instructor-

student rapport. H3 was supported.




the full model, nonverbal and support nonaccommodation were treated as random while

the remaining variables were treated as fixed. Following the analyses, content and

support nonaccommodation remained significant when controlling for other variables,

although the magnitude of the relative effects decreased slightly. Additionally, the

nonverbal and verbal/linguistic nonaccommodation effects disappeared when controlling

for other perceptions. The significance of the random slope for support accommodation

suggests that the relationship between the support nonaccommodation and instructor-

student rapport differs across course sections when controlling for other variables in the

model.

72

Table 4.7 Statistical Results of the Null Model of Course Section Effects on Instructor-

Student Rapport


Fixed Effects


Intercept (Instructor-Student

Rapport) γ00 3.92 0.06 67.24 <0.001

Random Effects





variability 0.50



73

Table 4.8 Student Level Model for Instructor-Student Rapport with Coefficient (Slopes)



Fixed Effects



Nonverbal Acc. γ10 0.30 0.06 5.12 <0.001┼


Content Acc. γ10 0.32 0.06 5.53 <0.001■

Support Acc. γ10 0.33 0.08 3.99 <0.001┼

Random Effects





Slope 0.00 36 27.60 >0.500■



Note. Symbols denote treatment of variable in full model based on absolute effects. ■= Fixed Effect ┼ = Random Effect ▲= Removal

74




Fixed Effects


Intercept (Instructor-Student

Rapport) γ00 3.94 0.05 83.65 <0.001


Content Acc. γ10 0.20 0.07 2.84 0.005

Support Acc. γ20 0.21 0.10 2.06 0.047

Random Effects





variability (Support Acc.

Slope)

0.15 36 77.57 <0.001


variability 0.42

Reliability



Support Acc. (random at the


75

Hypothesis Four: Nonaccommodation and Instructor Trustworthiness



related to instructor trustworthiness. The null model revealed a grand mean of 6.47 (p <

.001), suggesting high average instructor trustworthiness within course sections (see

Table 4.10, p. 76). Variance components for the null model were 0.45 for students and

0.09 for course section (p < .001). Thus, the proportion of variance attributable to

students was 82.59% and the proportion attributed to course section was 17.41%.

Student-level variance accounted for a large proportion of the variance in instructor

caring compared to course section.



significant absolute effect on instructor trustworthiness across course sections. The

significant nonaccommodation variables had similar effects, indicating that students who

perceived instructors as less nonaccommodative reported greater instructor

trustworthiness. Specifically, a one-unit increase in perceived nonverbal


increase in instructor trustworthiness. Second, a one-unit increase in perceived

verbal/linguistic nonaccommodation was associated with 0.22 increase in in instructor

trustworthiness. Third, a one-unit increase in perceived content nonaccommodation was

associated with 0.34 increase in in instructor trustworthiness. Fourth, a one-unit increase

in perceived support nonaccommodation was associated with 0.33 increase in in

instructor trustworthiness. H4 was supported.

76


The data from the previous step indicated that the slopes for nonverbal, content, and

support nonaccommodation differed between course sections. Therefore, in the

construction of the full model, nonverbal, content, and support nonaccommodation were

treated as random while the verbal/linguistic nonaccommodation was treated as fixed.

Following the analyses, content nonaccommodation remained significant when

controlling for other variables, and the magnitude of the relative effect remained the

same. Additionally, the nonverbal, verbal/linguistic, and content nonaccommodation

effects disappeared when controlling for other perceptions. The significance of the

random slope for content accommodation suggests that the relationship between the

support nonaccommodation and instructor trustworthiness differs across course sections

when controlling for other variables in the model.


Trustworthiness

Instructor Trustworthiness

Fixed Effects


Intercept (Instructor

Trustworthiness) γ00 6.47 0.06 113.82 <0.001

Random Effects





variability 0.45



77

Table 4.11 Student Level Model for Instructor Trustworthiness with Coefficient (Slopes)



Fixed Effects



Nonverbal Acc. γ10 0.33 0.06 5.46 <0.001┼


Content Acc. γ10 0.34 0.07 5.09 <0.001┼

Support Acc. γ10 0.33 0.07 4.43 <0.001┼

Random Effects





Slope 0.04 36 37.21 0.413■

Content Acc. Slope 0.06 37 58.06 0.015┼



78




Fixed Effects



Trustworthiness) γ00 6.48 0.05 126.57 <0.001


Content Acc. γ10 0.34 0.07 5.09 <0.001

Random Effects





variability (Content Acc.

Slope)

0.06 37 58.06 0.015


variability 0.40

Reliability



Content Acc. (random at the


79

Hypothesis Five: Nonaccommodation and Instructor Caring



related to instructor caring. The null model revealed a grand mean of 6.10 (p < .001),

suggesting that average instructor caring within course sections was high (see Table 4.13,

p. 80). Variance components for the null model were 0.71 for students and 0.17 for

course section (p < .001). Thus, the proportion of variance attributable to students was

81.01% and the proportion attributed to course section was 18.99%. Student-level

variance accounted for a large proportion of the variance in instructor caring compared to

course section.



significant absolute effect on instructor caring across course sections. The significant

nonaccommodation variables had similar effects, indicating that students who perceived

instructors as less nonaccommodative reported greater instructor caring. Specifically, a

one-unit increase in perceived nonverbal nonaccommodation (i.e., becoming less

nonaccommodative) was associated with a 0.45 increase in instructor caring. Second, a

one-unit increase in perceived verbal/linguistic nonaccommodation was associated with

0.35 increase in in instructor caring. Third, a one-unit increase in perceived content

nonaccommodation was associated with 0.45 increase in in instructor caring. Fourth, a

one-unit increase in perceived support nonaccommodation was associated with 0.51

increase in in instructor caring. H5 was supported.



80


the full model nonverbal and support nonaccommodation were treated as random while

verbal/linguistic and content nonaccommodation were treated as fixed. Following the

analyses, nonverbal and content nonaccommodation remained significant when

controlling for other variables, though the magnitude of the relative effects decreased.

Additionally, the verbal/linguistic and support nonaccommodation effects disappeared

when controlling for other perceptions. The significance of the random slope for

nonverbal nonaccommodation suggests that the relationship between nonverbal

nonaccommodation and instructor caring differs across course sections when controlling

for other variables in the model.


Caring

Instructor Caring

Fixed Effects


Intercept (Instructor Caring)

γ00 6.10 0.07 81.89 <0.001

Random Effects





variability 0.71



81

Table 4.14 Student Level Model for Instructor Caring with Coefficient (Slopes) of


Instructor Caring

Fixed Effects



Nonverbal Acc. γ10 0.45 0.08 5.41 <0.001┼

Verbal/Linguistic Acc. γ10 0.35 0.09 3.97 <0.001■

Content Acc. γ10 0.45 0.08 5.85 <0.001■

Support Acc. γ10 0.51 0.10 5.23 <0.001┼

Random Effects





Slope 0.06 36 37.58 0.397■




82


Instructor Caring

Instructor Caring

Fixed Effects


Intercept (Instructor Caring)

γ00 6.11 0.06 97.58 <0.001


Nonverbal Acc. γ10 0.35 0.11 3.28 0.002

Content Acc. γ20 0.22 0.11 2.02 0.044

Random Effects





variability (Nonverbal Acc.

Slope)

0.07 37 58.58 0.013


variability 0.60

Reliability



Nonverbal Acc. (random at

the course section level) 0.277

83

Research Question One: Nonaccommodation and Instructor Competence

RQ1 investigated the relationship between student perceptions of instructor a)

nonverbal, b) verbal/linguistic, c) content, and d) support nonaccommodation and

instructor competence. The null model revealed a grand mean of 6.47 (p < .001),

suggesting that average instructor competence within course sections was high (see Table

4.16, p. 84). Variance components for the null model were 0.71 for students and 0.17 for

course section (p < .001). Thus, the proportion of variance attributable to students was

81.01% and the proportion attributed to course section was 18.99%. Student-level

variance accounted for a large proportion of the variance in instructor competence

compared to course section.



significant absolute effect on instructor competence across course sections. The

significant nonaccommodation variables had similar effects, indicating that students who

perceived instructors as less nonaccommodative reported greater instructor competence.

Specifically, a one-unit increase in perceived nonverbal nonaccommodation (i.e.,

becoming less nonaccommodative) was associated with a 0.30 increase in instructor

competence. Second, a one-unit increase in perceived verbal/linguistic

nonaccommodation was associated with 0.18 increase in instructor competence. Third, a

one-unit increase in perceived content nonaccommodation was associated with 0.34

increase in in instructor competence. Fourth, a one-unit increase in perceived support

nonaccommodation was associated with 0.26 increase in instructor competence. The

results collectively answer RQ1.

84


The data from the previous step indicated that the slopes for nonverbal and content


the full model nonverbal and content nonaccommodation were treated as random while

verbal/linguistic and support nonaccommodation were treated as fixed. Following the

analyses, only content nonaccommodation remained significant when controlling for

other variables, and the magnitude of the relative effect was the same. Additionally, the

verbal/linguistic, content, and support nonaccommodation effects disappeared when

controlling for other perceptions. The significance of the random slope for content

nonaccommodation suggests that the relationship between content nonaccommodation

and instructor competence differs across course sections when controlling for other

variables in the model.


Competence

Instructor Competence

Fixed Effects



Competence) γ00 6.47 0.05 121.15 <0.001

Random Effects





variability 0.41



85

Table 4.17 Student Level Model for Instructor Competence with Coefficient (Slopes) of



Fixed Effects



Nonverbal Acc. γ10 0.30 0.06 4.77 <0.001┼


Content Acc. γ10 0.34 0.07 5.17 <0.001┼

Support Acc. γ10 0.26 0.06 4.50 <0.001■

Random Effects





Slope 0.02 36 36.22 0.459■


Support Acc. Slope 0.02 37 50.67 0.066■


86




Fixed Effects



Competence) γ00 6.47 0.05 137.62 <0.001


Content Adj. γ10 0.34 0.07 5.17 <0.001

Random Effects






Slope)

0.06 37 56.92 0.019


variability 0.37

Reliability





87

Hypothesis Six: Accommodation and Instructor Communication Competence



related to instructor caring. Student perceptions of accommodation served as independent

variables, and instructor communication competence as the outcome variable. The null

model revealed a grand mean of 6.21 (p < .001), suggesting that the average instructor

communication competence reported by students across course sections was relatively

high (see Table 4.19, p. 89). Variance components for the null model were 0.52 for

students and 0.09 for course section (p < .001). Thus, the proportion of variance

attributable to students was 85.94% and the proportion attributed to course section was

14.06%. Student-level variance accounted for a large proportion of the variance in

instructor communication competence compared to course section.



significant absolute effect on instructor communication competence across course

sections. The significant nonaccommodation variables had similar effects, indicating that

students who perceived instructors as less nonaccommodative reported greater instructor

communication competence. Specifically, a one-unit increase in perceived nonverbal


increase in instructor communication competence. Second, a one-unit increase in

perceived verbal/linguistic nonaccommodation was associated with 0.24 increase in

instructor communication competence. Third, a one-unit increase in perceived content

nonaccommodation was associated with 0.42 increase in instructor communication

competence. Fourth, a one-unit increase in perceived support nonaccommodation was

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associated with 0.35 increase in instructor communication competence. H6 was

supported.


The data from the previous step indicated that the slope for content nonaccommodation

differed between course sections. Therefore, in the construction of the full model content

nonaccommodation was treated as random while nonverbal, verbal/linguistic, and support

nonaccommodation were treated as fixed. Following the analyses, only content

nonaccommodation remained significant when controlling for other variables, and the

magnitude of the relative effect was the same. Additionally, the nonverbal,

verbal/linguistic, and support nonaccommodation effects disappeared when controlling

for other perceptions. The significance of the random slope for content

nonaccommodation suggests that the relationship between content nonaccommodation

and instructor communication competence differs across course sections when

controlling for other variables in the model.

89


Communication Competence


Fixed Effects



Communication

Competence) γ00

6.21 0.06 110.81 <0.001

Random Effects





variability 0.52



90

Table 4.20 Student Level Model for Instructor Communication Competence with

Coefficient (Slopes) of Nonaccommodation (Absolute Effects)


Fixed Effects



Nonverbal Acc. γ10 0.32 0.06 5.15 <0.001■

Verbal/Linguistic Acc. γ10 0.24 0.07 3.60 <0.001■

Content Acc. γ10 0.42 0.07 5.81 <0.001┼

Support Acc. γ10 0.35 0.07 5.10 <0.001■

Random Effects



Nonverbal Acc. Slope 0.03 37 39.90 0.342■


Slope 0.01 35 24.08 >0.500■


Support Acc. Slope 0.05 37 50.85 0.064■


91




Fixed Effects



Communication

Competence) γ00

6.21 0.05 121.05 <0.001


Content Acc. γ10 0.42 0.07 5.81 <0.001

Random Effects






Slope)

0.08 37 58.04 0.015


variability 0.46

Reliability





92

Research Question Two: Outcome Variability Across Course Sections

Within the investigation of effects of level-one predictors on classroom outcomes,

analyses also revealed the extent to which the data structure (i.e., students nested within

course sections) impacted the relationships among variables.

RQ2 was concerned with whether course sections varied in terms of students’

reported outcomes. To answer this question, Table 4.22 (p. 93) presents a summary of the

intraclass correlation coefficients (ICC) produced for each null model. The ICC indicates

the degree of variability in the outcome attributable to course section; it provides a

general sense of the extent to which the outcome differs across the multilevel data

structure.

In general, the ICCs indicate that some variability in each outcome variable is

attributable to course section. Thus, the results show that outcomes differed marginally

across course sections. However, the primary source of variability in each outcome

measure occurred at the student level. These results are promising for individuals seeking

to achieve standardization in learning outcomes or pedagogy across course sections;

however, the ICCs do indicate that some characteristic of the course section groupings is

contributing to differences in the mean values for the outcomes overall.

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Table 4.22 Summary of Intraclass Correlation Coefficients for Each Null Model

Outcome Variable ICC

Extraneous Load 0.065

Communication Satisfaction 0.169

Instructor-Student Rapport 0.161

Instructor Trustworthiness 0.179

Instructor Caring 0.190

Instructor Competence 0.167

Instructor Communication Competence 0.141

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Research Question Three: Relationship Differences Across Course Sections

RQ3 investigated whether the relationships between perceptions of

nonaccommodation and the outcomes (i.e., slopes) differed across course sections.

Results for this research question are available in the full model results for each outcome

variable (Tables 4.3, 4. 6, 4.9, 4.12, 4.15, 4.18, 4.21). Of the analyses conducted, only

those related to extraneous load did not include a slope that varied significantly.

Therefore, related to extraneous load, the various modes of nonaccommodation produced

no random effects; their respective effects are assumed to not vary between course

sections.

Related to the other outcome variables, analyses produced six different random

effects indicating that slopes differed across course sections. First, for communication

satisfaction, the effect of support nonaccommodation differed across course sections (p <

0.001). Second, the effect of support nonaccommodation on instructor-student rapport

varied across course sections (p < 0.001). Third, the effect of content nonaccommodation

on instructor-student trustworthiness varied across course sections (p = 0.015). Fourth,

the effect of nonverbal nonaccommodation on instructor-student caring varied across

course sections (p = 0.013). Fifth, the effect of content nonaccommodation on instructor-

student competence varied across course sections (p = 0.019). Finally, the effect of

content nonaccommodation on instructor communication competence varied across

course sections (p = 0.015).

Together, these results hint that students’ perceptions of similar levels of

instructor nonaccommodation may be resulting in various levels of outcomes across

course sections. Specifically, it appears that a) the strength of association between

nonaccommodation related to support and communication satisfaction and instructor-

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student rapport; b) the strength of association between nonaccommodation related to

nonverbal communication and instructor caring; and c) the strength of association

between nonaccommodation related to content and instructor trustworthiness,

competence, and communication competence differed depending on the section in which

the participant was enrolled.

The results within this chapter demonstrate specific testing of the hypotheses and

answering of the research questions. The next chapter provides further interpretation of

the results and offers several important implications related to CAT, instructional

communication, and the BCC.

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CHAPTER 5. DISCUSSION

Instructional communication scholars have devoted considerable effort to

understanding the factors and influences that shape classroom experiences in higher

education, as well as a variety of other contexts. Yet, as argued in chapter one, such

efforts could be enhanced by grounding research in a theory of adjustment. Additionally,

any theory of adjustment instituted to explain classroom communication should be rooted

in students’ identities that drive individual perceptions in order to be effective. To this

end, this dissertation incorporated communication accommodation theory (CAT) as a

means of theoretically and practically overcoming existing problems in instructional

communication research and advancing the discipline.

The results of this dissertation point to several implications. First, the

methodological steps undertaken in this dissertation provide researchers with a nuanced

operationalization of nonaccommodation that can be applied to future work concerning

individual perceptions of behavior. Second, in line with the hypotheses and research

questions, the varying degrees to which students’ perceptions of instructor behavior were

perceived as nonaccommodative were significantly associated with differences in

outcomes across course sections. A student’s ability to process information, their

relationship with the instructor, and their overall impressions of the instructor were

shaped in some way by feelings about the instructor’s communicative adjustment.

However, for several of the outcomes assessed, relationships were influenced by the data

hierarchy of students nested within course sections. Unexpectedly, analyses also revealed

that several of the hypothesized relationships between perceptions of nonaccommodation

and outcomes disappeared when modes were modeled simultaneously. For example,

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nonverbal, verbal/linguistic, support nonaccommodation were no longer significantly

related to the hypothesized outcomes when considered alongside content

nonaccommodation for four of the seven outcome variables. The following discussion

expands upon the significance and implications of these findings through connections

with the theoretical framework and existing literature. Several limitations of the results,

as well as potential areas for future directions, are also woven through the subsequent

discussion of implications where appropriate.

Operationalizing Students’ Perceptions of Nonaccommodation

Prior to investigating relationships between instructor nonaccommodation and the

facilitation of important classroom outcomes, steps were taken to operationalize students’

perceptions in accordance with CAT. A bipolar rating format, which aligns with a listener

approach to CAT by focusing on the appropriateness of behaviors, was used to account

for individual expectations for behaviors relative to their needs (e.g., millennial students’

desiring more relationship-oriented teaching; Frey & Tatum, 2016). The transformative

process detailed in chapter three resulted in a measure of the magnitude of perceived

nonaccommodation (Perceptions of Instruction Nonaccommodation Scale; PINS),

ranging from perceptions of the most inappropriate behavior (0; complete

nonaccommodation) to the most appropriate behavior (4; accommodation).

The new measure adds to the various phenomenological, experimental, and

empirical methods that have been used to assess speaker and listener behavior while

concurrently addressing several limitations of existing measurements of communicative

adjustment. First, the process improves existing measurement by explicitly

contextualizing nonaccommodation within the instructor-student interaction. When

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assessing listeners’ perceptions of accommodation or nonaccommodation, researchers

have previously asked participants to provide general perceptions of behavior without

regard for contextual features. For example, Giles et al., (2007b) used five items to assess

perceived accommodation by police officers in interactions with civilians. Items included

perceptions of the pleasantness, accommodativeness, respectfulness, politeness, and

clarity of the officers (p. 144). Although this research program has demonstrated the

importance of civilians perceiving police officers as accommodating figures (e.g., Barker

et al., 2008), the presence of high-inference items does not provide unique insights into

the effects of specific behaviors. Past research suggests that the context of an interaction

influences perceptions of adjustment (Brown et al., 1985; Gallois & Callan, 1991), and

police officers or others in high status positions (e.g., instructors) behave in ways unique

to their occupation, setting, and interactional goals (Dixon, Schell, Giles, & Drogos,

2008). Comparatively, the current procedure used in this dissertation incorporated

behaviors inimitable to the instructional setting (e.g., my instructor moved around the

classroom when speaking; my instructor incorporated examples to make course content

relevant; my instructor was concerned about my success in the class) that contextualized

interactions appropriately.

Second, compared to existing research, the bipolar rating format separates ratings

of the same behavior as accommodative for some students and nonaccommodative for

others. Other approaches to measuring and assessing accommodation depend on pre-

existing taxonomies explicitly identifying and labeling behaviors as either

accommodative or nonaccommodative. To illustrate, Williams et al. (1997) utilized

participants’ experiences of satisfying and dissatisfying intergenerational communication

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identified by Williams and Giles (1996) to build a measure that empirically separated

certain behaviors as only accommodative or nonaccommodative (i.e., young people’s

perceptions of conversations with older people). Other researchers have reported similar

scale development procedures (Cai et al., 1998; Ng, Liu, Weatherall, & Loong, 1997).

However, despite evidence suggesting that the behaviors identified in their measures

(e.g., I found elders to be attentive) may function as forms of accommodation in context

from a speaker perspective, listeners (i.e., students) can have different interpretations.

Whereas it is reasonable that an individual may view an elder providing compliments as

accommodative, it is equally plausible that an individual could view this same elder as

not providing enough or providing too many compliments (i.e., nonaccommodation). The

value added by specifying behaviors and separating types of accommodation is

overshadowed by the scale’s inability to account for the way a listener might perceive the

same behavior both ways. Thus, for researchers interested in perceptions, the bipolar

format may provide a promising alternative approach to assessing the experience.

Finally, the measure used in this dissertation demonstrates consistency between

theory and application by focusing solely on perceptions of the frequency of behaviors.

Building on the work by Williams and colleagues, Harwood (2000) further separated

nonaccommodation by grandparents and grandchildren into perspectives of under- and

overaccommodation. This work intended to build on the previously identified low-

inference measures (e.g., Williams et al. 1997), which use perceptions of objective

behavior, to characterize dimensions: accommodation (e.g., “shares personal thoughts

and feelings;” p. 751), overaccommodation (e.g., “talks down to me;” p. 751), and

underaccommodation (e.g., “talks about his/her health;” p. 751). This design led to a

100

string of studies empirically quantifying perceptions of accommodative behavior in an

intergenerational setting (e.g., Bernhold, Gasiorek, & Giles, 2018; Soliz & Harwood,

2003; Soliz & Harwood, 2006; Speer et al., 2013). Yet, despite the focus of the measure

on listener perceptions of accommodation, some items fail to differentiate between the

frequency of a behavior and participants’ individual judgments. Consequently, there are

inconsistencies in the formation of items in the scales.

Speer et al. (2013) provide an explicit example of this difference. In their study,

factor analysis of specific stepchild-stepparent behaviors resulted in a multidimensional

measure comprising dimensions of accommodation, underaccommodation, and

overaccommodation. When examining the accommodation factor, items included

“compliments me” (p. 227) and “is supportive’ (p. 227). The notion of a stepparent

offering a compliment is a concrete and tangible behavior, whereas the same individual

can demonstrate support in multiple ways. Participants are reporting on the frequency of

being complimented in their interactions versus their judgment of supportive behavior in

general. The items in this study, when used in conjunction with the bipolar format, focus

clearly on objective behaviors that a listener may interpret positively or negatively

relative to its frequency, rather than offering a participant the option of reporting a

judgment about a behavior.

Together, there are benefits of using the bipolar rating format and subsequent

transformation as a means of increasing isomorphism with the listener conceptualization

of nonaccommodation. This discussion is not meant to belittle or mitigate the important

research conducted using more objective, Likert-based approaches, but rather offers a

101

new direction that might collectively improve understanding of how nonaccommodation

functions differently for various individuals.

However, researchers using the described measure should also be wary of

limitations to this approach. Mainly, the process used to turn bipolar ratings into a linear

scale does not differentiate between perceptions of under- and overaccommodation.

Recall that CAT theoretically links individual perceptions of messages exceeding

expectations (i.e., overaccommodation) or not meeting expectations (i.e.,

underaccommodation) to negative outcomes for students and instructors (Jones et al.,

1994). This distinction is an important feature of the accommodative process when

framed from listeners’ experiences (Gasiorek, 2016b); however, students’ perceptions of

under- and overaccommodation, while both nonaccommodative, shape experiences

differently.

Under and overaccommodation are largely driven by attributions individuals

make about their experiences; evaluations of nonaccommodation may be tempered by the

intent attributed by a receiver to a speaker (Gasiorek & Giles, 2015). For instance,

research on instructor strategic ambiguity (Klyukovski & Medlock-Klyukovski, 2016)

posits that a lack of information necessary for understanding (i.e., underaccommodation),

coupled with positively inferred motivations, could lead to positive learning outcomes.

From the message receiver’s perspective, evaluations of underaccommodation are also

rated more negatively than overaccommodation (e.g., Gasiorek & Giles, 2012; Gasiorek

& Giles, 2015; Hewett, Watson, & Gallois, 2015; Jones et al., 1994). This pattern has

been attributed to the conceptualization of underaccommodation as a lack of engagement

viewed similarly to maintenance, in which a speaker is evaluated less positively because

102

of their inability or motivation to change their “default” form of communication.

Contrarily, speakers perceived to overaccommodate still maintain some engagement,

whereby communicative behavior has been modified to fit the needs of the interaction,

albeit exceedingly (Gasiorek & Giles, 2012; Gasiorek & Dragojevic, 2018). Thus,

researchers should be cautious that the measure forwarded in this study limits one’s

ability to tap into these different perceptual dimensions.

Perceptions of Nonaccommodation on Classroom Outcomes

The collective set of hypotheses and research question posited direct effects on

classroom outcomes depending on the extent to which students perceived an instructor as

nonaccommodative. Moreover, the effects were generally expected to exist across

nonverbal, verbal/linguistic, content, and support modes of communication. Although

instructional communication researchers have previously contributed to this thinking

through investigations of inappropriate or objectively poor teaching behaviors (see

Goodboy & Myers, 2015), CAT presents a new framework for understanding how

behaviors can be interpreted differently. The results point to two specific patterns

describing relationships between perceived nonaccommodation and outcomes across

course sections.

First, students’ perceptions of their instructors’ nonaccommodative behavior

across modes were independently associated with all classroom outcomes included in the

study. As expected, this suggests that student perceptions of nonaccommodation in any of

the four modes included, when evaluated in isolation. have the potential to detract from

classroom outcomes. Second, the HLM analyses revealed relative effects that

demonstrate the overall importance of content nonaccommodation compared to

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nonverbal, verbal/linguistic, and support nonaccommodation. When all variables were

included in the model, the absolute effects between nonverbal, verbal/linguistic, and

support nonaccommodation and classroom outcomes were no longer significant.

Relationships between perceived nonaccommodation and classroom outcomes appeared

to depend on the extent which certain modes of accommodation occurred together (i.e.,

multimodal accommodation; Giles, Taylor, & Bourhis, 1977). Examples are provided in

the paragraphs below.

Perceived Nonaccommodation and Information Processing

H1 investigated relationships between students’ perceptions of instructors’

nonaccommodation and extraneous load. The findings (Table 4.2, p. 63) reveal that

perceptions of nonverbal (-0.52, p < .05), verbal/linguistic (-0.20, , p < .05) content (-

0.75, p < .05), and support (-0.45, p < .05) nonaccommodation were associated with

significant decreases in extraneous load. That is, among course sections, when the

magnitude of nonaccommodation decreased, extraneous load also decreased. Thus, for

each mode, there were significant effects for nonaccommodation on extraneous load.

It is not surprising that instructors perceived as nonaccommodative across modes

forced students to process additional information not necessary for the instructional task.

CAT suggests that a failure to adapt communication in accordance with a listener’s needs

(i.e., perceived nonaccommodation) leads to difficulties in comprehension (Gasiorek,

2015), thus limiting the amount of information internalized into a student’s working

memory. The current results support the notion that inappropriate nonverbal,

verbal/linguistic, content, and support behavior divided students’ attention away from

processing content to focus on building cognitive schemas for information unrelated to

104

the instructional situation (Sweller et al., 1998; Paas, van Gog, & Sweller, 2010).

Essentially, inappropriate instruction in the form of nonaccommodation may occupy

cognitive resources that students need to encode knowledge into their long-term

memories (Paas et al., 2003; Sweller et al., 1998). At the same time, increases in

extraneous load also decrease the amount of working memory available to students to

deal with intrinsic load, or load stemming from the inherent complexity of the learning

material (Paas et al., 2010). Clearly, students’ perceptions of an instructor as

nonaccommodative presents the potential to overload working memories and inhibit

important learning processes.

Although intrinsic load is fixed for given knowledge levels of learners (Sweller,

2010), research has suggested that instructors can strategically design instruction to

reduce extraneous load. Much of the research surrounding these strategies involves

presenting materials in complex or unique ways (see Sweller, 2016). For example, Jong

(2010) forwarded the modality effect, arguing that extraneous load can be reduced by

combining auditory and visual forms of information. Or, instructors can deliberately

avoid presenting students with redundant information across multiple sources (i.e., the

redundancy principle; Sweller et al., 1998). Nonaccommodation represents a process

whereby instructors appear to violate several of these principles.

To illustrate, nonaccommodative instructors violate the redundancy principle by

using behaviors too frequently relative to student expectations. The use of more clarity

strategies to reduce uncertainty and clarify learning principles (see Bolkan, Goodboy, &

Kelsey, 2016) may in fact only be useful up to a certain threshold, at which point a

student perceives the instructor to be nonaccommodative and subsequently redundant.

105

Similarly, instructors can increase extraneous load by failing to provide enough clear

information when initially presenting a concept. Thus, this dissertation argues that it is

important for instructors to acknowledge and recognize their students’ expected levels of

behavior in order to determine the appropriate pedagogical strategies. To be effective,

instructors must find a balance between making sure specific behaviors are implemented

into their pedagogy while also not relying on them in a way that students see as

distracting (Jong, 2010). Consequently, students with instructors they perceive as

accommodative may experience lower extraneous load and have more cognitive

resources to invest in tasks necessitating germane load, which should eventually lead to

learning (Mavilidi & Zhong, 2019).

Perhaps even more interestingly, this research supports new frontiers in

examination of cognitive load theory related to gestures (Pouw, de Nooijer, van Gog,

Zwaan, & Paas, 2014) and emotions (Plass & Kalyuga, 2019). Specifically, researchers

have recently considered examining cognitive load in relation to behaviors separate from

learning content. The results from this dissertation provide evidence that perceptions of

physical movement like gestures, eye contact, enthusiasm, and movement can drain

students’ cognitive resources away from processing information. In addition, the

emotional support provided by instructors to students may also compete with students’

available working memory for processing relevant learning information. This is an

important step linking perceptions of nonaccommodation to cognitive load theory, yet

more research is needed to theoretically examine direct relationships between

communication, CAT, and outcomes.

106

Beyond these isolated effects, equally interesting was the disappearance of

nonverbal, verbal/linguistic, and support effects in the presence of content

nonaccommodation as exposed by the HLM analysis. The full model (Table 4.3, p. 64)

illustrates that significant associations between nonverbal, verbal/linguistic, and support

nonaccommodation on extraneous load disappeared when included alongside content

nonaccommodation. The content nonaccommodation effect remained significant with a

consistent magnitude to its absolute effect (-0.71, p < .05). The reduction in total variance

accounted for between the null and full models also suggests that the content

accommodation gap played a small, yet statistically significant, role explaining variance

in students’ extraneous load across course sections; only a small amount of variance was

explained by the model after accounting for content nonaccommodation.

When controlling for the various modes of communication simultaneously, only

content nonaccommodation remained associated with extraneous load. This suggests that

the magnitude of nonverbal, verbal/linguistic, and support nonaccommodation no longer

influenced student outcomes in the presence of content nonaccommodation. In relation to

cognitive load theory, the results reiterate the importance of the presentation of content in

addition to other factors like the inherent complexity of the content and the learner’s

expertise. The instructor clearly has an important responsibility to ensure that content is

presented to students in relevant and appropriate ways (Sexton, 2017). The results also

suggest that the relationship between content-specific behaviors may obfuscate the effects

of other modes of nonaccommodation. Consequently, although instructor nonverbal,

verbal/linguistic, or support nonaccommodation may influence the amount of working

memory available to students individually, students appear to prioritize the behavior most

107

relevant to the learning task (i.e., content behavior) when assessing appropriateness.

Future research should continue examining the collective effects between instructor

presentational behaviors and instructor content behaviors on extraneous load to determine

how instructors should prioritize their instruction.

H2 and H3 explored patterns between perceptions of nonaccommodation and

students’ beliefs about the instructor-student relationship.

Perceived Nonaccommodation and the Instructor-Student Relationship

The initial results for H2 (nonaccommodation on communication satisfaction;

Table 4.5, p.68) and H3 (nonaccommodation on instructor-student rapport; Table 4.8, p.

73) were similar to those obtained for H1. First, HLM analyses revealed that decreases in

the magnitude of nonverbal (0.45, p < .05), verbal/linguistic (0.24, p < .05), content

(0.41, p < .05), and support (0.45, p < .05) nonaccommodation were associated with

increases in communication satisfaction across course sections. Likewise, decreases in

nonverbal (0.30, p < .05), verbal/linguistic (0.21, p < .05), content (0.32, p < .05), and

support (0.33, p < .05) nonaccommodation were also significantly associated with

increases in instructor-student rapport across sections. Together, the models provide

evidence for independent associations between nonaccommodation across modes and

students’ impressions of the instructor-student relationship.

Much like the results for extraneous load, it was expected that perceptions of

nonaccommodation would be related to differences in students’ beliefs about their

relationships with their instructors. From a CAT perspective, listeners’ perceptions of

nonaccommodation are dictated by the appropriateness of a speaker’s behavior. When the

message is seen as appropriately adjusted, students should evaluate the experience and

108

the relationship more positively (Giles et al., 2007a). Similarly, when the message is

viewed as inappropriately adjusted, students should rate the experience negatively (Claus,

Booth-Butterfield, & Chory, 2012). The results reaffirm theoretical relationships between

perceptions of nonaccommodation and the development of interpersonal relationships in

yet another context (i.e., the instructional situation) where role-defined and hierarchically

supported social differences are present (see Bourhis et al., 1989; Buzzanell, Burrell,

Stafford, & Berkowitz, 1996; Gardner & Jones, 1999; McCroskey & Richmond, 2000).

Specific to modes of communication, the results affirm theoretical relationships

between perceptions of nonaccommodation and relational development across nonverbal,

verbal/linguistic, content, and support means. It appears that when students perceive

behavior of any kind to be inappropriate, their feelings about the relationship with the

instructor are subsequently lessened. Consequently, instructors should be mindful of how

both what (i.e., content and support) they say as well as how they say it (i.e., nonverbal

and verbal/linguistic) can be interpreted differently by students to impact the instructor-

student relationship. For example, Ackerman and Gross (2010) argued that instructors

should provide moderate amounts of feedback to students in order to nurture

relationships. The results of this study potentially add to this literature by suggesting that

what constitutes a moderate amount of feedback, among other behaviors, differs for

students; even if an instructor strategically intends to provide a moderate amount of

feedback, some students might still find this practice too underwhelming or excessive for

their needs.

Interestingly, the results for H2 and H3 differed from H1 when all variables were

simultaneously entered into the HLM models. First, the full model for communication

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satisfaction (Table 4.6, p. 69) illustrates that only the absolute effects for content and

support nonaccommodation remained significant when controlling for other variables, yet

the magnitude of the effects decreased (content: 0.30, p < .05; support: 0.29, p < .05).

Next, the full model for instructor-student rapport (Table 4.9, p. 74) also demonstrates

that only the effects for content and support nonaccommodation remained significant in

the model, decreasing slightly (content: 0.20, p < .05; support: 0.21, p < .05). The

reduction in total variance for each model could not be calculated because the slope for

support accommodation varied randomly across sections. In both cases, the absolute

effects for nonverbal and verbal/linguistic nonaccommodation disappeared in the

presence of content and support nonaccommodation.

The results from the full model for communication satisfaction and instructor-

student rapport run contrary to those obtained for extraneous load by highlighting the

dual importance of content and support behaviors. In both cases, students’ perceptions of

an instructor’s nonverbal or verbal/linguistic behavior no longer influenced their feelings

about the relationship when both content and support nonaccommodation were included.

Thus, when it comes to developing relationships with instructors, perhaps student again

prioritize the appropriateness of behaviors directly related to their understanding of the

academic task. It seems reasonable that students expect instructors to exercise appropriate

behaviors relative to the overarching goal of the instructional context: improvement in

some competency (Nyquist & Booth, 1977). If instructors can reduce the complexity of

the material by adjusting content while simultaneously recognizing students’ emotional

needs, they can develop strong relationships with their students. In fact, research does

support the notion that students prioritize clear, competent, and relevant instructor

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behaviors compared to immediacy (Goldman, Cranmer, Sollitto, Labelle, & Lancaster,

2017). Appropriate nonverbal and verbal/linguistic behavior may serve as an added

luxury for students that can individually enhance closeness between instructors and

students, yet appropriate levels of each may not be necessary for building effective

relationships. This adds to the body of literature linking students’ perceptions of

instructor behaviors including inappropriate conversations (Sidelinger, 2014), self-

disclosure (Sidelinger, Nyeste, Madlock, Pollak, & Wilkinson, 2015), and many others

(Myers et al., 2014) to students’ satisfaction.

This pattern of results is also fascinating in light of existing research linking

students’ perceptions of instructor-student rapport to learning (Frisby & Martin, 2010;

Frisby & Housley Gaffney, 2015; Frisby, Limperos, Record, Downs, & Kercsmar, 2013).

Past research has shown that students’ beliefs about nonverbal (Frisby & Housley

Gaffney, 2015) and supportive (Kim & Thayne, 2015) behaviors influence rapport, which

could then lead to greater accounts of affective and cognitive learning. However, based

on these results, an instructor’s ability to adjust content appropriately may also influence

beliefs about rapport. Research has argued that students’ academic needs may drive

antisocial classroom behavior to a greater extent than their relational needs (Claus et al.,

2012). In an academic environment where students are constantly evaluated and success

is often dictated by the distribution of grades, perhaps students initially base their

understanding of the enjoyableness of the interaction and the personal connection with an

instructor in how well that instructor facilitates their understanding of content.

Ultimately, each mode of nonaccommodation was significantly related to the

development of instructor-student relationships, but the collective results highlight

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students’ potential prioritization of specific types of instructor behaviors when it comes to

feelings of satisfaction and rapport.

Last, H4-H6 and RQ1 investigated patterns between perceptions of

nonaccommodation and students’ general impressions of the instructor.

Perceived Nonaccommodation and Student Impressions of Instructors

The findings for H4, H5, and RQ1 (Table 4.11, p. 77; Table 4.14, p. 81; Table

4.17, p. 85) and H6 (Table 4.20, p. 90) were again similar to those in the previous

analyses. Nonaccommodation within each mode was significantly related to each

dimension of instructor credibility across course sections: instructor trustworthiness

(nonverbal; [0.33, p < .05], verbal/linguistic; [0.22, p < .05], content; [0.34, p < .05],

support; [0.33, p < .05]), instructor caring (nonverbal; [0.45, p < .05], verbal/linguistic;

[0.35, p < .05], content; [0.45, p < .05], support; [0.51, p < .05]), and instructor

competence (nonverbal; [0.30, p < .05], verbal/linguistic; [0.18, p < .05], content; [0.34, p

< .05)], support; [0.26, p < .05]). Nonverbal (0.32, p < .05), verbal/linguistic; [0.24, p <

.05], content (0.42, p < .05), and support (0.35, p < .05) nonaccommodation was also

associated with decreases in instructor communication competence. For every mode

included, the research demonstrated significant independent effects of perceived

nonaccommodation on students’ impressions of the instructor.

Once again, these results are not surprising. Consistent with CAT,

nonaccommodation was significantly, negatively related to expected outcomes. It appears

that students’ perceptions of their instructors’ nonaccommodative behavior informed

their feelings about the instructor’s general content knowledge, whether they cared for

them, whether they had their best interests at heart, and whether they were effective

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communicators in general. This reinforces claims about the underlying function of

accommodative skills as an indicator of an individual’s communicative competence

(Greene & Burleson, 2003; Gallois et al., 2016b; Pitts & Harwood, 2015). Although

specific intentions on the part of the instructor were not examined, it follows from the

data and results that their behavior may have resembled a form of downward

convergence for many students. This pattern has been shown to increase perceptions of

competence elsewhere (Bradac, Mulac, & House, 1988; Mazer & Hunt, 2008), though

future research might reinforce this claim by objectively categorizing instructor behavior

in relation to students’ impressions.

The results when all variables were included in the HLM models were the exact

same as those obtained for extraneous load, with one exception. For trustworthiness

(Table 4.12, p. 78), competence (Table 4.18, p. 86), and instructor communication

competence (Table 4.21, p. 91), the full models demonstrated that nonverbal,

verbal/linguistic, and support nonaccommodative effects disappeared when included with

content nonaccommodation. The magnitude of the effect of content nonaccommodation

remained the same as the absolute effect for each: trustworthiness (content; 0.34, p <

.05), competence (content; 0.34, p < .05), and instructor communication competence

(content; 0.42, p < .05). The one exception to this pattern was instructor caring. When all

modes were included, nonverbal (0.35, p < .05) and content (content; 0.22, p < .05)

nonaccommodation both remained significant while the effects for verbal/linguistic and

support nonaccommodation disappeared.

After examining patterns across all four models, it seems students’ impressions of

instructors were also largely driven by feelings about content nonaccommodation. That

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is, in every case apart from instructor caring, the significant effects for other modes of

nonaccommodation disappeared while content nonaccommodation remained significant.

This would indicate that instructors can get away with increasing levels of inappropriate

nonverbal, verbal/linguistic, and support behavior as long as they make an effort to adjust

their presentation of content. Perceptions of instructor content-related communication

(Schrodt, 2013) seem to influence students’ impressions of instructors and their

communication abilities.

At least for this particular course, students’ perceptions of the instructor’s overall

caring also depended to some degree on nonverbal nonaccommodation. This finding

supports the important role of instructor nonverbal behavior in relation to students’

beliefs about caring (Teven, 2001; Teven & Gorham, 1998). Contrarily, the effect of

nonverbal accommodation on competence, trustworthiness, and instructional

communication competence disappeared when controlling for other variables in the

model. When it comes to the development of instructor caring, appropriate nonverbal

behavior may be a necessary condition and not an added benefit (Bolkan, Goodboy, &

Myers, 2017).

The significant associations between instructor nonaccommodation and instructor

competence may be unique to the instructional setting. In an instructional setting, the

ultimate goal is to improve specific competencies (Staton, 1989; Clark, 2002). And, as

Nyquist and Booth (1977) suggest, this is facilitated through roles predetermined and

hierarchically formalized by individual subject matter knowledge. This implies a

difference in subject-matter expertise between instructors and students. Instructors must

verbalize different forms of content-relevant pedagogy in order to ensure students are

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understanding content correctly and efficiently (Shulman, 1987; e.g., speech evaluation

training, Stitt; Simonds, & Hunt, 2003; Frey, Simonds, Hooker, Meyer, & Hunt, 2016).

Thus, within an instructional setting, students’ impressions of an instructor’s competence,

and instructor credibility in general, depend on their ability to adjust content in a way that

helps students meet learning goals. If a student feels that an instructor helps them meet

this goal by adjusting appropriately, they will view that instructor as more knowledgeable

and as having more expertise.

Collectively, the findings point to perceptions of nonaccommodation related to

content having significant, driving effects on classroom outcomes. When content

nonaccommodation was included in a model, the significant absolute effects of other

forms of nonaccommodation generally disappeared. Consequently, the results warrant

further consideration of the role of content-related adjustment in classroom settings. If the

effects of content-related nonaccommodation mask the influence of other forms of

nonaccommodation, instructors may want to devote extra time and energy adjusting these

behaviors to help students process information, develop relationships, and form positive

impressions. In this study, those content behaviors included appropriate adjustments

related to feedback, examples that increase relevance, explanations of course content, the

simplification of course content, and the repetition of ideas to increase understanding.

Theoretically, these findings also highlight the importance of role-related

differences influencing students’ perceptions (Hosek & Soliz, 2016). Inherent in each of

the content behaviors included in this study is an understanding of the instructor as an

individual possessing greater knowledge and power; there is a status differential between

instructors and students. The behaviors comprised by nonverbal, verbal/linguistic, and

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support behavior listed in Table 3.2 are contextually tied to the classroom context, yet

they may not reflect the difference in knowledge implicit in content behaviors. Thus, to

students in this study, perhaps nonaccommodation related to content remained significant

because of its reinforcement of the instructor’s social position relative to students in a

way not captured by the other modes of communication. This reinforces the significant

importance of context when considering the effects of nonaccommodation.

Also related to CAT research, findings raise the question of whether researchers

should continue modeling nonaccommodation across modes simultaneously. In several

cases, depending on whether the outcome was cognitive or affect in nature, the

combination of behaviors necessary to significantly effect that outcome differed.

Previous studies examining CAT across various modes of communication have provided

a similar distinction (e.g., Gnisci, 2005; Jones et al., 1998); however, their research

relates primarily to the development of affect. Studies of interpretability or

comprehension, where accommodation is intended to enhance or reduce mutual

understanding, might also depend on perceptions and attributions within and across

specific modes of behavior to influence outcomes (Giles & Gasiorek, 2013; Hewett et al.,

2015; Hewett, Watson, Gallois, Ward, & Leggett, 2009).

Moreover, these results hint that when nonaccommodation is characterized as

multimodal, message receivers may draw on the characteristics of the situation to

prioritize adjustment in some areas over others. Although research has frequently

documented the occurrence of accommodation across modes (Bilous & Krauss, 1988;

Gnisci & Bakeman, 2007; Zilles & King, 2005), the current study highlights the idea that

some nonaccommodative effects may depend on the presence of nonaccommodation in

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other areas in order to exert a significant influence. In other words, depending on the

context in which nonaccommodation is being evaluated, the relevant contextual goals

may determine which behaviors will be most important in influencing outcomes.

Nonaccommodation in an instructional context might depend heavily on an instructor’s

ability to adjust content to students needs as a means of optimizing student learning

conditions (i.e., enhancing information processing, building relationships, and creating

positive impressions.

The final set of results were concerned with the influence of the hierarchical

structure of students in the basic communication course. RQ2 and RQ3 investigated

investigate whether any of the effects present in the previous hypotheses were influenced

by the nested structure of the data.

Influence of the Data Hierarchy

Overall, the data hierarchy of students nested within course sections did have a

small, yet substantial influence on many of the variables in the study (for an overview,

refer to Table 4.22, p. 93). The relatively small intra-class correlation coefficients (ICCs)

indicate the variance in outcomes was primarily attributable to students (level-one) rather

than course sections (level-two). Stated differently, the relationships between students’

perceptions of instructor nonaccommodation and outcomes were fairly consistent

between sections. The ICC also indicates that there is some unexplained variance

occurring at the course section level, but outcomes appear to be primarily dictated by the

individual student experience rather than effects existing across course sections.

Regarding this point, course section did exert an influence on several of the

relationships among study variables; only the relationships between nonaccommodation

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and extraneous load did not feature any random variation. Six slopes across seven models

differed across course sections, revealing three general patterns of association. These

relationships imply that some nonaccommodation effects differed in size and magnitude

depending on a student’s course section. First, the strength of association between

support nonaccommodation and communication satisfaction and instructor-student

rapport differed across sections. Second, the strength of association between nonverbal

nonaccommodation and instructor caring differed across sections. Third, the strength of

association between content nonaccommodation and instructor trustworthiness,

competence, and communication competence differed depending on the section in which

the participant was enrolled. The significant random variations across course sections

present significant procedural and training implications for basic course instructors and

administrators, each of which are subsequently discussed.

To begin, the effect of students’ perceptions of an instructor’s support

nonaccommodation on both communication satisfaction and instructor-student rapport

differed across course sections. This means for students in different courses sections, a

similar magnitude of perceived instructor support nonaccommodation may have a large

influence on relational development in one section and a small influence in another. This

is especially important considering the effects of rapport on instructor’s professional

outcomes like satisfaction and teaching efficacy (Frisby et al., 2016). If instructors are

providing equal levels of support across sections, yet still developing various levels of

satisfaction and rapport with students, there may be a problem in the overall equity of

instructor effort in the program. That is, some instructors may be more

nonaccommodative to students in terms of their support behavior, yet they may have less

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of an impact on overall relationship development than other instructors who are less

nonaccommodative; the same amount of instructor support behavior produces different

outcomes in different course sections.

Likewise, the relationship between students’ perceptions of instructor nonverbal

nonaccommodation and instructor caring also differed within course sections. The effects

of nonverbal nonaccommodation on caring was larger in some course sections compared

to others. Like the aforementioned relational variables, instructor caring can lead to

instructor motivation, job satisfaction, and more positive student evaluations, so

instructors should be aware of how students are perceiving caring in their courses (Teven,

2007). Instructors may be using similar levels of eye contact, smiling, enthusiasm,

gestures, or movement across sections, yet students are reporting various levels of

instructor caring in response.

Although it is unclear what specific features of the instructional context may

account for this unexplained variation in these outcomes, the results still provide practical

suggestions for the BCC. First, course directors should be mindful that some unknown

characteristics of the context might potentially mitigate the effects of their instructor’s

nonaccommodative behavior so that students are experiencing satisfaction and

developing rapport differently. It might be helpful for instructors to know that students

are using perceptions of support-related behavior to internalize their feelings about the

instructor-student relationship in meaningful ways. It may be fruitful for the director of

the present BCC to apply these findings by training instructors to investigate the amount

of support desired by their students. This could involve quick methods that do not

markedly obstruct important instructional time like a brief questionnaire, an informative

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discussion about the instructor’s role in providing support, or informal interactions with

students. Once instructors have an idea of the amount of support desired by their specific

course sections, they can adjust their behavior appropriately in order to enhance

perceptions of satisfaction and rapport to potentially increase learning. This would allow

administrators or investigators to make targeted student-level or instructor-level training

and interventions in order to further increase standardization across a large number of

course sections, ensure students are meeting required general education outcomes, and

enhance the student-instructor experience.

However, it is also important to note that the effects of both support and

nonverbal nonaccommodation may not have as important of an influence on outcomes as

content nonaccommodation in an instructional context. Although the results point to the

notion that in certain course sections the effect of nonaccommodation is less pronounced,

both these modes of nonaccommodation no longer significantly influenced outcomes

when considered along perceptions of content behavior. Thus, BCD administrators and

directors may want to focus their attention specifically on the cause of this variation.

Implications for Instructor Content Behavior

The final slope that randomly varied across course sections was instructors’

content accommodation on instructor trustworthiness, competence, and communication

competence. This suggests that the effect of content nonaccommodation was marginal in

some course sections and larger in others. Notably, each outcome that varied across

course sections is related to students’ perceptions of the instructor’s ability in his or her

role. For example, trustworthiness is an integral component of instructor credibility, and

it has also been referenced as an important component in accommodative interactions

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between individuals of different status (e.g., police-officer civilian interactions; Giles et

al., 2007a). Recall that trustworthiness is defined by students’ beliefs about the integrity

of the instructor (McCroskey & Teven, 1999). This indicates that some students in

specific course sections viewed instructors who were nonaccommodative in their content

as less honest than instructors in other sections. Again, this finding could have

implications for the equity of instructor efforts across sections. Two instructors perceived

as exhibiting the same level of content adjustment in different sections appeared to

receive differing assessments of trustworthiness from students.

Ultimately, this variation in the effect of content behavior is of critical importance

for instructors of the BCD at the institution where data were collected. Findings suggest

that the general effects of support and nonverbal nonaccommodation disappear alongside

content nonaccommodation; the variation in the effects of support and nonverbal

nonaccommodation may be practically unimportant because outcomes stem from

perceptions in other areas. Unless instructors are interested specifically in developing

relationships with students or facilitating caring, administrators may want to train

instructors to prioritize the appropriateness of content behaviors within the classroom.

Stated differently, training time may be well spent to highlight the general importance of

effective content behaviors as opposed to the effects of support and nonverbal

appropriateness on specific goals.

To this end, contextual characteristics that may potentially account for the random

slope were not examined, but the results can be extended specifically to trainings or

professional development sessions regarding using appropriate content behaviors.

Instructors need to be able to effectively differentiate between situations where students

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might desire more feedback, increased explanations, or added simplicity. A training

covering these topics might include pedagogical strategies for encouraging a comfortable

climate and a culture of question asking. Additionally, directors could find ways to

incorporate more resources for instructors to help explain content in a variety of different

ways with different examples. Such strategies may help to mitigate potential increases in

nonaccommodative effects across sections.

However, those in charge of the basic course at the institution where the research

was conducted should find some satisfaction in the overall outcomes across and within

course sections. Table 3.3 shows that students in general experienced little extraneous

load, had good relationships with their instructors, and thought very highly of their

instructors. Students are clearly reporting positive outcomes in response to their

instructors’ communication behaviors. This becomes particularly critical in a time where

students in higher education face challenges beyond commonly considered concerns and

different from those of previous generations, including greater levels of substance abuse,

eating disorders, increased stress, anxiety, sexual orientation, and racial and sexual

discrimination. Whereas some instructors might not feel such challenges fall within the

realm of their organizational responsibilities, the current sample of instructors are

behaving in ways that students find appropriate, acceptable, and helpful. In turn, perhaps

this type of support in a course typically required for first-year students at many

institutions (Valenzano, Wallace, & Morreale, 2014) can lead to more positive

experiences like increased writing and oral communication skills desired by employers

(Hooker & Simonds, 2015), a greater awareness and desire to engage in social justice

related causes (Patterson & Swartz, 2014), greater information-literacy awareness (Meyer

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et al., 2008), or increased comfortability with the transition to college life (Hosek,

Waldbuesser, Mishne, & Frisby, 2018; Sidelinger & Frisby, 2019). Ultimately, this could

also lead to higher student success, retention rates, and graduation rates.

The collective results of this dissertation provide an initial exploration of the

effects of instructor communication behavior as framed through communication

accommodation theory. Although more objective and experimental approaches to CAT

provide valid and reliable estimates of the effects of nonaccommodative behavior, the

approach used in this study provides more conceptual alignment between the theory and

the format used to measure students’ perceptions. The findings also demonstrate support

for direct effects of unimodal forms of nonaccommodation across all outcomes while

introducing the possibility that some effects may disappear when multiple modes are

considered simultaneously. Lastly, the HLM analyses provide insight into the influence

of contextual effects that basic course directors can target to improve the overall basic

course experience. All of these findings represent substantial contributions to the

instructional communication and CAT literature by positing several new theoretical and

practical directions that set the stage for future analyses and research.

Limitations

The results of this dissertation should be interpreted within the scope of the

limitations. To begin, the descriptive statistics depicted in Table 3.3 suggest that all the

instructors responsible for teaching the course sections excelled in students’ reports of

each outcome. This is a positive finding for the current status of the basic communication

course, but it might also suggest minimal variation among the study outcomes. The

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results may be statistically significant, but they lack social significance in that students

still experienced very positive outcomes even amid nonaccommodation.

Essentially, although nonaccommodation leads to small differences in outcomes,

it is still unclear whether perceived nonaccommodative instructors are truly more

effective educators than perceived accommodative instructors. According to CAT, one

would expect perceived nonaccommodation to result in much more difficulty

comprehending content, as well as increasingly negative perceptions of the message

speaker. If students still report positive classroom outcomes when their instructors are

increasingly nonaccommodative, then an instructor’s ability to adjust their behavior may

not be a substantial factor contributing to students’ overall experiences in the classroom.

Students in this course clearly had good, positive experiences regardless of their

perceptions of the instructor’s level of adjustment; the significance of the theoretical

implications are eclipsed by the general capability of the instructors in the sample.

Considering this idea, basic course directors could easily justify bypassing future

trainings in communication adjustment to focus on more problematic areas that require

immediate attention and have a more significant impact on students’ experiences.

Second, the theoretical implications of the results become increasingly complex

when considering the instructional context in which the data were collected. Students

were asked to reflect on their general experiences interacting with instructors without

considering when these interactions occurred. To illustrate, instructors frequently shift

between addressing students as individuals (e.g., a student asks a question privately

before class) and the class as a group (e.g., delivering a lecture). The results fail to

differentiate between students who based perceptions on routine interpersonal

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interactions with the instructor and students who based perceptions on general instructor

communication with the entire class. Moreover, some students may have had more

frequent interactions with an instructor. This study is limited in that the different

available audiences, and students’ overall frequency of interaction with the instructor,

may have influenced their understanding of when and how nonaccommodation occurred.

Other features of the instructional context may have also limited the results. In a

classroom, students and instructors come into contact on a scheduled, reoccurring basis

with a definitive beginning and end. Perhaps students’ understanding that the formalized

length of the instructor-student relationship is terminal influenced their perceptions of

nonaccommodation. Students may be less interested in forming personal relationships or

establishing similarity with instructors when they know their required time for interaction

will eventually come to an end. Likewise, task success in an instructional setting is

determined largely by the instructor in the form of assessment (i.e., grades; Nyquist &

Booth, 1977). Students who are succeeding in a course or in pursuit of an instructional

goal may look back more positively on their experiences with an instructor compared to

students performing poorly (Gasiorek & Dragojevic, 2018). Thus, since data were

collected near the end of the semester, at which point final course grades were likely

close to being finalized, students may have reflected on their interactions with the

instructor in various ways.

Third, the decision to individually select 20 items and 4 supracategories to

represent the universe of instructor nonverbal, verbal/linguistic, content, and support

behavior limits the overall content and face validity of the measure. It is highly likely that

instructors accommodate to students using several different behaviors or across several

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different modes that were not included in the present research. Although the chosen

behaviors are grounded within the larger framework of instructional communication and

CAT literature, a preliminary or grounded study may have revealed insight into the

prominence of the behaviors chosen relative to students’ perceptions or revealed new

behaviors that were overlooked when constructing the measure. Stated differently, the

four categories are arbitrary in that they do not represent the entire universe of potential

instructor behaviors, yet they do still provide a meaningful framework for categorizing

behaviors and interpreting results.

Fourth, the inclusion of the dissertation survey items at the end of the basic course

assessment survey threatened the internal validity of the study by potentially influencing

the way students responded. It is likely that students did not answer the survey as

accurately or as thoughtfully as desired due to the nature of the number of questions and

the time it took to complete the assignment. In keeping with the general traditions of

CAT research, it may be worthwhile for scholars to observe or manipulate instructor

interactions within an actual classroom session in order to more objectively determine

how accommodation occurs across the various modes identified in this study (see Gnisci,

2005; Dixon et al., 2008). Additionally, the time period at which the data were collected

(last two weeks of the semester) may have influenced the truthfulness of the findings. It

may be the students become accustomed to certain patterns of communication with

instructors over time that diminish the present effects. Researchers might consider

collecting data at different time points or investigating how perceptions of

nonaccommodation change across the length of the semester.

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The design of the survey may have also contributed to an overall halo effect

among students who perceived their instructors as accommodative. Within the survey,

students were asked to rate the appropriateness of their instructor’s behavior prior to

completing the measures related to classroom outcomes. It may be that some students

overrated their responses to the outcome measures because of their ratings of the

instructor at the beginning of the survey. This is especially relevant for the outcomes

related to students’ perceptions of the instructor (i.e., credibility, instructor

communication competence); students who viewed the instructor as more

accommodative may have been more likely to have higher impressions of the instructor

following this rating.

Fifth, the study does not include covariates in the HLM analysis that could control

for potential biases in the sample. For example, HLM analyses investigating school

effects generally control for student demographics and second level factors that may bias

the overall results (Ma et al., 2008). It was argued that the overwhelming number of

Caucasian, female instructors and students may have diminished differences in

perceptions of verbal adjustment because they share similar vocal characteristics.

Separate HLM analysis in which student sex, race, course instructor sex, or course

instructor gender serve as additional control variables might lead to unbiased and

nuanced results that advance theory and practice further. Raudenbush and Bryk (2002)

also call for the control of individual-difference variables at the student level in order to

produce more accurate estimates of the quality of academic experiences within course

sections and more generalizable results about the equity of outcomes across course

sections. For example, the inclusion of individual student characteristics would provide

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helpful information related to how students across course sections are performing

regardless of their sex, race, socioeconomic status, family size, or other importance

demographic variables.

Finally, the present sample was underpowered for the HLM analysis. The a priori

power analysis suggested that a sample size of 874 students would be necessary for

significant power under the specified conditions. HLM is a large-sample technique

designed to provide descriptive, non-parametric analyses of large data sets.

Administrators or professionals wishing to implement similar procedures in the

evaluations of their basic communication courses must consider the parameters necessary

for conducting the tests and obtaining significant results. For this dissertation, a larger

sample of course sections (i.e., 50; Hox, 2002) may have produced different patterns of

results. Nonetheless, the sample still managed to produce statistical results under the set

research parameters (p. 50).

Beyond the noted threats to the relationships between variables in the study, there

are several external validity threats that limit the study generalizability. The lack of

control for potential sample biases limits the generalizability of the results to other course

sections. Researchers concerned with school effects sometimes also control for level two

contextual variables (e.g., location, mean socioeconomic status, mean status in school) in

order to make results applicable across a wide variety of settings. Additional course

sections characteristics could have severed as level-two covariates in order to make the

results more transferable to instructional communication researchers and basic course

directors across other institutions.

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Second, students in the basic course at this institution understand that their

participation in the research is required for a course grade. As stated, they are required to

complete a pre-test at the beginning of the semester and a post-test during the final two

weeks. This pre-test may have sensitized students to the overall nature of the assignment

and reiterated their completion as a path to a quick, easy grade as opposed to a thoughtful

analysis of their experience. Moreover, the post-test posed additional questions that were

not present in the pre-test. It is possible that students’ responses to the initial set of

questions desired by the assessment coordinator, which analyzed reports of students’ self-

efficacy behaviors, influenced students’ responses during the rest of the survey. Thus,

students may have experienced reactive effects due to the nature of the questions posed.

Next, potential methods for enhancing overall internal and ecological validity, as

well as new lines of theoretical development, are discussed as part of the future directions

of this line of research.

Future Directions

The findings in this dissertation advance instructional communication research

and CAT research by effectively linking students’ perceptions of instructor

nonaccommodative behavior to classroom outcomes. The implications provide a clear

direction for future researchers to assess listeners’ nonaccommodative experiences

intuitively and accurately while also providing empirical evidence for the importance of

students’ perceptions of instructor behavior. Consequently, the following section outlines

several future directions that should continue to enhance this line of research for both

CAT and instructional communication scholars.

129

As stated in the discussion of implications, the results provide an initial

examination of relationships between perceptions of nonaccommodation and individual

information processing ability in a classroom setting. Research can continue to

investigate how perceptions of instructor behavior impact this process by extending

research to include other forms of cognitive load (i.e., intrinsic, germane). To illustrate,

there is evidence that the type of load experienced differs depending on the level of

expertise of the learner (Kalyuga, Ayres, Chandler, & Sweller, 2003; Paas et al., 2003).

Researchers should consider controlling for students’ communication self-efficacy or

replicating results with higher level learners (e.g., a graduate seminar) to add even more

distinction to the relationship between nonaccommodation and load.

Next, this dissertation does not examine learning as the ultimate outcome in the

instructional situation. Clark (2002) argued that learning should be the focus of

instructional communication research, and the results of the present dissertation are

limited in that conclusions regarding how these outcomes relate to student learning are

omitted. Thus, while the argument can be made that instructors perceived as less

nonaccommodative put students in a better position to learn, research still needs to

examine conditions under which instructor accommodation instructors specifically lead

to student learning.

Beyond learning, however, both instructional communication and CAT

researchers often focus on compliance as a relevant theoretical outcome. This is

demonstrated in instructional communication research concerning concepts like challenge

behaviour (Simonds, 1997) and student misbehaviors (Johnson, Goldman, & Claus,

2019) among several others. CAT researchers have examined compliance as a response

130

to a speaker’s speech rate (Buller & Aune, 1992, Buller, LePoire, Aune, & Eloy, 1992)

and vocalics (Aune & Kikuchi, 1993; Buller & Aune, 1988), as well as across police-

civilian interactions (Hajek, Villagran, & Wittenberg-Lyles, 2007), reinforcing CAT as

an explanatory mechanism for persuasive effects. If instructors want students to follow

classroom rules and procedures, like those presented in syllabi, investigating the effects

of nonaccommodation on students’ compliance seems like a logical next step.

Depending on the researcher’s epistemological position (Hayes, 2006), several

other hierarchical structures are present beyond students nested within course sections.

Researchers might be interested in contextual effects from students nested within

instructors or students nested within various majors across the university. Or, HLM

presents an opportunity to contextually model the nested structure over students’ repeated

observations, which may more precisely model changes over time (Luke, 2004; Slater et

al., 2006). Essentially, HLM allows for greater ease when assessing trends through

specific growth curve models that accurately and efficiently account for the

interdependence between several student observations. Thus, instructional

communication researchers or CAT researchers should consider using HLM to answer

research questions related to repeated measures nested within individuals to provide

easier assessment of trends (e.g., learning; Lane, 2015) resulting from specific

communicative practices.

Perhaps the most interesting future direction of the present research concerns the

continued exploration of the bipolar rating format to differentiate between perceptions of

under- and overaccommodation. Researchers should contemplate integrating the Too

Little / Too Much rating format (TLTM; Vergauwe et al., 2017) as a possible mechanism

131

for overcoming the limitations of the measure in the current approach. Like this

dissertation, the TLTM incorporates a specific midpoint to differentiate between

perceptions of behavior occurring too infrequently (i.e., underaccommodative), behavior

occurring just the right amount relative to one’s needs (i.e., accommodative), and

behavior occurring too frequently (i.e., overaccommodative). The TLTM differentiates

itself from other approaches by relying on the standard error of measurement (SEM) to

precisely estimate a confidence interval containing a respondent’s true score. That is,

Vergauwe et al. posit that average scores which fall outside of particular SEM ranges are

conceptually indistinguishable from 0 (i.e., the midpoint), providing an outlet to

mathematically categorize responses in a manner that overcomes some of the

shortcoming of the coding process. Ultimately, there are several options for expanding

thinking about the measurement of students’ perceptions of nonaccommodation, and

future research efforts should seek to refine these processes in search of acceptable

theoretical solutions.

Next, a single, isolated study is not enough to provide concrete solutions, and

many of the critiques of instructional communication researchers highlight this problem

(Friedrich, 2002; Staton-Spicer & Wulff, 1984; Waldeck et al., 2001). Clearly, course

sections present different circumstances under which student observations are collected,

and future replications of this research across a variety of instructional contexts is

warranted. A replication of the linkages between nonaccommodative perceptions and

student outcomes in other basic courses, or other academic programs in general, may

reveal boundary conditions under which the current relationships differ. Also, researchers

should replicate the effects of the various modes of nonaccommodation or the lack of a

132

difference before making definitive conclusions regarding these theoretical

developments.

Importantly, the discussion of the random effects within course sections also

introduces the possibility of level-two predictor variables that influence relationships

between level-one variables in the hierarchical structure. A series of potential level-two

predictors, including instructor race, instructor age, average student age, overall climate,

length, format, composition, or time of day (e.g., morning, afternoon, evening), might

exert contextual effects on students’ overall classroom experiences. Future assessments

of the basic communication course, or similar academic programs, may benefit from the

application of HLM analyses that situate such variables as level-two predictors. As

discussed within the context of RQ2 and RQ3 in the discussion, level-two predictors can

also provide theoretical and practical insight into the reasons why predictor variables vary

randomly across course sections. Future research would benefit through a more advanced

understanding of why the relationships between nonverbal, content, and support

behaviors and student outcomes varied randomly.

For example, future research on instructor support and rapport might extend a

larger line of thinking suggesting that rapport may differ across cultures (Frisby &

Buckner, 2018). Frisby, Slone, and Bengu (2017) claimed that U.S. and Turkish students

perceived different levels of rapport in interactions with their instructors. At a contextual

level in the basic course, it may be that the relationship between support

nonaccommodation and instructor-student rapport depends on the influence of cultural

characteristics like the demographics of the section, the location of the university, or

students’ beliefs about the ethnicity of the instructor. As another example, there are

133

several second level factors that may also contribute to the magnitude and direction of the

instructor content nonaccommodation and trustworthiness relationship. To illustrate, the

instructors of the course sections in the sample differed in terms of age and experience.

Intergenerational communication has been a staple among CAT researchers for decades

(Soliz & Giles, 2014), suggesting that adjustment is often influenced by age-based

stereotypes and characteristics (e.g., Hummert, Shaner, Garstka, & Henry, 1998). As

such, age has also been shown to influence communication between students and

instructors. Edwards and Harwood (2003) reasoned that students were more favorable

towards younger instructors, while Semlak and Pearson (2008) suggested that students

rated older instructors as more credible. Thus, instructor age, or students’ perceptions of

instructor age, might account for this variation within sections.

Several additional areas for future research were also woven throughout the

implications of this dissertation research. Summatively, these future directions were

related to a focus on the relationship between student expectations for the type and

frequency of specific instructor behaviors and relational formation, objective

observations of instructor behavior to advance CAT in instructional settings from a

message-sender perspective, and the development of applied professional development

trainings forwarded by the results. This collective of future directions alludes to the

promise of conducting, replicating, and refining CAT research in an instructional setting

in order to move the discipline forward.

Apart from the noted limitations, the theoretical and practical implications

forwarded herein offering exciting next steps for instructional communication and CAT

researchers. First, CAT, a prominent and proven theory of communication adjustment,

134

appears to be a helpful framework for understanding students’ perceptions of their

instructor’s behavior. Across all 7 HLM models, the variance in outcomes was driven

primarily by student-level perceptions of nonaccommodation. The notion that instructor

behaviors can clearly serve different functions for different students paves the way for

researchers to rethink how the traditional process-product approach to understanding

classroom processes may be limiting our general understanding of the complexities of

classroom communication. Behaviors may not function as linearly or definitively as

previously conceptualized, and CAT presents a clear opportunity to advance the

discipline by forwarding new knowledge claims about what constitutes effective

communication across different types of students.

In this regard, CAT is a relevant theoretical platform for analyzing the influence

of context on interactive outcomes. Specifically, CAT is rooted in individuals’ identities

that determine their respective interactional needs and goals. It is likely that these

personal and social identities ultimately play a role in dictating whether the instructor’s

behavior was deemed appropriate or inappropriate. This approach presents a guiding

framework for further investigations of how cultural and contextual influences influence

overall instructional experiences (Hosek & Soliz, 2016). Pragmatically, this

understanding that an instructor’s ability to adjust his or her communication to meet

students’ individualized expectations also has important ramifications for creating

optimal learning environments. Instructors should potentially consider taking more time

to recognize both a) their students’ individual needs and b) the immediate instructional

goal to determine the most effective types of behavior.

135

Finally, this dissertation overcomes existing limitations in instructional

communication and CAT research through revamped approaches to operationalizing

student perceptions and statistical procedures that control for the influence of nested

observations. Others can continue to build on these methods in order to refine

understandings of the nuances of classroom communication. Researchers can employ the

rating format and coding procedures used in this study to more intuitively collect data

from students regarding their individual perceptions. The use of HLM to model

contextual influences and account for the interdependence of respondent observations can

also be replicated to answer important questions related to nonaccommodation, learning,

and the BCC.

When defining the boundaries of the instructional setting, Nyquist and Booth

(1977) stated that “students process any particular communication event in highly

personal ways. The message sent is not always the message received” (p. 17). Since then,

researchers have continued to advance thinking by advocating for the importance of

person perception variables (McCroskey, Teven, Minielli, & Richmond, 2014), making

calls to for scholars to be responsive to changing instructional environments (Hess &

Mazer, 2017) and identities (Witt, 2017), and promoting the use of advanced statistical

modeling techniques that add greater precision to scientific research examining instructor

messages and student reception (Goodboy, 2017). This dissertation is an attempt to

integrate these perspectives in the hopes of providing an outlet for improving students’

experiences in higher education. Communicative adjustment is and will continue to be a

fundamental component of human interaction, and scholars should reflect this principle

both within and across academic settings.

136

APPENDIX

Survey Questions

Perceptions of Instructor Nonaccommodation Scale

Think about your CIS 111 instructor. Use the scale below to respond to whether your

instructor used an appropriate or inappropriate amount of the specified behavior.

Too Little Just the Right

Amount

Too Much

-4 -3 -2 -2 -1 -0 1 2 2 3 4

Made eye contact with me

Smiled at me

Showed enthusiasm

Used gestures to emphasize points

Moved around the classroom when

speaking


Amount

Too Much

-4 -3 -2 -2 -1 -0 1 2 2 3 4

Used slang that I would use

Concentrated on articulating words

for clarity

Tried to use simple language

Used jargon that was tough to

understand

Made an effort to pronounce words

correctly


Amount

Too Much

-4 -3 -2 -2 -1 -0 1 2 2 3 4

Provided feedback to me

137

Incorporated examples to make

course content relevant

Explained course content thoroughly

Simplified course content for me

Repeated his/her ideas to help me

understand


Amount

Too Much

-4 -3 -2 -2 -1 -0 1 2 2 3 4

Provided emotional support

Made me feel comfortable

Was concerned about my success in

the class

Was responsive to my needs

Empathized with me

138

Cognitive Load

Instructions: The next set of questions are about your self-report of the cognitive load

imposed by the course content. Please answer as honestly as possible. Thinking about the

content in this course, please indicate your agreement with each item using the rating scale

below from 1 = not at all the case to 9 = completely the case.

Rate on a scale of 0 (not at all the case) to 9 (completely the

case)

The content of this

course is very

complex. o o o o o o o o o

The

problems/assignments

covered in this course

are very complex.

o o o o o o o o o

In this course, very

complex terms are

mentioned. o o o o o o o o o

I have invested a very

high mental effort in

the complexity of this

course

o o o o o o o o o

The explanations and

instructions in the

course are very

unclear.

o o o o o o o o o


instructions in the

course are full of

unclear language.

o o o o o o o o o


instructions in this

course are, in terms

of learning, very

ineffective.

o o o o o o o o o

139

I have invested a very

high mental effort in

unclear and

ineffective

explanations and

instructions in this

class.

o o o o o o o o o

This course really

enhances my

understanding of the

content covered.

o o o o o o o o o

This course really

enhances my

understanding of the


that are covered.

o o o o o o o o o

This course really

enhances my

knowledge of the

terms that are

mentioned.

o o o o o o o o o

The course really

enhances my

knowledge and

understanding of how

to deal with the


covered.

o o o o o o o o o

I invest a very high

mental effort during

this course to enhance

my knowledge and

understanding.

o o o o o o o o o

140


Instructions: Please select the number below that best represents your agreeance with the

following statements on a scale from strongly disagree (1) to strongly agree (7).

Strongly

disagree Disagree

Somewhat

disagree

Neither

agree

nor

disagree

Somewhat

agree Agree

Strongly

agree

My

communication

with my teacher

feels satisfying.

o o o o o o o

I dislike talking

with my teacher. o o o o o o o

I am not

satisfied after

talking to my

teacher.

o o o o o o o

Talking with my

teacher leaves

me feeling like I

accomplished

something.

o o o o o o o

My teacher

fulfills my

expectations

when I talk to

him/her.

o o o o o o o

My

conversations

with my teacher

are worthwhile.

o o o o o o o

When I talk to

my teacher, the

conversations

are rewarding.

o o o o o o o

141


Instructions: Please select the number below that best represents your agreeance with the

following statements on a scale from strongly disagree (1) to strongly agree (5).

Strongly

disagree

Somewhat

disagree

Neither

agree nor

disagree

Somewhat

agree Strongly

agree

In thinking

about my

relationship

with my

instructor, I

enjoy

interacting with

him/her.

o o o o o

My instructor

creates a feeling

of “warmth” in

our relationship. o o o o o

My instructor

relates well to

me. o o o o o

In thinking

about our

relationships, I

have

harmonious

relationships

with my

instructor.

o o o o o

My instructor

has a good sense

of humor. o o o o o

I am

comfortable

interacting with

my instructor. o o o o o

I feel like there

is a “bond”

between my o o o o o

142

instructor and

me.

I look forward

to seeing my

instructor in

class. o o o o o

I strongly care

about my

instructor. o o o o o

My instructor

has taken a

personal interest

in me. o o o o o

I have a close

relationship

with my

instructor. o o o o o

143

Instructor Credibility

Instructions: On the scales below, indicate your feelings about the instructor from the

scenario. Numbers 1 and 7 indicate a very strong feeling. Numbers 2 and 6 indicate a

strong feeling. Numbers 3 and 5 indicate a fairly weak feeling. Number 4 indicates you

are undecided.

1 2 3 4 5 6 7

Intelligent o o o o o o o

Unintelligent

Untrained o o o o o o o

Trained

Cares about

me o o o o o o o

Doesn't care

about me

Honest o o o o o o o

Dishonest

Has my

interests at

heart o o o o o o o

Doesn't have

my interests

at heart

Untrustworthy o o o o o o o

Trustworthy

Inexpert o o o o o o o

Expert

Self-centered o o o o o o o

Not self-

centered

Concerned

with me o o o o o o o

Not

concerned

with me

Honorable o o o o o o o

Dishonorable

Informed o o o o o o o

Uninformed

Moral o o o o o o o

Immoral

144

Incompetent o o o o o o o

Competent

Unethical o o o o o o o

Ethical

145

Communication Competence

My instructor / instructor’s …

Strongly

agree Agree

Somewhat

agree

Neither

agree

nor

disagree

Somewhat

disagree Disagree

Strongly

disagree

has a good

command of

the language. o o o o o o o

typically gets

right to the

point. o o o o o o o

can deal with

others

effectively. o o o o o o o

written

communication

is difficult to

understand.

o o o o o o o

expresses

his/her ideas

clearly. o o o o o o o

oral

communication

is difficult to

understand.

o o o o o o o

usually says

the right thing. o o o o o o o

is sensitive to

others' needs. o o o o o o o

146

pays attention

to what other

people say to

him/her.

o o o o o o o

is a good

listener. o o o o o o o

Is easy to talk

to. o o o o o o o

usually

responds to

messages

(memos, phone

calls, reports,

etc.) quickly.

o o o o o o o

147

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VITA

Terrell Kody Frey

EDUCATION

Instructional Communication Graduate Certificate, University of Kentucky,

Lexington, KY, May 2018

M.A. Communication, Illinois State University, Normal, IL, May 2015

Master’s Thesis - Raising the standard: Peer workshops as enhancers of speech

evaluation fidelity

Committee: Dr. Stephen K. Hunt (Chair), Dr. Cheri J. Simonds, Dr. John Hooker,

and Dr. Kevin Meyer

B.A. Communication Studies, Clemson University, Clemson, S, May 2013

Undergraduate Thesis: Leading online: An integration of technology into the

situational leadership model

Academic Leadership Certificate, Clemson University, Clemson, SC, December

2019

ACADEMIC EMPLOYMENT

July 2017 – Present Faculty Lecturer

University of Kentucky, Lexington, KY

SCHOLASTIC AND PROFESSIONAL HONORS

Faculty Teaching Excellence Award. (2018). Presented by the University of Kentucky

College of Communication and Information for excellence in teaching.

Research Fellowship in Interpersonal Communication. (2018). Presented by the

University of Kentucky College of Communication and Information and advised by Dr.

Renee Kaufmann. Awarded to students individually conducting over 40 hours of research

outside of doctoral dissertation.

University of Kentucky Microteaching Orientation Leader. (2017). Selected through

application to lead cohort of new graduate teaching assistants through training and

orientation.

States Advisory Council Outstanding Manuscript Award. (2017). Presented to the

top article published in a state or regional journal.

Graduate Student Fellow at Presentation U! (2016). Selected by a jury of peers to

participate in a series of workshops focusing on best practices for teaching in a

multimodal environment.

168

Ora Bretall Fellow. (2015). Presented by Illinois State University to a graduate student

whose proposal of master's thesis or doctoral dissertation deals with issues of educational

theory or religious thought.

CASE District III Marketing Award. (2013). Presented by the Council for

Advancement and Support of Education for work on Tigertown Bound recruitment

initiatives.

The David Malcolm Geddes/Mark Roderick Memorial Award. (2013). Presented by

Clemson University to an undergraduate or graduate student in recognition of their

outstanding performance in the classroom, academic achievement, involvement in the

program, creative effort, and character.

Clemson Rising Star Award. (2011). Presented by Clemson University to a first or

second year student who is currently involved in leadership or service activities at

Clemson University and actively pursuing future leadership roles.

Top Paper Awards:

Top 4 Paper. (2019). Basic Course Division, National Communication Association.

Top 4 Paper. (2019). Comm Theory Interest Group, Central States Comm Assc.

Top 4 Paper. (2018). Instructional Develop. Division, National Comm Association.

Top 4 Paper. (2018). Basic Course Interest Group, Central States Comm Assc..

Top Student Paper. (2017). Comm and the Future Division, National Comm Assc.

Top Paper. (2017). Basic Course Division, National Communication Association.

Top Student Paper. (2017). Instructional Develop., Southern States Comm Assc.

Top 4 Paper. (2017). Comm Education Interest Group, Central States Comm Assc.

Top 4 Paper. (2017). Comm and Instruction, Western States Comm Assc.

Top 3 Paper. (2016). Basic Course Division, National Communication Association.

Top 4 Paper. (2016). Basic Course Interest Group, Central States Comm Assc.

Top Student Paper. (2015). Basic Course Division, National Comm Association.

Top Student Paper. (2015). Comm and the Future Division, National Comm Assc.

Top Paper. (2015). Popular Culture Interest Group, Central States Comm Assc.

Top 4 Paper. (2015). Media Studies Interest Group, Central States Comm Assc.

Top Panel Awards:

Top Panel. (2017). Student Section, National Communication Association

PROFESSIONAL PUBLICATIONS

REFEREED PUBLICATIONS

Frisby, B. N., Vallade, J. I., Kaufmann, R., Frey, T. K., & Martin, J. C. (Accepted) Using

virtual reality for speech rehearsals: An innovative instructor approach to enhance

student public speaking efficacy. Paper accepted at The Basic Communication

Course Annual (Acceptance Received 7/10/2019).

Vallade, J. I., Kaufmann, R. M., & Frey, T. K. (Accepted). Facilitating students’

motivation in the basic communication course: A Self-Determination Theory

169

perspective. Paper accepted at The Basic Communication Course Annual

(Acceptance Received 5/20/2019).

Tatum, N. T. & Frey, T. K. (2018). Students as consumers: User responses to money-

back guarantees in higher education on Reddit. The Online Journal of Distance

Education and e-Learning, 6(3), 44-51. Retrieved from tojdel.com

Frey, T. K., Simonds, C. J., Hooker, J. F., Meyer, K. R., & Hunt, S. K. (2018). Assessing

evaluation fidelity between students and instructors in the basic communication

course: The impact of criterion-based speech. Basic Communication Course


Tatum, N. T., Olson, M., & Frey, T. K. (2018). Noncompliance and dissent with cell

phone policies: A psychological reactance theoretical perspective.


Frey, T. K., & Vallade, J. (2018). Assessing students’ perceptions of writing and public

speaking self-efficacy in a composition and communication course. Journal of

Communication Pedagogy, 1, 27-39. doi:10.31446/JCP.2018.08

Frey, T. K., & Tatum, N. T. (2017). The influence of classroom cell phone policies on

instructor credibility. Journal of the Communication, Speech & Theatre

Association of North Dakota, 29, 1-13.

Frey, T. K., & Tatum, N. T. (2016). Hoverboards and “hovermoms”: Helicopter parents

and their influence on millennial students’ rapport with instructors.


Frey, T. K., Hooker, J. F., & Simonds, C. J. (2015). The invaluable nature of speech

evaluation training for new basic course instructors. Basic Communication Course


BOOK CHAPTERS:

Frey, T. K. (Accepted). Classroom emotions measure. In E. E. Graham & J. P. Mazer

(Eds.), Communication research measures III: A sourcebook. New York, NY:

Routledge.

Frey, T. K. (Accepted). Teacher technology policies measure. In E. E. Graham & J. P.

Mazer (Eds.), Communication research measures III: A sourcebook. New York,

170

NY: Routledge.

Frey, T. K. (Accepted). Student academic support measure. In E. E. Graham & J. P.


NY: Routledge.

Frey, T. K. (Accepted). Parental academic support measure. In E. E. Graham & J. P.


NY: Routledge.

Frey, T. K. (Accepted). Instructor misbehaviors measure. In E. E. Graham & J. P. Mazer

(Eds.), Communication research measures III: A sourcebook. New York, NY:

Routledge.

Frey, T. K. (Accepted). Student perceptions of instructor understanding measure. In E.

E. Graham & J. P. Mazer (Eds.), Communication research measures III: A

sourcebook. New York, NY: Routledge.

Kaufmann, R. M., Tatum, N. T., & Frey, T. K. (2017). Current tools and trends of new

media, digital pedagogy, and instructional technology. In M. Strawser (Ed.). New

media and digital pedagogy. Lanham, MD: Lexington Press.

Lane, D. R., Frey, T. K., & Tatum, N. T. (2018). Affective approaches and methods. In

M. L. Houser & A. M. Hosek (Eds.), The handbook of instructional

communication: Rhetorical and relational perspectives (2nd ed.). New York, NY:

Taylor & Francis.

Frey, T. K., Tatum, N. T., & Beck, A. C. (2017). Is it really JUST Twitter!? In J. S.

Seiter, J. Peeples, & M. L. Sanders (Eds.), Great ideas for teaching students

(G.I.F.T.S.) in communication. Boston, MA: Bedford/St. Martin’s.

Tatum, N. T., & Frey, T. K. (2016). Be the change: Cardboard confessionals. In C K.

Rudick, K. B. Golsan, & K. Cheesewright (Eds.), Teaching from the Heart &

Learning to Make a Difference: Teaching the Introductory Communication

Course through Critical Communication Pedagogy. San Diego, CA: Cognella.

CONFERENCE PRESENTATIONS

Vallade, J. I., Kaufmann, R. M., & Frey, T. K. (2019, November). Facilitating students’

motivation in the basic communication course: A Self-Determination Theory

perspective. Paper to be presented in Basic Course Division at the annual meeting

171

of the National Communication Association Convention, Baltimore, MD.

Frey, T. K. (2019, April). Modeling classroom communication through hierarchical

linear modeling: Benefits to instruction, assessment, and theory. Paper presented

in Communication Education Interest Group at the annual meeting of the Central

States Communication Association Convention, Omaha, NE.

Frey, T. K. (2019, April). Communication Accommodation Theory: An overview and

adaptation to instructional communication. Paper presented in Communication

Theory Interest Group at the annual meeting of the Central States Communication

Association Convention, Omaha, NE.

Westwick, J., Chromey, K., Frey, T. K., Simonds, C. J., Sorensen, A., & Hosek, A.

(2019, April). PANEL: An exchange of ideas: A dialogue on the state of the basic

communication course. Paper presented in Basic Course Interest Group at the

annual meeting of the Central States Communication Association Convention,

Omaha, NE.

Frey, T. K., & Vallade, J. (2018, April). Assessing students’ perceptions of writing and

public speaking self-efficacy in a composition and communication course. Paper

presented on the top paper panel in Communication Education at the annual

meeting of the Central States Communication Association Convention,

Milwaukee, WI.

Mazer, J. P., Graham, E. E., Frey, T. K., Tatum, N. T. (2018, November). PANEL:

Measurement in instructional communication: Review, analysis, and

recommendations. Panel presented at the annual meeting of the National

Communication Association, Salt Lake City, UT.

Frey, T. K., Moore, K. P., & Dragojevic, M. (2018, November). Syllabus sanctions:

Controlling language and perceived fairness as antecedents to students’

psychological reactance and intent to comply. Paper presented on the top paper

panel in Instructional Communication at the annual meeting of the National


Frey, T. K., Hadden, A. A., Kaufmann, R. K., Beck, A. C. (2018, November). Classroom

“bae-haviors”: An examination of couples’ management of relational privacy in

the classroom. Paper presented at the annual meeting of the National


Bachman, A., Frey, T. K., & Beck, A. C. (2017, November). Identifying the personal

and professional impact of students’ self-disclosures. Paper presented at the

annual meeting of the National Communication Association, Dallas, TX.

Tatum, N. T., Olson, M., & Frey, T. K. (2017, November). Student compliance with

classroom cell phone policies: Antecedents and Consequences of Psychological

172

Reactance. Paper presented at the annual meeting of the National Communication

Association, Dallas, TX.

Frey, T. K., & Tatum, N. T. (2017, November). No homework, no problem? Using

expectancy violations theory to understand internet reactions to the elimination of

homework at a Texas elementary school. Paper presented at the annual meeting of

the National Communication Association, Dallas, TX.

Tatum, N. T., & Frey, T. K. (2017, November). Students as consumers: User responses

to money-back guarantees on Reddit. Top student paper in the Communication

and the Future interest group at annual meeting of the National

Communication Association, Dallas, TX.

Frey, T. K., Simonds, C. J., Hooker, J. F., Meyer, K. R., & Hunt, S. K. (2017,

November). Assessment in the basic communication course: The impact of

criterion-based speech evaluation training for students on evaluation fidelity with

instructors. Top paper in the Basic Course interest group at annual meeting of

the National Communication Association, Dallas, TX.

Frey, T. K., Kehoe, K., Reinig, L., Hughes, A. G., Fendley, S. A., Benedict, B. C., Hall,

R. D., & Kirschner, J. (2017). PANEL: The relevance of the communication

graduate student association. Top panel in the Student Section interest group

at annual meeting of the National Communication Association, Dallas, TX.

Tatum, N. T., Frey, T. K., & Beck, A. C. (2017, April). Rethinking student engagement:

Comparing behavioral and agentic orientations. Paper presented at the annual

meeting of the Southern States Communication Association, Greeneville, SC.

Frey, T. K. (2017, April). Drastic Measures: Teaching effective delivery through Phil

Davison. G.I.F.T. presented at the annual meeting of the Southern States

Communication Association, Greeneville, SC.

Tatum, N. T., Frey, T. K, & Olson, M. (2017, March). The development and validation

of the classroom cell phone policy attitudes and policy compliance instruments.

Paper presented at the annual meeting of the Central States Communication

Association Convention, Minneapolis, MN.

Frey, T. K. (2017, March). More than a clicker: Teaching audience analysis through fast-

response technology. G.I.F.T. presented at the annual meeting of the Central

States Communication Association Convention, Minneapolis, MN.

Frey, T. K. (2017, March). Communicating instructor presence: Construct development

and validation of a new measure. Paper presented at annual meeting of the

Teachers, Teaching, and Media Conference at Wake Forest University, Winston

Salem, NC.

173

Bachman, A. S., Beck, A. C., & Frey, T. K., (2017, April). What do I say? Investigating

students’ sensitive disclosures from the instructor’s perspective. Paper presented

at the annual meeting of the Western States Communication Association, Salt

Lake City, UT.

Frey, T K. (2016, November). Mediated misbehaviors: The mediating effect of online

classroom climate on instructor misbehaviors, credibility, and affective learning.

Paper presented at annual meeting of the National Communication Association,

Philadelphia, PA.

Tatum, N. T., & Frey, T. K. (2016, November). Classroom cell phone policy attitudes

and intended compliance in the basic course. Paper presented at annual meeting of

the National Communication Association, Philadelphia, PA.

Frey, T. K. (2016, June). Half manners, half lying: A review and critique of politeness

theory. Presented at the International Conference on Language and Social

Psychology, Bangkok, Thailand.

Frey, T. K., Tatum, N. T., & Beck, A. C. (2016, April). Is it really JUST Twitter!?.

G.I.F.T. presented at the annual meeting of the Central States Communication

Association, Grand Rapids, MI

Frey, T. K., Borella, B., & Hooker, J. F. (2016, April). Building a solid foundation for

the speech lab: A revised training plan for Graduate facilitators based on needs

assessment. Paper presented at annual meeting of the Central States

Communication Association, Grand Rapids, MI.

Frey, T. K., & Hunt, S. K. (2016, April). Increasing speech evaluation fidelity between

instructors and students through in-class peer workshops and assessment. Paper

presented at the Central States Communication Association, Grand Rapids, MI.

Frey, T. K. (2015, November). Peer assessment and use of written speech feedback in

the basic communication course. Dr. Lawrence W. Hugenberg top student

paper in the Basic Course Division at annual meeting of the National

Communication Association, Las Vegas, NV.

Frey, T. K., & Hook, O. L. (2015, November). Celebrating your choice: Online social

support for individuals facing traditional abortion stigmas. Paper presented at

annual meeting of the National Communication Association, Las Vegas, NV.

Frey, T. K. (2015, November). #BlackTwitter: An analysis of social sentiment in

temporarily imagined online communities. Top paper in the Communication

and the Future interest group at annual meeting of the National

Communication Association, Las Vegas, NV.

Frey, T. K., & Huxford, J. (2015, April). You’ll never walk alone: Liverpool Football

174

Club as an imagined fan community online. Top paper in the Popular Culture

Interest Group at annual meeting of the Central States Communication

Association, Madison, WI.

Frey, T. K., Hook, O. L., & Jamie, S. M. (2015, April). Text me back: Response time as

a relational predictor across text and email messages. Paper presented at annual

meeting of the Central States Communication Association, Madison, WI.

Worland, C., Shpall Wolstein, A., Frey, T. K., Malik, C. B., Johnson, E., & Strom, K.

(2015, January). Building bridges: Connecting knowledge-making practices

between Eng 101 and Com 110 for first year students. Presented at the Illinois

State University CTLT Teaching and Learning Symposium.

Frey, T. K., Baptist, R., Marver, T., & Huels, M. (2014, November). The science of

(dis)satisfaction: Satisfaction as a product of negative group experiences. Paper

presented at annual meeting of the National Communication Association,

Chicago, IL.

Signed: Terrell Kody Frey

Documents

CAT IN THE CLASSROOM: UNDERSTANDING INSTRUCTOR BEHAVIOR