Evaluating the Short- and Long-term Impacts of Didactic, Developmental, and Combined Approaches to

Kindergarten Teaching in Low- and High-SES Classrooms

by

Emiko Koyama

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Applied Psychology and Human Development University of Toronto

Ontario Institute for Studies in Education

© Copyright by Emiko Koyama 2015

ii

Evaluating the Short- and Long-term Impacts of Didactic,

Developmental, and Combined Approaches to Kindergarten

Teaching in Low- and High-SES Classrooms

Emiko Koyama

Doctor of Philosophy

Department of Applied Psychology and Human Development

University of Toronto

Ontario Institute for Studies in Education

2015

Abstract

The benefits of developmental vs. skills-based approaches to kindergarten have been debated, yet

the results of small-scale studies examining the effectiveness of high-quality programs remain

mixed. This dissertation examined the effectiveness of developmental, didactic, and combined

programs on children’s learning behaviors, reading, and math achievement from kindergarten to

Grade 5, with classroom SES as a moderator. To overcome methodological issues (e.g., lack of

control over pretreatment covariates and ignoring nested data structure) this dissertation utilized

multilevel modeling with a propensity score-based semi-parametric weighting method to remove

selection bias in the non-experimental data. The Early Childhood Longitudinal Study –

Kindergarten Cohort of 1998-1999 (ECLS-K) was used for analysis. The first question explored

the natural grouping of teachers’ classroom practice using confirmatory factor analysis and

cluster analysis. Analyses revealed four distinct groups of kindergarten programs, which differed

along the dimensions of developmental and didactic practices: developmental, didactic,

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combined(HI) and combined(LO) programs. The second question examined the school-, teacher-

and child-level characteristics associated with the four kindergarten programs. Notably, the

combined(HI) program teachers were more likely than the developmental program teachers to

endorse didactic practices in preschool and kindergarten. The third question explored the

interrelationships between kindergarten programs, time allocated to academic subjects, and

classroom SES. The results indicated that SES was associated with time allocation but not with

instructional approaches. Low SES classrooms spent more time on math, and high SES

classrooms spent more time on the arts. The combined(HI) programs spent the largest

proportion of time on academic subjects. The final question examined the effectiveness of the

kindergarten programs on children’s learning behaviors, reading and math outcomes from

kindergarten to Grade 5. Results showed a fairly consistent pattern in program effectiveness as a

function of SES. There were very few significant differences in effectiveness across the

programs in the high-SES classrooms. In the low SES group, interestingly, the didactic program

displayed positive sleeper effects that manifested up to Grade 5 in each of these

outcomes. Theoretical implications concerning the conceptualization of teaching practices and

practical implications of how SES affects kindergarten effectiveness are discussed. (342 words)

iv

Acknowledgments

Laudate Dominum!

I would like to express my most sincere gratitude to my thesis supervisor, Dr. Guanglei Hong,

for instilling in me the love for statistics, and for providing patient, caring, and careful

supervision throughout my Ph.D studies. My deepest gratitude goes to Dr Esther Geva. Without

her help, guidance, counsel, and provision, I would not have been able to finish my studies. I am

deeply grateful to her for taking me in as your own student. I thank Dr Michel Ferrari for his

continual support and encouragement from the beginning of my graduate studies. I thank Dr

Michal Perlman for her most helpful feedback on my dissertation work. I am grateful to Dr

Janette Pelletier for agreeing to be the back-up committee member. Special thanks to Dr Doug

Willms, who served as the external examiner for my final oral exam – I am most grateful for his

careful reading of my dissertation and for the excellent feedback and encouragement he

provided.

There are many friends that have helped me get through my dissertation process. I am most

grateful to Ljiljana Vuletic for reading over my many drafts, checking my references, and

keeping me motivated; Norman Himel for his statistical skills; Raghad Al-Khanati, Sae

Shigematsu, and Shamila Karunakaran, for their help in formatting tables. I am most grateful to

Grace Yee-Yue Luk who gave me great advice on the final presentation and kept me company

until the late hours before the final oral exam. Special thanks to Kyoko Kato, my very best friend

since elementary school, who came all the way from Ottawa to be with me on the day of my

defense. Many thanks to my former labmates Bing Yu, Yihua Hong, Marija Petaroudas, and

Natasha Jamal, for their friendship, company and encouragement. I would also like to thank the

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students from Dr Geva’s lab, especially Dr Fataneh Farnia, Lucja Segal-Seiden, and Joyce Mak

for attending to my moc defense and giving me constructive feedback on my presentation.

I am most indebted to Fr Michael Eades for his prayers and admonitions that got me through the

toughest times, to Fr Martin Hilbert, Fr Dominic Borg, Fr Daniel Utrecht, Sr Therese of Zephyr

Carmel, and Pastor Paul Johansen for their tireless encouragement and continual prayers, and

many brothers and sisters from Knox Presbyterian Church and Holy Family Catholic Church

who prayed for me on the days leading up to my defense. I thank especially Marijka Kuzma,

Karen Williams, Carol Leeda Crawford, Chao Hu, Steven Burger and Margaret Ma (deceased)

for their prayers. Many thanks also to my dear housemates Norine Love, Heidi Love, and Mary-

Ann Stanic for their kindness, prayers, and encouragement.

Finally, I would like to thank my parents Akira and Fujino Koyama, for their patience,

encouragement and loving support. Thank you so much for giving me birth and raising me up,

and loving me so unconditionally. I am also thankful to my sister, Akiko Sakai, who always

made sure that I was staying healthy and well.

It is done! Deo Gratias!

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Table of Contents

Acknowledgments.......................................................................................................................... iv

Table of Contents ........................................................................................................................... vi

List of Tables ................................................................................................................................. ix

List of Figures ................................................................................................................................ xi

List of Appendices ........................................................................................................................ xii

Chapter 1 Introduction .....................................................................................................................1

1 A Brief History of Kindergarten in the United States .................................................................4

2 The Contemporary Kindergarten ................................................................................................9

2.1 Allocated Instructional Time .............................................................................................10

2.2 Classroom Teaching Practice .............................................................................................11

2.2.1 Developmental Kindergartens ...............................................................................12

2.2.2 Didactic Kindergartens ..........................................................................................19

2.2.3 DAP vs DIP: A false dichotomy? ..........................................................................20

2.2.4 Empirical Studies on Developmentally Appropriate Practice and

Developmentally Inappropriate Practice................................................................22

2.2.5 Combined Programs...............................................................................................25

2.2.6 Effects for Children of Low Socioeconomic Status ..............................................25

3 Research Questions ...................................................................................................................27

Chapter 2 Methods .........................................................................................................................32

4 Chapter Overview .....................................................................................................................32

4.1 ECLS-K Dataset Characteristics ........................................................................................32

4.2 Analytic Sample .................................................................................................................33

4.2.1 Full-day Kindergarten ............................................................................................33

4.3 Measure of Classroom SES ...............................................................................................41

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4.4 Types of Kindergarten Classrooms ....................................................................................42

4.5 Time Allocation for Academic Subjects ............................................................................43

4.6 Outcome Variables.............................................................................................................45

4.6.1 Teacher Ratings of Children’s Approaches to Learning........................................45

4.6.2 Reading and Math Teacher Academic Rating Scale ..............................................45

4.6.3 Reading and Math Direct Assessment ...................................................................46

4.7 Analytic procedure .............................................................................................................49

4.7.1 Rubin's Causal Model ............................................................................................50

4.7.2 Marginal Mean Weighting with Stratification (MMW-S) Method .......................52

Chapter 3 Results ...........................................................................................................................61

5 Question 1: Teacher Groupings Based on Didactic and Developmental Teaching Practices ...61

6 Question 2: Examining the Pretreatment Characteristics .........................................................68

6.1 School characteristics.........................................................................................................68

6.2 Teacher characteristics .......................................................................................................69

6.3 Child characteristics ...........................................................................................................71

7 Question 3: Examining the Inter-relationships among Time Allocation, Kindergarten

Program, and SES .....................................................................................................................73

8 Question 4: How Kindergarten Program Relates to Outcomes of Motivation, Reading, and

Math from Kindergarten to Grade 5..........................................................................................81

8.1 Approaches to Learning .....................................................................................................81

8.1.1 Without Marginal Mean Weighting .......................................................................81

8.1.2 With Marginal Mean Weighting ............................................................................82

8.2 Teacher Rating of Children’s Reading Skills ....................................................................85

8.2.1 Without Marginal Mean Weighting .......................................................................85

8.2.2 With Marginal Mean Weighting ............................................................................85

8.3 Teacher Rating of Children’s Math Skills .........................................................................89

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8.3.1 Without Marginal Mean Weighting .......................................................................89

8.3.2 With Marginal Mean Weighting ............................................................................89

8.4 Reading Direct Assessment Scores ....................................................................................93

8.4.1 Without Marginal Mean Weighting .......................................................................93

8.4.2 With Marginal Mean Weighting ............................................................................94

8.5 Math Direct Assessment Scores.........................................................................................97

8.5.1 Without Marginal Mean Weighting .......................................................................97

8.5.2 With Marginal Mean Weighting ............................................................................98

Chapter 4 Discussion ...................................................................................................................101

9 Is Developmentally Appropriate Practice for Everyone? .......................................................101

9.1.1 Limitations and Future Studies ............................................................................113

9.1.2 Conclusion ...........................................................................................................116

References ....................................................................................................................................118

Appendix A. .................................................................................................................................133

Appendix B. .................................................................................................................................136

Appendix C. .................................................................................................................................137

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List of Tables

Table 1. School Characteristics of Full-Day and Half-Day Kindergartens ................................. 35

Table 2. Teacher and Classroom Characteristics of Full-Day and Half-Day Kindergartens ..... 38

Table 3. Characteristics of Children in Full-Day and Half-Day Kindergartens ......................... 40

Table 4. Descriptive Statistics for Approaches to Learning, Teacher Rating of Reading and Math,

and Direct Assessment of Reading and Math for Full-Day Kindergarteners (n = 8393) ............ 47

Table 5. Correlations of Variables Used in the Confirmatory Factor Analysis ........................... 64

Table 6. Model of Invariance across High and Low SES Classrooms ......................................... 66

Table 7. Characteristics of the Didactic, Combined (HI/LO), and Developmental Kindergartens

....................................................................................................................................................... 67

Table 8. Proportion of Time Spent on Academic Subjects by Kindergarten Types and Classroom

Socioeconomic Status.................................................................................................................... 75

*Note: Total time in minutes spent daily on reading, math, social studies, science, music, art,

dance, theatre, ESL, foreign language, recess and lunch. ............................................................. 75

Table 10. Time (per Week) Allocated for Different Subjects and Activities among the

Developmental, Combined (HI/LO), and Didactic Programs ...................................................... 78

Table 11. Hierarchical Linear Modeling for Approaches to Learning in Low-SES Classrooms 83

Table 12. Hierarchical Linear Modeling for Approaches to Learning in High-SES Classrooms 84

Table 13. Hierarchical Linear Modeling for Teacher Rating of Children’s Reading Skills in Low-

SES Classrooms ............................................................................................................................ 87

Table 14. Hierarchical Linear Modeling for Teacher Rating of Children’s Reading Skills in

High-SES Classrooms ................................................................................................................... 88

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Table 15. Hierarchical Linear Modeling for Teacher Rating of Children’s Math Skills in Low-

SES Classrooms ............................................................................................................................ 91

Table 16. Hierarchical Linear Modeling for Teacher Rating of Children’s Math Skills in High-

SES Classrooms ............................................................................................................................ 92

Table 17. Hierarchical Linear Modeling for Direct Assessment Scores in Reading for the Low-

SES Classrooms ............................................................................................................................ 95

Table 18. Hierarchical Linear Modeling for Reading Direct Assessment Scores in High-SES

Classrooms .................................................................................................................................... 96

Table 19. Hierarchical Linear Modeling for Math Direct Assessment Scores in Low-SES

Classrooms .................................................................................................................................... 99

Table 20. Hierarchical Linear Modeling for Math Direct Assessment Scores in High-SES

Classrooms .................................................................................................................................. 100

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List of Figures

Figure 1.Confirmatory Factor Analysis of Didactic and Developmental Practices...................... 65

Figure 2. The Distribution of the Four Kindergarten Types with Reference to Developmental and

Didactic Practices…………………………………………………………………………..…….68

Figure 3. Line Graph with Error Bars for Approaches to Learning from Kindergarten to Grade

5…………………………………………………………………………………………………137

Figure 4. Line Graph with Error Bars for Teacher Ratings of Reading from Kindergarten to

Grade 5………………………………………………………………………………………….138

Figure 5. Line Graph with Error Bars for Teacher Ratings of Mathfrom Kindergarten to Grade

5…………………………………………………………………………..…………………….139

Figure 6. Line Graph with Error Bars for Reading Theta Scores from Kindergarten to Grade

5…………………………………………………………………………………………..…….140

Figure 7. Line Graph with Error Bars for Math Theta Scores from Kindergarten to Grade

5……………………………………………...………………………………………………….141

xii

List of Appendices

Appendix A. List of Potential Pretreatment Covariates ………….………………………….133

Appendix B. Marginal Mean Weights for Teachers in Low- and High-SES Groups by

Kindergarten Type …………………………………………………………………………...136

Appendix C. Graphs for the MMW-S Results from Question 4 ...….……………….………137

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Chapter 1 Introduction

Over the past several decades, kindergarten has gone through a significant transformation (Miller

& Almon, 2009). The original kindergarten was built on the foundation of a romantic view of

childhood1, which viewed the ideal child as one growing “in the order of nature” (Rousseau,

1762/1979, p. 86), under the care and protection of the educator, without excessive interference

from sociopolitical arenas or economic pressure. Building on Rousseau’s philosophy, Freidrich

Froebel founded the first kindergarten in 1837. He offered the mission statement of his

institution as follows:

As in a garden under God’s favor, and by the care of a skilled, intelligent gardener,

growing plants are cultivated in accordance with Nature’s laws, so here in our

childgarten, our kindergarten, shall the noblest of all growing things, men (that is

children, germs and shoots of humanity), be cultivated in the accordance with the laws of

their own being, of God and Nature. (Downs, 1978, p. 42)

For many children, kindergarten was the first educational institution they encountered away from

home. In the first kindergarten, as well as in subsequent early childhood institutions that

followed the same philosophy, great care was taken by the educators to meet the children where

they are and prepare them for the demands of formal schooling, physically, socially,

psychologically and academically (Copple & Bredekamp, 2009). In recent years, the educational

philosophy that emphasizes children’s development as a basis for teachers’ decision making on

1 The agricultural metaphor of human development and spirituality has been utilized since the Biblical times, yet it

was the Renaissance scholars such as Erasmus and Comenius that systematized these thoughts and utilized them in

their educational treatises.

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the appropriate environment, instruction, curriculum and assessment has come to be collectively

known as Developmentally Appropriate Practices (DAP). These practices were based on the

constructivist and cognitive learning theories represented by theorists such as Piaget and

Vygotsky. It emphasized child-initiated learning activities and experiential learning activities

that activate the senses, such as free play, drama, arts, music, dance and sports were

recommended. The primary role of the educators was to protect the children from danger and

facilitate and guide children’s exploration endeavors (Howes & Olenick, 1986; McCartney,

1984).

In recent years, however, the nature and purpose of kindergarten is said to have shifted to adopt a

more academic focus (Beatty, 1995; Dombkowski, 2001). With the growing number of children

attending preschools, kindergarten is no longer the first educational setting that children

encounter away from home. There are growing demands from the global economy and

educational legislation to meet national standards, prepare children for high-stakes testing, and

adopt a more academic emphasis at the kindergarten level (Bassok, 2012; Gullo & Hughes,

2011; Kagan & Kauerz, 2006). To better prepare young children for high-stakes testing in the

middle elementary grades and beyond, those that advocate for more academics-based

kindergartens maintain that children should spend more time being taught academic subjects.

Behavioral approaches such as academic skills building, worksheets, and drill activities are often

adopted to achieve this goal. To maximize time on academics, time for play was frequently

reduced or eliminated. As a result, the proportion of traditional kindergartens with abundant time

allocated to play, music, art, dance, physical education and recess have significantly decreased

(Miller & Almon, 2009). This transformation from a highly playful to a highly academic

kindergarten program is described as the “crisis in kindergarten” (Miller & Almon, 2009).

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Both of these approaches, play-based and academics-based, have their perceived benefits and

concerns. One of the major strengths of the academics-based programs is that they tend to offer

clearly defined and measurable educational goals (Kessler & Swadener, 1992; Stone, 1995). The

increased time spent teaching academic subjects, especially on reading and language arts, may

also provide children with an increased opportunity to learn the academic content. On the other

hand, some of the major concerns expressed with regard to academics-based kindergarten

programs are that they tend to narrowly focus on academic subjects such as reading readiness

and math, sometimes together with social studies and sciences, but at the expense of other

enrichment activities such as arts, music, physical education and free play at recess. It has been

suggested that excessive time allocated to academic subjects may have adverse effects on student

outcomes such as motivation and perseverance (Carroll, 1989). Additionally, critics of the

academics-based kindergarten maintain that the didactic, behaviorist approaches may negatively

impact children’s curiosity, creativity, and motivation. A play-based, developmentally-oriented,

child-initiated program, on the other hand, may offer classroom environments that are more

holistic, self-motivating, and developmentally appropriate, and these are likely to be more

flexible in accommodating children at various maturity levels and needs. Nevertheless, it is often

difficult to assess the educational effectiveness of the spontaneous child-directed activities. In

addition, teachers generally find it difficult to implement developmentally appropriate practices

in their classrooms.

Empirical studies have shown that kindergarten teachers tend to subscribe to either one of the

dichotomized philosophies: academics-based or play-based (Copple & Bredekamp, 2009;

Gallant, 2009; Pruitt, 2011). Nevertheless, it is at least theoretically conceivable that there could

be a third group of teachers who subscribe to neither one of these two extreme philosophies, but

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instead, try to strike a balance between both the academics and play, didactic and child-initiated

practices, and strive to integrate play-based, developmentally appropriate approaches into the

teaching of academic content. Some researchers have argued that kindergarten should adopt a

balanced model (Ray & Smith, 2010). They believe a more “balanced” approach may be more

appropriate to address both the unchanging developmental needs of the children and the

changing demands of the social, political, and economic forces (Gullo & Hughes, 2011;

Bredekamp, 1987).

An empirical question that arises is to what extent the different approaches to kindergarten

education actually impact the short- and long-term academic and motivational outcomes of the

developing child. The available research for the long-term effects of developmentally

appropriate, didactic, and the balanced kindergarten programs is limited, and the conclusions are

often extrapolated from studies on the preschool children ages 3 to 4 (Bryant, Clifford, &

Peisner, 1991). Moreover, the available research evidence suffers from methodological issues

including small sample sizes, lack of pretest scores, and not using appropriate statistical methods

for analyzing nested data. The primary objective of my dissertation, therefore, is to fill this gap

in the early childhood education literature by examining how the different program emphases

affect children’s reading, math, and motivational (approaches to learning) outcomes from the end

of kindergarten to Grade 5.

1 A Brief History of Kindergarten in the United States

During the beginning stages of the industrial revolution in the mid-18th century, Jean Jacques

Rousseau wrote his educational treatise, Emile, ou De L’education (1762/1979). Rousseau

viewed childhood as a brief period characterized by innocence. He maintained that early

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childhood education should be guided by the natural maturation process. Rousseau

revolutionized the educational process by putting the child, and not the teacher, at the center of

education. The educators’ role was to support the children’s natural development and protect

children from harm, both from nature and from the corruptible influence of the society.

Building on Rousseau’s philosophy, and that of Heinrich Pestalozzi, Friedrich Froebel created

his kindergarten. Froebel explicitly rejected the rigidity of the teacher-directed, didactic

instruction, following Rousseau’s ideals (Fromberg, 2006). Instead of directly teaching the

children the basic academic concepts, he insisted on the value of free play and learning through

exploration. To this end, the “educational materials” that Froebel developed were presented in

the form of games and fun activities that children can engage in to discover various elementary

principles for themselves. One of the best known innovations of Froebel’s kindergarten was the

use of a set of toys called “gifts,” which consisted mostly of different geometric shapes, such as

balls, wooden blocks, tiles, and rings, and materials with varying attributes such as color, size,

and flexibility. These “gifts” were presented to the children in a sequential manner, along with

what he called “occupations,” or activities that the children engaged in to discover basic

mathematical concepts (Bryant & Clifford, 1992). Froebel’s gifts and occupations had a great

influence on education worldwide. Frank Lloyd Wright, a prominent architect in the U.S., for

example, was greatly inspired by these Froebelian “gifts” in his work (Rubin, 1989). Froebelian

gifts remain a prominent feature in the contemporary Japanese and Korean kindergartens

(Jeynes, 2006).

When kindergarten was introduced to North America, its original proponents strictly subscribed

to Froebelian approaches to early childhood education. In 1856, Margarethe Meyer Schurz, who

was trained under Froebel, founded the first German-speaking kindergarten in the U.S. In 1860,

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Elizabeth Peabody followed suit and founded the first English speaking kindergarten. By the

late 19th century and during the progressive era, however, modifications started to be made to

the strict Froebelian methods of kindergarten education, incorporating the ideas of social

reformers and psychologists such as John Dewey and G. Stanley Hall (Finkelstein, 1988). For

example, Dewey’s idea of kindergarten incorporated the value of independence and self-

sufficiency and took a pragmatic approach including playing with real-life objects and situations

such as washing clothes and cooking their own lunches in place of Froebelian abstract play

(Dewey, 1915; Weber, 1984).

Dewey (1915) writes:

The imaginative play of the child’s mind comes through the cluster of suggestions,

reminiscences, and anticipations that gather about the things he uses […] The simple

cooking, dishwashing, dusting, etc., which children do are no more prosaic or utilitarian

to them than would be, say, the game of the Five Knights. To the children these

occupations are surcharged with a sense of the mysterious values that attach to whatever

their elders are concerned with. The materials, then, must be as “real” as direct and

straightforward, as opportunity permits. (pp. 118-119)

G. Stanley Hall emphasized the importance of building a kindergarten program based on the

understanding of the stages of child development. He also utilized methodologies from

psychological research such as questionnaires and child study to inform educational decision-

making regarding the kindergarten programs and instruction (Weber, 1984).

Before and during the 1870s, privately-funded kindergartens were predominant, mainly serving

children of immigrants and the inner-city poor. As such, kindergartens were perceived as an

7

institution for promoting social welfare (Dombkowski, 2001). In 1873, however, Susan Blow

started the first publicly-funded kindergarten in St. Louis, Missouri. After this, kindergartens

slowly started to become integrated into the public school system. At the same time, the pressure

to conform to didactic instruction increased (Cuban, 1992). Preparing the children for a

successful transition into formal schooling became one of kindergarten’s main objectives

(Beatty, 2011). As the push for kindergartens to be fully institutionalized into the public

elementary school system continued during the late 19th and early 20th centuries, tensions

started to grow among educational professionals between those who advocated the child-initiated

and didactic approaches to kindergarten (Dombkowski, 2001). External economic and

sociopolitical factors such as the Great Depression in the 1930’s and the cold war in the 1950’s

accelerated the push toward academization to win the global competition (Dombkowski, 2001).

In the 1960s, promising results from well-designed preschool studies such as the Abecedarian

(Barnett & Masse, 2007) and the High/Scope Perry School programs (Schweinhart et al., 2005)

started to draw the public’s attention (Barnett & Masse, 2007) and bring hope that early

childhood education could greatly enhance later academic achievement and narrow the

achievement gaps between the advantaged and the disadvantaged segments of society. The

popular media became the leading force for promoting the academic vision of kindergarten, and

the state policy and professional organizations followed suit (Russell, 2011). Under the

legislation of President Johnson in the mid-1960s, the Head Start program was launched to assist

children from low-income families to overcome their initial educational disadvantages (U. S.

Department of Health and Human Services, Administration for Children and Families, 2010).

In the 1990s and the beginning of the 21st century, calls for universal public preschools were

being made. As a result, kindergarten was no longer considered the first step away from home,

8

but a transitional year between preschool and elementary school. It was no longer a preparation

year before formal academic schooling, but started to be considered “the new first grade” itself

(Tyre, 2006).

The education reform of the late 20th century mainly focused on increasing students’ opportunity

to learn through the provision of additional time spent on teaching academic subjects. At the

beginning stages of this reform, educational legislators compiled a report entitled A Nation at

Risk (National Commission on Excellence in Education, 1983), which called for additional time

to be spent in schools, with longer school days and extended school years, and promoted an

increased focus on core subject matters such as English, mathematics, science, and social

studies.

At the kindergarten level, this resulted in an increase in scheduled time through the provision of

full-day kindergartens (Votruba-Drzal, Li-Grining, & Maldonado-Carreño, 2008), and an

increase in instructional time allocated to academic subjects. Correlating with the growing

number of women participating in the workforce, the proportion of young children attending full-

day kindergartens increased steadily from about 10% in the 1960s (Bruno & Adams, 1994), to

about 15% in the 1970s (Elicker & Mathur, 1997), 33% in the late 1980s (Bruno & Adams,

1994), and by the end of 1990s, the number increased to 55% (Walston & West, 2004). The

proportion of full-day kindergartens have continued to grow until the beginning of the 21st

century, in which this figure increased to roughly 63% (Ackerman, Barnett, & Robin, 2005;

Kauerz, 2005). As of 2012, eleven states and the District of Columbia offer mandatory full-day

kindergartens (Workman, 2013).

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The tendency toward a greater academic focus was further intensified by the passage of the No

Child Left Behind Act (NCLB) in 2001, which stipulated high expectations for all students, as

well as sanctions for schools that failed to meet the Adequate Yearly Progress (AYP) based on

standardized test results. Although kindergarten is not directly the target of NCLB-mandated

high-stakes standardized testing, scholars suggest that the kindergarten teachers are nevertheless

indirectly influenced into preparing the children academically for testing in Grades 3 (Bryant et

al., 1991). A significant increase in the time spent on the core academic subjects, especially in

early literacy/reading and language arts, was observed in kindergarten and the elementary school

grades, which is attributed to the NCLB (Bassok & Rorem, 2013; Morton & Dalton,

2007). Some of the children that are more likely to be affected by the increased academic

emphasis are children attending public kindergartens, especially with a high proportion of

children at risk for academic failure, such as immigrants, children with disabilities, and children

from low socioeconomic backgrounds (Bassok & Rorem, 2013).

2 The Contemporary Kindergarten

While the contemporary educational policy generally endorses the academic emphasis in

kindergarten, teacher education institutions and professional organizations for early childhood

education continue to advocate a play-based, developmental approach to kindergarten. The effect

of such a debate have manifested itself in “what is being taught” and “how it is being taught” in

early childhood settings (Bryant, Clifford, & Peisner, 1991; Hatch, 2012). Although curriculum

and pedagogy have been considered separate areas of inquiry in the educational literature, these

two areas have often been confounded in the literature of early childhood education (Kagan &

Kauerz, 2006). I will examine this issue further, by investigating the concept of allocated

instructional time and classroom teaching practice.

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2.1 Allocated Instructional Time

The issue of time allocation has been examined in various levels in educational research. The

most encompassing unit of instructional time during an academic year, sometimes referred to as

scheduled time, is the total time the students spend in school (Karweit & Slavin, 1981). In

general, the State education agencies determine the length of the school year, whereas the local

school districts are responsible for determining the length of the school day (Karweit & Slavin,

1981). The provision of full-day kindergartens was an attempt to extend the scheduled time with

the assumption that additional scheduled school time would increase students’ learning

outcomes.

Within the scheduled time, school principals and classroom teachers are generally responsible

for allocating instructional time within the school day (Walberg, Niemiec, & Frederick,

1994). Allocated instructional time, sometimes referred to as opportunity to learn (Carroll, 1963,

1989), is the time spent on instruction and includes organizational and housekeeping activities

such as transitional and waiting time (Berliner, 1990). The amount of time the students are

actually focusing on the lesson is called engaged time, or time on task (Scheerens & Hendriks,

2014). Allocated instructional time is the upper bound of the students’ engaged time (Berliner,

1990). Engaged time excludes the time students spend being inattentive to the learning tasks,

such as daydreaming, going outside of the classroom (such as running errands or going to the

washroom during class time), and socializing (Scheerens & Hendriks, 2014). Finally, academic

learning time refers to the portion of engaged time the students spend working on tasks that are

neither too easy nor too difficult, at the appropriate difficulty level while achieving high levels of

success (Scheerens & Hendriks, 2014). Although the more fine-grained time indicators such as

academic learning time and engaged time have generally been considered better predictors of

11

academic achievement (Berliner, 1990; Scheerens & Hendriks, 2014), policymakers have

generally been interested in the effects of allocated instructional time and scheduled time, as

these are easier to measure in large-scale assessments and can be directly manipulated by

educational legislators (Berliner, 1990; Karweit & Slavin, 1981).

Given the research evidence that suggests that giving sufficient time is especially important for

weaker students (Scheerens & Hendriks, 2014), it is conceivable that the pressure to spend more

time on academic subjects would be higher for teachers and schools with high proportions of

disadvantaged students. This increased focus on academic subjects, however, may inevitably

come at the expense of opportunities to develop skills such as creativity, problem-solving, social

skills, oral language and communication skills, self-initiative, self-control, and motivation

through enrichment activities including arts, music, physical education, dance, and free play

(Dunn & Kontos, 1997). All of these skills are necessary and important, not only for the child’s

future academic and career success, but for the child’s wellbeing and personal development

(Copple & Bredekamp, 2009). If disadvantaged students are systematically being excluded from

these learning opportunities, this could also constitute a significant equity concern that needs to

be addressed (Winfield, 1991), over and above opportunity to learn.

2.2 Classroom Teaching Practice

Criticisms regarding academic kindergartens have not only been directed toward increased time

spent on academic subjects, but also toward the classroom practices that are considered less age

appropriate for kindergarten children. For example, the major concern for the National

Association for the Education of Young Children (NAEYC), the largest early childhood

education advocacy group in the world, was the “trend toward increased emphasis on formal

instruction in academic skills” (Bredekamp, 1987, p.1). These educators were concerned that the

12

teaching practices in kindergarten and other early childhood institutions were becoming

increasingly didactic with lack of consideration to child development. Since the first adoption of

the framework in 1986, the NAEYC has continued to endorse what they called the

developmentally appropriate practice (DAP) for children from birth to age 8 (Bredekamp, 1987;

Bredekamp & Copple, 1997; Copple & Bredekamp, 2009). The original framework presented in

the first two editions of the book Developmentally Appropriate Practice in Early Childhood

Programs Serving Children from Birth through Age 8 provided a stark contrast between what

they regarded as developmentally “appropriate” and developmentally “inappropriate” practices

(Bredekamp, 1987; Bredekamp & Copple, 1997). In the third edition of the book, the term

developmentally “inappropriate” practice was replaced with the term “in contrast” practice

(Copple & Bredekamp, 2009). To avoid the value judgment associated with such terms, the

more neutral terms, “developmental” and “didactic” practices will be used to describe the

“developmentally appropriate” and “developmentally inappropriate” or “in contrast” practices,

respectively, while using DAP and DIP when discussing the NAEYC’s theory and framework.

The characteristics and theoretical underpinnings of each of these practices, as well as the

possibility of a third, balanced approach will be discussed in the following sections.

2.2.1 Developmental Kindergartens

According to the NAEYC, developmentally appropriate practice is defined as the “ways of

teaching that engage children’s interest and adapt for their age, experience, and ability to help

them meet challenging and achievable learning goals.” (Copple & Bredekamp, 2009, p. 70) Its

recommended practices heavily rely on developmental theories, especially those of Piaget,

Erikson, and Vygotsky (Bredekamp & Rosegrant, 1992; Hatch, 2012). The guidelines

recommend that the teaching materials and instructional methods be examined for their age

13

appropriateness, individual appropriateness, and cultural appropriateness. Age appropriateness is

based on the premise that there are “universal, predictable sequences of growth and change that

occur in children during the first 9 years of life” (Bredekamp, 1987, p. 8). The idea is closely

linked to developmental stage theories represented by those of Piaget and Erikson. These

universal and predictable changes occur in all domains of development, including the physical,

cognitive, emotional and social domains, and provide an overarching framework for educators to

consider the appropriateness of their lesson plans and practices.

Despite these universal sequences and stages, it is acknowledged that there is a great amount of

variations in the timing, trajectories, and patterns of development across domains. Each child is

also unique with different interests, preferences, personalities, and learning styles. While the

original 1986 guideline emphasized the age appropriateness of the teaching practices, the 1996

revision called for greater attention to the children’s individual differences within the universal

stages. It was suggested that, to support the development and learning of all the children,

teachers should take into account all that they know about each child, not just his or her stage of

development, but his or her family history, culture, and social background as well as the

individual differences such as temperament and likes and dislikes (Bredekamp & Copple, 1997).

In the original guideline, cultural appropriateness was considered part of individual

appropriateness. In the 2007 revision, however, cultural appropriateness was given its own

recognition. As the U.S. population becomes increasingly more diverse socioeconomically,

ethnically, and linguistically, teachers are facing the need to understand the variations in

sociocultural values, expectations, and experiences that exist in the classrooms. For this reason,

Vygotsky’s socio-cultural theory has become more important to the conceptualization of

developmentally appropriate practices. The next section will review how the developmental

14

theories of Piaget, Erikson, and Vygotsky contributed to the conceptualization of

developmentally appropriate practices, with a special focus on kindergarten children’s

development.

2.2.1.1 Piaget

Piaget started his career as a biologist but later spent 30 years conducting research in child

development at the faculty of science at Geneva, and was the director of the Institut Jean-

Jacques Rousseau, a pedagogical institute in Geneva with a vision of transforming educational

theory and practice into a science (Lonergan, 1988), and its associated lab school, Maison de

Petits. His overarching theoretical framework is referred to as genetic epistemology. The goal of

genetic epistemology was to examine how biological developmental processes relate to the

development of knowledge structures. Piaget’s contributions to the field of developmental

psychology and education are extensive, ranging from research in language, mathematics, moral

reasoning and judgment, and emotional development. The neo-Piagetian scholars have

expanded on Piaget’s original work (Case, 1998; Ferrari & Vuletic, 2010; Fischer, 1990), and

applied his theoretical framework to such areas as mathematics (Kamii, 1994) and early literacy

development (Ferreiro & Teberosky, 1982). Piaget also had great insight into the role of

symbolic and make-believe play in children’s cognitive development. Yet, his major theoretical

contributions to the framework of developmentally appropriate practice relate to stage theory and

to the mechanisms of cognitive development.

Piaget proposed that the human cognitive development progresses through four universal stages:

the sensorimotor, preoperational, concrete operational, and formal operational

stages. Kindergarten children will most likely be in the second, preoperational stage of

development, which lasts from approximately 2 to 7 years of age. During this period, children

15

grow rapidly physically, socially, and cognitively. Their physical growth and refined motor

skills enable them to explore their environment in new ways. Through the use of symbols and

language, children’s cognitive capacity is also greatly facilitated. In this stage, children start to

engage in symbolic play, where one object (e.g., a stone) could represent something else (e.g.,

food). Nevertheless, the preoperational children’s ability to communicate is still limited. One

characteristic of this stage is the children’s use of egocentric speech, or speech whose purpose is

not to communicate ideas to others. While various scholars have attributed different functions to

egocentric speech, Piaget viewed it primarily as evidence of the children’s self-centeredness and

immature social communication skills. In addition to the children’s language ability, their

cognitive capacities are also limited and their thinking is often considered self-centered and

illogical. For example, children in the preoperational stage lack understanding of the principle of

conservation, which is the knowledge that a certain quantity is preserved despite its arrangements

or physical appearance. A preoperational child, when presented with two balls of clay of the

same size, is capable of answering correctly that the two balls are of the same size. Nevertheless,

when the experimenter stretches out one of the balls out into an oblong shape, the child would

say that the oblong-shaped clay is larger than the ball-shaped clay. According to Piaget,

development precedes learning. Due to the preoperational children’s limited cognitive abilities,

their learning capacities are also capped.

Piaget theorized that cognitive development occurs as a result of children actively constructing

their own knowledge through interaction with the environment. According to Piaget, learning

and development occur through two distinctive cognitive processes: assimilation and

accommodation. Assimilation occurs when an activity proceeds from the child’s own pre-

existing schema of operations and new information is processed to fit the child’s pre-existing

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cognitive schema. On the other hand, accommodation occurs when the pre-existing cognitive

scheme is modified. This happens when the existing cognitive schema does not work, and needs

to be adjusted to fit the new information (Piaget, 1973). Through trial-and-error, active

exploration, and repeated practice initiated by the children, they are able to refine their cognitive

schemas. Activities such as writing with inventive spelling may be encouraged in classrooms to

facilitate children’s schema development and knowledge of phonics and letter-sound

correspondence (Copple & Bredekamp, 2009).

Piaget’s theory contributed to the idea of developmentally appropriate practice in multiple ways.

Firstly, it contributed to the acknowledgement that children are active learners and that children

learn through interacting with their environment. In this framework, teachers are expected to

facilitate children’s learning mostly by providing a rich and stimulating environment, rather than

providing the information that the children are supposed to learn. Manipulatives and other real-

life, hands-on experience may help children to develop their cognitive schema. Secondly, the

developmental stages and universal developmental trajectories in each domain, although highly

criticized by modern scholars (Flavell, 1982; Smith, 2002; Walsh, 1991), provide a broad

framework for lesson planning and setting realistic expectations.

2.2.1.2 Erikson

Similar to Piaget’s cognitive stage theory, Erikson proposed a psychosocial stage theory for

personality development (Erikson, 1963). At each stage of Erikson’s psychosocial development,

a person is confronted with a psychosocial crisis which manifests itself as a conflict between a

person’s biological or psychological needs and the needs of the society. When these conflicting

needs between self and society are successfully negotiated, the person emerges from the stage

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acquiring what Erikson calls a virtue. According to Erikson, each stage builds on the success of

previous stages, although a success of one stage is not required to advance to the next stage.

Erikson’s psychosocial development occurs in eight stages, which covers the whole lifespan

(Erikson, 1963). Most kindergarteners and elementary school children will be proceeding

through stage 3 of Erikson’s psychosocial stages. In stage 3, children ages 3 to 5 are confronted

with the conflict between initiative versus guilt. Building on the previous stages of the conflicts

between basic trust and mistrust (stage 1 - first year of life) and autonomy versus shame and

doubt (stage 2; 2nd and 3rd year of life), children in the third stage try to take initiative, building

on the autonomy, or “the quality of undertaking, planning, and attacking a task for the sake of

being active and on the move” (Erikson, 1963, p. 233). Children at this stage are generally very

eager to try new skills and reach new goals, and may try to act on every impulse. However, the

environment and other individuals may provide restrictions to the activities the children can

do. Therefore, the challenge for children at this stage is to maintain the zeal for activity while

understanding the limitations that sometimes they cannot get what they want. When children

successfully master this stage, they will acquire the virtue of purpose.

Erikson and Piaget’s theories both point to the importance of giving children the initiative in

their learning experience. Developmentally appropriate practice therefore recommends that

kindergarten teachers allow children to take initiative in their own learning and choose their own

learning materials such as their own books to read.

2.2.1.3 Vygotsky

Finally, Vygotsky’s theory helped educators recognize the importance of social interaction and

the mediating role of culture and society for the development of higher order thinking

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skills. Vygotsky was a contemporary of Piaget. Piaget and Vygotsky were interested in similar

issues regarding cognitive development, although their theoretical viewpoints were distinct. For

Piaget, “learning is subordinated to development and not vice-versa” (Piaget, 1964, p. 17). In

other words, Piaget believed that development precedes learning and provides the cognitive

schema necessary for learning to occur. Vygotsky, on the other hand, believed that learning

leads to development (Hatch, 2012). In addition, while Piaget sought to examine the biological

correlates of cognitive development, Vygotsky sought to investigate the social origins of higher

psychological processes (sociogenesis). Vygotsky maintained that social interaction is

imperative to cognitive development. Vygotsky believed, following the views of psychologist

Pierre Janet, that language originally developed as a means of communicating with others and

only later became a device directed to oneself (Vygotsky, 1978). In the process of language

acquisition, therefore, the child initially develops social speech. Around ages 3 to 7, the child

begins to use egocentric speech -- speech that is said out loud but not directed toward others. For

Vygotsky, egocentric speech was not an evidence of immature social skills as Piaget considered,

but a process of internalizing external speech. Gradually, according to Vygotsky, this egocentric

speech becomes internalized, and becomes private, inner speech.

More generally, Vygotsky (1978) maintained that

Every function in the child’s development appears twice: first on the social level, and

later, on the individual level; first between people (interpsychological) and then inside the

child (intrapsychological). This applies equally to voluntary attention, to logical

memory, and to the formation of concepts. All the higher functions originate as actual

relationships between individuals. (p. 57)

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Vygotsky, who worked as a school teacher himself before embarking on his research career,

applied this formulation to educational settings, which he called the zone of proximal

development (ZPD). Zone of proximal development refers to “the distance between the actual

developmental level as determined by independent problem solving and the level of potential

development as determined through problem solving under adult guidance, or in collaboration

with more capable peers” (Vygotsky, 1978, p. 86). This level of potential development is

considered the ideal level at which the student should be instructed. The DAP principle of

“meeting children where they are” and providing “challenging, yet achievable” goals with

sufficient support to facilitate development and learning can be said to be derived from

Vygotsky’s theory of zone of proximal development (ZPD).

Another practical application of Vygotsky’s theory for developmentally appropriate practice is to

be attentive to the diverse cultural values that exist in the classrooms and to the cultural context

in which instruction takes place. As the U.S. educational context becomes more diverse and the

developmentally appropriate practice framework becomes more recognized internationally, more

attention is being paid to what developmentally appropriate practice looks like in various

contexts, especially to support the development of children from disadvantaged backgrounds

(Burger, 2010; Brown & Lan, 2015).

2.2.2 Didactic Kindergartens

According to the DAP framework and the theoretical perspectives of Piaget and Vygotsky,

learning occurs through exploration and interaction with objects and other individuals. On the

other hand, didactic instruction, which is considered a synonym of developmentally

inappropriate practice in the NAEYC’s developmentally appropriate practice framework, is

commonly associated with behaviorist learning theories. Behaviorists define learning as

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acquisition of new behavior through environmental conditioning. In a behaviorist classroom,

learning occurs through repeating the correct response to the teacher’s stimuli. Tasks are taught

in small sequential steps, and when errors occur, feedback is provided immediately so that the

wrong responses are not learned (Stipek, 2002). Rewards and punishments are used effectively

so that the desired responses will be rewarded and inappropriate responses will be punished and

eliminated. In many of the experiments conducted by behaviorists, animals such as rats and

pigeons were used to examine how the response is learned from a given stimuli. As such, the

behaviorists’ perspective might be considered an attempt to reduce human learning to basic

instinctive behavior shared with animals. Therefore, it can be said that their methods and

principles of learning can be universally applied, rather than being “inappropriate” for

development. Advocates of behaviorist approaches to learning include Watson (1913), Skinner

(1972) and Bandura (1986).

Some researchers have theorized that didactic approaches may be more appropriate for meeting

the needs of certain segments of the population, including economically disadvantaged children

and children from diverse cultural backgrounds. Stipek and Byler (1997) for example, suggest

that it may be more consistent with the cultural values of low-income families to emphasize

basic skills and academic outcomes. In addition, educators have insisted that the focus on active

learning and meeting individual needs may be inconsistent with the cultural values of some

ethnic groups such as the Native American (Williams, 1994) and the traditional Taiwanese

cultures (Hsue & Aldridge, 1995).

2.2.3 DAP vs DIP: A false dichotomy?

One of the criticisms against DAP relates to its implementation. While developmental practices

are advocated by most teacher education programs and early childhood organizations, several

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studies have indicated that teachers struggle with their implementation. One classroom

observation study found that only 20 percent of the sampled U.S. kindergarten classrooms met

the criteria of DAP (Bryant et al., 1991). Further studies have found that while teachers may

hold beliefs consistent with DAP, they actually tend to engage in non-DAP practices (McMullen,

1999). The teachers often cite environmental and work-related stress, pressure from parents and

upper-grade teachers to prepare students for testing as some of the reasons for the discrepancy

between their beliefs and practices. On the other hand, the teachers who believe in didactic

teaching practice what they believe. The discrepancies between beliefs and practices for the

teachers who hold developmental beliefs may be due to the discrepancy in ideals held by the

early childhood education community and the educational policy environment of the U.S. While

it is more “politically correct” to admit to holding DAP beliefs in the community of early

childhood education, the educational policy environment may generally endorse the didactic

teaching practices.

Another criticism of the NAEYC’s framework is that DAP and DIP are depicted as completely

dichotomous, distinct and mutually exclusive concepts (Stipek, Feiler, Daniels, & Milburn,

1995). While empirical evidence tends to support the fact that teachers subscribe to either one of

the two pedagogical philosophies, Stipek et al. (1995) suggest that these two approaches are not

mutually exclusive and could be used for different goals and purposes. Indeed, some have

speculated that an integration of the two approaches would be ideal for early childhood education

(Ray & Smith, 2010). The full-day kindergarten programs in Ontario, Canada, is an example of

an initiative to integrate both developmental and didactic perspectives by having two teachers

lead the classroom -- one an academic teacher and the other an early childhood educator (Pascal,

2009). They are expected to bring complementary skills, perspectives and educational

22

backgrounds to the classroom. The academic teacher generally has at least a bachelor of

education and is trained to teach children from kindergarten to Grade 8. On the other hand, an

early childhood educator usually has a college degree and is trained in early childhood

education. The early childhood educator is expected to bring the perspective of child

development from earlier stages of development. On the other hand, the academic teacher is

expected to bring the perspectives of elementary school education.

2.2.4 Empirical Studies on Developmentally Appropriate Practice and Developmentally Inappropriate Practice

Effectiveness studies have examined how attending developmental, didactic, and sometimes the

combined programs affect children in various areas of development. These areas include stress

(Burts et al., 1992; Burts, Hart, Charlesworth, & Kirk, 1990; Love, Ryer, & Faddis, 1992),

creativity (Hirsh-Pasek, Hyson, & Rescorla, 1990), confidence (Mantzicopoulos, Neuharth-

Pritchett, & Morelock, 1994; Stipek et al., 1995), motivation (Stipek et al., 1995), and cognitive

and academic outcomes (Huffman & Speer, 2000; Stipek et al., 1995). Most studies, however,

are done in preschool settings and the conclusions about the effectiveness of kindergarten

programs are usually extrapolated from these preschool studies. In addition, the vast majority of

these studies adopt a non-experimental research design where selection bias is a major concern.

Finally, many studies suffer from significant methodological problems, including lack of pretest

(Marcon, 1999), small sample size in intervention studies, and lack of attention to the nesting

that exists in the data.

The primary concern for the NAEYC when they published the initial policy statement was that

children are being rushed into formal education too early, especially through inappropriate

classroom practices, and was experiencing considerable amount of stress. Therefore, from the

23

earliest studies on the effect of developmental and didactic programs, there was a focus on

children’s stress, motivation and emotional development (Dunn & Kontos, 1997).

Several studies have shown favorable results in the socioemotional and motivational domains for

children attending developmentally-oriented programs. In a classroom observation study by

Hyson, Hirsch-Pasek, and Rescorla (1990), the researchers reported a moderate negative

correlation (r= -.23) between children's test anxiety and the degree to which the preschool

program was considered child-initiated (as measured by Classroom Practice Inventory).

Another study using the same sample found that children in the child-initiated programs

appeared to be more willing to engage in the classroom learning activities, had higher self-

confidence, and held more positive attitudes toward school (Hirsh-Pasek et al., 1990). Similarly,

in an observational study by Stipek et al. (1995) that involved 32 preschool and kindergarten

classrooms, the children attending developmentally-oriented programs were found to be more

motivated, less worried about school, took more pride in their accomplishments, were less

dependent on adults for approval, and had higher expectations for their success in school.

Studies have also documented the negative impact of attending didactic classrooms on children’s

socioemotional and motivational development. In a study by Burts et al. (1990) involving 37

kindergarten children from one didactic classroom and one developmental classroom, the

children attending didactic classrooms displayed more stress-related behaviors (such as nail

biting, exhibition of physical hostility or fights, tremors, tics and nervous laughter) than children

attending developmental programs, especially when the children were in a group setting or

engaged in activities using workbooks or worksheets. Similarly, children from didactic

preschool classrooms received worse behavior evaluation from their classroom teachers than

24

children from developmental preschool classrooms (Mantzicopoulos & Neuharth-Pritchett, 1995;

Marcon, 1994).

The findings from research studies examining academic outcomes of developmental and didactic

programs are mixed. Several studies have found positive effects of attending developmental

programs on children’s academic outcomes. A cluster randomized study by Sherman and

Mueller (1996), for example, examined how implementation of developmental programs from

kindergarten to Grade 2 could affect children’s reading and math outcomes. They found that,

even when developmentally appropriate practices were only modestly adhered to, there was a

significant positive impact on children’s reading and math (Woodcock-Johnson) scores. Burts et

al. (1993) found similar results for reading. On the other hand, Hyson et al. (1990) and Burts,

Charlesworth, and Fleege (1991) found no significant achievement differences in relation to

developmental appropriateness of the program.

There is also empirical support for didactic instruction. Marcon’s (1999) observational study

found a slight positive effect of didactic programs compared to the developmental and combined

programs on math outcomes. In a study with 227 poor and middle-class children from ages 4

and 6 years, Stipek et al. (1995) found that children attending didactic programs scored

significantly higher on a test of reading and letter recognition than the children attending

developmental programs. However, the results showed that there were no significant differences

between groups in the numbers achievement test, and that the children in the didactic programs

were doing significantly worse on most measures of motivation.

25

2.2.5 Combined Programs

In the second iteration of the guidelines, NAEYC advocated for a more balanced approach, using

the child-initiated and didactic instruction models flexibly (Charlesworth, Hart, Burts, &

DeWolf, 1993). However, some studies imply that this eclectic approach may not be the most

beneficial approach. Marcon's (1999) observational study compared three preschool models

derived through cluster analysis: the child-initiated, academically-directed, and the combination

models. The researcher found that children in the combination model scored significantly worse

on almost all socioemotional and cognitive measures compared with the other two models.

Children in the child-initiated programs demonstrated better mastery over basic skills than

children in the other two programs. However, this study is limited due to the lack of pretest data.

2.2.6 Effects for Children of Low Socioeconomic Status

A concern for children from impoverished backgrounds has been one of the greatest motivating

factors for early childhood education research and policy making, especially since the 1960s.

Nevertheless, most studies examining the effect of kindergarten programs for disadvantaged

children's motivation and academic achievements are limited to relatively small-scale, high-

quality programs (Frede & Barnett, 1992). Some of the earlier intervention studies for children

from socioeconomically disadvantaged backgrounds found no particular model to demonstrate

superiority over others on academic achievement (Smith & James, 1975). There were, however,

a few well-designed, highly-effective child-centered programs located in impoverished

neighborhoods such as the Abecedarian Project of Chapel Hill, North Carolina (Campbell et al.,

2012) and the High/Scope Perry Preschool Project of Ypsilanti, Michigan (Barnett, 1996;

Belfield, Nores, Barnett, & Schweinhart, 2006). The purpose of the High/Scope Perry School

Project was to examine the effectiveness of early childhood education for children at high risk

26

for academic failure, as determined by their initial IQ scores being less than 78. Sixty-eight

children ages 3 and 4 from low income households were randomly assigned to one of three

treatment groups: (1) The didactic, Direct Instruction model group, in which the teachers

followed a script to directly teach children academic skills; (2) The traditional Nursery School

model group, in which the teachers supported children in self-initiated free play in a safe, social

setting, and (3) The High/Scope model group, in which the teachers would set up activity areas

and the students would plan, do, and review their own daily educational activities with the

assistance of their teachers. The children in each group received 2 ½ hours of classroom

instruction 5 days a week and 1 ½ hour home visits every 2 weeks. The only major difference at

the end of the early childhood program intervention was that the Direct Instruction group had

significantly higher IQ scores than the traditional Nursery School group (103 vs. 93). However,

the three groups did not differ in subsequent school achievement, or high school graduation rates.

Nevertheless, at the follow-up study at ages 15 and 23, significant socio-emotional outcomes

were found (Schweinhart & Weikart, 1997; Schweinhart, Weikart, & Larner, 1986). At age 15,

those who received High/Scope and traditional Nursery School groups showed half the

delinquent behaviors as those in the Direct Instruction group did. Similarly, at age 23, the

High/Scope and traditional Nursery School groups had fewer felony arrests and fewer years in

special education due to emotional impairment. The High/Scope and traditional Nursery School

groups did not differ statistically from each other. Thus, the researchers concluded that while

well-implemented early childhood programs is expected to show positive effects on IQ in

general, child-initiated early childhood programs may have long-term socio-emotional benefits

over didactic programs (Schweinhart & Weikart, 1997; Schweinhart et al., 1986; Weikart,

Epstein, Schweinhart, & Bond, 1978).

27

Both the High/Scope and Abecedarian projects are well designed randomized control studies

with longitudinal follow-up into adulthood. However, both are limited to one neighborhood and

the effects of such programs have not yet been investigated nation-wide and across different SES

neighborhoods. Moreover, such well-designed developmental programs are not typical of the

regular kindergarten programs, and may not be considered the regular outcome of the typical

developmental programs in the U.S. Given that there have also been well-designed didactic

intervention programs that have successfully improved the achievement of children from poor

families (Becker & Gersten, 1982; Bereiter, 1986), it seems important to compare the

effectiveness of the kindergarten programs with a large-scale, national level sample.

3 Research Questions

In this dissertation, I address four main research questions. The research questions and the

hypotheses for each question are as follows:

(1) What are the natural groupings of teachers that emerge from the data based on their

didactic and developmental teaching practices?

According to the NAEYC’s developmentally appropriate practice framework, there are

two distinct practices commonly observed in early childhood and early elementary

school classrooms: the “developmentally appropriate” and the “developmentally

inappropriate” or “in contrast” practices. Most studies following this framework have

assumed the existence of these two distinct approaches and proceeded to examine the

effectiveness of teachers who adhere to either one of these practices. However, some

researchers have argued that there may be a third group of teachers who may combine

elements of both the developmental and didactic approaches (e.g., Marcon, 1999; Stipek

28

& Byler, 1997). The aim of the first research question is to empirically derive from the

national-level data the natural grouping of kindergarten teachers in the 1998-1999

school year based on their teaching practices.

(2) With reference to the kindergarten programs identified under the first research question:

a. What kinds of schools are more likely to adopt these programs?

b. What kinds of teachers are more likely to implement each of these programs in their

classrooms?

c. What are the demographic characteristics of the children attending each of these programs?

Building on the first research question, the purpose of the second research question was to

examine the school-, teacher-, and child-level characteristics associated with each type of

programs identified in question 1. From previous studies (e.g., Maxwell, McWilliam, Hemmeter,

Ault, & Schuster, 2001), it is expected that schools with more disadvantaged students will be

more likely to adopt a didactic orientation, whereas schools with more advantaged students will

be more likely to adopt a developmental orientation. Due to the lack of empirical studies, there

are no specific predictions regarding the school characteristics of programs that combine the

elements of developmental and didactic practices, if such groups were found.

Previous research indicates that teachers who adopt a developmental approach tend to have

greater knowledge about developmentally appropriate practices (Bryant et al., 1991). Therefore,

teachers who have taken more courses in child development, or have more experience teaching

preschool may be more likely to adopt a developmental approach. On the other hand, teachers

29

with more experience teaching upper grades may be more likely to adopt a didactic approach to

kindergarten. Moreover, teacher beliefs about what children should learn during kindergarten

may have an effect on teaching practices. Previous research has shown that teachers holding DIP

beliefs were more likely to adopt didactic practices, but teachers who hold DAP beliefs do not

necessarily engage in developmental practices. Therefore, I predict that teachers who believe

that learning specific academic skills is important before or in kindergarten would more likely

engage in didactic practices whereas teachers who believe in the importance of developmental

practices such as playing math games and reading stories to children may or may not engage in

developmental practices.

(3) How does the time allocation differ among the didactic, combination, and developmental

kindergartens? How does classroom SES affect time allocation?

It is often assumed that didactic instruction is used in academically-oriented programs that spend

more time on academics, and that developmental instruction is used in programs where more

time is allocated to enrichment activities and recess (Miller & Almon, 2009), yet the inter-

relationship among time allocation, kindergarten program, and SES has rarely been investigated.

I expected that didactic programs and programs that allocate more time to academic subjects will

be found in low SES classrooms, because they would be more under pressure to achieve

academically, maybe at the expense of enrichment activities and recess.

(4) How does attending didactic, combination, or developmental kindergartens affect the

children’s motivational and academic outcomes from kindergarten to Grade 5? Does the

program effectiveness depend on the SES level?

30

The lack of longitudinal studies investigating the effect of kindergarten developmental, didactic,

and combined programs on children’s academic achievement and motivational outcomes has

long been acknowledged by researchers in the field (Burts et al., 1993; Van Horn & Ramey,

2003). In addition, there is a paucity of research on how socioeconomic status affects program

effectiveness.

It is expected that advantaged children would score higher than disadvantaged children on all

measures of academic test scores, both at the end of kindergarten and longitudinally to the end of

Grade 5. From previous studies, I hypothesize that children who attended developmental

programs would receive better teacher ratings on learning-related behavior and academic

outcomes, both at the end of the kindergarten year and longitudinally, irrespective of their

socioeconomic background (Kumtepe, 2005). The standardized test scores for students attending

didactic kindergartens may be higher than those for students attending developmental

kindergartens at the end of the kindergarten year but not longitudinally. The difference may be

more pronounced for children living in poverty than those who do not.

As discussed earlier, most non-experimental studies examining the outcomes of developmental,

didactic, and combined kindergarten programs have significant methodological limitations

including potential selection bias, small sample size, lack of pretest data and lack of

consideration of the nested data structure. While a few small-scale, randomized control trials of

high-quality early childhood programs have been conducted, the results of such studies have yet

to be replicated on a larger scale. Conducting a randomized control study on the effectiveness of

developmental, didactic and combined programs on a national level is practically infeasible.

Moreover, long-term follow-up studies of kindergarten programs have rarely been conducted. In

my dissertation, I use marginal mean weighting with stratification (MMW-S) method, which is a

31

semi-parametric application of the propensity score method using Rubin’s counterfactual causal

inference framework (Hong, 2012). Using the MMW-S method on the Early Childhood

Longitudinal Study – Kindergarten Cohort of 1998-1999 (ECLS-K), a US-based, national-level,

observational dataset, allowed me to emulate a pseudo-randomized study of the effectiveness of

developmental, didactic, and combined kindergarten programs. Moreover, thanks to the

longitudinal nature of the dataset, the effectiveness of kindergarten programs can be examined

throughout the elementary school grades.

32

Chapter 2 Methods

4 Chapter Overview

In this chapter, I describe the methodology used to answer my research questions. I first describe

the characteristics of the ECLS-K dataset. Second, I provide details of how the treatment

variable was created, and describe the child- and teacher-level predictors and outcome variables

used in my analyses. Finally, I provide details of the analytic procedures used to answer my

research questions.

4.1 ECLS-K Dataset Characteristics

I used the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K),

from National Center for Education Statistics (NCES) of the U.S. Department of Education for

analysis (Najarian, Pollack, Sorongon, & Hausken, 2009) . ECLS-K was the first nationally

representative dataset to follow the educational attainment and development of a sample of

children, kindergarten through to Grade 8. Data were collected using a multistage probability

sampling design, with geographic areas of counties and groups of counties serving as Primary

Sampling Units (PSUs). In the fall of 1998, 21260 kindergarten students were sampled from

3305 public and private schools within the PSUs. The ECLS-K contains a total of seven rounds

of longitudinal data: Fall and spring of kindergarten, fall and spring of Grade 1, and spring of

Grades 3, 5, and 8 (Najarian, Pollack, Sorongon, & Hausken, 2009). The fall of Grade 1 was

excluded from my analysis because only a subsample of children was recruited to participate in

33

this round. Additionally, the Grade 8 data were not used, as some of the outcome measures were

missing (e.g., teacher ratings of approaches to learning).

4.2 Analytic Sample

The analytic sample consisted of full-day kindergarten teachers who had valid information

regarding their classroom instructional characteristics and their students.

4.2.1 Full-day Kindergarten

I utilized 3 variables from the Fall and Spring Kindergarten Teacher Questionnaires to define the

full-day kindergarten sample: (1) Whether the teachers taught full-day kindergarten in the fall

and (2) in the spring, and (3) whether the teachers taught full-day kindergartens 5 days a

week. The reason for restricting the sample to those that held daily full-day classes throughout

the academic year is to maintain consistency in the treatment condition (e.g., some classes

offered full-day kindergartens only 2 or 3 days a week and some had shortened school days

during the fall of kindergarten). Of the 3305 kindergarten teachers in the base year full sample,

377 teachers (11.4%) had missing data on at least one of the three selection variables. Of the

valid sample of 2928 kindergarten teachers, 1581 (54%) were identified as teaching full-day

kindergartens. These full-day kindergarten teachers were nested in 492 schools and were

teaching 8393 students in the sample.

Tables 1, 2, and 3 show the demographic characteristics of schools, teachers, and children

affiliated with full-day and half-day kindergartens. Full-day kindergartens were more likely than

half-day kindergartens to be located in the Southern U.S. and in a central city. Compared to the

half-day kindergartens, the full-day kindergartens were more likely to be serving disadvantaged

students. Full-day kindergartens were more likely to be receiving Title 1 funds and have a

34

higher percentage of students eligible for the federal free and reduced lunch programs than half-

day kindergartens. The principals of schools with full-day kindergartens tended to be female with

less experience as principals than the principals of schools that provided half-day kindergartens.

The class size of full-day kindergartens was larger than that of half-day kindergartens. Full-day

kindergartens tended to allocate proportionally less time to academic subjects and more time to

non-academic subjects than their half-day kindergarten counterparts. The full-day kindergarten

teachers tended to be females and Black or African Americans, and less likely to be Hispanic or

White.

Finally, the children in the sample attending full-day kindergartens were more likely to be Black,

Native American, or Pacific Islander students living below the poverty threshold but spoke

English at home. There was a higher proportion of kindergarten retainees in the full-day

programs than in the half-day programs, and the children were less likely to be White, Hispanic

and Asian.

35

Table 1. School Characteristics of Full-Day and Half-Day Kindergartens

School

Full-day

kindergarten

Half-day

kindergarten p-value

Census region

<.01

Northeast 62 92

15% 21%

Midwest 96 132

23% 30%

South 202 84

48% 19%

West 62 136

15% 31%

Location type

<.01

Central city 195 143

46% 32%

Urban

fringe and

large town 128 205

30% 46%

Small town

and rural 99 96

23% 22%

Public school

.73

Yes 303 325

72% 73%

No 117 119

28% 27%

% Hispanic

students

.13

0% 89 65

36

School

Full-day

kindergarten

Half-day

kindergarten p-value

23% 17%

More than 0

and less

than 5 155 164

41% 42%

5 to less

than 10 36 46

9% 12%

10 to less

than 25 36 49

9% 13%

25 or more 66 65

17% 17%

% Black students

<.01

0% 54 69

13% 16%

More than 0

and less

than 5 127 215

32% 51%

5 to less

than 10 43 46

11% 11%

10 to less

than 25 66 50

16% 12%

25 or more 112 41

28% 10%

Received Title 1

funds

.02

Yes 256 236

37

School

Full-day

kindergarten

Half-day

kindergarten p-value

61% 53%

No 163 207

39% 47%

% Free lunch

eligible students Mean (SD)

32.70

(28.79)

23.49

(25.97) <.01

% Reduced lunch

eligible students Mean (SD)

7.97

(6.70) 6.59 (6.38) <.01

% LEP students

in Kindergarten,

Transitional

Kindergarten,

and Transitional

Grade 1 Mean (SD)

9.97

(16.93) 14.96(23.89) .03

38

Table 2. Teacher and Classroom Characteristics of Full-Day and Half-Day Kindergartens

Teacher Full-day

kindergarten

Half-day

kindergarten p-value

Teacher

gender Male 24 56 <.01

2% 3%

Female 1541 1643

98% 97%

Teacher age

Mean

(SD)

38.62

(13.78)

39.13

(14.61) .30

Teacher

Hispanic Yes 114 190 <.01

7% 11%

No 1435 1489

93% 89%

Teacher

American

Indian or

Alaska

Native Yes 25 17 .17

2% 1%

No 1490 1564

98% 99%

Teacher

Asian Yes 48 42 .40

3% 3%

No 1467 1539

97% 97%

Teacher

Black or

African

American Yes 156 86 <.01

10% 5%

No 1359 1495

90% 95%

39

Teacher Full-day

kindergarten

Half-day

kindergarten p-value

Teacher

White Yes 1286 1445 <.01

85% 91%

No 229 136

15% 9%

Proportion of

time spent on

academic

subjects

Mean

(SD) 3.68 (3.34) 2.09(2.40) <.01

Proportion of

time spent on

non-

academic

subjects

Mean

(SD)

.66

(0.14)

.67

(0.14) <.01

40

Table 3. Characteristics of Children in Full-Day and Half-Day Kindergartens

Child

Full-day

kindergarten

Half-day

kindergarten p-value

Race Hispanic 1359 1915 <.01

16% 20%

American Indian 283 191 <.01

3% 2%

Asian 514 790 <.01

6% 8%

Black 1985 1052 <.01

23% 11%

Pacific Islander 181 102 <.01

2% 1%

White 5153 6950 <.01

60% 71%

Home

language Non-English 1006 1534 <.01

12% 16%

Poverty

Below poverty

threshold 1997 1893 <.01

23% 19%

First-time

kindergartner Yes 7828 8934 <.01

94% 96%

Gender Female 4470 5019 .21

49% 49%

Non-parental

care

arrangements

pre-K Yes 1624 1899 .14

19% 19%

41

4.3 Measure of Classroom SES

Classroom SES was defined using the child-level household SES measure,

WKSESQ5. WKSESQ5 was a 5-level, composite SES measure which utilized information from

the parent interview regarding parental education, parental occupation prestige and household

income. Low SES households were defined as those belonging to the two lowest SES

quintiles. Of the total sample of 21409 students, 3770 students were identified as coming from

low SES households. Within the 8393 students attending full-day kindergarten, 3233 students

(40.0% of the valid sample) was identified as coming from low SES households.

By aggregating this child-level SES variable to the teacher level, I obtained the proportion of

sampled students in the classroom who came from low SES households. In a typical full-day

kindergarten classroom in the analytic sample, roughly 4 out of 10 students came from low SES

households (M = .431, SD = .346). Classrooms with more than 50% of the sampled students

coming from low SES households were defined as low-SES classrooms. Of the 1581 teachers in

the sample with valid child poverty information, 474 teachers were teaching low-SES classes.

The aggregated SES composite variable was used in the present study to reflect the students’

educational resources at home which may not be captured by household income alone. However,

due to the small number of sampled students in each classroom, there may be a considerable

amount of measurement error involved in this variable.

In an attempt to validate this SES measure (because there was no classroom level SES measure

in the dataset), the school-level percentage of free-lunch eligible students was de-aggregated to

the classroom level and used as the criterion variable. As expected, there was a significant

42

difference between high and low poverty classrooms as defined by student composite SES on the

school percentage of free-lunch eligible students, t(853.764) = -12.336, p <.001.

4.4 Types of Kindergarten Classrooms

The treatment variables were created using multi-group confirmatory factor analysis and two-

step cluster analysis. The variables that are theoretically related to child-initiated and didactic

practices were derived from the teacher questionnaire regarding classroom activities and

instructional practices.

I used two items from the Teacher Questionnaire B, question 1, which asked how much time in a

typical day the children engaged in teacher-directed whole class activities and child-selected

activities. These two items were used to represent didactic and developmental practices

respectively2. The teachers indicated how much time they spent on each activities during a

typical day. The possible responses were (1) no time, (2) half an hour or less, (3) about one

hour, (4) about two hours, and (5) three hours or more.

In addition to the classroom activities, items related to instructional activities and curricula focus

were derived from questions 28 and 31 of Teacher Questionnaire A. These questions asked how

often the children engage in specific reading and language arts (question 28) and math (question

31) activities. The items theoretically selected to represent didactic practices were (1) the use of

worksheets in reading and (2) the use of worksheets in math. The items theoretically chosen to

2 There were two other items - teacher-directed small group activities and teacher-directed individual activities – in

the teacher questionnaire B question 1. However, these two items were not included in the current analysis as it was

theorized that they could be applicable to either didactic or developmental practices. These two items were found to

have negative correlations with items related to didactic approaches and to have positive correlations with

developmental practices.

43

represent developmental practices were: (1) peer tutoring in reading, (2) peer tutoring in math,

(3) writing with inventive spelling, (4) children choosing their own books to read, (5) using

geometric manipulatives, (6) counting manipulatives, (7) playing math games, and (8) solving

real-life math. The possible alternatives for these items were (1) never, (2) once a month or less,

(3) two or three times a month, (4) once or twice a week, and (5) three or four times a week, and

(6) daily.

4.5 Time Allocation for Academic Subjects

The proportion of time allocated to academic subjects during a typical week was calculated by

multiplying the frequencies and the durations of each of the following activities or subjects:

Reading, math, social studies, science, music, art, dance, theatre, physical education, recess, and

lunch. Information regarding the frequency and duration of academic subjects, enrichment

courses, and recess was derived from a series of items in the Spring Kindergarten Teacher

Questionnaire A (Questions 10-15).

Question 10 asked the teachers how often and how much time the children in their class engage

in lessons or projects in each of the following areas: (a) reading and language arts, (b)

mathematics, (c) social studies, (d) science, (e) music, (f) art, (g) dance/creative movement, and

(h) theater/creative dramatics. The frequency of these activities/subjects was reported on a 5-

point scale: (1) Never, (2) Less than once a week, (3) 1-2 times a week, (4) 3-4 times a week, and

(5) Daily. The duration was estimated on a 4-point scale: (1) 1-30 minutes a day, (2) 31-60

minutes a day, (3) 61-90 minutes a day, and (4) More than 90 minutes a day.

Similarly, question 11 and question 12 asked the teacher how often and how much time their

classes engaged in physical education. The probes for the frequency question were (1) Never,

44

(2) Less than once a week, (3) Once or twice a week, (4) Three or four times a week, and (5)

Daily. The probes for the duration question were (1) Do not participate in physical education,

(2) 1-15 minutes / day, (3) 16-30 minutes / day, (4) 31-60 minutes / day, (5) More than 60

minutes / day.

Questions 13, 14, and 15 asked the teacher how many days a week, how many times, and how

long the children participate in recess, respectively. Question 13 asked the teacher to write the

number of days per week the children have recess. Question 14 asked how many times in a day,

between the starting bell and the dismissal bell, the children have recess: (1) Once, (2) Twice, or

(3) Three or more times. Finally, question 15 asked the duration of lunch and recess in a typical

day: (1) 1-15 minutes, (2) 16-30 minutes, (3) 31-45 minutes, or (4) longer than 45 minutes.

Utilizing this information, I calculated the proportion of time the teacher spent on academic

subjects. First, the total number of minutes per week the teacher spends on each of the activities

was calculated by assigning a middle value to each of the categorical measures and multiplying

the frequency and duration variables for each of the subjects and activities. For questions 10 and

11, the assigned values were 0 = never, 0.5 = less than once a week, 1.5 = one to two times a

week, 3.5 = three to four times a week, and 5 = daily. For the duration questions in question 10,

the following values were assigned: 15 = 1-30 minutes a day, 45 = 31 – 60 minutes a day, 75 =

61 to 90 minutes a day, and 105 = more than 90 minutes a day. For question 12 the assigned

values were 0 = do not participate in physical education, 7.5 = 1-15 minutes / day, 22.5 = 16-30

minutes / day, 52.5 = 31-60 minutes / day, 67.5 = more than 60 minutes / day. For question 13, I

used the raw number of days that the teachers reported. For question 14, the assigned values

were 1 = once, 2 = twice, and 3.5 = three or more times. Lastly, for question 15, the assigned

45

values were 7.5 = 1 – 15 minutes, 22.5 = 16-30 minutes, 37.5 = 31 – 45 minutes, and 52.5 =

longer than 45 minutes.

4.6 Outcome Variables

The outcome variables included children’s direct assessments of reading and math using IRT

theta scores, teacher ratings of the children’s reading and math ability, as well as teacher ratings

of children’s approaches to learning. All outcomes were available from kindergarten to Grade 5.

The mean and standard deviation for each of the measures for each grade are shown in Table 4.

4.6.1 Teacher Ratings of Children’s Approaches to Learning

The teacher rating of children’s approaches to learning estimated how often the sampled child

exhibited certain positive learning-related behaviors. In kindergarten and Grade 1, the following

statements were rated on a 4-point scale from 1= never to 4 = very often: (1) Keeps belongings

organized, (2) Shows eagerness to learn new things, (3) Works independently, (4) Easily adapts

to changes in routine, (5) Persists in completing tasks, and (6) Pay attention well. In Grades 3

and 5, an additional item was included to increase variance: (7) Follows classroom rules. The

children’s approaches to learning score were an average of all the items that the teacher rated on

the child’s behavior. The teachers who rated at least 4 out of 6 items in kindergarten and Grade

1, and at least 5 out of 7 items in Grades 3 and 5 were included in the analysis.

4.6.2 Reading and Math Teacher Academic Rating Scale

The advantage of using IRT theta scores was that it provided a direct measure of a child’s ability

and could be used to assess change over time. The disadvantage, on the other hand, was that

children deemed unsuitable for direct testing due to the children’s English language ability or

special needs were excluded from the testing. To augment the disadvantage of the direct

46

assessment, ECLS-K collected data from teachers of each of the sampled children, and rated

their ability in academic subjects relative to other children of the same grade level. The

advantage of using such indirect measure of child’s ability is that the researcher could gain

insight into the ability level of the children excluded from the direct testing. The disadvantage,

however, is that the rating scale is specific for that child’s grade level, and cannot be used as a

measure of change over time. While the Academic Rating Scale included math, reading, and

general knowledge at the kindergarten and Grade 1 level, and math, reading and science in

Grades, 3, 5, and 8, I use only the teacher assessment of children’s reading and math skills from

Kindergarten to Grade 5 in my analysis.

4.6.3 Reading and Math Direct Assessment

For the direct assessment measures of children’s overall ability in reading and math, I used

children’s Item Response Theory (IRT) theta scores from kindergarten to Grade 5. The IRT

theta scores are estimates of children’s underlying ability based on Item Response Theory.

These scores are vertically equated and are considered ideal measures for assessing children’s

growth over time. The IRT theta scores form a normal distribution at all assessment times and

the score distribution ranges from approximately -3 to 3 (NCES, 2009). The reliability estimates

across grades ranged from .87 to .96 for reading, and .91 to .95 for math.

47

Table 4. Descriptive Statistics for Approaches to Learning, Teacher Rating of Reading and

Math, and Direct Assessment of Reading and Math for Full-Day Kindergarteners (n = 8393)

Mean Std. Deviation Skewness

Approaches to Learning

Beginning K 2.96 0.68 -0.26

End K 3.10 0.69 -0.46

Grade 1 3.04 0.71 -0.39

Grade 3 3.06 0.68 -0.34

Grade 5 3.07 0.68 -0.35

Reading - Teacher Rating

Beginning K 2.50 0.73 0.14

End K 3.37 0.80 0.06

Grade 1 3.44 0.92 -0.24

Grade 3 3.32 0.88 -0.10

Grade 5 3.44 0.84 -0.11

Math - Teacher Rating

Beginning K 2.57 0.82 0.37

End K 3.54 0.85 -0.27

Grade 1 3.48 0.89 -0.28

Grade 3 3.11 0.74 0.27

Grade 5 3.42 0.71 0.14

Reading - Child Assessment

Beginning K -1.09 0.57 0.53

End K -0.45 0.56 0.03

48

Mean Std. Deviation Skewness

Grade 1 0.49 0.51 -0.63

Grade 3 1.30 0.39 -0.44

Grade 5 1.30 0.35 -0.12

Math - Child Assessment

Beginning K -1.02 0.59 0.08

End K -0.42 0.56 -0.11

Grade 1 0.48 0.52 -0.68

Grade 3 1.31 0.47 -0.20

Grade 5 1.39 0.46 -0.18

49

4.7 Analytic procedure

Question 1: Items in teacher questionnaire B question 1 regarding the frequency of engaging in

teacher-directed whole class activities and child-initiated activities, and items in teacher

questionnaire A question 28 and 31 regarding the teachers’ reading and math instructional

practices were used to define the developmental and didactic teaching practices. Multigroup

confirmatory factor analysis was used to examine the dimensionality of these items across high

and low SES classrooms. After establishing the dimensionality, the latent factor scores were

extracted and used for further analysis. A two-step cluster analysis was used to examine the

natural groupings of teachers based on the factor scores.

Question 2: To examine the school, teacher, and child characteristics associated with each type

of kindergarten program, a series of bivariate analyses (chi-square for categorical variables and

ANOVA for continuous variables) were run using variables from the school and teacher

questionnaires as well as parent interviews that might theoretically explain the reasons for

adopting one approach to kindergarten over another. The list of these covariates is found in

Appendix A.

Question 3: The third research question examined the interrelationship among time allocation,

classroom practice, and SES. Firstly, to examine whether socioeconomic status was related to

the prevalence of the different types of programs, I performed a 2 x 4 contingency table analysis.

Secondly, a two-way analysis of variance was used to examine whether kindergarten program

and SES each had a main effect and jointly had an interaction effect on time allocation. Finally, a

two-way multivariate analysis of variance was used to investigate whether kindergarten program

and SES was related to the time spent on individual subjects and activities in kindergarten.

50

Question 4: For the fourth question, regarding the causal effect of receiving different types of

kindergarten program on children’s end-of-kindergarten and longitudinal outcome, I ran a series

of three-level cross-sectional hierarchical linear models with and without applying the marginal

mean weighting with stratification (MMW-S) method to adjust for the imbalance in the

pretreatment covariates associated with the treatment groups due to observational data.

The MMW-S method is based on Rubin’s causal model and is a non-parametric extension of the

propensity score stratification methods (Hong, 2012). I will describe Rubin’s causal model and

the MMW-S method, followed by the specification of the hierarchical linear models.

4.7.1 Rubin's Causal Model

The current research is based on Rubin's causal model (also known as the counterfactual model

or potential outcomes framework; Rubin, 1978), which provides a framework for examining the

causal effect of treatment using observational data. In an ideal scenario, a researcher would

have all the units participating in the study be assigned to the treatment conditions at random.

When a unit is assigned to one of the treatment conditions, the potential outcomes associated

with all other treatment conditions will become counterfactual and missing. This is known as the

“fundamental problem of causal inference” (Holland, 1986).

When units are randomly assigned to treatments, it becomes possible to draw inferences about

the average causal effect for a population of units, although it is impossible to estimate the causal

effect of a specific unit. The average causal effect can be considered equivalent to the expected

difference when all the units in the population were assigned to the treatment group and when all

the units in the population were assigned to the control group. Random assignment balances

both the observed and the unobserved pretreatment covariates across all the treatments and thus

51

eliminates alternative explanations except for the effect of the treatment (the treatment

assignment becomes independent of the potential outcomes).

Observational studies pose a unique challenge to causal inference for two reasons: First, due to

the potential influence of the unobserved pretreatment covariates, and second, due to the large

number of observed pretreatment covariates that distinguish the groups. Selection bias becomes

a concern if the groups differ in systematic ways prior to treatment assignment and the pre-

existing differences have the potential to contaminate the estimation of the treatment

effect. Rubin’s causal model provides a framework for adjusting for the imbalance in the

pretreatment covariates among the groups and thus making causal inference possible using

observational data under specific assumptions.

There are three major assumptions associated with Rubin’s causal model. The first assumption

is that each unit has a non-zero probability of being assigned to each of the treatment

conditions. The second assumption is that each unit has only one potential outcome

corresponding to each treatment, and this potential outcome is not influenced by the assignment

mechanism or the treatments the other units are assigned to. In other words, there is only one

potential outcome per treatment for each unit. This is called the stable unit treatment value

assumption, or SUTVA (Rubin, 1978). In educational settings, this assumption is frequently

violated due to the interdependence among units. For example, a child’s end of the year reading

outcome could be influenced, not only by the treatment variable, but also by who was assigned to

the same treatment (e.g., a disruptive child present in the classroom). If this was the case, the

child would have multiple outcomes within the same treatment, which would violate the

SUTVA.

52

The third assumption is that, given a set of observed pretreatment covariates, the treatment

assignment is conditionally independent of the potential outcomes. This is called the ignorability

assumption and relates to the hidden bias in the treatment assignment mechanism due to the

unobserved covariates. It assumes that the observed covariates capture all relevant information

about the treatment assignment and thus the unobserved covariates can be ignored. With a

reasonably large number of variables in the dataset, the strong ignorability assumption becomes

increasingly plausible. To assess the possibility of major unmeasured covariates affecting the

results, sensitivity analysis can be used.

Rosenbaum and Rubin (1983) devised methods for using propensity scores to adjust for a large

number of observed pretreatment covariates in observational data. A propensity score is defined

as the probability of a unit being assigned to a particular treatment given a set of pretreatment

covariates. By statistically adjusting for the propensity score, selection bias associated with the

selected set of observed pretreatment covariates could be removed. Namely, when the treatment

and control groups have the same propensity score, it is expected that the groups will have the

same joint distribution on all of the pretreatment covariates included in the propensity score

(Rosenbaum & Rubin, 1984).

4.7.2 Marginal Mean Weighting with Stratification (MMW-S) Method

For binary treatments, propensity score matching or stratification is a viable method for adjusting

for the pretreatment differences in observed covariates, thus approximating a randomized

experiment. However, propensity score matching and stratification become increasingly

cumbersome and infeasible when applied to multivalued or multiple treatments. The inverse

probability of treatment weighting (IPTW) method is sometimes used in cases of multivalued

treatments. IPTW weighs each unit with the inverse of the propensity for receiving the treatment

53

actually received (Robins, 1999; Rosenbaum, 1987). However, IPTW is known to be sensitive

to model misspecification, which will reduce precision and potentially increase bias, especially

when only a portion of the sample provides support for causal inference (Hong, 2010).

To address the issue, marginal mean weighting with stratification (MMW-S) was devised as a

nonparametric weighting method that combines the strengths of propensity score stratification

and IPTW methods (Hong, 2010, 2012). Similar to propensity score stratification, MMW-S uses

the estimated propensity score for a given treatment to stratify the sample. However, rather than

computing the treatment effect within each strata, MMW-S computes weights for each of the

treated units based on its representation within each stratum. Under the strong ignorability

assumption, the weighted mean difference between the treatment and control groups would

approximate the mean difference in outcome in a randomized experiment.

There are 7 major steps for applying the MMW-S method to observational data:

1. Creating a list of pretreatment covariates

2. Building the propensity score model

3. Identifying the analytic sample

4. Stratifying the analytic sample

5. Computing the marginal mean weight

6. Checking balance in the observed pretreatment covariates across treatment groups with

weighting, and

7. Estimating the treatment effect using the marginal mean weight.

54

Each of these steps will be described in detail below.

Step 1: Creating a List of Pretreatment Covariates

The first step of data preparation was to create a list of candidate pretreatment covariates that

would potentially predict both the treatment and the outcome (confounders) based on time

precedence and theory. These candidate pretreatment covariates and all outcomes were then

subject to multiple imputation procedures for high and low SES groups separately. One of the

imputed datasets was used for the analysis. For categorical variables, I created missing

indicators and no imputations were performed. For continuous variables, I created missing

indicators and multiple imputation procedures were performed. Upon completion of the data

imputation step, all candidate pretreatment covariates were aggregated to the teacher level and

bivariate associations between the treatment group membership and the continuous and

categorical pretreatment covariates were run.

Step 2: Building the Propensity Score Model

I used all the pretreatment covariates that were found to be significant in the series of bivariate

associations in the previous step to build the propensity score model. The list of covariates that

were entered in the propensity score model can be found in Appendix A. A multinomial logistic

regression was run to build the propensity score model for the high and low SES groups

separately. The estimated propensity scores obtained from the multinomial logistic regression

were saved and converted to the logit scale for each of the four treatment conditions.

Step 3: Identifying the Analytic Sample

55

To identify the analytic sample, I obtained the maximum and the minimum values on the

estimated logit propensity score for each of the treatment groups and added a caliper of 0.2

standard deviation of the logit propensity score to both sides of the spectrum as recommended by

Austin (2011). Teachers who had no corresponding counterparts in alternative treatment groups

were excluded from the analyses. This is because these teachers would have no counterfactual

outcomes in the observed data and, therefore, lack empirical basis for causal inferences. Teachers

who fell outside of this overlapping range on the logit propensity score were dropped from

subsequent analysis.

Step 4: Stratifying the Sample on the Estimated Logit Propensity Score

For all the teachers in the analytic sample, I sorted the estimated logit propensity scores for each

of the treatment groups in an ascending order one at a time and then divided the sample into 6

strata with equal proportion of units. For example, to calculate the weights for classes that

adopted the developmental program, the entire sample was sorted and stratified on the logit

propensity score for the developmental program. The weights for the classes adopting the other

programs were calculated in a similar fashion. In each stratum, the distribution of the observed

pretreatment covariates is expected to be equal between those who were assigned to the

particular treatment and the rest of the sample (Hong, 2012). When this was not the case, I re-

stratified the whole analytic sample until 95% of the observed pretreatment covariates were

balanced. Previous research has shown that dividing the sample into five or six strata would

typically remove 90% of the selection bias, although with larger samples, more strata may be

required (Cochran, 1968; Rosenbaum & Rubin, 1983, 1984). Six strata were sufficient for

balancing the pretreatment covariates for each of the logit propensity score corresponding to

each treatment.

56

Step 5: Computing the Marginal Mean Weight

For each teacher, I computed the marginal mean weight: (1)

Where, nsz is the total number of teachers in stratum Sz that was created on the

logit of z, where z = 1 to 4 correspond to didactic, combined (LO), combined (HI), and

developmental programs, respectively.

Pr(Z = z) is the marginal probability of actually being assigned to treatment group z

(that is, the grand total of all teachers in the analytic sample divided by the total number of

teachers in treatment group z),

is the number of teachers assigned to treatment group z in stratum Sz.

The numerator is the number of teachers in stratum s that would be assigned to treatment group z

in a completely randomized experiment, and the denominator is the number of teachers in

stratum s that were actually assigned to treatment group z.

The weights assigned to the teachers in each stratum by treatment group are shown in Appendix

B.

Step 6: Checking Balance

57

When the MMW-S method is successfully applied, the distribution of the observed pretreatment

covariates in each treatment group would resemble that of the entire sample had it been subjected

to a complete randomized experiment. If important pretreatment covariates were omitted from

the propensity score model, or if the sample was inappropriately stratified, however, MMW-S

will fail to balance the pretreatment covariates, in which case the results will remain biased. To

test whether stratification was successful, I applied a weighted analysis of variance with the logit

propensity score for each group as the outcome and the four treatment groups as the factor. In

addition, I checked whether balance was achieved in each of the pretreatment covariates using a

weighted ANOVA for continuous pretreatment covariates or a weighted cross-tab analysis for

categorical pretreatment covariates.

Step 7: Estimating the Treatment Effect with Marginal Mean Weighting

I analyzed the weighted outcome models using hierarchical linear modeling. I included strong

predictors of the outcome including the fall kindergarten assessment scores as covariates to

further reduce bias and improve precision in the treatment effect estimation. I used a series of

three-level, cross-sectional hierarchical linear models with children at level 1, classrooms at level

2, and schools at level 3.

The equation for the hierarchical linear modeling is specified as follows for high and low SES

groups separately:

Level 1: Child-level

Level 2: Classroom-level

58

Level 3: School-level

+

where,

is the outcome at each assessment time point for child i in classroom j in school k. Y can

represent the raw scores of teacher-ratings of student’s approaches to learning, teacher-ratings of

student’s reading or math, or direct assessment of student’s reading or math at the end of

kindergarten, Grades 1, 3, or 5.

is the level-1 intercept; the mean outcome of children in classroom j in school k at a

particular assessment time point as a function of the SES composition and kindergarten program.

is the level-1 error term for child i in classroom j in school k, with an assumed distribution

59

is the level-2 intercept, which is the mean outcome for didactic classrooms in school k when

SES composition is given.

is the coefficient associated with the combined(LO) kindergarten classrooms in school k

when SES composition is given.

is the coefficient associated with the combined(HI) kindergarten classrooms in school k

when SES composition is given.

is the coefficient associated with the developmental kindergarten classrooms in school k

when SES composition is given.

is the level-2 error term, which is assumed to be normally distributed with a mean of 0 and

variance , i.e., .

is the level-3, school-level intercept.

is the level-3 error term, which is assumed to be normally distributed with a mean of 0 and

variance .

60

, , and represent the level-3 fixed effects associated with the level-2 coefficients

, , and respectively.

61

Chapter 3 Results

5 Question 1: Teacher Groupings Based on Didactic and Developmental Teaching Practices

The first research question examined how the U.S. kindergarten teachers of the 1998-1999

school year can be categorized based on their developmental and didactic teaching practices.

The correlations of the variables used for the confirmatory factor analysis are shown in Table 5.

The correlation matrices of the high and low SES classrooms do not appear to differ

considerably. In both SES groups, there were positive and significant, but low correlations

among the variables related to didactic instruction; namely, there were correlations among the

variables regarding how often the teachers engaged in teacher-directed whole class activities,

used reading workbooks and worksheets and used math workbooks and worksheets. There were

also positive and significant, but low correlations among the items corresponding to

developmental practices; namely, there were correlations among variables concerning child-

selected activities, reading and math peer tutoring, choosing books to read, writing with invented

spelling, handling geometric manipulatives, counting manipulatives, playing math games and

solving real-life math. There were negative correlations observed among the items

corresponding to didactic instruction and the items corresponding to developmental instruction3.

A two-factor model with developmental and didactic practices as latent factors was specified for

the multi-group confirmatory factor analysis as specified in Figure 1. A multi-group comparison

3 The reliability of the didactic and developmental dimensions were .58 and .73 respectively.

62

was performed to examine whether the same two factor model holds for both the high and low

SES classrooms. The first unconstrained model imposed no constraints to the model parameters

between the high and low SES groups. The second model constrained the measurement weights

to be equal between the two groups. Building on the second model, the third model further

constrained the structural covariances to be equal. The final model constrained the measurement

weights, structural covariances, and measurement residuals to be equal.

When evaluating model fit, the chi-square statistic is known to be very sensitive, especially with

large sample sizes, and not very practical when testing either the model fit or the relative fit of

the nested equivalence models. Thus, three model fit indices were examined alongside the chi-

square test: the Root Mean Squared Error of Approximation (RMSEA), the Comparative Fit

Index (CFI), and the Goodness of Fit Index (GFI). As a rule of thumb, RMSEA values between

0.06 and 0.08 are considered good fit, whereas values below 0.06 are considered excellent fit

(Hu & Bentler, 1999). For GFI and CFI, values greater than 0.90 are considered good model fit

and values greater than or equal to 0.95 are considered excellent fit (Bentler, 1990, Hoyle, 1995).

In addition to examining the difference in chi-squares among the nested models, the change in

CFI less than or equal to -0.01 was used as the criterion for examining the relative fit of the

nested equivalence models (Cheung & Rensvold, 2002).

The chi-square values and their degrees of freedom, the RMSEA, GFI and CFI goodness-of-fit

indices, as well as CFI change for each of the invariance models are presented in Table 6.

Although the chi-square statistic for each of the models was statistically significant, RMSEA,

GFI, and CFI demonstrate adequate fit to the data. For each of the models, RMSEA values were

below 0.03 and GFI values were approximately 0.95, indicating excellent model fit. CFI values,

although slightly lower, were all approximately 0.91, indicating good fit.

63

A comparison across the nested models shows that only one of the chi-square changes was

statistically significant. The model with measurement residuals was the only model that had a

significant chi-square change (in comparison with the structural covariances model: Δ χ²(15, N =

1581) = 30.82, p < .01). All four models showed adequate fit when using the criterion of CFI

change (i.e., Δ CFI less than or equal to -0.01), indicating that all observed parameters can be

considered invariant between the high and low SES classrooms.

64

Table 5. Correlations of Variables Used in the Confirmatory Factor Analysis

1 2 3 4 5 6 7 8 9 10 11 12

1

Teacher-directed whole

class activities --- .16** .25** -.30** -.13** -.12** -.09* -.16** -.08* -.11** -.13** -.11**

2 Reading workbooks /

sheets .09* --- .56** -.19** -.02 -.18** -.10** -.10** -.02 -.07 -.01 -.07*

3 Math worksheets .11** .57** --- -.24** -.02 -.18** -.09** -.11** -.005 -.10** .01 -.08*

4 Child-selected activities -.15** -.14** -.21** --- .08* .17** .15** .18** .13** .17** .08* .11**

5 Reading Peer tutoring .03 .04 -.01 .09* --- .23** .27** .14** .22** .21** .76** .21**

6

Write with invented

spellings -.05 -.08* -.15** .07* .20** --- .48** .18** .19** .23** .24** .33**

7 Choose books to read -.06 -.02 -.13** .14** .18** .42** --- .17** .24** .24** .25** .25**

8 Geometric manipulatives -.02 -.07 -.03 .07* .15** .13** .13** --- .50** .48** .15** .18**

9 Count manipulatives -.05 -.03 .002 .13** .18** .18** .21** .51** --- .50** .25** .26**

10 Math games -.06 -.02 -.02 .08* .19** .21** .19** .44** .49** --- .25** .22**

11 Math peer tutoring -.03 .06 .02 .06 .73** .17** .15** .19** .20** .23** --- .27**

12 Real life math .001 .07* .03 .10** .29** .28** .28** .21** .29** .31** .36** ---

Note: * p =<.05, ** p =<.01

The upper and lower triangular areas contain the correlations among the variables for the higher and lower SES groups, respectively.

65

Figure 1.Confirmatory Factor Analysis of Didactic and Developmental Practices.

66

Table 6. Model of Invariance across High and Low SES Classrooms

df χ² RMSEA GFI CFI

Unconstrained 122 511.82 0.03 0.95 0.91

Measurement weights 133 524.70 0.03 0.95 0.91

Structural covariances 136 528.39 0.03 0.95 0.91

Measurement residuals 151 559.21* 0.03 0.95 0.91

As the same two-factor model was found to be adequate for both the high and low SES

classrooms, the latent factor loadings of two factors from the same confirmatory factor analysis

model were saved and used for further analysis. There was a negative correlation between the

child-initiated, developmental practices and the teacher-directed, whole-class didactic practices

(r = -.49, p <.001).

A two-step cluster analysis was performed to explore the patterns of the group of teachers from

the data. The analysis yielded four clusters of approximately equal size, with good cohesion and

separation. The resulting clusters and their features are presented in Table 7. The first cluster

had 391 teachers (208 high-SES classroom teachers and 183 low-SES classroom teachers) and

was named teacher-directed didactic practice (Didactic). This cluster had the highest latent

factor scores (m = 0.89) for teacher-directed whole class activities and the lowest scores for

child-initiated activities (m = 0.71). The second cluster, named combined (LO) practice, was

represented by 403 teachers (193 high-SES and 210 low-SES classroom teachers) and had the

second lowest scores on both the child-initiated (m = 0.88) and teacher-directed activities (m =

67

0.54). The third cluster, named combined (HI) practice, was represented by 393 teachers (201

high-SES and 192 low-SES classroom teachers) and had the second highest scores on both the

child-initiated (m = 0.99) and teacher-directed activities (m = 0.82). The fourth cluster was

named developmental practice, and had the highest factor score on child-initiated activities (m =

1.07) and the lowest factor score on the teacher-directed whole class activities (m = 0.39). There

were 394 teachers (222 high-SES and 172 low-SES classroom teachers) in the developmental

practice cluster. The distribution of the four clusters with regards to the developmental and

didactic latent factor loadings is shown in Figure 2.

Table 7. Characteristics of the Didactic, Combined (HI/LO), and Developmental Kindergartens

Didactic Combined (LO) Combined (HI) Developmental

Size (n (%)) 391 (25%) 403 (26%) 393 (25%) 394 (25%)

Developmental

Dimension (mean) 0.71 0.88 0.99 1.07

Didactic Dimension

(mean) 0.89 0.54 0.82 0.39

68

Figure 2. The Distribution of the Four Kindergarten Types with Reference to Developmental and

Didactic Practices.

6 Question 2: Examining the Pretreatment Characteristics

The second research question investigated the school, teacher, and child characteristics

associated with the developmental, combined (HI and LO), and didactic kindergarten programs.

6.1 School characteristics

Geographically speaking, developmental programs tended to be most common in the south.

Didactic programs tended to be most common in the midwest. Combined (HI) and combined

(LO) programs were found in midwestern, southern, and western census regions of the U.S.

69

The school principals of high SES combined (HI) programs tended to be highly educated, many

of them having a master’s degree. The schools with low-SES combined (HI) programs, on the

other hand, were more likely to be public schools located in a large city, receiving Title I funding

for the entire education program.

6.2 Teacher characteristics

As expected, teacher certification and course-taking were related to the types of kindergarten

programs the teachers adopt. The teachers who taught the developmental programs were more

likely to be certified in early childhood education and have taken more courses in child

development and early education, irrespective of classroom SES. Additionally, teachers of high-

SES developmental programs tended to have spent more years teaching preschool than teachers

of other types of programs.

The teachers of didactic programs, on the other hand, were less likely to be certified in early

childhood education and tended to have taken fewer courses in child development and early

childhood education, irrespective of classroom SES. Moreover, the teachers of low-SES didactic

programs were more likely to have spent a longer time teaching Grade 6 and above.

The teachers of combined (HI) programs were more likely to be certified in elementary school

education, and less likely to be certified in early childhood education, irrespective of classroom

SES. Compared to the teachers of didactic programs, the combined (HI) teachers were more

likely to have taken more courses in early childhood education and reading pedagogy. The

teachers of high-SES combined (LO) programs, on the other hand, were found to have taken

fewer child development and early childhood education courses, compared to the teachers of

developmental programs.

70

There were notable characteristics in the teachers’ kindergarten readiness beliefs and

expectations for what their kindergarteners should learn based on their teaching practices. There

were significant differences in the teachers’ beliefs about behavioral and academic requirements

to enter kindergarten. The teachers of the Combined (HI) programs were most likely to consider

knowing English as a prerequisite to attending kindergarten. In addition, in low SES classrooms,

Combined (HI) program teachers were more likely than other teachers to consider counting up to

20 or more and knowing the names of colors and shapes as important skills to acquire prior to

kindergarten. In both the low and high SES classrooms, there were no significant differences

among the teachers of the four kindergarten programs on the socioemotional and communicative

aspects of kindergarten readiness such as expecting their children to be able to finish tasks, take

turns, share, solve problems, be sensitive to others, follow directions, and tell teachers their

needs and thoughts.

Teacher beliefs about the benefit of preschool education and expectations of what children

should learn in kindergarten also differed among kindergarten programs. Combined (HI) teachers

tended to believe, more than other teachers, that preschool reading and math was beneficial for

school. Combined (HI) teachers were also more likely than other teachers to believe that

children should learn how to read in kindergarten and that parents should assist their children’s

schoolwork at home. Teachers of developmental programs, on the other hand, were less likely to

agree that preschool reading and math programs were beneficial for school, that the children

should know the alphabet before kindergarten, and that the children should learn to read in

kindergarten. There were no significant differences among the teachers, however, regarding

whether the parents should read to their children and play math games with their children.

71

In the high SES condition, the developmental and combined(HI) programs were more likely to

cover more advanced academic content than were the didactic and combined(LO) programs. In

reading and language arts, for example, the students in the high SES developmental and

combined(HI) programs were more likely to be taught how to read multi-syllable words, identify

the main idea of a story and make predictions, use punctuation, and compose stories during

kindergarten than the students in the high SES didactic and combined(LO) programs were.

Similarly, in math, teachers of the high SES developmental and combined(HI) programs were

more likely to cover content such as counting by 2’s, 5’s, and 10’s, how to use measurement

instruments accurately, estimating quantities, and adding two-digit numbers than were the high

SES didactic and combined(LO) programs. The teachers of the high SES didactic and

combined(LO) program indicated that these more advanced skills should be taught at a higher

grade level. In the low SES condition, however, no such differences in content coverage were

observed among the four kindergarten programs.

6.3 Child characteristics

Irrespective of socioeconomic status, children attending developmental programs showed more

readiness for academic teaching compared to the didactic, combined(LO) and combined(HI)

groups. They received higher scores in the direct assessment and teacher rating of general

knowledge at the beginning of kindergarten, and, children attending high-SES developmental

programs in particular, were rated by their teachers as being less impulsive and as having better

interpersonal relationships at the beginning of kindergarten.

On the other hand, children attending didactic classrooms seemed less ready, as they were more

likely to receive lower scores in general knowledge at the beginning of kindergarten compared to

the children attending developmental programs. In high-SES classrooms in particular, children

72

attending didactic classrooms were rated by their teachers as being more impulsive and having

lower self-control than children in developmental programs.

Similarly, children attending combined (HI) programs were more likely to score significantly

lower on general knowledge compared to children in developmental programs, irrespective of

SES. Children of high-SES combined (LO) programs also scored lower in general knowledge

and self-control at the beginning of kindergarten, compared to the children attending high-SES

developmental programs.

The Limited English Proficient (LEP) students were most likely to be assigned to didactic

programs if they were high SES, or to the combined (LO) programs if they were low SES. There

also tended to be a higher percentage of Hispanic / Latino teachers in the low-SES combined

(LO) programs. Within the low-SES condition, the developmental program had the fewest

Hispanic/Latino students. Low-SES developmental programs had about 5-10 % African

American students.

In sum, there were significant differences in the demographic characteristics at the school,

teacher, and child levels for the four types of kindergarten programs. Most notably, school

funding, teacher beliefs, teacher education, certification type, and experience, student academic

and social skills were associated with the types of kindergarten program the children will

receive. Schools that were receiving Title I funding for the entire education were more likely to

adopt a combined(HI) program that provides a high dosage of both the didactic and

developmental practices. Teachers who had taken more courses in child development, held early

childhood education certifications, and had more experience with preschool education were more

likely to choose a developmental approach to kindergarten. Teachers who held elementary

73

school education certification and had more experience with teaching Grade 6 and up, on the

other hand, were more likely to adopt a combined(HI) approach. Teachers who had taken fewer

courses in child development were more likely to provide didactic instruction. Advantaged

students with higher academic and social skills at the beginning of kindergarten were more likely

to receive the developmental approach to kindergarten, and less advantaged students were likely

to receive other types of programs including the combined(LO), combined(HI), or didactic

programs. These pre-existing differences in demographic characteristics, if not controlled for

statistically, are also likely to confound the true effects of kindergarten programs.

7 Question 3: Examining the Inter-relationships among Time Allocation, Kindergarten Program, and SES

The third research question examined the interrelationship among time allocation, kindergarten

program, and SES. Firstly, I ran a contingency table analysis to investigate whether there is a

difference in the distribution of kindergarten program types as a function of classroom

socioeconomic status. Secondly, I examined how time allocated for academic subjects differed

as a function of program types and classroom socioeconomic status using a two-way ANOVA.

Thirdly, I ran a multivariate analysis of variance to examine the relationship among kindergarten

programs, SES, and time allocation by dividing the time allocation variable into separate subjects

and activities.

The 2 (low or high SES) by 4 (kindergarten programs) contingency table analysis showed no

significant associations between SES and kindergarten program types, χ2(3, N =1581) = 6.04, p

= .11. This indicates that low and high SES classrooms were not significantly different from

each other in their choices of which of the four programs to adopt.

74

The means and standard deviations for the time allocation for academic subjects by kindergarten

type and SES are presented in Table 8. With the proportion of time allocated to academic

subjects as the outcome variable, the ANOVA showed a significant main effect for SES. Low

SES classrooms allocated significantly more time to academic subjects than did the high SES

classrooms, F (1, 1526) = 23.45, p <.001. The total time (in minutes) spent in school per day on

reading, math, social studies, science, music, art, dance, theatre, ESL, foreign language, recess

and lunch differed among the four programs. Regardless of SES, the developmental and

combined(HI) programs were more likely to spend more time on these activities than were the

didactic and combined(LO) programs. However, the variability in the developmental and

combined(HI) programs was greater than those in the didactic and combined(LO) programs.

Although this general trend was observed in both the high and low SES classrooms, statistical

significance was observed only for the high SES classrooms, (F(3, 1389) = 30.41, p <.001), and

not for the low SES classrooms (F(3, 147) = 1.97, p =.12. There was also a significant main

effect for kindergarten program types. A post-hoc test showed that the Combined (HI)

kindergartens allocated significantly more time to teaching academic subjects, compared to the

other types of kindergartens, F(3, 1526) =5.68, p =.001.

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Table 8. Proportion of Time Spent on Academic Subjects by Kindergarten Types and Classroom

Socioeconomic Status

Proportion of Time

Spent on Academic

Subjects S.D.

Total Time

(minutes/day)* S.D.

High

SES

Didactic 0.54 0.15 222.59 78.59

Combined

(LO)

0.54 0.16 233.86 80.64

Combined (HI) 0.58 0.14 274.62 106.51

Developmental 0.54 0.16 279.31 115.18

Low

SES

Didactic 0.59 0.17 234.37 85.70

Combined

(LO)

0.58 0.16 235.92 91.38

Combined (HI) 0.62 0.16 277.46 164.72

Developmental 0.58 0.15 284.39 102.13

*Note: Total time in minutes spent daily on reading, math, social studies, science, music, art,

dance, theatre, ESL, foreign language, recess and lunch.

76

Finally, as there were significant differences in the proportion of time allocated for academic

subjects among the four types of kindergartens and between the low and high SES conditions, I

used MANOVA to examine how the four kindergarten types and SES conditions differ in their

time allocation for each subject and activity.

The MANOVA showed a significant main effect for SES F(13,843)=4.32 p <.001, Wilk’s

lambda = .94; and kindergarten type, F(39, 2535) = 3.71, p <.001, Wilk’s lambda = .85. The

interaction between SES and kindergarten type was not significant.

Univariate results showed that there was a significant main effect for SES for math, F(1, 855) =

5.68, p = .02; art, F(1, 855) = 4.56, p = .03; ESL, F(1, 855) = 27.80, p <.001; and recess, F(1,

855) = 10.80, p <.001. On average, the low SES classrooms spent 16 more minutes on math, 44

more minutes on ESL, but spent 16 fewer minutes on art, and 44 fewer minutes on recess per

week compared to the high SES classrooms (see Table 8).

There was also a significant univariate main effect for kindergarten type for math, F(3, 855) =

7.84, p <.001; social studies, F(3, 855) = 7.26, p <.001; science, F(3, 855) = 8., p <.01; music,

F(3, 855) = 5.05, p =.002; art, F(3, 855) = 11.65, p <.001; dance, F(3, 855) = 8.40, p <.001;

theatre, F(3, 855) = 9.16, p <.001; physical education, F(3, 855) = 9.35, p <.001; and lunch, F(3,

855) = 7.57, p <.001. The combined (HI) and developmental approaches spent significantly

more time on math and social studies compared to the combined (LO) and didactic approaches.

Moreover, developmental kindergartens spent more time on science, music, art, dance, and

theatre than the combined (LO) and didactic approaches. Didactic approaches spent significantly

less time on physical education, but significantly more time on lunch than the other three

kindergarten types.

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Table 9. Time (per Week) Allocated for Different Subjects and Activities in High- and Low-SES

Classrooms

Classroom SES

Mean

(minutes) S.D.

Reading High-SES 355.44 142.22

Low-SES 366.42 134.96

Math High-SES 218.17 109.72

Low-SES 233.92 118.47

Social Studies High-SES 93.71 88.27

Low-SES 89.90 81.24

Science High-SES 84.31 80.03

Low-SES 81.39 80.09

Music High-SES 67.79 67.03

Low-SES 66.61 71.35

Art High-SES 99.34 85.18

Low-SES 82.90 75.60

Dance High-SES 34.38 42.43

Low-SES 34.79 47.63

Theatre High-SES 27.33 45.93

Low-SES 23.84 46.04

ESL High-SES 36.04 103.83

Low-SES 80.27 149.85

Physical

Education

High-SES 73.42 55.26

Low-SES 73.65 58.04

Recess High-SES 234.30 206.72

Low-SES 189.85 184.16

Lunch High-SES 120.57 32.67

Low-SES 121.22 33.58

78

Table 10. Time (per Week) Allocated for Different Subjects and Activities among the

Developmental, Combined (HI/LO), and Didactic Programs

M S.E. 95% Confidence Interval

Lower Bound Upper Bound

Reading Didactic 347.22 9.88 327.84 366.60

Combined (LO) 347.76 10.01 328.12 367.41

Combined (HI) 360.36 11.42 337.95 382.76

Developmental 364.09 11.49 341.55 386.64

Math Didactic 196.18 7.49 181.49 210.87

Combined (LO) 206.62 7.59 191.73 221.51

Combined (HI) 242.68 8.66 225.69 259.67

Developmental 236.74 8.71 219.65 253.84

Social

Studies

Didactic 71.89 5.83 60.44 83.33

Combined (LO) 80.53 5.91 68.94 92.13

Combined (HI) 98.90 6.74 85.67 112.13

Developmental 109.38 6.78 96.07 122.69

Science Didactic 61.94 5.33 51.49 72.40

Combined (LO) 76.31 5.40 65.72 86.91

Combined (HI) 92.05 6.16 79.96 104.14

Developmental 99.25 6.20 87.08 111.41

Music Didactic 54.11 4.47 45.34 62.89

Combined (LO) 58.73 4.53 49.84 67.63

Combined (HI) 71.49 5.17 61.34 81.64

Developmental 77.58 5.20 67.37 87.79

Art Didactic 75.39 5.60 64.41 86.37

Combined (LO) 79.10 5.67 67.96 90.23

79

Combined (HI) 87.79 6.47 75.09 100.49

Developmental 122.18 6.51 109.40 134.95

Dance Didactic 23.89 2.87 18.26 29.52

Combined (LO) 31.05 2.91 25.34 36.76

Combined (HI) 34.50 3.32 27.98 41.01

Developmental 45.71 3.34 39.15 52.26

Theatre Didactic 14.13 2.99 8.25 20.00

Combined (LO) 20.60 3.04 14.64 26.55

Combined (HI) 26.02 3.46 19.23 32.81

Developmental 37.53 3.48 30.69 44.37

ESL Didactic 55.05 7.61 40.12 69.98

Combined (LO) 46.20 7.71 31.07 61.33

Combined (HI) 41.90 8.79 24.63 59.16

Developmental 62.75 8.85 45.38 80.12

Physical Ed Didactic 53.69 3.76 46.31 61.07

Combined (LO) 71.07 3.81 63.59 78.55

Combined (HI) 68.09 4.35 59.56 76.62

Developmental 83.71 4.37 75.13 92.30

Recess Didactic 217.88 12.86 192.65 243.11

Combined (LO) 197.42 13.03 171.84 222.99

Combined (HI) 204.71 14.86 175.54 233.88

Developmental 205.56 14.96 176.21 234.92

Lunch Didactic 130.55 2.26 126.12 134.97

Combined (LO) 116.63 2.29 112.14 121.11

Combined (HI) 117.85 2.61 112.74 122.96

Developmental 120.58 2.62 115.43 125.72

80

In this section, I explored the relationship among time allocation, kindergarten type, and SES.

Firstly, the analysis showed that socioeconomic status was not significantly related to the

kindergarten types but was significantly related to the proportion of time these kindergartens

allocate to academic subjects. Low SES kindergartens allocated proportionally more time to

academic subjects than high SES kindergartens. Secondly, there was a relationship between

kindergarten type and time allocation, irrespective of SES. Contrary to the expectations that

didactic kindergartens would spend proportionally more time on academic subjects than other

types of kindergartens, it was the combined(HI) kindergartens that spent proportionally the most

time on academic subjects. Thirdly, I examined what academic subjects and other activities the

kindergartens spend time on. Interestingly, among the academic subjects, the time allocated to

reading instruction was not found to be significantly different between low and high SES

classrooms, nor among the different types of kindergartens. The low SES classrooms spent 16

more minutes on math, 44 more minutes on ESL, 16 less minutes on art, and 44 less minutes on

recess than high SES classrooms. The relationship between the types of kindergarten and time

allocation was more complicated, given that the combined(HI) and developmental kindergartens

spent more time on math and social studies, and developmental kindergartens spent more time on

science, than the other types of kindergartens. As expected, however, the developmental

programs spent significantly more time on enrichment activities such as music, art, theatre, and

dance, especially compared to combined(LO) programs, and didactic programs spent less time

on physical education compared to other types of programs.

81

8 Question 4: How Kindergarten Program Relates to Outcomes of Motivation, Reading, and Math from Kindergarten to Grade 5

In the fourth research question, I examine how kindergarten programs affect children’s

motivational and academic outcomes from Kindergarten to Grade 5. The results of the cross-

sectional multilevel modeling are reported for approaches to learning, teacher ratings of reading

and math, and children’s direct assessment of reading and math. Both the results unweighted

and weighted by the marginal mean weighting with stratification (MMW-S) are reported for each

outcome. A summary table of all the significant results can be found in appendix C. There were

425 schools, 1187 classrooms, and 6882 children in the analytic sample, In the low-SES

classrooms, 97% of all candidate pretreatment covariates were balanced. In the high-SES

classrooms, 96% of all the candidate pretreatment covariates were balanced.

8.1 Approaches to Learning

The results of the unweighted and weighted three-level hierarchical models for approaches to

learning in the low-SES and the high-SES classrooms are shown in table 12 and table 13,

respectively.

8.1.1 Without Marginal Mean Weighting

There was a significant difference in the low SES condition between the combined (HI) and

didactic programs in Grade 3. In Grade 3, children who attended low-SES didactic kindergartens

were scoring higher by 0.14 points in approaches to learning than children who attended

combined (HI) programs, p =.03. There was also a significant mean difference in the high SES

condition in Grade 5 between the combined (HI) and didactic programs. The children who

82

attended high-SES didactic kindergarten programs were scoring 0.09 points higher in approaches

to learning than children who attended combined (HI) programs in Grade 5, p = .05.

8.1.2 With Marginal Mean Weighting

The weighted results of the hierarchical linear models showed no program impact on approaches

to learning, for neither the low-SES nor the high-SES groups, throughout kindergarten to Grade

5.

83

Table 11. Hierarchical Linear Modeling for Approaches to Learning in Low-SES Classrooms

Low-SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.83 0.05 59.81 .00

2.85 0.07 43.95 .00

Combined (L) -0.03 0.06 -0.55 .59

-0.11 0.08 -1.37 .17

Combined (H) 0.03 0.06 0.48 .63

0.04 0.07 0.53 .59

Developmental 0.05 0.06 0.81 .42

-0.01 0.07 -0.09 .93

End K Intercept 2.93 0.05 65.50 .00

2.95 0.05 55.10 .00

Combined (L) -0.02 0.06 -0.26 .79

-0.08 0.07 -1.18 .24

Combined (H) 0.02 0.06 0.32 .75

0.03 0.08 0.36 .72

Developmental 0.03 0.06 0.57 .57

0.03 0.07 0.43 .67

Grade 1 Intercept 2.83 0.04 66.76 .00

2.80 0.06 46.06 .00

Combined (L) 0.09 0.05 1.66 .10

0.12 0.07 1.70 .09

Combined (H) 0.05 0.06 0.80 .42

0.06 0.08 0.78 .44

Developmental 0.06 0.06 1.13 .26

0.10 0.08 1.31 .19

Grade 3 Intercept 2.99 0.05 60.97 .00

2.97 0.06 48.78 .00

Combined (L) -0.09 0.07 -1.32 .19

-0.09 0.09 -1.06 .29

Combined (H) -0.15 0.07 -2.25 .03

-0.10 0.07 -1.37 .17

Developmental -0.07 0.06 -1.14 .26

-0.09 0.08 -1.15 .25

Grade 5 Intercept 3.00 0.05 63.12 .00

2.98 0.05 60.10 .00

Combined (L) -0.07 0.07 -1.00 .32

-0.05 0.07 -0.63 .53

Combined (H) -0.04 0.07 -0.63 .53

-0.03 0.07 -0.37 .71

Developmental -0.11 0.07 -1.67 .10 -0.13 0.08 -1.64 .10

Note: The didactic program is the comparison group

84

Table 12. Hierarchical Linear Modeling for Approaches to Learning in High-SES Classrooms

High SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.99 0.03 93.46 .00

2.99 0.04 69.17 .00

Combined (L) 0.02 0.05 0.52 .60

0.02 0.07 0.22 .83

Combined (H) 0.02 0.04 0.40 .69

0.00 0.06 0.07 .94

Developmental 0.05 0.05 1.03 .30

0.02 0.06 0.39 .69

End K Intercept 3.10 0.03 94.39 .00

3.08 0.15 21.18 .00

Combined (L) 0.02 0.05 0.37 .71

0.07 0.25 0.28 .78

Combined (H) 0.06 0.05 1.39 .17

0.10 0.25 0.39 .70

Developmental 0.08 0.05 1.67 .10

0.07 0.16 0.43 .67

Grade 1 Intercept 3.09 0.03 98.16 .00

3.09 0.07 43.95 .00

Combined (L) 0.00 0.05 -0.09 .93

0.00 0.09 0.05 .96

Combined (H) 0.02 0.04 0.39 .70

0.04 0.09 0.50 .62

Developmental -0.06 0.05 -1.35 .18

-0.06 0.12 -0.48 .63

Grade 3 Intercept 3.10 0.03 93.66 .00

3.09 0.07 43.48 .00

Combined (L) 0.01 0.05 0.25 .80

0.02 0.09 0.26 .79

Combined (H) 0.03 0.05 0.59 .56

0.02 0.12 0.13 .90

Developmental 0.06 0.05 1.24 .22

0.07 0.12 0.59 .56

Grade 5 Intercept 3.15 0.04 89.22 .00

3.16 0.05 60.82 .00

Combined (L) -0.02 0.05 -0.36 .72

-0.04 0.07 -0.61 .54

Combined (H) -0.10 0.05 -1.98 .05

-0.13 0.10 -1.32 .19

Developmental -0.09 0.05 -1.81 .07 -0.13 0.08 -1.56 .12

Note: The didactic program is the comparison group

85

8.2 Teacher Rating of Children’s Reading Skills

The unweighted and weighted results of the three-level hierarchical linear models for teacher

ratings of children’s reading skills in the low-SES and the high-SES classrooms are shown in

tables 14 and 15, respectively.

8.2.1 Without Marginal Mean Weighting

In the low-SES condition, the naïve, unweighted results show that the children attending

developmental programs received higher teacher ratings of reading skills than children who

attended the didactic program at the end of kindergarten, p = .009. In Grade 3, the children who

attended the combined (HI) kindergarten program received significantly lower teacher ratings of

their reading skills than children who attended the didactic kindergarten program, β = -0.21, p =

.006.

In the high-SES condition, the children who attended the didactic program received the lowest

teacher ratings of their reading skills out of the four programs at the end of the kindergarten year.

The mean teacher rating scores of the children who attended the didactic program was 0.14

points lower than that of the combined(LO) program (p = .03), 0.15 points lower than that of the

combined(HI) program (p = .01), and 0.21 points lower than that of the developmental program

(p =.001).

8.2.2 With Marginal Mean Weighting

Similar to the unweighted results, at the end of kindergarten, the children in the low-SES

developmental programs were rated higher by their teachers on their reading skills than children

86

who attended low-SES didactic programs (p = 0.04). However, this result did not carry over to

the future grades. In Grades 3 and 5, children who attended developmental kindergarten

programs were scoring significantly lower than children who attended didactic kindergarten

programs, p =.04 and p = .05, respectively. In addition, in Grade 5, children who attended

combined(HI) programs were scoring significantly lower than children who attended didactic

programs, p =.05.

In the high-SES classrooms, again similar to those of the unweighted results, at the end of

kindergarten, children in the combined(LO) program were scoring 0.21 points higher than

children in the didactic program (p = .02), and children in the developmental program were

scoring 0.17 points higher than children in the didactic program (p = .01). However, these

program benefits disappeared after kindergarten.

87

Table 13. Hierarchical Linear Modeling for Teacher Rating of Children’s Reading Skills in Low-SES Classrooms

Low-SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.30 0.06 36.01 .00

2.32 0.07 33.14 .00

Combined (L) 0.02 0.08 0.29 .77

-0.07 0.09 -0.76 .45

Combined (H) 0.02 0.07 0.21 .83

0.04 0.09 0.42 .68

Developmental 0.06 0.09 0.64 .52

0.04 0.10 0.41 .68

End K Intercept 3.20 0.05 59.92 .00

3.22 0.06 51.50 .00

Combined (L) 0.02 0.07 0.34 .74

-0.05 0.09 -0.59 .56

Combined (H) 0.08 0.08 1.08 .28

0.03 0.09 0.34 .73

Developmental 0.20 0.08 2.66 .01

0.18 0.09 2.09 .04

Grade 1 Intercept 3.27 0.05 61.10 .00

3.24 0.06 50.49 .00

Combined (L) -0.01 0.07 -0.14 .89

0.00 0.08 0.01 .99

Combined (H) -0.01 0.07 -0.12 .91

0.03 0.08 0.33 .74

Developmental -0.02 0.08 -0.27 .79

-0.08 0.09 -0.85 .40

Grade 3 Intercept 3.25 0.06 52.94 .00

3.24 0.07 49.74 .00

Combined (L) -0.13 0.08 -1.68 .09

-0.17 0.10 -1.75 .08

Combined (H) -0.21 0.07 -2.81 .01

-0.14 0.08 -1.64 .10

Developmental -0.16 0.09 -1.74 .08

-0.22 0.10 -2.07 .04

Grade 5 Intercept 3.36 0.07 47.80 .00

3.37 0.07 46.11 .00

Combined (L) -0.15 0.09 -1.69 .09

-0.15 0.10 -1.59 .11

Combined (H) -0.15 0.09 -1.71 .09

-0.20 0.10 -2.01 .05

Developmental -0.13 0.09 -1.37 .17 -0.21 0.10 -1.99 .05

Note: The didactic program is the comparison group

88

Table 14. Hierarchical Linear Modeling for Teacher Rating of Children’s Reading Skills in High-SES Classrooms

High SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.64 0.05 56.85 .00

2.62 0.05 49.80 .00

Combined (L) -0.02 0.06 -0.29 .77

0.00 0.08 0.03 .97

Combined (H) -0.01 0.06 -0.23 .82

0.01 0.07 0.18 .86

Developmental 0.06 0.06 0.96 .34

0.03 0.08 0.34 .73

End K Intercept 3.43 0.04 79.95 .00

3.44 0.05 73.61 .00

Combined (L) 0.14 0.06 2.23 .03

0.21 0.09 2.40 .02

Combined (H) 0.15 0.06 2.53 .01

0.13 0.07 1.90 .06

Developmental 0.21 0.06 3.36 .00

0.17 0.06 2.85 .01

Grade 1 Intercept 3.58 0.04 82.18 .00

3.61 0.05 70.96 .00

Combined (L) 0.02 0.06 0.35 .73

-0.03 0.07 -0.43 .67

Combined (H) 0.04 0.06 0.64 .52

0.01 0.07 0.19 .85

Developmental -0.05 0.06 -0.74 .46

-0.10 0.07 -1.50 .13

Grade 3 Intercept 3.39 0.04 77.66 .00

3.36 0.05 71.32 .00

Combined (L) 0.06 0.06 0.99 .32

0.08 0.07 1.26 .21

Combined (H) 0.06 0.06 1.05 .30

0.06 0.07 0.83 .41

Developmental 0.10 0.06 1.56 .12

0.09 0.07 1.25 .21

Grade 5 Intercept 3.51 0.05 75.65 .00

3.53 0.06 64.17 .00

Combined (L) 0.10 0.07 1.52 .13

0.05 0.08 0.67 .50

Combined (H) 0.02 0.06 0.25 .81

-0.03 0.07 -0.45 .65

Developmental 0.05 0.07 0.76 .45 0.00 0.08 0.05 .96

Note: The didactic program is the comparison group

89

8.3 Teacher Rating of Children’s Math Skills

The unweighted and weighted results of the three-level hierarchical linear models for teacher

ratings of children’s math skills in the low-SES and the high-SES classrooms are shown in tables

16 and 17, respectively.

8.3.1 Without Marginal Mean Weighting

In the low-SES condition, the results of the unweighted analyses for the teacher rating of

children’s math skills are similar to those of the unweighted analyses for the teacher rating of

children’s reading skills. At the end of kindergarten, the children in the low-SES developmental

programs were receiving significantly higher teacher ratings for their math skills than children in

the low-SES didactic programs, β = 0.27, p = .001. In Grade 3, children who attended the low-

SES combined(HI) program were rated lower than children who attended the low-SES didactic

program, β = -0.15, p = .04.

In the high-SES condition, there was only one mean difference among the kindergarten programs

that reached statistical significance. In Grade 1, children who attended the combined(LO)

program were rated by their teachers as significantly better in their math skills than children who

attended the didactic program, β = 0.12, p = .05.

8.3.2 With Marginal Mean Weighting

The weighted results at the end of kindergarten show that children in the low-SES,

developmental programs were receiving higher teacher ratings in math than children in the low-

SES didactic programs, β = 0.28, p = .002. However, this result did not carry over to the other

90

grades. In Grade 3, the children who attended low-SES developmental programs were receiving

lower teacher ratings in math than children who attended low-SES didactic programs, β = - 0.19,

p = .004. There were no significant differences in the teacher ratings of children’s math skills

among the kindergarten programs in the high-SES condition from kindergarten to Grade 5.

91

Table 15. Hierarchical Linear Modeling for Teacher Rating of Children’s Math Skills in Low-SES Classrooms

Low-SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.29 0.07 31.87 .00

2.29 0.08 28.10 .00

Combined (L) 0.14 0.09 1.51 .13

0.14 0.10 1.38 .17

Combined (H) 0.07 0.09 0.83 .41

0.10 0.10 0.98 .33

Developmental 0.15 0.09 1.58 .12

0.19 0.13 1.50 .13

End K Intercept 3.27 0.06 53.07 .00

3.29 0.08 40.91 .00

Combined (L) 0.13 0.09 1.40 .16

0.05 0.11 0.51 .61

Combined (H) 0.09 0.09 1.10 .27

0.06 0.11 0.59 .56

Developmental 0.27 0.08 3.38 .00

0.28 0.12 2.41 .02

Grade 1 Intercept 3.30 0.06 58.34 .00

3.30 0.06 52.86 .00

Combined (L) 0.00 0.07 0.00 1.00

-0.04 0.08 -0.49 .62

Combined (H) -0.02 0.08 -0.30 .77

0.00 0.08 -0.01 1.00

Developmental -0.09 0.08 -1.09 .28

-0.16 0.10 -1.65 .10

Grade 3 Intercept 3.09 0.06 51.05 .00

3.08 0.07 46.62 .00

Combined (L) -0.10 0.07 -1.31 .19

-0.11 0.09 -1.21 .23

Combined (H) -0.15 0.07 -2.07 .04

-0.07 0.09 -0.81 .42

Developmental -0.15 0.09 -1.73 .08

-0.19 0.09 -2.05 .04

Grade 5 Intercept 3.40 0.09 37.68 .00

3.27 0.14 23.24 .00

Combined (L) -0.12 0.11 -1.15 .25

-0.01 0.16 -0.06 .95

Combined (H) -0.17 0.11 -1.50 .13

-0.02 0.16 -0.13 .90

Developmental -0.09 0.11 -0.83 .41 -0.05 0.17 -0.30 .77

Note: The didactic program is the comparison group

92

Table 16. Hierarchical Linear Modeling for Teacher Rating of Children’s Math Skills in High-SES Classrooms

High SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept 2.68 0.06 44.51 .00

2.74 0.12 22.31 .00

Combined (L) 0.01 0.08 0.15 .88

-0.08 0.17 -0.46 .64

Combined (H) -0.01 0.08 -0.18 .86

-0.18 0.15 -1.21 .23

Developmental 0.12 0.08 1.53 .13

0.00 0.14 -0.02 .99

End K Intercept 3.63 0.05 72.17 .00

3.64 0.06 62.72 .00

Combined (L) 0.08 0.07 1.10 .27

0.14 0.09 1.58 .12

Combined (H) 0.10 0.07 1.48 .14

0.08 0.08 1.02 .31

Developmental 0.13 0.07 1.88 .06

0.11 0.08 1.42 .16

Grade 1 Intercept 3.55 0.05 79.67 .00

3.58 0.05 67.19 .00

Combined (L) 0.12 0.06 1.95 .05

0.06 0.08 0.84 .40

Combined (H) 0.05 0.06 0.90 .37

0.05 0.07 0.69 .49

Developmental 0.00 0.06 -0.03 .98

-0.04 0.07 -0.63 .53

Grade 3 Intercept 3.15 0.04 75.63 .00

3.12 0.05 63.33 .00

Combined (L) 0.11 0.06 1.80 .07

0.08 0.06 1.34 .18

Combined (H) 0.07 0.06 1.14 .25

0.05 0.07 0.74 .46

Developmental 0.10 0.06 1.63 .10

0.09 0.06 1.46 .14

Grade 5 Intercept 3.46 0.05 68.10 .00

3.45 0.07 51.96 .00

Combined (L) 0.09 0.07 1.21 .23

0.12 0.10 1.22 .22

Combined (H) 0.04 0.07 0.60 .55

-0.02 0.10 -0.25 .81

Developmental 0.10 0.08 1.34 .18 0.13 0.09 1.36 .17

Note: The didactic program is the comparison group

93

8.4 Reading Direct Assessment Scores

The unweighted and weighted results of the three-level hierarchical linear models for children’s

direct assessment scores in reading from kindergarten to Grade 5 in the low-SES and the high-

SES classrooms are shown in tables 18 and 19, respectively.

8.4.1 Without Marginal Mean Weighting

Even at the beginning of kindergarten, children in the low-SES combined(HI) program were

scoring significantly lower in reading than children in low-SES didactic programs, β = -0.07, p =

.04. This trend continued from kindergarten to Grade 5. At the end of kindergarten, children in

the low-SES combined(HI) program scored on average 0.08 points lower than children in the

low-SES didactic program, p = .05. Similarly, in Grades 3 and 5, children who attended the low-

SES combined(HI) program continued to score significantly lower in reading than children who

attended the low-SES didactic programs (β = -0.08, p = .02 and β = -0.08, p = .008 for Grades 3

and 5, respectively). Additionally, in Grades 3 and 5, children who attended the low-SES

combined(LO) programs were also scoring significantly worse in their direct assessment of

reading than the children who attended the low-SES didactic programs (β = -0.07, p = .03, and β

= -0.08, p = .01 for Grades 3 and 5, respectively).

In the high-SES condition, there was a significant mean difference among the four kindergarten

types only at the end of kindergarten. Children in the combined(LO) program scored

significantly higher than the children in the didactic program, β = 0.07, p = .04. From Grade 1 to

Grade 5, there were no significant mean differences among the kindergarten programs.

94

8.4.2 With Marginal Mean Weighting

There was a consistent pattern in the results of the low-SES condition from the end of

kindergarten to Grade 5. At the end of Kindergarten and in Grades 3 and 5, children who

attended the combined(LO) program scored significantly lower in their direct assessment of

reading than children in the didactic program β = -0.12, p = .01, β = -0.09, p = .05, and β = -0.07,

p = .04 for end of kindergarten, and Grades 3 and 5, respectively. A similar pattern emerged

between the combined(HI) and didactic programs. At the end of kindergarten and in Grade 5,

children in the combined(HI) program were scoring significantly lower in reading than children

in the didactic program, β = -0.13, p = .005 and β = -0.08, p = .03 for end of kindergarten and

Grade 5, respectively. The weighted results showed no significant differences among the

kindergarten types for the high-SES conditions from kindergarten to Grade 5.

95

Table 17. Hierarchical Linear Modeling for Direct Assessment Scores in Reading for the Low-SES Classrooms

Low-SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept -1.24 0.03 -41.85 .00

-1.22 0.13 -9.47 .00

Combined (L) -0.05 0.04 -1.33 .19

-0.10 0.14 -0.67 .50

Combined (H) -0.07 0.03 -2.02 .04

-0.07 0.17 -0.39 .69

Developmental 0.01 0.04 0.31 .76

-0.01 0.15 -0.07 .94

End K Intercept -0.53 0.03 -16.12 .00

-0.50 0.04 -12.92 .00

Combined (L) -0.05 0.04 -1.22 .22

-0.12 0.05 -2.59 .01

Combined (H) -0.08 0.04 -2.01 .05

-0.13 0.05 -2.85 .01

Developmental -0.02 0.05 -0.38 .71

-0.09 0.05 -1.71 .09

Grade 1 Intercept 0.36 0.03 11.16 .00

0.36 0.03 10.42 .00

Combined (L) -0.05 0.04 -1.30 .19

-0.08 0.05 -1.70 .09

Combined (H) -0.05 0.04 -1.24 .22

-0.05 0.05 -1.04 .30

Developmental -0.02 0.05 -0.45 .65

-0.08 0.05 -1.58 .11

Grade 3 Intercept 1.19 0.03 47.00 .00

1.21 0.05 26.57 .00

Combined (L) -0.07 0.03 -2.18 .03

-0.09 0.05 -1.95 .05

Combined (H) -0.08 0.03 -2.38 .02

-0.09 0.05 -1.86 .06

Developmental -0.03 0.03 -0.95 .34

-0.07 0.05 -1.45 .15

Grade 5 Intercept 1.21 0.02 50.71 .00

1.19 0.03 43.46 .00

Combined (L) -0.08 0.03 -2.49 .01

-0.07 0.04 -2.04 .04

Combined (H) -0.09 0.03 -2.68 .01

-0.08 0.04 -2.20 .03

Developmental -0.02 0.03 -0.59 .56 -0.06 0.04 -1.43 .15

Note: The didactic program is the comparison group

96

Table 18. Hierarchical Linear Modeling for Reading Direct Assessment Scores in High-SES Classrooms

High SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value

Coefficie

nt S.E. t p-value

Beginning K Intercept -0.98 0.03 -33.55 .00

-0.97 0.04 -28.18 .00

Combined (L) 0.07 0.04 1.70 .09

0.06 0.05 1.17 .24

Combined (H) 0.02 0.04 0.53 .59

0.01 0.05 0.22 .83

Developmental 0.07 0.04 1.62 .11

0.04 0.05 0.77 .44

End K Intercept -0.32 0.03 -12.24 .00

-0.32 0.03 -10.58 .00

Combined (L) 0.08 0.04 2.07 .04

0.08 0.04 1.92 .06

Combined (H) 0.03 0.04 0.72 .47

0.02 0.04 0.54 .59

Developmental 0.06 0.04 1.53 .13

0.05 0.04 1.36 .17

Grade 1 Intercept 0.56 0.02 23.43 .00

0.57 0.03 20.12 .00

Combined (L) 0.06 0.03 1.70 .09

0.05 0.04 1.37 .17

Combined (H) 0.04 0.03 1.24 .21

0.02 0.04 0.52 .60

Developmental 0.04 0.03 1.20 .23

0.03 0.04 0.70 .48

Grade 3 Intercept 1.36 0.02 72.36 .00

1.36 0.02 55.40 .00

Combined (L) 0.03 0.03 0.96 .34

0.03 0.03 1.04 .30

Combined (H) 0.02 0.03 0.74 .46

0.03 0.03 0.84 .40

Developmental 0.03 0.03 0.96 .34

0.01 0.03 0.36 .72

Grade 5 Intercept 1.36 0.02 67.84 .00

1.37 0.02 58.97 .00

Combined (L) 0.04 0.03 1.27 .20

0.02 0.03 0.62 .53

Combined (H) 0.01 0.03 0.46 .64

0.00 0.03 0.07 .94

Developmental 0.00 0.03 0.14 .89 -0.01 0.03 -0.39 .70

Note: The didactic program is the comparison group

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8.5 Math Direct Assessment Scores

The unweighted and weighted results of the three-level, cross-sectional hierarchical linear

models of children’s direct assessment scores in math from kindergarten to Grade 5 in the low-

SES and the high-SES classrooms are shown in tables 20 and 21, respectively.

8.5.1 Without Marginal Mean Weighting

From the beginning of kindergarten, there was a significant difference in math scores between

children who were assigned to attend the low-SES combined(HI) program and children who

were assigned to attend the low-SES didactic program. In the low-SES condition, children in the

combined(HI) program were scoring significantly lower than children in the didactic program, β

= -0.09, p = .02. The children in the low-SES combined(HI) program continued to score lower

than the children in the low-SES didactic program in Grades 1, 3, and 5, β = -0.12, p = .004, β = -

0.10, p = .008, and β = -0.12, p = .008 for Grades 1, 3, and 5, respectively. In addition, the mean

math scores of children who attended the low-SES combined(LO) program was significantly

lower than the children who attended the low-SES didactic program in Grades 1 and 5, β = -0.08,

p = .04 and β = -0.10, p = .05 for Grades 1 and 5, respectively.

In the high-SES condition, however, the general pattern is reversed. From the end of

kindergarten to Grade 5, children in the combined(LO) program consistently scored higher than

children in the didactic program, β = 0.08, p = .03 for end of kindergarten, β = 0.08, p = .01 for

Grade 1, β = 0.07, p = .03 for Grade 3, and β = 0.07, p = .04 for Grade 5.

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8.5.2 With Marginal Mean Weighting

In the low-SES condition, the weighted results showed significant group differences in reading in

Grades 1 and 5. In Grade 1, children who attended the didactic program scored significantly

higher than children in the combined(LO) program, β = -0.10, p = .02, the combined(HI)

program, β = -0.10, p = .02, and developmental program, β = -0.10, p = .04. Similarly, in Grade

5, children in the didactic program scored significantly higher in reading than children in the

combined(LO) program, β = -0.12, p = .03, and the combined(HI) program, β = -0.13, p = .01.

In the high-SES condition, there was only one significant group difference, which was between

the combined(LO) and didactic programs in Grade 1. Children in the combined(LO) program

were scoring significantly higher in reading than children in the didactic program, β = 0.08, p =

.04.

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Table 19. Hierarchical Linear Modeling for Math Direct Assessment Scores in Low-SES Classrooms

Low-SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept -1.18 0.03 -37.39 .00

-1.15 0.13 -9.16 .00

Combined (L) -0.05 0.04 -1.29 .20

-0.09 0.14 -0.65 .51

Combined (H) -0.09 0.04 -2.37 .02

-0.10 0.17 -0.58 .56

Developmental 0.01 0.04 0.12 .91

-0.02 0.14 -0.15 .88

End K Intercept -0.55 0.03 -17.31 .00

-0.53 0.04 -12.04 .00

Combined (L) -0.02 0.04 -0.53 .59

-0.08 0.05 -1.48 .14

Combined (H) -0.06 0.04 -1.42 .16

-0.10 0.05 -1.83 .07

Developmental -0.01 0.04 -0.19 .85

-0.05 0.05 -0.87 .39

Grade 1 Intercept 0.40 0.03 13.32 .00

0.38 0.03 12.31 .00

Combined (L) -0.08 0.04 -2.08 .04

-0.10 0.04 -2.32 .02

Combined (H) -0.12 0.04 -2.95 .00

-0.10 0.04 -2.39 .02

Developmental -0.07 0.04 -1.66 .10

-0.10 0.05 -2.07 .04

Grade 3 Intercept 1.20 0.03 37.82 .00

1.23 0.06 19.57 .00

Combined (L) -0.05 0.04 -1.34 .18

-0.07 0.07 -1.13 .26

Combined (H) -0.10 0.04 -2.69 .01

-0.11 0.06 -1.74 .08

Developmental -0.02 0.04 -0.38 .70

-0.07 0.07 -0.92 .36

Grade 5 Intercept 1.29 0.04 33.18 .00

1.28 0.04 30.65 .00

Combined (L) -0.10 0.05 -1.96 .05

-0.13 0.06 -2.22 .03

Combined (H) -0.12 0.05 -2.67 .01

-0.13 0.05 -2.56 .01

Developmental -0.01 0.05 -0.16 .87 -0.06 0.06 -0.95 .34

Note: The didactic program is the comparison group

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Table 20. Hierarchical Linear Modeling for Math Direct Assessment Scores in High-SES Classrooms

High SES

Without MMW-S

With MMW-S

Coefficient S.E. t p-value Coefficient S.E. t p-value

Beginning K Intercept -0.89 0.03 -32.97 .00

-0.90 0.03 -26.54 .00

Combined (L) 0.05 0.04 1.37 .17

0.06 0.04 1.42 .16

Combined (H) -0.01 0.04 -0.23 .82

0.00 0.04 0.11 .92

Developmental 0.05 0.04 1.43 .15

0.06 0.04 1.38 .17

End K Intercept -0.30 0.03 -11.97 .00

-0.28 0.04 -7.59 .00

Combined (L) 0.08 0.04 2.16 .03

0.07 0.05 1.53 .13

Combined (H) 0.05 0.03 1.48 .14

0.02 0.04 0.57 .57

Developmental 0.06 0.04 1.62 .11

0.04 0.04 0.93 .35

Grade 1 Intercept 0.55 0.02 23.26 .00

0.56 0.03 17.33 .00

Combined (L) 0.08 0.03 2.47 .01

0.08 0.04 2.02 .04

Combined (H) 0.04 0.03 1.22 .22

0.00 0.04 -0.04 .97

Developmental 0.05 0.03 1.35 .18

0.05 0.04 1.21 .23

Grade 3 Intercept 1.36 0.02 56.61 .00

1.36 0.03 48.11 .00

Combined (L) 0.07 0.03 2.13 .03

0.06 0.04 1.70 .09

Combined (H) 0.05 0.03 1.61 .11

0.06 0.04 1.43 .15

Developmental 0.05 0.03 1.55 .12

0.04 0.04 1.02 .31

Grade 5 Intercept 1.44 0.03 56.95 .00

1.45 0.03 48.01 .00

Combined (L) 0.07 0.04 2.08 .04

0.04 0.04 0.92 .36

Combined (H) 0.06 0.03 1.72 .09

0.04 0.04 0.91 .37

Developmental 0.04 0.04 1.10 .27 0.02 0.04 0.42 .68

Note: The didactic program is the comparison group

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Chapter 4 Discussion

9 Is Developmentally Appropriate Practice for Everyone?

Is Developmentally Appropriate Practice for Everyone? – this was the title of an article written

by Lubeck (1998), which raised concerns whether the developmentally appropriate practice

framework was broad enough to encompass the diverse needs of the various segments of the

U.S. population. In response, Charlesworth (1998) wrote an article entitled Developmentally

Appropriate Practice is for Everyone, affirming the universal applicability of developmentally

appropriate practices. In my dissertation, I sought to examine this issue further. First, I

investigated how many natural groupings of kindergarten teachers there were based on their self-

reported didactic and developmental teaching practices. Second, I examined program

availability of each of the programs based on school, teacher, and child demographics. Third, I

examined whether there were any differences among these kindergarten programs in how they

make use of their time in kindergarten. Lastly, I investigated which kindergarten program was

the most beneficial choice for children from different socioeconomic backgrounds in their

motivational and academic development.

The finding from the cluster analysis showed that there were 4 distinctive types of kindergarten

programs in the ECLS-K dataset for the 1998-1999 school year. These programs were labeled

didactic, combined (LO), combined (HI), and developmental kindergarten programs,

respectively. In the majority of previous studies which examined the effectiveness of

developmentally appropriate practices, the comparison group entailed only the didactic

“developmentally inappropriate” or “in contrast” practice (Copple & Bredekamp, 2009; Gallant,

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2009; Pruitt, 2011). Only rarely did it include another eclectic, intermediate “mid-way”

program that entails elements of both the didactic and developmental programs (e.g., Marcon,

1994, 1999, Stipek, Daniels, Galluzo, & Milburn, 1992). In my study, in addition to the didactic

and developmental practices commonly found in other empirical studies, I found two types of

“mid-way” combined programs – one that provides a high dosage of both the didactic and the

developmental instructions and another that provides a low dosage of both types of instructions.

The existence of these two types of combined programs has practical and theoretical

implications. Firstly, thisimplies the need to consider the existence of the “intermediate”

program in future studies, as indicated by Marcon (1994) and Stipek, Daniels, Galluzo, &

Milburn, (1992). This is especially true since roughly half of the kindergarten teachers in both

the low and high SES conditions fell into this category in naturalistic settings in the 1999-2000

school year. The existence of the combined program points to a need to further elaborate on the

NAEYC’s developmentally appropriate practice framework to reflect the complexity of real-life

early childhood teaching practices. Secondly, the existence of the high and low combined

programs implies the need to revisit the theoretical framework which conceptualizes the didactic

and developmental practices as opposite sides of a unidimensional axis of teaching practice. The

limitations of conceptualizing teaching practices as a unidimensional construct is that the

teachers could be categorized as highly developmental, highly didactic, or in between, but not

high or low on both the developmental and the didactic practices. The existence of programs

that provide high and low dosages of both the didactic and the developmental practices, however,

implies that the didactic and developmental practices may not be opposite sides of a

dichotomous, unidimensional construct, but rather may be better conceptualized as a bi-

dimensional construct on two separate axes. This finding provides support to a

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multidimensional conceptualization of early instructional practices exemplified by Stipek &

Byler’s (2004) early childhood classroom observation measure (ECCOM). ECCOM provides

two separate scales for didactic and child-centered practices, and allows for different degrees of

practices on both scales, including low scores on both, if neither types of instruction was

predominant, as was the case for the combined (LO) program in the current thesis. These

multidimensional measures can be expected to provide a more real-to-life assessment of the

teaching practices in early childhood education and contribute to a better understanding and

theorization of how teaching practices affect child outcomes.

The findings from the second research question documented some systematic differences at the

school, teacher, and child levels associated with the developmental, didactic, combined (LO) and

combined (HI) programs. The major characteristics of the developmental program was that the

children tended to be more advantaged in their general knowledge and self-control at the

beginning of kindergarten compared to the children in other programs, and the teachers tended to

have taken more courses in child development and be certified in early childhood education. The

teachers of the didactic program, on the other hand, tended to have taken fewer courses in child

development and have more experience teaching the upper elementary grades than the

developmental program teachers. The children of the combined (LO) program tended to have

lower scores in general knowledge and self-control, and the teachers tended to have taken fewer

courses in child development compared to those in the other programs. Finally, the combined

(HI) program tended to be public schools with high kindergarten entry requirements and high

academic goals during kindergarten, despite their children scoring low on general knowledge at

the beginning of kindergarten compared to the developmental program. In the low SES

condition, schools with combined (HI) programs tended to receive Title I funds applied to the

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entire education program. In the high SES condition, the principals of schools with combined

(HI) programs tended to be highly educated, most of whom held a master’s degree. The teachers

of the combined (HI) program were more likely to hold an elementary school education

certificate and have taken more courses in early childhood education and reading pedagogy.

Concerning the second research question, it was hypothesized that schools with more

disadvantaged students were more likely to adopt the didactic approach, whereas schools with

more advantaged students were more likely to adopt a developmental approach (Bassok &

Rorem, 2013; Maxwell et al., 2001). Consistent with this hypothesis, children who scored higher

in general knowledge and self-control at the beginning of kindergarten were more likely to be

assigned to a developmental program compared to other types of programs. Furthermore,

consistent with the hypothesis that the disadvantaged children tended to be assigned to the

didactic approach, the LEP children in the high SES condition were more likely to be assigned to

a didactic program. However, there were two notable exceptions. Firstly, in the low SES

condition, the LEP students were more likely to be assigned to a combined (LO) program rather

than a didactic program. While the reason for this is still speculative due to lack of previous

research evidence, it is possible that the assignment of LEP students to the combined (LO)

condition in the low SES condition reflects the unfavorable learning environments for these

students, especially given that the teachers in the combined (LO) program had taken the fewest

number of courses in child development, and the students tended to score low in self-control and

general knowledge at the beginning of kindergarten. One interesting question to be investigated

in future studies is whether and how the teaching approaches for LEP students have changed,

especially after the No Child Left Behind Act of 2002 mandated high expectations for all

disadvantaged students including the LEP students. While previous studies have theorized that

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some teachers may not subscribe to either of the didactic or the developmental approaches

(Stipek & Byler, 2004), the combined (LO) program provides such real-life instances of such

programs. Further observational studies are necessary to examine the characteristics of the

combined (LO) program.

Secondly, low SES schools that received Title I funding for the entire education program tended

to adopt a combined (HI) approach rather than a didactic approach. These schools also tended to

spend more time teaching the academic subjects compared to the other kindergarten programs.

The schools receiving Title I funding for the entire educational program may have favored the

combined (HI) approach, as some theorists suggest that the balanced approach is ideal for

striking a balance between meeting the developmental needs of the children and the academic

demands imposed by educational agencies (Gullo & Hughes, 2011; Ray & Smith, 2010). More

specifically, evidence-based literacy and math programs tend to endorse practices that explicitly

teach basic skills in a meaningful and rich environment (IRA/NAEYC, 1998; NAEYC/NCTM,

2010; National Reading Panel, 2000), which may be why public schools with Title I funding

would be utilizing combined (HI) approaches.

According to a study by Maxwell et al., (2001), teacher characteristics and beliefs were the best

predictors of early childhood teaching practices, above and beyond school and classroom

demographics. Irrespective of SES, teacher characteristics such as certification type, number of

courses taken, and experience teaching different grades were associated with teaching practices

in the predictable manner. As expected from previous research (Bryant, Clifford, & Peisner,

1991), teachers with greater knowledge of developmentally appropriate practices as indirectly

demonstrated by being certified in early childhood education, taking more courses in child

development, and having spent longer years teaching preschool, were more likely to adopt a

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developmental approach. This is consistent with Vartuli’s (1999) research which reports that

teachers with early childhood education certification were likely to believe and adopt

developmentally appropriate practices. Similarly, teachers who had more experience teaching

upper elementary grades, and teachers who took fewer courses in child development were more

likely to mention they adopted a didactic approach.

An interesting hypothesis based on McMullen’s (1999) research was that teachers who hold

highly academic beliefs were more likely to adopt a didactic approach to kindergarten, but

teachers who hold highly developmental beliefs may not necessarily engage in developmental

practices. This was because previous studies have found that teacher education institutions

generally endorse values and practices consistent with the developmental approach (Jones, Burts

Buchanan, & Jambunathan, 2000) but teachers tend to be pressured into adopting a more didactic

approach, especially when the need for educational accountability is high (McMullen, 1999).

Consistent with this hypothesis, the current study found that the developmental beliefs (e.g.,

taking turns and sharing, not being disruptive, being sensitive to others and beliefs about the

importance of parents reading to children and playing games with their children) failed to

differentiate among the four kindergarten programs. On the other hand, kindergarten readiness

beliefs regarding basic skills (e.g., counting up to 20 or more, knowing English, knowing most of

the alphabet), benefits of preschool basic skills instruction, and kindergarten academic goals

differentiated the kindergarten teachers. Interestingly, however, the main contrast was not

between didactic kindergarten teachers and developmental kindergarten teachers as previously

assumed, but rather between the combined (HI) teachers who held highly academic beliefs and

goals, and developmental kindergarten teachers who were largely resistant to direct academic

instruction before elementary school. The fact that the developmental kindergarten teachers

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were resistant to direct instruction is not surprising, especially since the primary concern for the

early childhood educators who advocated for the NAEYC’s developmental appropriate practice

framework was that the formal academic instruction was infiltrating early childhood education.

However, the fact that the didactic kindergarten teachers were not holding the highest academic

beliefs was somewhat unexpected.

The third research question investigated how time allocation differs among the didactic,

combined (LO), combined (HI) and developmental kindergartens, and how classroom SES was

related to teaching practices and time allocation. It was often assumed that teachers who adopt

didactic practices spend more time teaching academic subjects, while teachers who adopt a

developmental approach allocate more time to enrichment activities and recess (Miller & Almon,

2009). It was also hypothesized that low SES classrooms will allocate more time to academic

subjects than the high SES classrooms due to the pressure to achieve academically.

The hypothesis that low SES classrooms will spend more time teaching academic subjects than

high SES classrooms was supported by this study. Previous research found that children from

disadvantaged backgrounds benefit from the extra time spent on academic subjects (Scheerens &

Hendricks, 2014) and therefore it is likely that the low SES classrooms tried to maximize the

time spent teaching academic subjects, especially math and ESL, at the expense of time spent on

enrichment activities and recess. On the other hand, the hypothesis that low SES classrooms

would be more likely than high SES classrooms to take a didactic approach was not supported.

In fact, teaching approaches was largely orthogonal to classroom SES. Teachers in both the high

and low SES classrooms were equally likely to adopt each of the four teaching approaches. This

finding is inconsistent with previous studies that have found that low SES classrooms were more

likely to favor didactic approaches to instruction, partly because the parents of disadvantaged

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children more likely favored the direct instruction of basic skills, which they believed would

improve their children’s chances to succeed in school (Pruitt, 2011). It can be speculated that

this discrepancy may be due to the previous studies confounding teaching approaches with

content and time use. As low SES classrooms tended to spend more time teaching academic

subjects, it could have been assumed that the teachers teach didactically, too. Future research

would benefit from investigating how the two concurrent treatments of time allocation and

teaching approaches affect the children’s motivational and academic outcomes, for both the high

and low SES students.

It was found that the developmental and combined(HI) programs in the high-SES programs

differed in content coverage and time use from the high-SES didactic and

combined(LO)programs. First, the former programs covered more academic content during the

school year than did the latter programs. Second, the developmental and combined(HI)

programs spent more time on reading, math, social studies, science, music, art, dance, theatre,

ESL, foreign language, recess and lunch than the didactic and combined(LO) programs did.

However, it should be noted that there was also greater variability in developmental and

combined(HI) programs in their time allocated to these activities. The same patterns in time

allocation differences were observed irrespective of SES, although statistical significance was

reached only in the high SES condition.

The reasons for the differences in time allocation among the programs are unclear. Factors such

as teacher expectations and school policies could contribute to these variability, yet further

research is needed to explore the sources of these observed differences.

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The fourth research question examined how kindergarten programs affect approaches to learning

as well as reading and math outcomes (teacher ratings and direct assessment scores) from

kindergarten to Grade 5. The results show that, for both the low and high SES classrooms and

throughout kindergarten to Grade 5, kindergarten programs did not have a big impact on learning

behavior, as defined by the approaches to learning measure. Previous studies have raised

concerns that didactic kindergarten programs may negatively impact children’s motivation and

learning-related behaviors. A study by Hirsh-Pasek, Hyson, & Rescorla (1990), for example,

found that children in the developmental programs were more willing to engage in classroom

learning activities, were more likely to hold more positive attitudes toward schooling, and tended

to be more confident than children in didactic programs. Stipek et al. (1995) also found that

children attending developmental programs were more motivated, had higher expectations for

school success, were less worried about school, were less dependent on adults, and took more

pride in their academic accomplishments than children in didactic programs. From these results,

it was expected that children in developmental programs would have more positive approaches

to learning than children in didactic programs. A possible explanation for this discrepancy with

previous studies is that it is due to the nature of the approaches to learning measure used in this

study. In the ECLS-K, the approaches to learning measure not only had items related to

motivational and learning-related behaviors such as eagerness to learn, task persistence,

independence, and attention, but also had items examining other constructs such as organization

and adaptability. Future studies may examine which elements of approaches to learning were

affected by kindergarten programs and which elements were not by conducting analyses at the

item level. In addition, all studies cited above used a convenient observational sample and used

a single-level analysis which ignored the nested structure of the sample without adequately

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controlling for possible pre-existing differences. The current study uses a national-level dataset,

appropriate multilevel analyses that takes into account the nested data structure, and a semi-

parametric propensity-score based method that controls for observed pretreatment covariates,

which may also be the source of the difference observed in the previous studies and the current

study.

While there was very little impact of kindergarten programs on approaches to learning, the

teacher ratings and direct assessments of academic outcomes suggest that kindergarten programs

had impact on the children’s academic achievement. The effectiveness of the programs

manifested itself in different grades. In general, children in high-SES kindergarten classrooms

seemed to be less affected by the nature of kindergarten program types than were children in

low-SES kindergarten classrooms. When there were significant differences in program

effectiveness in the high-SES condition, the didactic program was found consistently to be less

effective than the contrasting programs. This was not the case however in the low SES condition

where the general pattern of the results for academic outcomes was in fact contrary to the one

noted in the high-SES condition. In fact, in the low-SES condition, the didactic programs tended

to produce better academic outcomes than the other three kindergarten programs.

Some previous research comparing didactic and developmental approaches in kindergarten and

elementary school found that didactic approaches to instruction tend to be associated with

improved academic achievement for low-income, minority students (Becker & Gersten, 1982;

Bereiter, 1986; Stipek, Feiler, Daniels, & Milburn, 1995). Stipek, Feiler, Daniels, and Milburn

(1995), for example, found that children from low- (and middle-) income households who

received didactic, basic-skills instructions had higher scores on letter identification and pre-

reading test scores than children who received child-centered, developmentally-oriented

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instructions at the end of kindergarten. In general, it appears that the effects of kindergarten

programs on academic outcomes in general are inconclusive. The majority of studies reviewed

by Kumpete (2005), for example, found a positive academic effect for developmental programs

compared to didactic programs. These discrepancies in program effects could be due to selection

bias, that is, that the developmental programs tended to attract more advantaged students.

Without random assignments to different kindergarten programs or adequately controlling for

pretreatment covariates, previous studies may have failed to account for pre-existing differences

that already existed among the students in various programs. Additionally, previous studies

tended to have relatively small sample sizes and insufficient attention has been paid to the

hierarchical structure in the data. One major contribution of the current study was in

investigating the effect of kindergarten programs at the national level, and utilizing appropriate

statistical analyses for nested data and controlling for selection bias. The discrepancy between

the non-weighted and weighted results of the hierarchical linear modeling demonstrates the

importance of controlling for pre-existing differences among the kindergarten programs.

There was a sleeper effect in each of the academic outcome measures in the low-SES condition,

suggesting that the kindergarten program effects may not only manifest themselves immediately

at the end of kindergarten but also after several years following kindergarten. Interestingly, all

delayed effects were associated with an advantage for the didactic programs over other programs

in producing positive academic effects. This is not the only study that has noted such delayed or

“sleeper” effects. Delayed and long-term effects of intervention were also found in several of the

intervention studies, such as the High/Scope Perry Preschool Study (Belfield, Nores, Barnett, &

Schweinhart, 2006) and the Abecedarian Studies (Campbell et al., 2012).

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There was one noteworthy exception to this pattern for the low-SES condition, however. The

teacher ratings of reading and math were significantly higher for low-SES children who attended

developmental kindergartens than low-SES children who attended didactic kindergartens at the

end of kindergarten. This pattern was only observed in the teacher ratings at the end of

kindergarten and not in other grades, and was not reflected in the direct assessment of reading

and math.

It is important to note that discrepancies between the findings from the direct assessment and

teacher ratings have been also reported by Kumtepe’s (2005). Some potential reasons for this

finding include teacher bias, features of the teacher ratings measure used in the present study,

and selection bias. Studies examining the validity of teacher assessments have generally found

the correlation of the teacher ratings with standardized measures of student achievement to be

moderate to high (Hoge & Coladarci, 1989), and the reliability of the academic rating scale used

in the ECLS-K in particular was found to be very high (NCES, 2009). Some researchers

maintain that teacher ratings are more accurate than one-time direct assessments because

teachers have the opportunities to observe and interact with their children on a daily basis

(Meisels, Bickel, Nicholson, Xue, & Atkins-Burnett, 2001). However, other researchers voice

concerns about the validity of teacher ratings, arguing that teachers’ judgments could be biased

and affected by their own expectations (Hoge & Coladarci, 1989). This is especially the case

because the teachers are both the providers of the kindergarten instruction and the assessors of

their children’s progress. The discrepancy between teacher ratings and direct assessment

measures could be also attributed to the academic rating scale containing some constructs that

were not assessed in the direct cognitive assessments used in ECLS-K, such as components of

writing, speaking, listening in the reading subscale, and the use of various measuring instruments

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in the math subscale. Therefore, the gain in the teacher rating at the end of kindergarten could

also be attributed to actual change in children’s abilities not captured in the direct assessments.

Finally, it should be noted that, even when statistical significance was observed among the

groups, the effect sizes were small. The largest observed effect was between the didactic and

combined(HI) programs in Grade 5 for the direct assessment in math, with the effect size of 0.28

(95% CI: 0.06, 0.50). Therefore, the magnitude of the group differences, even when

significant,were found to be modest.

9.1.1 Limitations and Future Studies

One of the limitations of the current study is the potential measurement error associated with the

SES variable. The SES variable used in this dissertation was a classroom-level aggregate of the

child-level compound SES variable, which was created using information from the parent

interview regarding parental education, occupation prestige and household income. This

variable was used instead of the school-level measure of free and reduced lunch, because

research shows that parental educational resources can be considered more important than mere

household income, especially for children from immigrant families. Including an SES composite

measure typically reduces measurement error compared to a single indicator of SES.

Nevertheless, the measurement error of this SES measure, when used as a measure for classroom

SES, may have increased due to the small number of students sampled from each class. An

attempt was made to validate this classroom SES measure using school-level percentage of free-

lunch eligible students, de-aggregated to the classroom level as the criterion variable. There was

a significant association between the classroom SES measure used in this study and the

114

percentage of free-lunch eligible students. Yet, future studies should examine whether the

findings from the current study can be replicated with alternate measures of SES.

A second limitation concerns the use of teachers’ self-report and teacher report of students’

performance. While large-scale studies frequently rely on self-report measures as a relatively

low-cost and efficient means of collecting information (Mayer, 1999), relatively little is known

about their validity and reliability for measuring teaching practices. Although researchers may

use survey items as proxy measures to represent certain constructs (e.g., developmental

practices) the items may not necessarily measure the intended construct (Rowan, Correnti, &

Miller, 2002). In addition, the teachers’ ratings related to their instructional practices and student

performance may reflect their ideals and expectations rather than their actual instructional

practices and student performance (Berger & Kaiser, 2007).

The third limitation relates to the latent construct for didactic practices. The didactic practices

construct relied only on 3 observed measures (teacher-directed whole-class activities, reading

workbooks / worksheets, and math workbooks / worksheets), and the reliability of the measure

was low (Cronbach’s alpha =.58). Therefore, the validity and reliability of this latent construct is

restricted.

The fourth limitation is related to the use of two-step cluster analysis for classifying teachers into

naturally-occurring, distinct groups. Cluster analysis was used in former studies examining

kindergarten teaching practices (e.g., Marcon, 1994; 1999). The two-step cluster analysis has

advantages over other conventional clustering algorithms such as k-means and hierarchical

clustering because it allows for different types of measurement to be used in one analysis, and

because the number of clusters is automatically determined (Bacher, Wenzig, & Vogler, 2004).

In the present study, the cluster analysis yielded four groups of approximately equal size, with

115

good cohesion and separation. Nevertheless, it could be argued that there are very small

differences among teachers on the borderline of each of the program types. Future studies may

conduct sensitivity analyses by removing cases at the border of each of the clusters. Moreover,

cluster analysis is less powerful than model-based techniques such as latent class or mixture

model analysis for clustering, because it is data-driven rather than based on theory. Therefore

future studies should examine whether the same cluster structure holds using model-based

techniques for determining the clusters.

The present study focused on the impact of socioeconomic status on children’s responsiveness to

kindergarten teaching practices. Other important child demographic characteristics, such as

gender, ethnicity, and limited English proficiency status, were considered merely as part of the

pretreatment covariates in the propensity model. At the same time it is important to acknowledge

that these variables have also been shown to impact kindergarten teaching effectiveness, and

therefore future studies should examine the potential impact of these covariates more explicitly

as potential moderators in the statistical model.

Relatedly, one potential direction for future research concerns the examination of the combined

programs, especially the combined(HI) programs, which were found to have limited

effectiveness in the current study, especially in the low-SES condition. There is a possibility that

the effectiveness of the combined(HI) programs depends on certain teacher and student

characteristics. Further research is necessary to examine whether certain conditions make

combined approaches more effective.

A major assumption of propensity-score based methods is that there are no major unobserved

confounders that were not included in the propensity score model. Although great care was taken

116

to control for all major pretreatment covariates, there may be some unobserved pretreatment

covariates that were not taken into account. A sensitivity analysis would have shown how

unobserved pretreatment covariates would have influenced the program effects.

Finally, some schools may be philosophically committed to certain types of teaching programs

(e.g., the Montessori programs) and this kind of commitment may have influenced the

curriculum of the entire school across multiple grades. The current study only focused on the

impact of kindergarten program on the outcome at the end of kindergarten and in subsequent

grades. Future studies could also examine the cumulative effects of receiving certain types of

programs across a longer time span.

9.1.2 Conclusion

Notwithstanding these limitations, this research makes some important contribution to the

literature, and the outcomes have important implications. Firstly, the study provided evidence of

the long-term effects of kindergarten programs on children’s academic achievement, a

connection that has long been argued by educational researchers (Burts et al., 1993; Van Horn &

Ramey, 2003). Secondly, this study overcame several methodological difficulties in this field by

using large-scale observational data, appropriate multilevel techniques and causal inference

methods to examine the effects of kindergarten teaching practices on children’s approaches to

learning, as well as on teacher ratings and direct assessments of children’s reading and math

outcomes from kindergarten to Grade 5. Thirdly, the existence of the two combined programs

points to the need to refine the current theory of developmentally appropriate teaching practices

and examine further the effects of such eclectic practices. Fourthly, the significant difference

observed in the effectiveness of teaching practices based on socioeconomic status contributes to

117

the discussions of developmentally appropriate teaching practices for children living in various

social contexts. The results suggest that “one size does not fit all”. Each of these results has

significant implications for both the theorization and the practices of early childhood instruction,

and they point to the necessity for further studies to examine the differential effectiveness of

kindergarten practices based on pivotal background characteristics.

118

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Appendices

Appendix A.

List of Potential Pretreatment Covariates

School characteristics

H,L 1 Census Region

H, L 2 Location type (city, town, suburb)

3 School type (Catholic, Public, Other)

4 Total school enrollment

H, 5 Total K enrollment

H, 6 LEP children in school

H, L 7 % LEP students in Kindergarten

8 School perceived security

9 School received schoolwide Title 1

10 School received Title 1 funds

L 11 School received Title 1 funds for entire education

12 School retention policy

H 13 # FTE classroom teachers

14 School % White students

H, L 15 School % Black students

L 16 School % Hispanic students

L 17 School % Other minority students

18 School free and reduced lunch eligible students

19 School teacher race %

20 Grade level available at school

21 Principal gender

22 Principal race

H 23 Principal highest level of education

24 Principal experience teaching preK/Headstart

25 Principal experience teaching K

26 Principal experience teaching Grade 1

27 Principal experience teaching Grade 2-5

L 28 Principal experience teaching Grade 6+

29 Principal years teaching

30 Principal years as principal

31 Principal years as principal at this school

134

32 Principal years teaching art or music

33 Principal years teaching bilingual ed

34 Principal years teaching ESL

35 Principal years teaching phys ed

36 Principal years teaching spec ed.

37 Principal courses in math pedagogy

38 Principal courses in reading pedagogy

L 39 Principal courses in science pedagogy

40 Principal courses in spec education

Teacher characteristics

41 Preschool read/math good for school (teacher belief, didactic)

42 Have child know alphabet before k (teacher belief, didactic)

43 Child should learn reading in k (teacher belief, didactic)

44 Parents should read to and play math games with child (teacher belief, developmental)

45 Classroom teacher gender L 46 Classroom teacher race 47 Classroom teacher age L 48 Classroom teacher certification type 49 Classroom teacher highest education level achieved H 50 Classroom teacher experience teaching pre-K 51 Classroom teacher experience teaching K 52 Classroom teacher experience teaching Grade 1 53 Classroom teacher experience teaching Grade 2-5 54 Classroom teacher experience 6+ Grade 55 Classroom teacher experience teaching at this school 56 Classroom teacher experience teaching ESL 57 Classroom teacher experience teaching Bilingual ed 58 Classroom teacher experience teaching art or music 59 Classroom teacher experience teaching Phys ed. 60 Classroom teacher experience teaching Spec ed. H, L 61 Classroom teacher # Early Ed courses H 62 Classroom teacher # Elementary ed courses H, L 63 Classroom teacher child development courses 64 Classroom teacher ESL college courses 65 Classroom teacher math pedagogy courses H 66 Classroom teacher reading pedagogy courses L 67 Classroom teacher science pedagogy courses 68 Classroom teacher spec ed courses

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Child characteristics

L 69 Sampled children's average math theta scores at beginning K

H, L70 Sampled children's average reading theta scores at beginning K

H, L71 Sampled children's average general knowledge scores at beginning K

72 Sampled children's # of books at home

73

Sampled children's family structure (Single parent with siblings, single parent no

siblings,

74 2 parents with siblings, 2 parents without siblings)

H 75 Sampled children's household SES

H 76 Sampled children's mother's age

77 % above age 5 in class

78 % below age 5 in class

79 % Black students in class

80 % Hispanic students in class

81 % children read letters at beginning K

82 % children read words at beginning K

83 % children with preschool records in class

84 Classroom % Child in center-based care before K

85 % LEP students at beginning K

86 % K repeaters in class

Significant pretreatment covariates for H = high-SES, L = low-SES classrooms

136

Appendix B.

Marginal Mean Weights for Teachers in Low- and High-SES Groups by Kindergarten Type

Low SES

Stratum Didactic n Combined(LO) n Combined(HI) n Developmental n

1 5.9333 3 3.0984 7 5.9888 4 3.7430 6

2 2.5429 7 2.5672 7 3.9925 6 1.6041 14

3 1.6182 11 2.2721 8 1.4091 17 1.8715 12

4 1.5000 12 1.4536 18 .8652 28 1.1355 20

5 .8091 22 .8442 27 .6654 36 .7486 30

6 .3423 52 .6217 33 .4520 53 .4237 53

7 ---

.4426 49 ---

---

Unweighted n 107 149 144 135

High SES

Stratum Didactic n Combined(LO) n Combined(HI) n Developmental n

1 3.9045 7 5.3006 5 6.9156 4 2.9448 9

2 4.5974 6 2.2290 12 2.3266 12 3.3436 8

3 1.5325 18 1.7832 15 1.2690 22 1.6718 16

4 1.0609 26 .9553 28 .9006 31 .9224 29

5 .6896 40 .8106 33 .6980 40 .8629 31

6 .4019 68 .3956 67 .4769 58 .3956 67

Unweighted n 165 160 167 160

137

Appendix C.

Graphs for the MMW-S Results from Question 4

Figure 3. Line Graph with Error Bars for Approaches to Learning from Kindergarten to Grade 5

138

Figure 4. Line Graph with Error Bars for Teacher Ratings of Reading from Kindergarten to

Grade 5

139

Figure 5. Line Graph with Error Bars for Teacher Ratings of Math from Kindergarten to Grade 5

140

Figure 6. Line Graph with Error Bars for Reading Theta Scores from Kindergarten to Grade 5

141

Figure 7. Line Graph with Error Bars for Math Theta Scores from Kindergarten to Grade 5