ADMIN 820: Measurement, Factor Analysis and Structure ... · ADMIN 820 Measurement, Factor Analysis and Structure Equation Modelling ... Third Edition, Barbara M. Byrne (2016

ADMIN 820 Measurement, Factor Analysis and Structure Equation Modelling Fall 2017

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Department of Supply Chain and Business Technology Management

1. COURSE DESCRIPTION/OBJECTIVES This course is intended to provide a comprehensive introduction to social science measurement models and struc-

tural equation modelling (SEM) commonly known as the LISREL/EQS models Structural equation modeling. This

is a regression-based technique that incorporates elements of path analysis and confirmatory factor analysis to mod-

el structural relationships and measurement properties, respectively. The general goal of the course is to provide a

thorough background in the conceptual aspects, broad statistical underpinnings, and application of this method that

will facilitate your ability to conceptualize hypothesis testing in an SEM framework, estimate and evaluate hypoth-

esized models, write results sections detailing SEM findings, and be able to competently review the SEM analyses

of others (as in peer review). The emphasis is on applications and applying the relevant techniques wisely for data

analysis which requires a good understanding of the corresponding theories and the relevant assumptions. At the

end of the course, we expect participants to have a solid, conceptual foundation of structural modeling issues, be

able to analyze data using an SEM package (AMOS and/or EQS), critically evaluate professional articles, and write

up SEM results.

2. COURSE MATERIALS

Principal text * Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Pro-

gramming, Third Edition, Barbara M. Byrne (2016), Routledge, Taylor & Francis Group, New York

ISBN: 978-1-138-79702-4 (hbk), ISBN: 978-1-138-79703-1 (pbk), ISBN: 978-1-315-75742-1 (ebk)

*Note: Acquisition of the principal text, or of some other book at a similar level, is advised. However, the lectures may

not directly follow any particular textbook and may contain materials not found in any of them. The chapters and sec-

tions listed below correspond to the principal text. (See tentative schedule)

Other Materials:

Textbooks: Hoyle, R. H., Editor (2012), Handbook of Structural Equation Modeling, The Gilford

Press, NY.

Bollen, Kenneth A., (1989), Structural Equations with Latent Variables, John

Wiley and Sons, New York. (A good classical book – difficult to obtain)

ADMIN 820: Measurement, Factor Analysis and Structure Equation Modelling

Fall 2017

Instructor: Dennis KIRA Tuesday – 8h45-11h30 Venue of Lectures: MB. 12.314

Office: MB012.355 Office hours: Monday 16h00–18h00 or by appointment

E-mail: [email protected] Likely Moodle for communication

mailto:[email protected]


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Geoffrey M. Maruyama (1997), Basics of Structural Equations Modeling, Sage

publications, California

Kline, R. B. (2011), Principles and practice of structural equation modeling, 3rd

edition, Guilford Press.

Articles and class notes to be posted on Moodle

3. INSTRUCTIONAL METHODS

The course will be taught using a lecture, discussion, and report model. Major issues and concepts will be

initially introduced by the instructor in a lecture format with discussion of related readings. To consolidate

understanding, students are expected to study these concepts and report on a topic of their choice with

consent and guidance from the instructor. In addition to lecture and discussion, to facilitate students’ un-

derstanding and usage of the software, the instructor will provide examples of computer input and output

of relevant topics and an opportunity for students to reproduce them in a computer lab. The instructor

notes will be posted in advance on Moodle. Students are encouraged to peruse and review these notes pri-

or to attending each class and read the relevant chapters of the text.

Lectures /Article Discussions

Assignments/Homework

Term Project (individual)

4. COURSE OBJECTIVES

1. Understand the process of measurement in social science, the meaning of reliability, validity and apply

them for operationally defining various concepts that they may use in their research.

2. Differentiate between the classical approaches and the modern approaches for assessing psychometric

properties of a measurement instrument.

3. Understand the applications of factor analysis in the literature and evaluate them critically.

4. Demonstrate skill and self-confidence in undertaking a study involving instrument development and

modelling.

5. Apply confirmatory factor analysis and the related techniques to an area of empirical research.

6. Evaluate the effect of measurement error on validity of statistical inference and utilize techniques for

correcting parameter estimates in the presence of measurement error.

7. Conduct path analysis and formulate a research problem involving latent variables into testable hypoth-

eses through structural equation modelling.

8. Use a structural equation-modelling program such as AMOS, EQS or LISREL in a meaningful fashion

(if you are familiar with R then we can discuss the use of sem package).


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5. INTEGRATION

Class examples taken from various disciplines.

Use of software in some assignments for analyzing data.

Projects in an application setting related to business and economics.

6. EVALUATION

Evaluation will be based on assignments/homework, presentation, participation and a term project.

Students will be required to carry out a project. You will be expected to attend all class (Tuesday) having read

all assigned materials. You will be expected to complete two homework assignments wherein you analyze data

via SEM, and two peer review of SEM articles (one to be presented in the class).

Finally, you will be required to complete a final project, where you test a particular hypothesis using

SEM techniques and write an APA style methods and results section detailing your findings. You will turn in

an outline of your project idea approximately 1/2 of the way through the semester so that we can evaluate the

appropriateness of the idea for the final project. You will give a brief overview of your project and findings to

the class via PowerPoint presentation during the last week of class.

Specific EVALUATION*

The final grade will be based on at least 2 assignments which will be due at various points during the term, a

term project and presentation. Assigned tasks are to be handed in at the beginning of class on due dates.

Evaluation will be based on assignments, data analysis projects, a final project, presentation of a topic of your

choice, and active class participation as follows:

Participation (active) 5%

Assignments/Data Analysis 20%

Presentation (article) 20%

Final Project (Individual) 40%

Presentation (project) 5%

Letter Grades

A+ 95- 100% B+ 75 < 80% C 60 <65

A 90< 95 B 70 < 74 F 0 < 60

A- 89< 90 B- 65 < 69

*To get a passing grade, a student must obtain at least 60%

Assignment/Homework/Data Analysis

A typed cover page giving the student's name, assignment number, and due date should accompany

the submissions. As much as possible, answers should be typed and supporting output clearly labeled.

The homework assignments will constitute 25% of the total mark for the course.

Project ( Individual project)


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The Objective of the term project is the resolution of one important problem of choice and a clear in-

terpretation in terms of the subject-matter concepts. Individual proposal describing the project for ap-

proval as to its suitability for the course must be submitted no later than the beginning of week 6 of

the term.

Projects will involve both statistical and pragmatic issues. Grades will be awarded based on student

proficiency in handling both the theoretical and the pragmatic challenges of each proposed project.

The quality of the presentation in the report will also be a significant factor. The students who have

well thought out justified reasons for their results and who are able to explain and demonstrate their

thinking will receive the highest grades. Statistical results without analysis, justification, and explana-

tion is of no value. The project report is worth 40% of the total mark for the course. Additional 5% is

assigned for the short presentation.

Policy on assigned work:

Students are free to discuss homework assignments and projects among each other on a conceptual

level. However, wholesale copying of problem solutions is prohibited. Late assignments are unac-

ceptable.


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Reading List and Weekly Schedule (tentative)

(Make sure to read at least the indicated chapters of the handbook or the references marked with an as-

terisk before the class and participate in class discussion)

(Papers/Chapters other than Byrne available on Moodle):

Week 1:

Introduction

Tuesday, September 5, 2017: Discussion of syllabus/expectations/course structure, Introduction to

terms. This class provides a road map of the course, the rational for studying SEM and introduces the

path analysis notation. The AMOS and EQS models both in the form of path analysis and matrix nota-

tions will be also introduced.

READINGS:

*Byrne, Chapters 1 and 2

*Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodo-

logical revolution. American Psychologist, 65, 1-12.

*Christof Nachtigall, Ulf Kroehne, Friedrich Funke, Rolf Steyer (2003). (Why) Should We Use

SEM?: Pros and Cons of Structural Equation Modeling, Methods of Psychological Research Online

2003, Vol.8, No.2, pp. 1-22

Hashem Salarzadeh Jenatabadi, A Tutorial for Analyzing Structural Equation Modelling

Dawn Iacobucci, Everything you always wanted to know about SEM (structural equations modeling)

but were afraid to ask, Journal of Consumer Psychology 19 (2009) 673–680

Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York: John Wiley and

Sons. (Chapter 2)

Geoffrey M. Maruyama (1997), Basics of Structural Equations Modeling, Sage publications, Califor-

nia (chapters 3 & 8)

Week 2: (September 12)

Model specification Estimation and Identification rules

*Chapter 3 Byrne

Geoffrey M. Maruyama (1997), Basics of Structural Equations Modeling, Sage publications, Califor-

nia (chapters 3 & 4)


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Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York: John Wiley and

Sons. (Chapter 4)

*Greenland, Sander, James J. Schlesselman, and Michael H. Criqui (1986), The Fallacy of Employing

Standardized Regression Coefficients and Correlations as Measures of Effect, American Journal of

Epidemiology, 123 (February), 203-208.

Subhadip Roy1 and Monideepa Tarafdar, T.S. Ragu-Nathan and Erica Marsillac, The Effect of Mis-

specification of Reflective and Formative Constructs in Operations and Manufacturing Management

Research. Electronic Journal of Business Research Methods Volume10 Issue 1 2012.

Week 3 (September 19):

Continuation of the AMOS/EQS model and demo of the AMOS/EQS program

*Chapter 2 Byrne

*Get Running with AMOS Graphics, IBM® SPSS® Amos™ 22 User’s Guide by James L. Arbuckle

*Chapter 19, Handbook of Structural Equation Modeling.

*Byrne, B. M., (2006), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming,

3nd edition, Rutledge Publications

EQS 6 Structural Equations Program Manual, Multivariate Software, Inc. Peter M. Bentler (2006). (Chapter 2, pag-

es 21-43, Chapter 3)

Structural Equation Modeling Using AMOS, The Division of Statistics + Scientific Computation, The University of

Texas at Austin.

Week 4 (Sept. 26):

Effect of Measurement Error, Classical Measurement Model and Exploratory Factor Analysis

*Chapter 2 and 3 Byrne

*Bollen, K.A., & Hoyle, R.H. (2012). Latent variables in structural equation modeling. In R. H. Hoyle

(Ed.), Handbook of Structural Equation Modeling (pp. 56-67). New York, NY: Guilford Press.

Bagozzi, R.P., & Yi, Y. (2011). Specification, evaluation, and interpretation of structural equation

models. Journal of the Academy of Marketing Science, 40, 8-34.

Rigdon, Edward E. (1994), Demonstrating the Effects of Unmodeled Random Measurement Error, Structural Equa-

tion Modeling, 1 (4), 375-380.

Chambless, D.L., Bryan, A.D., Aiken, L.S., Steketee, G.S. & Hooley, J.M. (1999). The structure of expressed emo-

tion: A three-construct representation. Psychological Assessment, 11, 67-76.

Diana D. Suhr, Ph.D., Exploratory or Confirmatory Factor Analysis?

Week 5 (October 3)

Confirmatory Factor Analysis and demonstration of AMOS/EQS

*Byrne chapter 3, 4

Chapters 4 and 22, Handbook of Structural Equation Modeling

Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York: John Wiley and Sons. (Chapter

7) Stage, F.K., Hasani, C.C., Amaury, N. (2004). Path analysis: An introduction and analysis of a decade

of research. The Journal of Educational Research, 98 (Sep-Oct 2004), 5-12.


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Week 6 (October 10)

Measurement Models continued (Reliability and Validity) and Fit indices *Byrne chapter 5, 6

*Chapterd 28, Handbook of Structural Equation Modeling

Zimmerman, D.W., Zumbo, B.D., & Lalonde, C. (1993). Coefficient alpha as an estimate of test reliability under vio-

lation of two assumptions. Educational and Psychological Measurement, 53, 33-49.

Miller, Michael B. (1995), Coefficient Alpha: A Basic Introduction From the Perspectives of Classical Test Theory

and Structural Equation Modeling, Structural Equation Modeling, 2 (3), 255-273.

Fit indices *Chapter 13, Handbook of Structural Equation Modeling

Hu Li-tze and Bentler Peter.M (1999), "Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conven-

tional Criteria Versus New Alternativs" Structural Equation Modeling 6(1), 1-55.

Ding, Lin, Wayne F. Velicer, and Lisa L. Harlow (1995), Effects of Estimation Methods, Number of Indicators Per

Factor, and Improper Solutions on Structural Equation Modeling Fit Indices, Structural Equation Modeling, 2 ( 2),

119-143.

Week 7 (October 17)

Model modification and Treatment of Missing Data.

*Chapters 23, Handbook of Structural Equation Modeling *Byrne chapter 13

Bagozzi, R. P., & Yi, Y. (1992). Testing hypotheses about methods, traits, and commonalties in the direct-product

model. Applied Psychological Measurement, 16, 373-380.

Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991) Assessing construct validity in organizational research. Administra-

tive Science Quarterly, 36, 421-458.

Week 8 (October 24)

The General Structural Equation Model, Formative and reflective measures, MIMIC and PLS

*Bollen, Kenneth A. (1989), Structural Equations with Latent Variables, New York: John Wiley and Sons. (Chapters

6, 8).

*Byrne 10

Formative and reflective measures

Bollen KA, Lennox R. Conventional wisdom in measurement: a structural equation perspective. Psychological Bul-

letin 1991; 110(2):305–314.

Diamantopoulos A, Siguaw JA (2006). Formative versus reflective indicators in organizational measure develop-

ment: A comparison and empirical illustration. British Journal of Management 17(4):263–282.

Diamantopoulos A (2006). The error term in formative measurement models: interpretation and modelling implica-

tions. Journal of Modeling in Management;1(1):7–17.

Edwards J, Bagozzi R (2000). On the nature and direction of relationships between constructs and measures. Psycho-

logical Methods 5(2):155–174.

MIMIC


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Bollen, K.A. And Lennox, R. (1991). Conventional wisdom on measurement: a structural equation perspective.

Psychological Bulletin, 110 (2): 305-314.

Bollen, K.A. (1989). Structural equation with latent variables. New York: Wiley Interscience.

Byrne, B.M. and Goffin, R.D. (1993). Modeling MTMM data from additive and multip.

PLS

Fornell, C., & Cha, J. (1994), Partial least squares. In R.P. Bagozzi (Ed.), Advanced Methods of Marketing Re-

search (pp. 52-78), Cambridge, MA: Blackwell Business.

Joreskog, K.G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and

comparative aspects. In K.G. Joreskog, & Ketterlinus, R.D. et al. (1990). Partial least squares analysis in develop-

mental psychopathology. Development and psychopathology, 1, 351-371.

Wold, H. (1982). Soft modeling: the basic design and some extensions. In K.G. Joreskog & H. Wold (ed.), Systems

under Indirect Observation: Causality, Structure, Prediction, Vol.2, North-Holland, Amsterdam, p1-54.

Week 9 (October 31)

Causality, Analysis of Experimental data, Multiple Groups and Analysis of Categorical Data.

Causality *Chapter 5, Handbook of Structural Equation Modeling

*Byrne 5, 6, 9, 10

Bullock, Heather E., Lisa L. Harlow, and Stanley A. Mulaik (1994), Causation Issues in Structural Equation Model-

ing Research, Structural Equation Modeling, 1 (3), 253-267.

Mulaik, Stanley A., and Lawrence R. James (1995), Objectivity and Reasoning in Science and Structural Structural

Equation Modeling: Concepts, Issues and Applications, Thousand Oaks, CA: Sage, 118-137.

Analysis of Experimental data *Chapter 24, Handbook of Structural Equation Modeling

Russell, D. W., Kahn, J. H., Spoth, R., & Altmaier, E. M. (1998). Analyzing data from experimental studies: A la-

tent variable structural equation modeling approach. Journal of Counseling Psychology, 45, 18-29.

Cole, D.A., Maxwell, S.E., Arvey, R., & Salas, E. (1993). Multivariate group comparisons of variable systems:

MANOVA and structural equation modeling. Psychological Bulletin, 114, 174-184.

Hancock, G.R., Lawrence, F.R., & Nevitt, J. (2000). Type I error and power of latent mean methods and

MANOVA in factorially invariant and noninvariant latent variable systems. Structural Equation Modeling, 7, 534-

556.

Muthen, B.O., & Curran, P.J. (1997). General longitudinal modeling of individual differences in experimental de-

signs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.

Green, Samuel B; Thompson, Marilyn S; Babyak, Michael A. (1998). A Monte Carlo investigation of methods for

controlling Type I errors with specification searches in structural equation modeling. Multivariate Behavioral Re-

search, 33, 365-383.

Multiple Groups *Chapter 23, Handbook of Structural Equation Modeling

Raykov, T., & Marcoulides, G.A. (2000). A method for comparing completely standardized solutions in multiple

groups. Structural Equation Modeling, 7, 292-308.

Byrne, B. M., (2006), Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming,

2nd edition, Sage Publications

Categorical Data *Chapter 12, Handbook of Structural Equation Modeling


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Week 10 (November 7)

Second-Order Factor Model, Structural Analysis of Correlation Matrix, Means and Covariance Structure,

and Sample Size and Power *Byrne chapter 5

Bagozzi, Richard P. and Todd F. Heatherton (1994), A General Approach to Representing Multifaceted Personality

Constructs: Application to State Self-Esteem, Structural Equation Modeling, 1 (1), 35-67.

Byrne, Barbara M. (1995), Strategies in Testing for an Invariant Second-Order Factor Structure: A Comparison of

EQS and LISREL, Structural Equation Modeling, 2 (1), 53-72.

Structural Analysis of Correlation Matrix Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation modeling in practice : A review and recommended

two-step approach. Psychological Bulletin, 103(3), 411-423.

Cudek, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 2,

317-327.

Means and Covariance Structure Aiken, L.S., Stein, J.A., & Bentler, P.M. (1994). Structural equation analyses of clinical subpopulation differences

and comparative treatment outcomes: Characterizing the daily lives of drug addicts. Journal of Consulting and Clin-

ical Psychology, 62, 488-499.

Browne, M.W., & Arminger, G. (1995). Specification and estimation of mean- and covariance-structure models. In

G. Arminger, C.C. Clogg, & M.E. Sobel (eds.), Handbook of statistical modeling for the social and behavioral sci-

ences (pp. 185-241). New York: Plenum Press.

Sorbom, D. (1982). Structural equation models with structured means. In K.G. Joreskog & H. Wold (Eds.) Systems

under indirect observation: Causality, structure.

Yuan, K.-H., & Bentler, P. M. (1997). Mean and covariance structure analysis: Theoretical and practical improve-

ments. Journal of the American Statistical Association, 92, 767-774.

Sample Size and Power *Chapter 11, Handbook of Structural Equation Modeling

Sarris, W. E., & Satorra, A. (1993). Power evaluations in structural equation models. In K. A. Bollen & J. S. Long

(Eds.), Testing structural equation models (pp. 181-204). Newbury Park, CA: Sage.

Kaplan, D. (1995). Statistical power in structural equation modeling. In R. Hoyle (Ed). Structural Equation Model-

ing: Concepts, Issues, and Applications. pp. 100-117. Thousand Oaks, CA: Sage.

MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multi-

variate Behavioral Research, 32(2), 193-210.

Week 11 (November 14) Mediator and Moderator, Latent Variables Interaction and Analysis of Time Series

data *Chapter 25, Handbook of Structural Equation Modeling

Brown, R.L. (1997). Assessing specific mediational effects in complex theoretical models. Structural equation

modeling, 4, 142-156.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological re-

search: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology,51, 1173-

11.

Holmbeck, G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators 10

and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Con-

sulting and Clinical Psychology, 65, 599-610.

Finch, J.F., West, S.G., & MacKinnon, D. (1997). Effects of sample size and nonnormality on the estima-

tion of mediated effects in latent variables models. Structural Equation Modeling, 4, 87-107.

MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002, A comparison of methods to test mediation and

other intervening variable effects. Psychological Methods, 7(1), 83-104).


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Judd, C.M., Kenny, D.A., & McClelland, G.H. (2001). Estimating and testing mediation and moderation

in within-subjects designs. Psychological Methods, 7, 115-134.

MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation

Review, 17, 144-158.

MacKinnon, D.P., Lockwood, C.M., Hoffman, J. M.,West, S.G., & Sheets, V. (2002) A comparison of

methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83-104.

Interaction and nonlinear models *Chapter 26, Handbook of Structural Equation Modeling

Joreskog, K. G. (1998). Interaction and nonlinear modeling: Issues and approaches. In G. A. M. R. E. Schumacker

(Eds.), Interaction and nonlinear effects in structural equation modeling (pp. 239-250). Mahwah, NJ, Lawrence Erl-

baum Associates.

Jöreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction

effects. In G. A. Marcoulides and R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 57-88).

Mahwah, NJ: Lawrence Erlbaum.

Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psycholog-

ical Bulletin, 96, 201-210.

Bollen and Paxton (1998), "Interactions of Latent Variables in Structural Equation Modeling," Structural Equation

Modeling, 5 (3), 267-293.

Week 12 (November 21) Longitudinal and Multi-level modeling *Chapters 27 and 35, Handbook of Structural Equation Modeling

Allison, P. (1990). Change scores as dependent variables in regression analysis. Sociological Methodology, 20, 93-

114.

Collins, L.M., & Sayer, A.G. (1991). New methods for the analysis of change. Washington, D.C.: American Psy-

chological Association. (ISBN: 1557987548)

Finkel, S.E. (1995). Causal analysis with panel data. Thousand Oaks, CA: Sage.

Gottman J.M. (1995). The Analysis of Change (pp. 261-276). Mahwah, NJ: Lawrence Erlbaum).

Marsh, H. W. (1993). Stability of individual differences in multiwave panel studies: Comparison of simplex models

and one-factor models. Journal of Educational Measurement, 30, 157-183.

Latent growth curve Model

Byrne, B. M., (2006), Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming,

2nd edition, Sage Publications

Multi-level modeling *Chapter 30, Handbook of Structural Equation Modeling

Kaplan and Elliott (1997), "A Didactic Example of Multilevel Structural Equation Modeling Applicable to the

Study of Organizations," Structural Equation Modeling, 4 (January), 1-24..

Week 13 (November 28) Presentation of projects, review, and a group lunch (optional)

The following chapter/papers provide guidelines for reporting SEM results *Chapters 21, Handbook of Structural Equation Modeling

Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461-483.

Raykov, Tenko, Adrian Tomer, and John R. Nesselroade (1991), Reporting Structural Equation Modeling Results

in Psychology and Aging: Some Proposed Guidelines, Psychology and Aging, 6 (4), 499-503.

McDonald, R.P., Ho, M-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychologi-

cal Methods, 7, 64-82.

Hoyle, Rick H. and Abigail T. Panter (1995), Writing About Structural Equation Models, in Rick H. Hoyle (Ed.),

Structural Equation Modeling: Concepts, Issues and Applications, Thousand Oaks, CA, Sage, 158-176.


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Project Presentation on December 5, 2017

We will discuss this later


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Academic Integrity

“The integrity of University academic life and of the degrees, diplomas and certificates the University confers is

dependent upon the honesty and soundness of the instructor-student learning relationship and, in particular, that of

the evaluation process. As such, all students are expected to be honest in all of their academic endeavors and rela-

tionships with the University,” (Academic Code of Conduct, art. 1)

All students enrolled at Concordia are expected to familiarize themselves with the contents of this Code. You are

strongly encouraged to visit the following web address: http://johnmolson.concordia.ca/images/stories/undergrad_prog/undergrad_landing_page/docs/academic_integrity_may2009.pdf.

___________________________________________________________________________

Plagiarism:

The most common offense under the Academic Code of Conduct is plagiarism which the Code defines as "the

presentation of the work of another person as one's own or without proper acknowledgement." This could be

material copied word for word from books, journals, internet sites, professor's course notes, etc. It could be material

that is paraphrased but closely resembles the original source. It could be the work of a fellow student, for example,

an answer on a quiz, data for a lab report, a paper or assignment completed by another student. It might be a paper

purchased through one of the many available sources. Plagiarism does not refer to words alone _ it can also refer to

copying images, graphs, tables, and ideas. "Presentation" is not limited to written work. It also includes oral presen-

tations, computer assignments and artistic works. Finally, if you translate the work of another person into French or

English and do not cite the source, this is also plagiarism.

In Simple Words:

DO NOT COPY, PARAPHRASE OR TRANSLATE ANYTHING FROM ANYWHERE WITHOUT

SAYING FROM WHERE YOU OBTAINED IT!

(Source: The Academic Integrity Website: http://provost.concordia.ca/academicintegrity/plagiarism/)

___________________________________________________________________________

DISCLAIMER

In the event of extraordinary circumstances beyond the University's control, the content

and/or evaluation scheme in this course is subject to change.

http://johnmolson.concordia.ca/images/stories/undergrad_prog/undergrad_landing_page/docs/academic_integrity_may2009.pdf

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Documents

ADMIN 820: Measurement, Factor Analysis and Structure ... · ADMIN 820 Measurement, Factor Analysis and Structure Equation Modelling ... Third Edition, Barbara M. Byrne (2016