THE EFFECTS OF KINDERGARTEN THROUGH SECOND GRADE READING INSTRUCTION AND

THE EFFECTS OF KINDERGARTEN THROUGH SECOND GRADE

READING INSTRUCTION AND INTERVENTION ON

THIRD GRADE READING ACHIEVEMENT

A Dissertation Presented to the Faculty

of California State University, Stanislaus

In Partial Fulfillment of the Requirements for the Degree

of Doctor of Education in Educational Leadership

Teresia Chevalier-Metzger May 2013

CERTIFICATION OF APPROVAL

THE EFFECTS OF KINDERGARTEN THROUGH SECOND GRADE

READING INSTRUCTION AND INTERVENTION ON

THIRD GRADE READING ACHIEVEMENT

by Teresia Chevalier-Metzger

__________________________________________ ________________________ Dr. John Borba Date Professor of School Administration __________________________________________ ________________________ Dr. Dawn Poole Date Professor of Educational Technology __________________________________________ ________________________ Dr. Karen Schauer Date Superintendent Galt Joint Union Elementary School District

Signed Certification of Approval Page is On File with the University Library

© 2013

Teresia Chevalier-Metzger ALL RIGHTS RESERVED

iv

DEDICATION

To my husband, JR. You always believe in me, even when I don’t believe in

myself. I couldn’t do what I do without you. XO

v

ACKNOWLEDGMENTS

My deepest thanks go to many who have influenced me on this journey. I had

the privilege of conducting this study alongside many exemplary educators. Some

were coaches, some were classroom teachers, some were instructional assistants, but

all were teachers who wanted kids to be successful readers.

Special thanks to Dr. John Borba for guiding me through the process,

encouraging me, and keeping me going.

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TABLE OF CONTENTS PAGE

Dedication ............................................................................................................ iv

Acknowledgments................................................................................................ v

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

List of Figures ...................................................................................................... xii

Abstract ................................................................................................................ xiii

CHAPTER I. Introduction .......................................................................................... 1

Statement of the Problem ............................................................. 8Research Questions ...................................................................... 9

Research Question 1 ........................................................ 9Research Question 2 ........................................................ 9Research Question 3 ........................................................ 10

Significance of the Study ............................................................. 11Limitations and Delimitations ...................................................... 12

Limitations ....................................................................... 12Delimitations .................................................................... 12

Definitions of Terms .................................................................... 13Summary ...................................................................................... 15

II. Review of the Related Literature ........................................................ 17

National Reading Panel Identifies Key Components of Reading ..................................................................................... 17

The Effects of Educational Reform Efforts on Reading Instruction and Achievement .................................................... 27

Academic and Demographic Factors That Predict Reading Success ...................................................................................... 34

Evaluation of Interventions Used in the School’s Multitiered Intervention Model................................................. 45

Summary ...................................................................................... 47

III. Methodology ...................................................................................... 48

Restatement of the Research Questions ....................................... 49Research Question 1 ........................................................ 49

vii

Research Question 2 ........................................................ 50Research Question 3 ........................................................ 50

Population and Sample ................................................................ 53Demographic Data Collection...................................................... 56Achievement Data Collection ...................................................... 57

District Reading Achievement Data Collection Background ...................................................................... 57Instrumentation ................................................................ 58State Reading Achievement Measures ............................. 60Identifying Factors that Contributed to Student Performance in Specific Interventions ............................. 63

Data Analysis ............................................................................... 64Summary ...................................................................................... 68

IV. Results................................................................................................ 69

Research Question 1 .................................................................... 69Hypothesis 1.1.................................................................. 70Hypothesis 1.2.................................................................. 77

Research Question 2 .................................................................... 87Hypothesis 2.1.................................................................. 87Hypothesis 2.2.................................................................. 95

Research Question 3 .................................................................... 103Hypothesis 3.1.................................................................. 104Hypothesis 3.2.................................................................. 106Hypothesis 3.3.................................................................. 107Hypothesis 3.4.................................................................. 109Hypothesis 3.5.................................................................. 111

Summary ...................................................................................... 113

V. Discussion, Implications, and Conclusions......................................... 114

Summary of the Study ................................................................. 114Summary of the Methods ............................................................. 114Discussion .................................................................................... 116

Research Question 1 ........................................................ 117Research Question 2 ........................................................ 121Research Question 3 ........................................................ 125

Implications.................................................................................. 129Limitations and Delimitations ...................................................... 133Recommendations for Further Study ........................................... 133Conclusion ................................................................................... 134

viii

References ............................................................................................................ 136

ix

LIST OF TABLES

TABLE PAGE 1 Number and Percentage of Test Items for Each English Language

Arts Subtest on the California Standards Test ............................................ 61

2 Score Ranges for Third Grade Performance Levels on English Language Arts California Standards Test ................................................... 62

3 Score Ranges for Performance Levels on Third grade English Language Arts California Modified Assessment ........................................ 63

4 Descriptive Statistics by Demographics for CST/CMA and Subtests ........ 71

5 Pair-Wise Correlations for Kindergarten Demographic Factors Included in Multiple Regression Analyses ................................................. 72

6 Bivariate and Partial Correlations of the Demographic Predictors With CST/CMA .......................................................................................... 73

7 Bivariate and Partial Correlations of the Demographic Predictors With Word Analysis and Vocabulary Subtest ............................................ 75

8 Bivariate and Partial Correlations of the Demographic Predictors With Reading Comprehension Subtest ....................................................... 76

9 Bivariate and Partial Correlations of the Demographic Predictors With Literary Response and Analysis Subtest ............................................ 78

10 Descriptive Statistics by Demographics for District Benchmark Test Subtests ....................................................................................................... 79

11 Bivariate and Partial Correlations of the Demographic Predictors With Basic Phonics Skills Test Subtest ...................................................... 81

12 Bivariate and Partial Correlations of the Demographic Predictors With Silent Comprehension Subtest ........................................................... 82

13 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Accuracy Subtest ............................................................... 84

14 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Fluency Subtest ................................................................. 85

x

15 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Comprehension Subtest ..................................................... 87

16 Pair-Wise Correlations for Kindergarten Reading Readiness Factors Included in Multiple Regression Analyses ................................................. 89

17 Bivariate and Partial Correlations of the Reading Readiness Predictors With the California Standards Test/California Modified Test.............................................................................................................. 90

18 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Word Analysis and Vocabulary Subtest ..................... 92

19 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Reading Comprehension Subtest ................................ 93

20 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Literary Response and Analysis Subtest..................... 95

21 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Basic Phonics Skills Test ............................................ 97

22 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Silent Comprehension Subtest .................................... 99

23 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Accuracy Subtest ........................................ 100

24 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Fluency Subtest .......................................... 102

25 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Comprehension Subtest .............................. 103

26 Bivariate and Partial Correlations of the Demographic Predictors With First grade Basic Phonics Skills Test Performance ........................... 105

27 Bivariate and Partial Correlations of the Demographic Predictors With Second grade Basic Phonics Skills Test Performance ....................... 107

28 Bivariate and Partial Correlations of the Demographic Predictors With Second grade End-of-Intervention Fluency Test Performance.......... 109

29 Bivariate and Partial Correlations of the Demographic Predictors

xi

With First grade End-of Intervention Basic Phonics Skills Test Performance ................................................................................................ 111

30 Bivariate and Partial Correlations of the Demographic Predictors With Second grade End-of-Intervention Comprehension Test Performance ................................................................................................ 113

xii

LIST OF FIGURES

FIGURE PAGE 3.1. Model of variables represented in Research Questions 1 and 2 ................. 51

3.2. Model of variables represented in Research Question 3............................. 53

xiii

ABSTRACT

State and local assessment data from 117 students in one school in the California

Central Valley were examined to determine how much participation in a kindergarten

through second grade multitiered reading intervention model contributed to third

grade reading achievement, over and above the demographic factors of ethnicity,

socioeconomic status, parent education level, English language level in kindergarten,

gender, and age at kindergarten entry. State and local assessment data were also

examined to determine how much participation in a kindergarten through second

grade multitiered reading intervention model contributed to third grade reading

achievement, over and above the kindergarten reading readiness factors of letter

sounds, oral blending, oral segmenting, consonant-vowel-consonant (CVC) word

reading, and high frequency word (HFW) reading. Multiple regression analyses with

ordered sets of predictors were conducted to identify the relationship of the factors to

students’ third grade end-of-year reading performance. Additionally, multiple

regression analyses were conducted to examine the contributions of three

interventions–Systematic Instruction in Phonemic Awareness, Phonics, and Sight

Words (SIPPS); Reads Naturally; and teacher-created interventions–over and above

demographic factors on end-of-intervention assessments. Kindergarten reading

readiness and demographic factors significantly contributed to third grade reading

achievement, while the effects of participation in a multitiered intervention model

were not significant.

1

CHAPTER I

INTRODUCTION

Recent educational reform efforts in the United States have brought

unprecedented attention to the academic achievement of the nation’s children,

particularly those whose background includes historically underrepresented groups

and those who are at-risk of school failure (Slavin, 2003). Federal legislation such as

the Elementary and Secondary Education Act (ESEA) and the Individuals with

Disabilities Education Act (IDEA) provides regulations to govern how schools

instruct and intervene for students across the country. In 2002, No Child Left Behind

(NCLB) was enacted as a reauthorization of the ESEA. Shortly after NCLB became

law, Learning Point Associates published a series of Quick Keys to help educators

understand how NCLB would affect schools. Several provisions in the area of

reading were summarized as follows:

NCLB mandates that all public schoolchildren should be proficient in reading by the end of the 2013-14 school year. States are required to assess students in reading and to hold schools and districts accountable for ensuring that students make adequate yearly progress toward meeting this deadline. (2002, p. 2)

Adequate Yearly Progress (AYP) can be achieved by meeting a federally-set

target percentage of students scoring proficient on the annual assessment, or by

achieving “safe harbor,” which involves decreasing the number of students scoring

below proficient by 10% in each target area. NCLB provides “corrective action” (Sec.

1116 b7A) for schools that are unable to achieve AYP. Such schools are identified as

Program Improvement (PI) schools and are subject to additional oversight by district

2

or state academic improvement teams. Each year of PI status requires actions that are

progressively more intensive and may result in replacement of teaching and/or

administrative staff, closure, or reorganization of the school. As a result of the AYP

requirement and potential corrective action for failure to meet AYP, schools across

the nation have been urgently searching for ways to increase student proficiency

(Mead, 2007).

Additionally, NCLB included requirements for schools to use evidence-based

practices, including “research that shows how interventions are expected to improve

student achievement” (Brown-Chidsey & Steege, 2005, p. 14). These ideas were

further reinforced by the 2004 reauthorization of the Individuals with Disabilities in

Education Act (IDEA), which introduced Response to Intervention (RtI). Previously,

special education eligibility was determined by the presence of at least one federally-

defined condition, plus evidence that the condition adversely affected the student’s

academic performance; that is, a significant discrepancy between ability and

achievement existed. The 2004 IDEA guidelines allowed states to utilize a problem-

solving approach called RtI as an alternative to the discrepancy model traditionally

used to qualify students for special education services. Many states quickly embraced

the RtI model, not specifically for the purpose of changing special education

eligibility criteria, but because the tiered intervention model made sense for student

learning (Brown-Chidsey & Steege, 2005). The RtI model also made sense in the

context of NCLB’s AYP requirements, as demonstrated by increases in overall

student achievement by many districts and schools that implemented multileveled

3

intervention based on students’ needs (Fox, Carta, Strain, Dunlap, & Hemmeter,

2009).

The three main components of RtI are the use of scientifically-based practices,

evaluation of students’ responses to intervention, and emphasis of data in the decision

making process (Brown-Chidsey & Steege, 2005). The implementation of RtI is most

often represented as a three-tiered model, in which all students receive Tier I high-

quality instruction, as well as general interventions such as additional guided practice.

At the Tier I level, intervention is teacher directed, short term, and available to all

students. About 80% of students will respond well to Tier I (Brown-Chidsey &

Steege, 2005). For the 20% of students who do not make adequate progress with Tier

I, a new layer of support is added. Tier II intervention consists of instruction that

targets specific skills or concepts and often utilizes small group instruction. Tier II

intervention may be facilitated by the teacher or a specialist and often takes place

outside the classroom (Brown-Chidsey & Steege, 2005).

Only about 5% of students do not respond adequately to Tier I and II

intervention. Those students receive intensive intervention called Tier III. Tier III

includes specialized materials and very small group size, with frequent progress

monitoring (Bender & Shores, 2007; Brown-Chidsey & Steege, 2005; McDowell,

Graney, & Ardoin, 2009). For example, students who do not respond to the Tier I

and II interventions may work with a teacher who delivers specialized instruction in a

one-on-one setting. While some RtI models designate Tier III as special education,

most do not. As specified by IDEA, RtI is a general education process and students’

4

responses to Tier III interventions should be evaluated before a student is considered

for special education (National Center for Learning Disabilities, 2012).

For schools and districts that adopt RtI, the most common starting point is

early reading intervention. Reading instruction is complex and learning foundational

reading skills is crucial to academic success in the subsequent grades (Adams, 1990).

Foundational skills are identified as those phonemic awareness and phonics tasks that

comprise accurate and fluent decoding. These skills are generally taught in

kindergarten and first grade. Early student proficiency in the basics often predicts

success on high stakes tests in third and sixth grades (Chard et al., 2008).

The use of kindergarten and first grade reading achievement as a means for

predicting academic success or failure has been studied for several decades. While

earlier research was more focused on the specific skills that predicted reading

success, recent studies have explored the effects of skills such as self-regulation and

social competence on reading achievement. Regardless of the scope of the study, the

literature consistently has shown that early reading performance is strongly connected

to subsequent student achievement (Adams, 1990; Book, 1980; Butler, Marsh,

Sheppard & Sheppard, 1985; McClelland, Acock & Morrison, 2006).

In the 1990s, reading instruction was hotly debated by educators across the

United States. In 1998, President Clinton commissioned a National Reading Panel

(NRP) to study the available research, hear testimony from practitioners in the field,

and determine guidelines for effective instruction in reading. The panel considered

only findings directly related to reading instruction and intervention, even though

many of the studies the panel reviewed found significant differences in student

5

performance based on demographic factors. For example, the panel did not consider

whether students’ ethnicity or economic status impacted the efficacy of a specific

type of instruction. The final NRP report set forth five components of reading

instruction deemed to be the most significant: phonemic awareness, phonics, oral

reading fluency, vocabulary, and comprehension (National Institute of Child Health

and Human Development [NICHD], 2000). Although it was published over a decade

ago, the panel’s findings continue to have a significant influence on reading

instruction throughout the nation (Hattie, 2009).

Researchers found that early identification and subsequent intervention for

students who initially experienced difficulty learning to read contributed to long-term

positive effects on subsequent reading achievement (Shaywitz & Shaywitz, 1999;

Vaughn, Gersten, & Chard, 2000). Chard et al. (2008) found that multilevel support

models “can decrease the gap between struggling and proficient readers and can

reduce the number of students who are identified as needing special education

services” (p. 1). Determining variables that most accurately predict student

achievement in reading as early as possible becomes paramount to designing efficient

systems that support students who find learning to read difficult (Neuman & Roskos,

2002).

While learning to read can be considered a mechanical process, there are

many components other than decoding text that are required for students to truly

become proficient readers (Adams, 1990; Cunningham & Stanovich, 2001; National

Institute for Child and Human Development, 2000). Fluency and comprehension

skills are usually developed after decoding has been mastered to some degree and are

6

necessary for understanding longer, more complex text presented in subsequent

grades. Researchers also found that vocabulary and background knowledge are

critical for reading proficiency and should be an integral part of explicit early reading

instruction and intervention (Shefelbine, 2001).

The concept of reading characterized as more than a mechanical process was

underscored in Friere’s writings. In 1983, he wrote, “Reading the world always

precedes reading the word, and reading the word implies continually reading the

world” (p. 10). Friere discussed the importance of reading in the context of readers

making sense of their world through the written word. His assertion was that the act

of reading and thinking about the reading shapes readers, and in turn, readers shape

their own world. Reading therefore is much more than just an important skill; it is

something that can transform a life.

The multifaceted nature of reading instruction has been most recently

addressed in the 2010 Common Core State Standards (CCSS) initiative, a national

effort that “establishes a single set of clear educational standards for kindergarten

through 12th grade in English language arts and math” (CoreStandards.org). The

English language arts CCSS were developed using scholarly research; state, national,

and international assessment data and National Assessment of Educational Progress

(NAEP) frameworks in reading and writing. The CCSS were written to provide

clarity, consistency, and alignment of expectations for college and career success.

The English Language Arts (ELA) CCSS include six strands: Reading Standards for

Literature, Reading Standards for Informational Text, Reading Foundational Skills,

Writing, Speaking and Listening, and Language.

7

The ELA CCSS require students to read increasingly complex text over the

course of their academic career. The ability to independently read and understand

complex text is important for college success and necessary in many other aspects of

life. The importance of foundational reading skills as a means to improve students’

overall reading ability is highlighted by their inclusion in the CCSS. The introduction

to the Reading Foundational Skills section states in part, “they are necessary and

important components of an effective, comprehensive reading program designed to

develop proficient readers with the capacity to comprehend texts across a range of

types and disciplines” (ELA CCSS, 2010, p. 7). Additionally, the interdependence of

all six strands of the ELA CCSS is underscored by the overlapping of standards

throughout the documents.

The CCSS reinforce findings from studies that suggest that the mechanical,

affective, and experiential aspects of reading are all important to the overall

development of a proficient reader (Nelson, Benner & Gonzalez, 2003; Vellutino,

Scanlon, Sipay, Small, Pratt & Chen, 1996). In addition, many aspects of reading,

including vocabulary development and attitude toward reading seem connected to

demographic factors such as socioeconomic status and ethnicity (Goldenberg, 2002;

Juel, 1988; Noble, Wolmetz, Ochs, Farah & McCandliss 2006). The question then is,

can schools that identify signs of early reading difficulties be proactive by facilitating

reading instruction and intervention that develops proficient readers across all

subgroups of students?

8

Statement of the Problem

The National Assessment of Educational Progress (NAEP) issues the Nation’s

Report Card ™ annually based on the assessment results of nationally represented

samples of students ages 9, 13, and 17. The NAEP reported a long-term trend of

improvement in reading for 9- and 13-year-olds between 1971 and 2008, with no

statistically significant difference in reading performance for 17-year-olds during the

same time period. The most significant change occurred during the period from 2004

to 2008, with three to four point gains in scale scores for all three groups. Although

the trend seems to be positive, an examination of scores at each performance level

shows no significant differences since 1971. While approximately 90% of 9-year-

olds can read at the lowest, most discrete levels, only about one in five can interrelate

ideas and make generalizations (Snyder, 2009). These percentages have remained

virtually unchanged in the last three decades.

Despite the large knowledge base about teaching reading, there remains a

large gap between students who simply perform the mechanics of reading, and those

who are able to use reading as a means for increasing knowledge. The problem

remains twofold. One, schools must find accurate and efficient ways to identify

students who are struggling with the reading process as early as possible, so that

interventions can be implemented before students become dependent on their reading

ability to expand their knowledge—and increase proficiency—in later grades. Two,

schools must design interventions that effectively transform nonreaders or struggling

readers into proficient readers. These interventions must address students’ needs in

all key components of reading.

9

Research Questions

This study examines factors that influence third grade reading achievement,

specifically the predictability of demographic factors and reading readiness at the

beginning of kindergarten on students’ subsequent reading performance at the end of

third grade when a multitiered response to intervention model of instruction is

utilized. The following questions will be addressed and hypotheses tested.

Research Question 1

The first research question was, how well do demographic factors and

participation in a multitiered reading intervention model explain third grade reading

achievement? Two hypotheses were posed for this question.

Hypothesis 1.1. Participation in a multitiered reading intervention model

explains more about third grade reading achievement on the California Standards Test

or California Modified Assessment than ethnicity, socioeconomic status, parent

education level, gender, age upon kindergarten entry, or English language level in

kindergarten.


explains more about third grade reading achievement on the District Benchmark Tests

than ethnicity, socioeconomic status, parent education level, gender, age upon

kindergarten entry, or English language level in kindergarten.

Research Question 2

The second research question was, how well do reading readiness factors and



10



or California Modified Assessment than kindergarten scores on letter names, letter

sounds, oral blending, oral segmenting, consonant-vowel-consonant reading, or sight

word tasks.


explains more about third grade reading achievement on District Benchmark Tests

than kindergarten scores on letter names, letter sounds, oral blending, oral

segmenting, consonant-vowel-consonant reading, or sight word tasks.

In addition to examining factors that may predict or explain third grade

reading achievement, the effectiveness of intervention programs for different groups

of students will be evaluated with the following research question and hypotheses:

Research Question 3

The third research question was, how much do kindergarten demographic

factors and participation in multitiered interventions explain student reading

achievement on the end-of-intervention assessment? Five hypotheses were posed for

this question.

Hypothesis 3.1. For first grade students, participation in multitiered SIPPS

intervention explains more about first grade reading achievement on the end-of-

intervention BPST than the kindergarten demographic factors of socioeconomic

status, parent education level, ethnicity, or English language level in kindergarten.

Hypothesis 3.2. For second grade students, participation in multitiered SIPPS

intervention explains more about second grade reading achievement on the end-of-

11



Hypothesis 3.3. For second grade students, participation in the Reads

Naturally intervention explains more about second grade reading achievement on the

end-of-intervention District Benchmark Fluency Test than the kindergarten

demographic factors of socioeconomic status, parent education level, ethnicity, or

English language level in kindergarten.

Hypothesis 3.4. For first grade students, participation in a teacher-created




Hypothesis 3.5. For second grade students, participation in a teacher-created


intervention District Benchmark Comprehension Test than the kindergarten



Significance of the Study

Research has shown that the effects of early reading success or failure

accumulate over time and carry implications for later student reading achievement.

As schools are held increasingly accountable for academic performance, primarily the

proficiency rates of subgroups of students on high-stakes, standards-based

assessments, they must accurately identify students from all subgroups who may

struggle with learning to read. Schools must then implement interventions that will

12

assist students with both skill development and the motivation to persevere through

the complex process of becoming proficient readers. This study will contribute

additional findings to the existing literature by identifying significant predictors of

reading performance and examining factors that potentially increase the reading

ability of students. Information gathered from this study may be useful to teachers,

school administrators and policy makers who wish to increase student reading

achievement by implementing kindergarten through second reading interventions.

Limitations and Delimitations

The following limitations and delimitations are presented to help the reader

moderate the generalizations that may otherwise apply to this research topic:

Limitations

Only interventions offered at each grade level in the sample school will be

examined in this study. Because of the fluid nature of the intervention system,

caution should be used in generalizing the findings to other schools. It is uncertain if

some of the interventions are replicable.

Delimitations

For purposes of this study, changes in the district-adopted curriculum

materials will not be taken into consideration. Both the previous and current state-

approved curriculum address the key components of reading instruction as defined by

the National Reading Panel’s report in 2000.

Changes in staffing at the school will not be taken into consideration for

purposes of this study, although there were changes in teachers over the course of the

two cohorts, the majority of the teachers on any given grade level team remained

13

stable, with no more than one new teacher per team per year. Over the course of the

two cohorts, kindergarten had two teacher changes, first grade had no changes, and

second grade had one change.

Definitions of Terms

The following definitions of terms are outlined to provide clarity to the reader

and aid in interpreting the results of this study:

Basic Phonics Skills Test III (BPST). A test of basic phonics skills

beginning with consonant sounds normally taught in kindergarten and ending with polysyllabic word patterns encountered in third and fourth grade. The BPST is an informal test of (a) high-utility, spelling-sound relationships for reading single syllable words and (b) syllabic and morphemic strategies for reading polysyllabic words. (Santa Clara County Office of Education, 2012a)

Blending. The act of putting “sounds together to form words” (Shanahan,

2006, p. 6).

Comprehension. “The act of understanding and interpreting the information

within a text…the construction of meaning (not just) passive remembering”

(Shanahan, 2006, p. 28).

California Modified Assessment. A criterion referenced test that allows

students with disabilities greater access to an assessment that helps measure how well they are achieving California’s content standards and to provide information about how well schools and school districts are meeting state and federal accountability requirements regarding English language arts (ELA), mathematics, and science. (California Department of Education, 2012b)

California Standards Test. A criterion-referenced test

developed by California educators and test developers specifically for California. They measure students’ progress toward achieving California’s state-adopted academic content standards in English language arts (ELA), mathematics, science, and history/social science, which describe what

14

students should know and be able to do in each grade and subject tested. (California Department of Education, 2012)

District Benchmark Test (DBT). A test developed by the district and used by

the school in this study to measure students’ reading achievement in the areas of

decoding, fluency, and comprehension.

Intervention. Instruction that “targets a specific problem” (Fuchs & Fuchs,

2006, p. 94).

Fluency. “The ability to read text aloud with accuracy, speed, and proper

expression” (Shanahan, 2006, p. 18).

Literary Response and Analysis Subtest. A reading subtest of the California

Standards Test (CST) and California Modified Assessment (CMA), administered

each spring to students in Grades 2–11 (California Department of Education, 2012).

Phonemic Awareness. “The ability to hear and manipulate the individual

sounds within words” (Shanahan, 2006, p. 6). Phonemic awareness is wholly

separate from phonics in that the former does not involve print.

Phonics. Using “the relationship between letters and sounds to translate

printed text into pronunciation” (Shanahan, 2006, p. 11).

Proficiency. A score of 350 or greater on the California Standards Test (CST)

or California Modified Assessment (CMA), which is administered each spring to

students in Grades 2–11 (California Department of Education, 2012); also used

synonymously with “at benchmark” or “meets benchmark” on the District Benchmark

Tests.

15

Proficient Reader. To “decode accurately, and read fluently and with

understanding” (Snowling & Hulme, 2011).

Reading Comprehension Subtest. A reading subtest of the California



Segmenting. To “perceive the separable sounds within words” (Shanahan,

2006, p. 6).

Word Analysis and Vocabulary Subtest. A reading subtest of the California



Summary

Chapter I provided an overview of the mounting pressure from national and

state educational reform efforts to increase student proficiency in English language

arts and mathematics. As school systems urgently search for ways to ensure that all

students reach proficiency as measured by high-stakes standardized tests, the

examination of early reading performance as a predictor for academic proficiency in

later grades is a promising practice that can assist schools in the development of

effective interventions that include both skill-based instruction and motivational

strategies.

Chapter II includes a review of the findings and recommendations of the

National Reading Panel; a discussion of the effects of No Child Left Behind, the

Individuals with Disabilities in Education Act, and the new focus of the Common

Core State Standards Initiative; and a review of the literature related to early reading

16

achievement and demographic factors as predictors to later student success. Chapter

II concludes with a review of different types of reading intervention programs.

17

CHAPTER II

REVIEW OF THE RELATED LITERATURE

Chapter I provided an overview of the increasing pressure from national and

state educational reform efforts to raise student proficiency in English language arts

and mathematics. As school systems across the nation work to help all students

reach proficiency as measured by standardized tests, ensuring that students are skillful

readers has become critically important. The examination of early reading

performance as a predictor for reading proficiency in later grades is a promising

practice that can assist schools in the development of interventions to remediate

weaknesses in key reading skills.

Chapter II includes (a) a review of the findings and recommendations of the

National Reading Panel, (b) a discussion of the effects of educational reform efforts

on reading instruction and reading achievement, (c) a discussion of academic and

demographic factors that predict reading success, and (d) an evaluation of two

intervention programs.

National Reading Panel Identifies Key Components of Reading

In 1998 in the midst of the heated debate about how to best predict, teach, and

remediate reading, President Clinton commissioned a National Reading Panel (NRP)

to study the available research, hear testimony from practitioners in the field, and

determine guidelines for effective instruction in reading. The NRP adopted rigorous

guidelines for the research to be reviewed. Although this meant only a small portion

of the total available research would meet the criteria, the rigorous procedures

18

allowed the panel to narrow the studies to those of moderate to high quality, which

would help the group reach consensus in its findings. Based on the research, the final

NRP report set forth five components of reading instruction deemed to be the most

significant: phonemic awareness, phonics, oral reading fluency, vocabulary, and

comprehension (National Institute of Child Health and Human Development

[NICHD], 2001; Shanahan, 2005).

Under the broader category of alphabetics, the NRP addressed phonemic

awareness (PA) and phonics instruction as two of the five essential components of

reading instruction. PA was defined as “the ability to focus on and manipulate

phonemes in spoken words” (NICHD, 2001, p. 2-1). Phonics was defined as “a set of

prespecified associations between letters and sounds” (NICHD, 2001, p. 2-1). The

NRP’s findings discussed the positive effects of systematic phonemic awareness and

phonics instruction, based on meta-analyses of nearly 100 studies in the two areas.

The NRP reported overall effect sizes (ES) of 0.86 on PA skill acquisition when

students received focused and explicit instruction on one or two PA skills at a time.

Small group treatments that ranged from 5 to 9 hours produced larger effect sizes (d =

1.37, p < .05) than treatments that were shorter (less than 5 hours d = .61, p < .05) or

longer (greater than 20 hours d = .65, p < .05). Blending was identified as the PA

skill most closely associated with decoding. The effects of PA on overall reading and

spelling outcomes were moderate with ES of 0.53 and 0.59, respectively (NICHD,

2001).

Systematic phonics instruction showed moderate effect sizes of 0.56 in

kindergarten and 0.54 in first grade. In second grade, the effect of phonics instruction

19

dropped to 0.27, indicating that phonics instruction may be more beneficial in

building foundational skills for early readers. For first grade students who showed

signs of early reading difficulties, the ES of phonics instruction was 0.74. The NRP

also found that the overall ES of phonics instruction on first grade reading

comprehension was 0.51 (NICHD, 2001). All of these findings supported Chall’s

(1967) assertion that systematic phonics instruction is beneficial to reading

achievement.

Not long after the NRP released its findings, two studies were conducted to

dispute the magnitude of the effects of systematic phonics instruction as reported by

the NRP (Camilli, Vargas, & Yurecko, 2003; Hammill & Swanson, 2006). While the

NRP showed a moderate effect size (d = .41, p < .05) for end-of-intervention results,

Camilli, Vargas, and Yurecko found a much lower ES (d = .24, p < .05). They argued

that variance in the amount of phonics instruction in the control groups examined by

the NRP served to overestimate the effect sizes of the treatment. Additionally,

Hammill and Swanson (2006) conducted a study in which they converted effect sizes

(Cohen’s d values) to coefficients (r and R2 values) to better analyze the variance in

the differences accounted for by the NRP. For example, the NRP’s finding that

psuedoword decoding had a moderate effect on reading performance (d = .67, p <

.05) was converted to an r value of .32, with a R2 of .10. The R2 value meant that

only 10% of the mean reading performance could be attributed to students’ ability to

decode psuedowords. Based on their analysis of each variable used by the NRP, the

researchers concluded that the variance in reading achievement could be “attributed

to factors other than the systematic phonics instruction” (p. 18).

20

However, in 2008, Stuebing, Barth, Cirino, Francis, and Fletcher reexamined

the Camilli et al. (2003) study and found that it was based on a slightly different set of

studies and characteristics than those used by the NPR. Stuebing et al. also

challenged the application of the statistical analysis used by Hammill and Swanson,

saying that although the effect sizes identified were small using Cohen’s guidelines,

they were practically significant as educational interventions. Stuebing et al.

reevaluated the Camill and Hammill studies (including the studies originally used by

the NRP) using a multilevel regression analysis and controlled for several

instructional characteristics. They calculated mean effect sizes for each combination

of predictors and identified those with the largest ES. For example, systematic

phonics instruction when combined with 1:1 tutoring had a very large predicted effect

(d = .913, p < .01). Stuebing et al. supported the NRP findings, stating that although

Camilli et al. furthered the discussion about other factors that affected reading

outcomes, the larger effects were consistently associated with explicit phonics

instruction regardless of the other factors; therefore, explicit phonics interventions

could be considered effective for assisting struggling readers.

Two recent studies have also supported the findings of the NRP in the area of

alphabetics. In 2007, Puolakanaho et al. studied early phonological and language

skills in Finland. Using logistic regression models, the researchers examined early

predictors of reading disabilities, particularly dyslexia, at the end of second grade.

The sample included 198 children who were assessed in phonological awareness,

vocabulary, memory, and letter naming tasks at 3.5, 4.5, and 5.5 years of age. Parent

education level and performance IQ were also studied in relation to student

21

performance. The researchers found that a combination of letter knowledge,

phonological awareness (PA), rapid automatic naming (RAN), and familial risk

provided a prediction probability > .80 as early as 3.5 years of age. When controlling

for familial risk, the combination of the PA and RAN variables was statistically

significant at ages 3.5 and 5.5 years (R2 = 33.4 and 34.6, respectively, p < .05). At

age 4.5 years, the combination of PA and letter knowledge was a significant predictor

(R2 = 32.0, p < .05).

Hulme, Bowyer-Crane, Carroll, Duff, and Snowling (2012) conducted a study

of 152 five-year-olds from 19 schools who participated in one of two 20-week

intervention programs, a phonology and reading group (P+R) or an oral language

group (OL). The team wanted to determine “if improvements in reading and spelling

produced …would be mediated by changes in phoneme awareness and letter-sound

knowledge” (p. 573). After controlling for initial differences, students in both groups

made gains in the skills they were explicitly taught, but the P+R group also improved

significantly in word-level literacy skills (d = .49, p < .001). When the students were

reassessed five months after the intervention ended, the students in the P+R group

continued to show strong performance in word-level literacy skills. The researchers

concluded that the levels of letter-sound knowledge and phoneme awareness “fully

mediated the improvements seen in the children’s word level literacy skills” (p. 576)

and the skills were indeed causally related to improvements in reading.

Another key component of reading related to this study identified by the NRP

was fluency. While the NRP focused primarily on word reading fluency studies in

grades three through five, its discussion of fluency and automaticity supported the

22

findings of many studies that concluded rapid automatic naming (RAN) is a good

predictor of reading achievement. Through its meta-analysis of 77 studies, the NRP

found that “the development of efficient word recognition skills is associated with

improved comprehension” and that beginning readers may be accurate decoders, “but

the process is likely to be slow and effortful” (NICHD, 2001, pp. 3–8). As Adams

(1990) asserted, when the young reader becomes automatic with the phoneme or

phoneme-grapheme tasks, fewer cognitive resources are required, which allows the

reader to focus on other processes such as understanding.

To that end, Ehri’s (1998) work on alphabetic phases remains a powerful

model of how children become fluent readers. Her framework included five phases:

pre, partial, fully, consolidated, and automatic. In the prealphabetic phase, children

have little or no knowledge of how to use alphabetic knowledge to read words. In

this phase, children often use clues such as pictures to determine what the words are

“saying.” In the partial alphabetic phase, children begin to understand that letters and

sounds are related but lack strategies for sounding out words. The full alphabetic

phase includes understanding of phoneme-grapheme connections and development of

sight-word vocabularies. Only in the consolidated alphabetic phase do children

recognize whole words automatically and use spelling patterns to decode unfamiliar

words. When children can read known and unknown words accurately and

effortlessly, they are considered to have reached the automatic alphabetic phase.

Only then can they be “fluent readers” as defined by the NRP (NICHD, 2001, pp. 3–

5).

23

Recent studies that support this idea include one by Schatschneider, Francis,

Carlson, Fletcher and Foorman (2004) who examined phonological, language, and

vocabulary measures in kindergarten as a predictor of second grade reading

achievement. The researchers followed the progress of 189 students in three suburban

elementary schools from kindergarten through second grade. Students with cognitive

disabilities, visual or hearing disabilities, and beginning-level English learners were

excluded from the study. Students were assessed four times in kindergarten to

determine their proficiency with early reading skills such as blending, segmenting,

alphabetic knowledge, rapid naming, vocabulary, and visual-motor integration. At

the end of first and second grades, standardized tests were administered to determine

reading achievement.

After an initial dominance analysis of predictors, language and vocabulary

predictors were found to be “consistently less related to early reading achievement”

(p. 270) than the other variables. Multiple regression analyses were then conducted

to determine the unique contribution of the most predictive variables: phonological

awareness (R2 = .34 and .21 in grades 1 and 2 respectively, p <.05), rapid naming of

letters (R2 = .51 and .41 in grades 1 and 2 respectively, p <.05), rapid naming of

objects (R2 = .40 and .34 in grades 1 and 2 respectively, p <.05), and letter sound

knowledge (R2 = .30 and .20 in grades 1 and 2 respectively, p <.05). Of the four

variables, the two fluency measures together accounted for 51% of the variance on

the end-of-first grade (and 41% on the end-of-second grade) measures

(Schatschneider, Francis, Carlson, Fletcher, & Foorman, 2004).

24

Ritchey and Speece (2006) studied sublexical fluency in kindergarten. Their

study included 92 students in a full-day kindergarten setting during the second

semester of the school year. They broke three fluency tasks (letter name fluency,

letter sound fluency, and phoneme segmenting fluency) into two components each

(speed and accuracy) and assessed the students in January and May. Using multiple

regression analyses, they determined three sublexical measures were significant to

end-of-year kindergarten word reading: letter name fluency ( = .159, p < .05), letter

sound fluency ( = .192, p < .05), and phonemic segmentation accuracy ( = .201, p

< .05). These findings, along with those of Schatschneider et al. support the NRP

findings that fluency is a key factor in reading achievement.

The final two components of reading studied by the NRP were vocabulary and

comprehension. While not directly studied by researchers seeking early predictors of

reading achievement, vocabulary and comprehension can be tied to language skills.

Many researchers have identified proficiency with oral language as a predictor of

reading achievement beyond third grade. The NRP acknowledged that vocabulary

and comprehension instruction were much more abstract and complex than phonemic

awareness, phonics, and fluency instruction. The latter three components are more

discrete and more easily measured while vocabulary and comprehension are much

more integrated cognitive skills. As a result, available studies were so varied that no

meta-analysis was performed by the panel for vocabulary, and in the area of

comprehension, the analysis was split into sixteen different categories of instruction.

One recent study conducted by Pullen, Tuckwiller, Konold, Maynard and

Coyne (2010) examined the effectiveness of explicit vocabulary instruction in a

25

multitiered model. Pullen et al. identified a storybook strategy that could be used to

instruct students in both a Tier I (whole class) and a Tier II (more intensive, small

group) setting. The participants were 224 first graders from 12 classrooms in one

school district. Using a standardized test, with three subtests (receptive, contextual,

and expressive), researchers identified 126 students who were not at risk (NAR) of

reading failure, and 98 students whose scores indicated they may have been at risk

(AR) of reading failure. The NAR group received Tier I vocabulary instruction only,

while the AR group was split into two groups: one that received Tier I instruction

only (control), and one that received Tier I and Tier II instruction (treatment).

Interrater reliability was determined through observations, checklists, and multiple

scoring of measures.

A multivariate analysis of variance (MANOVA) was conducted to examine

the differences among mean scores of the three groups of students, on three measures:

pretest, post-test, and delayed post-test. The results of the analyses were statistically

significant in every category. While the study did not specifically mention a

Bonferroni adjustment to control for Type I error, most of the p values reported were

< 0.01. For example, on the receptive level for the combined post-test between

groups, the results were statistically significant in favor of the treatment group [F(6,

404) = 10.33, p < 0.01]. Additionally, the treatment AR group outperformed the AR

control group at the receptive level (Mat-risk treatment = 6.09, Mat-risk control = 5.43; p <

0.05) and the contextual level (Mat-risk treatment = 4.66, Mat-risk control = 4.03; p < 0.05).

The results were similar at the delayed post-test. The researchers determined effect

sizes (ES) for each area on both the post-test and delayed post-test. ES ranged from

26

small (d = 0.20) on the delayed post-test context level to moderate (d = 0.64) on the

post-test context level. Limitations for this study included a small sample in one

grade level from one school district. Also, the sole focus of the study was vocabulary

knowledge. Further study could examine other grade levels, as well as the impact of

vocabulary knowledge on reading comprehension.

Adlof, Catts, and Lee (2010) conducted a longitudinal study, following 433

students with and without language impairments from kindergarten through eighth

grade. In kindergarten, students were given a standardized-test battery that included

vocabulary, comprehension, alphabetic knowledge, and IQ assessments. The students

were retested in second and eighth grades, also using standardized tests, in the areas

of word reading and reading comprehension. Logistic Regression analyses were

conducted to identify predictors of second and eighth grade reading achievement.

Area Under the Curve (AUC) values were determined, with a range of .5 for models

with no better than chance predictive value and 1.0 for models with perfect prediction

of outcomes. Values between .8 and .9 were considered excellent.

The second grade model with the best prediction value included vocabulary,

comprehension, and alphabetics factors: sentence imitation, letter identification,

mother’s education level, phoneme deletion, rapid naming, narrative comprehension,

and picture vocabulary (AUC = .91). In eighth grade, the strongest model included

these predictors: phoneme deletion, sentence imitation, mother’s education level,

grammatical completion, narrative expression, narrative comprehension, and oral

vocabulary (AUC = .87). Adlof, Catts, and Lee’s findings support the NRP’s

27

conclusion that vocabulary and comprehension are important components of reading

instruction.

The findings of the NRP and more current research that supports its findings

are important because one part of the present study examined how well alphabetics—

particularly phonemic awareness—explained overall reading achievement in the third

grade. Third grade reading achievement was examined in the context of all five key

components as determined by the NRP.

The Effects of Educational Reform Efforts

on Reading Instruction and Achievement

School systems in the United States have been under attack by critics who

have cited a host of problems, particularly for students from lower socioeconomic

status and minority backgrounds. Problems such as a resegregation of schools by

SES as described by Kozol (2000), along with public outrage about low graduation

rates and stagnant performance on the National Assessment of Educational Progress

led to the federal government increasing its involvement in monitoring the

performance of all schools across the nation. Two significant pieces of legislation

have had a direct impact on how schools have carried out the business of educating

students in the twenty-first century. The first was No Child Left Behind (NCLB),

enacted in 2002.

The purpose of NCLB was to close the achievement gap in the United States,

thus “leaving no child behind.” NCLB included provisions for measuring the

progress of all significant subgroups of students within school systems. This

accountability is achieved through the reporting of Adequate Yearly Progress (AYP),

28

based on standardized testing that occurs each spring. AYP targets increased each

year, with the goal of all students scoring proficient in English language arts and

mathematics by 2014. Schools that did not meet AYP were subject to increasingly

stringent sanctions, which included replacing the principal, staff, and/or closing the

school. Additionally NCLB included requirements for the use of research-based

methodologies and materials. The NCLB mandates created a sense of urgency

among many educators to rethink their practices in the areas of instruction,

intervention, and assessment.

Shortly after NCLB was implemented, the Individuals with Disabilities in

Education Act (IDEA) was reauthorized, becoming the Individuals with Disabilities

in Education Improvement Act (IDEIA) of 2004. One significant provision of IDEIA

was Response to Intervention (RtI). RtI has largely been implemented as a problem-

solving model in which students are provided appropriate levels of support as

evidenced by their response to instruction and intervention. Many states, districts,

and schools use a three-tiered RtI model in which all students are universally

screened in Tier I, then provided instruction and intervention by the classroom

teacher. Students who do not make adequate progress with Tier I receive more

strategic support and monitoring in Tier II. Students who do not make adequate

progress in Tier II receive intensive support in Tier III. This type of RtI model

requires a method for universal screening and progress monitoring through a battery

of tests as early as possible, usually starting in or before kindergarten.

In 2007, the California Department of Education convened a task force to

develop state guidelines and regulations for this type of model and as a result,

29

Response to Instruction and Intervention (RtI2) was born. According to the CDE’s

Technical Assistance Guide,

RtI2 is a systematic, data-driven approach to instruction that benefits every student. RtI2 integrates resources from general education, categorical programs, and special education through a comprehensive system of core instruction and tiered levels of interventions to benefit every student. The CDE work group expanded the notion of RtI to RtI2 to emphasize the full spectrum of instruction, from general or core to intensive, to meet the academic and behavioral needs of students (Ventura County Office of Education [VCOE] & California Department of Education [CDE], 2011).

The recognition that RtI included classroom instruction and multitiered

interventions for all students was an important shift for educators and policy makers.

California’s RtI2 Framework included ten core components, which started with High

Quality Classroom Instruction and ended with Determination of a Specific Learning

Disability. The Framework also included three tiers: Core, Strategic, and Intensive,

which described the levels of assistance students could receive based on their

response to the previous level of instruction or intervention (VCOE & CDE, 2011).

Chard et al. (2008) studied reading achievement predictor variables within the

context of RtI multitiered intervention models. The study was unique to previous

studies in that it allowed the researchers to examine student characteristics measured

early in the students’ school career in relation to later reading achievement after

students had received instructional supports. Over 650 students in Oregon and Texas

were identified for participation in the study based on kindergarten or early first grade

DIBELS performance. The study focused on the predictive validity of demographic

factors (such as SES and home language), early reading skills, and social behaviors

on at-risk students’ success on third grade standardized tests. Based on scatterplots of

30

first grade measures, the sample was split into high versus low groups to better

examine the interaction of the variables. The researchers found student growth to be

curvilinear in fashion, decelerating slightly between second and third grades, and

found that the only significant predictors of third grade SAT-10 performance were

fall of first grade phoneme segmentation fluency and spring first grade passage

comprehension (R2 = .097, z = 4.360, p < .001). For third grade oral reading fluency,

alphabetic principle was a significant predictor only for the low academic group (R2 =

.11, p < .01).

In addition to the initial assessments completed in kindergarten and first

grade, Chard et al. periodically reassessed students through the end of third grade.

Students also received interventions from school personnel during the study. The

researchers felt that because students received evidence-based reading interventions,

as called for in NCLB and IDEIA, most of the early reading achievement factors were

mitigated. The researchers turned their attention to demographic and social behavior

variables and found three that had significant impacts on student achievement:

gender, Hispanic ethnicity, and social skills. They concluded that the effects of

intervention practices are changing educational contexts in meaningful ways and that

a variety of factors that impact student achievement should continue to be studied.

The present study examined similar demographic and reading achievement data, also

in the context of the presence of a multitiered intervention program.

Simmons, Coyne, Kwok, McDonagh, Harn, and Kame’enui (2008) also

conducted a longitudinal study, following 41 students from kindergarten through third

grade. The purpose of the study was to evaluate whether reading status (at-risk or

31

out-of-risk) could be altered through intervention. The researchers examined risk

patterns and conducted logistic regression analyses to determine the probability that

students who were at risk in kindergarten would still be at risk in third grade. The

students in the study began their school careers below the 30th percentile on the

Letter Naming Fluency (LNF) of the Dynamic Indicators in Early Literacy Skills

(DIBELS) screening battery. These students received intervention in kindergarten

and were reassessed at the end of the year. Students who remained below the 30th

percentile in reading as measured by the Woodcock Reading Mastery Test, Revised

(WRMT-R) in word attack and word identification and by Dynamic Indicators in

Early Literacy Skills (DIBELS) in nonsense word fluency (NWF) and phonemic

segmentation fluency (PSF) continued to receive intervention in first grade. The

process to determine students who would receive interventions was repeated at the

end of first and second grades, with WRMT-R passage comprehension replacing PSF

and the addition of DIBELS oral reading fluency (ORF).

By the end of third grade, 95% of students were out of risk for word attack,

93% were out of risk in word identification and passage comprehension, and 49%

were out of risk for oral reading fluency. The researchers attributed the alteration of

reading status directly to the interventions provided in each grade. The probability of

a student who started below the 30th percentile in kindergarten and received reading

interventions, being out of risk by the end of third grade was nearly 93% (Pout of risk =

.927, Pstill at risk = .073, p < .05). To explain further, when the first cohort attended

kindergarten through second grade, teachers did not use a multitiered intervention

model; that is, they instructed their assigned students within their own classroom and

32

provided uniform reading instruction to all students in the class. Each student

received one small group reading lesson per day, and the lowest four or five students

may have received additional help of some sort. When the second cohort of students

began kindergarten in 2008-2009, the school had shifted to a more collaborative

model in which teachers shared the responsibility of providing reading instruction for

all students in their grade level. Students in the second cohort all participated in a

multitiered intervention model where the teachers worked as a grade level team,

along with paraeducators and support teachers, to deliver one to three doses of small

group, leveled reading instruction to all students per day. Students were grouped

based on their performance on regularly administered formative assessments and the

groups were fluid and flexible, meaning that the amount of reading instruction a

student received could change every few weeks.

While NCLB and IDEIA legislation were focused on student performance,

adequate yearly progress, and evidence-based interventions, both the National

Assessment of Educational Progress (NAEP) and the National Assessment of Adult

Literacy (NAAL) continued to show a decline in the number of students and adults

who are able to read complex text at a proficient level. A 2010 educational reform

effort that called for more depth and complexity in students’ thinking, reading, and

writing has the potential to make a large impact on what and how language arts is

taught in the United States. The Common Core State Standards (CCSS), a state-led

initiative, has been adopted by most states in the nation as the “next generation”

content standards. The CCSS is anchored by a set of college and career ready

standards, with the goal of having all students ready to successfully participate in

33

college or a career—without need for remediation—upon graduation from high

school (ELA CCSS, Appendix A, 2010).

In English language arts, a key component of the CCSS is increasing the level

of text complexity that students are expected to read independently throughout their

academic careers. Lexile levels (a numeric representation of the difficulty of a text)

of college textbooks and scientific periodicals have remained constant or increased in

complexity since the 1930s, while the complexity of textbooks used in K-12

classrooms has declined in sentence length and vocabulary demand (Hayes, Wolfer,

& Wolfe, 1996). In addition to the decline in text complexity, K-12 students often

read very little expository text and “when they do, it is with considerable teacher

scaffolding and support” (CCSS Appendix A, 2010, p. 3). The CCSS focus on

increasing students’ work with complex texts should benefit all students, but may

most benefit those from disadvantaged backgrounds (Bettinger & Long, 2009).

While the effects of the CCSS standards have not yet been seen, its

components are consistent with the findings of the National Reading Panel. Each of

the CCSS reading standards in the areas of Literature, Informational Text, and

Foundational Skills has the potential to increase students’ ability to show proficiency

with complex text. Whether or not federal legislation continues to mandate

proficiency for all students, the CCSS provide a clear, consistent expectation for what

American students should know and be able to do upon completion of their K-12

schooling. Using these standards, schools should investigate factors that lead to

student success and those that are associated with learning difficulties. NCLB’s 2014

deadline for 100% of all students proficient in English language arts and mathematics

34

has created a sense of urgency for educators, but success for all students is a worthy

goal and one that should be pursued regardless of federal or state legislation.

Determining the most accurate predictors, developing interventions to address those

predictors, and monitoring student progress toward improvement is a promising

practice for a 21st century educational system.

Academic and Demographic Factors That Predict Reading Success

A review of the existing literature revealed a number of studies that identified

factors that contribute to a child’s ability to read well. During the early to mid-20th

century, most children were taught to read through a whole-word, meaning-first

approach, also known as “look-say” (Adams, 1990, p. 38). Phonics was not

commonly taught. Yet even early studies dating to the 1920s found a very strong

positive correlation (r = .87) between students’ first grade reading ability and their

ability to name the letters of the alphabet (Smith, 1928). As this strategy came under

fire as insufficient in the latter half of the 20th century, the debate about how to teach

reading spurred a large number of studies about how children learn to read.

In 1967, after her involvement in the National Conference on Research in

English, Chall published the seminal work, Learning to Read: The Great Debate.

The book was a result of three years of interviews, program evaluations, observations

in schools, and a broad review of available research. Chall found a strong positive

correlation between letter and phonic knowledge and reading achievement in students

through third grade. Chall’s recommendations included explicit, systematic phonics

instruction for all beginning readers. In response to Chall’s findings, many new

35

studies were conducted on the effectiveness of different types of reading instruction

and programs.

Bond and Dykstra (1967) coordinated 27 projects for the United States Office

of Education—called The Cooperative Research Program in First Grade Reading

Instruction—to study three research questions:

1. To what extent did various community, family, teacher and school

characteristics influence student reading and spelling achievement in first

grade?

2. Which approaches to initial reading instruction produced better results in

reading and spelling at the end of first grade?

3. Did any program stand out as particularly effective for students with either

high or low readiness for reading?

Collectively, the studies involved over 600 schools and more than 20,000 students

across the United States. Bond and Dykstra’s Coordinating Center was responsible

for collecting, organizing, analyzing, and interpreting the data collected from each

study.

Bond and Dykstra computed Pearson product-moment correlation coefficients

to identify whether relationships existed between first grade reading achievement and

demographic characteristics of students, teachers, and classes. Several characteristics

showed little or no relationship with first grade word reading achievement. For

example, class size and teacher absences both showed nearly zero correlation (r = .01

and r = .07, respectively). Child age entering first grade had a weak correlation (r =

.17, p < .05), as did teacher years of experience (r = .27, p < .05).

36

In contrast, when individual student achievement measures were examined,

first grade end-of-year reading achievement on the Stanford Achievement battery was

strongly related to students’ entering ability to name uppercase and lowercase letters,

with r > .51 for all subtests. Overall, this single factor accounted for 25 to 36% of the

variation in reading ability. The next best predictor of first grade end-of-year

achievement on the Stanford Achievement battery was the ability to discriminate

phonemes at the beginning of the year, with r values in the .40 to .50 range. The

correlation between intelligence test results and Stanford Achievement battery scores

was weaker, mostly in the r = .30 range (p < .05).

In addition to the correlational study, Bond and Dykstra also examined the

effectiveness of the varied reading programs used in the 27 studies. They examined

data both “across projects” and then “within projects” by examining student

achievement after 140 days of first grade instruction. They conducted Analyses of

Covariance (ANCOVAs) to control for initial differences among students and chose

covariates based on their potential to eliminate project by treatment interactions. For

example, the letter name and phoneme subtests were both chosen as covariates for

this reason. The researchers found that regardless of the program or instructional

methods used to teach reading, some students markedly outperformed others even

when the differences in reading readiness were statistically controlled. After

examining classroom delivery methods and program usage in the studies without

finding any obvious predictors, Bond and Dykstra concluded that both program and

pedagogy influenced students’ reading performance.

37

The research that followed Chall’s and Bond and Dykstra’s work confirmed

over and over that letter knowledge was a strong predictor of reading achievement.

However, studies like the one conducted by Vellutino and Scanlon (1987) presented

evidence that simply teaching students to name the letters of the alphabet was not

enough to ensure that high levels of reading success were attained. Instead, the

researchers found that ability to store and retrieve phonological representation

corresponding to words was an important skill for success on a second and sixth

grade reading measure. The study involved 565 students in three consecutive cohorts

in Albany NY. Students were tested in kindergarten and retested with an oral reading

measure at the end of second and sixth grades.

Using One-Way Analyses of Covariance (ANCOVA), the researchers

controlled for IQ and found that mean scores on the oral reading test showed

significant differences for pseudoword decoding in both second and sixth grades

[second grade: F(1, 139) = 166.08, p < .001; sixth grade F(1, 139) = 69.77, p < .001],

and phonemic segmenting in second and sixth grades [second grade: F(1, 139) =

11.25, p < .001; sixth grade F(1, 139) = 7.65, p < .01]. Several researchers found that

the speed with which students could name letters was a strong predictor of reading

success in both prereaders and beginning readers. For example, Speer and Lamb

(1976) found a relationship between student scores on standardized measures in first

grade and both letter and grapheme (group of letters that make one sound, e.g., /ck/)

fluency measures (N = 25; letter fluency r = .68, p < .001; grapheme fluency r = .77,

p < .001). This finding matches Adams’ assertion that students who were fluent with

letter recognition (that is, they had both speed and accuracy with naming letters) were

38

more confident and could more easily learn sounds and patterns of sounds that made

up words (1990).

In 1974, Adams first published Beginning to Read: Thinking and Learning

about Print. The comprehensive ideas, processes, and models of early reading

presented in her book (and its subsequent reprintings) were cited in nearly every other

study for this review of the literature. Adams (1990) called reading a “complex

system of skills and knowledge” (p. 3) and further stated, “the parts are not discrete.

We cannot proceed by completing each individual subsystem and then fastening it to

another. They must grow to one another and from one another” (p. 6). Adams made

a strong case for how letter naming ability and phonemic awareness could predict

reading success in young readers and also discussed the rapid naming of any visual

stimuli as reflective of a “deep capacity” important for reading (p. 64).

In her discussion of phonemic awareness, Adams (1990) made distinctions

between simply hearing or producing phonemes and abstracting and manipulating

phonemes as part of the language. She asserted that a conscious attention to

phonemes does not come naturally, but overlearning the nuances of spoken language

proved to be an important part of freeing up cognitive resources for learning to

navigate printed language. This connection between spoken sounds and print was the

subject of a study by Hohn and Ehri (1983) who examined phonemic awareness with

and without graphemes. The researchers divided 62 kindergarten students into three

groups: control, ear, and letter. Each student received daily, individual segmentation

training. The ear group used markers to represent phonemes, and the letter group

used markers with the corresponding letters to represent phonemes. The control

39

group used no markers. Significant differences between the two experimental groups

and the control group were found at the end of the study. Both the ear and letter

groups scored better than the control group [F(2, 14) = 11.98, p < .01]. The letter

group also performed better than the ear group [t(7) = 3.55, p < .01]. The researchers

concluded that the use of letters allowed students to establish a sound-symbol

visualization system to better commit the sounds to memory.

As noted by Adams (1990), phonemic awareness is not a single skill and

many studies have tried to determine which subskill or skill-set might best predict

reading ability. There are five generally agreed upon levels of phonemic awareness

(PA). The first level is simply recognizing and producing sounds (phonemes). The

second level involves oddity tasks in which similarities and differences are

recognized, including rhyme and alliteration. The third and fourth levels require

blending and segmenting phonemes to construct and deconstruct words (and nonsense

words). The most complex phonemic awareness tasks involve manipulation of the

phonemic structure of words to regenerate different words.

The level or levels of PA that are the best predictors of reading achievement

continues to be studied today, with mixed results. Savage and Carless (2005)

conducted a three-year study to determine if onset-rime ability contributed to

phoneme manipulation as a strong predictor of reading achievement. Onset-rime is

the rhyming of the initial sound in a word rather than the final sound, and fits within

Adams’ second level of PA. For example, using boat as a target word and choice

words such as foot, bike, and coat, a student who says bike is the correct rhyming

word would be using onset-rime to match the sounds at the beginning of the word.

40

Ultimately, Savage and Carless concluded that onset-rime manipulation tasks were

not significant predictors of student reading success. However, their study did

confirm the findings of earlier studies that phoneme manipulation skill at age 5

strongly predicted reading comprehension at age 7.

Most studies on the effects of phonemic awareness instruction showed that a

combination of PA skills are strong predictors of subsequent reading achievement.

For example, Scarborough (1998) conducted a meta-analysis of 61 studies and found

that letter naming, phonological awareness, and rapid automatic naming accounted

between 16 and 28% of the variance in reading outcomes (R2 = .28, R2 = .18, and R2 =

.16, respectively; all p < .01). Felton (1992) conducted a step-wise discriminant

function analysis for a sample of 221 students in North Carolina and determined that

a combination of IQ, rapid naming of letters, and a phoneme deletion task accounted

for 41% of the variance in third grade reading performance on a standardized test (R2

= .41, p < .001).

Schatschnieder, Fletcher, Francis, Carlson, and Foorman (2004) conducted a

dominance analysis to determine predictors of end-of-first grade and end-of-second

grade reading efficacy. The linear combination of students’ beginning-of-year-

kindergarten performance on phonemic awareness, letter sounds, and rapid naming of

letters tasks accounted for almost half of the variance in student performance on end-

of-year first and second grade measures (N = 189, R2 = .49, p < .05). Additionally,

when used to predict second grade reading group classification (superior readers,

average readers, poor readers, severely poor readers), the three kindergarten factors

41

accurately placed 100% of the severe group, 76% of the poor group, 22% of the

average group, and 11% of the superior group.

Adlof, Catts, and Lee (2010) examined the “best predictors of early and later

reading impairments” by following over 400 students from kindergarten through

eighth grade (p. 333). The sample included children with and without language

impairments so the researchers could measure the effects of broader language skills

on later reading achievement. Participants in the study were assessed on a wide range

of phonemic awareness, phonics, and language skills in kindergarten. In second and

eighth grades, students were assessed in reading comprehension and word

recognition. Significant (all p < .001) strong correlations between kindergarten and

second grade measures were reported, particularly in letter identification and sentence

imitation (both r = .61). Other moderately strong correlations included phoneme

deletion (r = .59) and oral vocabulary (r = .56). There were moderate correlations

between kindergarten and eighth grade measures in sentence imitation and

grammatical completion (both r = .59), as well as with phoneme deletion (r = .49).

Upon further analysis, Adlof, Catts, and Lee described phoneme deletion as “one of

the most important predictors of eighth grade status…more important for predicting

eighth grade than second grade outcomes” (p. 340). While many of their conclusions

were consistent with the earlier research finding that phonemic awareness and

alphabetic knowledge helped identify students at risk for reading difficulties, Adlof,

Catts, and Lee found that kindergarten broad language skills were better predictors

than phonological awareness of eighth grade reading outcomes.

42

Similarly, a seven-year longitudinal study in Australia conducted by Butler,

Marsh, Shepard, and Shepard (1985) found that kindergarten language skill was the

number one predictor of sixth-grade reading achievement (R2 = .50, p < .01).

Language as defined for this study included repeating nonsense syllables,

distinguishing similarities and differences in word pairs, and repeating and retelling

words, sentences, and stories. Other predictive factors identified in the study

included psycholinguistic abilities, spatial/form perceptions and figure drawing,

although none rivaled the predictive strength of kindergarten language skills.

As noted in the discussion of Bond and Dykstra’s work, reading and

prereading skills were not the only factors studied by twentieth century researchers in

the quest to predict reading achievement. Ethnicity, socioeconomic status (SES),

gender, and mother’s education level were all examined as part of many studies on

reading achievement. Most studies, including one by Scarborough (1998) found that

student demographic variables, including SES, gender, age at school entry, and home

literacy environment were not strongly related to reading outcomes (all demographic

factors were reported with r values less than .28, none significant). Butler, Marsh,

Shepard, and Shepard (1982) also found that mother’s primary language indirectly

impacted students’ second grade reading success, but a follow up study found that

gender and parents’ primary language had minimal effects on later student reading

achievement (Butler, Marsh, Shepard, & Shepard, 1985).

However, several researchers found socioeconomic level to be a variable

related to reading achievement (Butler, Marsh, Shepard, & Shepard, 1982; Felton,

1992; Satz, Taylor, Friel, & Fletcher, 1978). One study conducted by Noble, Farah,

43

and McCandliss (2006) specifically studied socioeconomic status (SES) as a

moderating factor in reading achievement and found that because children from lower

SES households had decreased access to resources, it amplified the risk factors of low

phonological awareness skills, which were strongly tied to poor decoding. Noble,

Wolmetz, Ochs, Farah, and McCandliss (2006) used neuroimaging technology to

investigate the relationship between SES and reading. The researchers defined SES

as “a proxy measure for a child’s environment and experiences” (p. 650). They found

that as SES decreased, activity in brain-to-behavior regions of interest (ROI)

increased, but when SES increased, activity in brain-to-behavior ROIs decreased and

activity in brain-to-literature ROIs increased.

Aikens and Barbarin (2008) used hierarchical linear modeling techniques to

examine variables—including SES—that contributed to reading achievement for a

nationally-represented sample of 21,260 kindergarten students. When they

controlled for all other variables, Aikens and Barbarin discovered that as SES

increased, reading achievement at the beginning of kindergarten increased. For those

students 1 SD above the mean SES level, performance was about 2.24 points higher

than those at the mean SES level, and 4.48 points above those who were 1 SD below

the mean SES level. Since the average monthly learning rate was 1.86 points, this

equaled a 2.24 point head start in reading achievement. The researchers reported that

children’s reading readiness was correlated with the home literacy environment and

number of books owned. SES impacted student achievement because parents with

less income and resources were less likely to be able to create a literacy-rich

environment. Additionally, Coley (2002) used the same nationally-represented

44

sample to study home reading experiences and found that 36% of parents in the

lowest-income quintile read to their children daily, compared with 62% of parents

from the highest-income quintile.

Factors such as family history of learning disability, birth history, and birth

order also differentiated poor readers from good readers (Felton, 1992). In a 2003

study, Snowling, Gallagher and Frith classified 56 children as high or low risk based

on their parents’ scores on a battery of psychometric tests. They found that 66% of

children in the high-risk group were diagnosed with reading disabilities by age 8,

compared with only 14% of children in the control group. Conlon, Zimmer-Gembeck,

Creed and Tucker (2006) studied factors related to the adolescent reading skills of

190 students and found that both family history and student self-perceptions were

predictors of student performance on foundational reading skills tests and reading

comprehension tests. Family history accounted for about 17% of the variance in

student achievement (R2 = .17, F(3, 168) = 11.1, p < .001), and student self-

perception accounted for about 8% of the variance in student achievement (R2 = .08,

F(2, 163) = 12.8, p < .001). Overall, previous studies showed that while demographic

factors played a role in student reading achievement, the strongest predictors

remained phonemic awareness, alphabetic knowledge, and broad language skills in

kindergarten. With pressure from national educational reforms, such as NCLB and

the new Common Core State Standards, for students in all subgroups of students to

perform at or above proficiency, the present study examined both demographic and

academic factors in a multitiered intervention model.

45

Evaluation of Interventions Used in the School’s

Multitiered Intervention Model

The NRP’s recommendation for phonemic awareness and phonics instruction

was that it be systematic and explicit. While state-adopted reading curriculum must

meet this requirement and seems adequate for students who are not having difficulty

with early reading skills, children who have difficulty with learning to read often need

more time and practice with these components (Adams, 1990). When that is the case,

supplemental programs or materials are usually used to provide intervention. One

such intervention is Systematic Instruction in Phonemic Awareness, Phonics, and

Sight Words (SIPPS). SIPPS was designed to provide explicit and systematic

instruction for beginning readers through well-established routines that support

practice in each area of decoding instruction, including blending, segmenting,

phoneme manipulation, phoneme deletion, and rhyming. The Developmental Studies

Center (2002) conducted an evaluation of SIPPS and found positive results, with

effect sizes (ES) ranging from 0.24 to 0.38 when compared to control groups. Two

qualitative studies were conducted to examine the effectiveness of SIPPS. Garner

(2007) and Haselman (2003) reported increases in student performance with reading

skills such as oral blending and oral segmenting. They also reported that student self-

confidence increased, as did the positive relationship between the teachers and

students. Additional studies about the effectiveness of SIPPS were not found. The

present study may be able to contribute to the existing body of knowledge about the

effectiveness of this intervention.

46

Fluency is another area in which supplemental materials or programs are used

for intervention. Given the evidence for fluency as a predictor of later student

achievement, a study by Heistad (2004) was examined to determine the effects of the

Read Naturally intervention. Heistad’s sample included students in third through

fifth grade in four schools, with 78 students in a control group and 78 students in a

treatment group. Students were identified as at-risk readers by parent and/or school

recommendation. Student achievement was evaluated through three pretest/post-test

measures: a standardized test administered to all students each year (to test

comprehension), a standardized state assessment given to third and fifth graders each

year (to test comprehension), and the Read Naturally fluency monitor assessment (to

test fluency) given once in the fall and once in the spring to all students in the control

and treatment groups. Each assessment was considered to be valid and reliable. The

Read Naturally intervention was implemented for an entire school year.

Using paired t-tests, the researchers determined that there were statistically

significant differences on both comprehension assessments. Students in the treatment

group had higher mean scores than those in the control group on both comprehension

assessments (t = 2.46, p = 0.02, ES .24; and t = 2.24, p = 0.03, ES .39, respectively).

On the fluency assessment, the two schools with high levels of fidelity to the program

(at least 95% attendance and completion rate) showed statistically significant gains

according to dependent t-tests. The two schools that implemented the program with

lower levels of attendance and completion did not show significant gains. It appears

that the intervention is beneficial for both fluency and comprehension support, but

47

due to the small sample size, and participant selection process, further study is needed

to generalize the effects to the greater population.

The third intervention type used by the school in the present study is teacher-

created intervention. In this type of intervention, teachers use formative assessments

to design interventions that assist students through games, quiet environment after

school, one-on-one attention, and/or through additional practice time with high

frequency words, reading aloud, and spelling/writing activities. A review of the

literature revealed a large variety of studies, but none with conclusive findings on

interventions similar to the teacher interventions offered at the sample school.

Summary

Chapter II included a review of the findings and recommendations of the

National Reading Panel, summary of the educational reforms impacting reading

instruction, discussion of the academic and demographic factors that predict reading

success, and a review of intervention programs used by the school participating in the

present study. Chapter III will discuss the methodology of the current study.

48

CHAPTER III

METHODOLOGY

According to the National Assessment of Educational Progress (NAEP), there

was a slight positive trend in the reading scores of 9- and 13-year-olds between 1971

and 2008 but no statistically significant change in overall reading performance during

that time period (National Center for Educational Statistics, 2009). Approximately

90% of 9-year-olds read at the lowest, most discrete levels, yet only one in five can

interrelate ideas and make generalizations from text. These percentages have

remained virtually unchanged in the last three decades.

Despite the large knowledge base about teaching reading, there continues to

be a large gap between students who are able to perform only the mechanics of

reading and those who are able to proficiently read and comprehend grade appropriate

text. The problem remains twofold. First, schools must find accurate and efficient

ways to identify students who are struggling with the reading process as early as

possible. Doing so allows interventions to be implemented before students are

expected to use their reading ability to expand their knowledge and show proficiency

in all content areas in subsequent grades. Second, schools must utilize and design

interventions that effectively help nonreaders or struggling readers become proficient

readers. This requires teachers to isolate students’ areas of weakness and design

plans of action that will allow students to acquire all of the skills needed for mastery

of reading.

49

Restatement of the Research Questions

As stated in Chapter I, this study examined factors that influence third grade

reading achievement, specifically the predictability of demographic factors and

reading readiness at the beginning of kindergarten on students’ subsequent reading

performance at the end of third grade when a multitiered response to intervention

model of instruction is utilized. The following questions were addressed and

hypotheses tested.

Research Question 1






or California Modified Assessment than ethnicity, socioeconomic status, parent

education level, gender, age upon kindergarten entry, or English language level in

kindergarten.


explains more about third grade reading achievement on the District Benchmark Tests

than ethnicity, socioeconomic status, parent education level, gender, age upon

kindergarten entry, or English language level in kindergarten.

50

Research Question 2






or California Modified Assessment than kindergarten scores on letter names, letter

sounds, oral blending, oral segmenting, consonant-vowel-consonant reading, or sight

word tasks.


explains more about third grade reading achievement on District Benchmark Tests

than kindergarten scores on letter names, letter sounds, oral blending, oral

segmenting, consonant-vowel-consonant reading, or sight word tasks.

Figure 3.1 presents a model for variables examined in the first two research questions

in the present study.

In addition to examining factors that may predict or explain third grade

reading achievement, the effectiveness of intervention programs for different groups

of students was evaluated with the following research question and hypotheses.

Research Question 3

The third research question was, how much do kindergarten demographic

factors and participation in multitiered interventions explain student reading

achievement on the end-of-intervention assessment? Five hypotheses were posed for

this question.

Figure 3.1. Model of variables represented in Research Questions 1 and 2.

variables represented in Research Questions 1 and 2.

51

52

Hypothesis 3.1. For first grade students, participation in multitiered SIPPS




Hypothesis 3.2. For second grade students, participation in multitiered SIPPS




Hypothesis 3.3. For second grade students, participation in the Reads

Naturally intervention explains more about second grade reading achievement on the

end-of-intervention District Benchmark Fluency Test than the kindergarten



Hypothesis 3.4. For first grade students, participation in a teacher-created




Hypothesis 3.5. For second grade students, participation in a teacher-created


intervention District Benchmark Comprehension Test than the kindergarten



53

Figure 3.2 presents a model for the variables examined in research question

three.

Figure 3.2. Model of variables represented in Research Question 3.

Population and Sample

This study was limited to students enrolled in one public elementary school

located in the northern Central Valley of California. According to the United States

Census, the community had a population of 19,472 in 2000 and 23,647 in 2010, a

growth of 4,175 residents, or 21.4%. In 2010, the median household income was

$58,476, with 26% of the households reporting an annual income of less than $35,000

and 16% reporting an annual income of greater than $100,000 (U.S. Census Bureau,

2010). Claritas Marketplace (2007) reported 24% of residents had less than a high

school diploma, 29% graduated from high school, 32% had some college, and 15%

held a bachelor’s or graduate degree. The largest employers in the community were

the elementary and high school districts, which together employed 625 people in

2007 (Dun & Bradstreet, 2008). Other local industries included retail, manufacturing,

service, and agriculture. In 2010, the city’s annual average unemployment rate was

20.3% (California Employment Development, 2012).

54

The present study examined student demographic and reading achievement

data for students who attended kindergarten at the school in either the 2004–2005 or

2008–2009 academic years. In August 2004, 128 students were enrolled in

kindergarten at the school. Of those, 62 continued at the school through third grade.

The steep decline in enrollment from 2004–2005 to 2007–2008 was due to the

opening of a new school in the district in August 2005. In August 2008, 83 students

were enrolled in kindergarten and 55 continued at the school through third grade.

The kindergarten and third grade reading achievement of the two student cohorts was

compared to analyze the impact of a kindergarten through second grade multitiered

reading intervention model, which was fully implemented beginning 2008—the year

the second cohort entered kindergarten.

According to school attendance records, the total school population at the time

the first cohort entered kindergarten in 2004–2005 was 842 students. Of those

students, 144 (17%) were classified as English Learners (EL). Males accounted for

57% of the population and females accounted for 43%. Forty-nine percent of the

students were white; 41% were Hispanic; 3% were African American; and 3% were

Asian. The remaining 4% were American Indian, Pacific Islander, and Filipino. Four

hundred forty two students (52%) participated in the National School Lunch Program.

Seven percent of parents had graduate degrees, 63% of the parents attended or

graduated from college, 17% were high school graduates, and 12% did not graduate

from high school. The kindergarten cohort in 2004–2005 was representative of the

total school population.

55

According to school attendance records, in 2008–2009 when the second

cohort entered kindergarten, the school population was 665. Of those students, 105

(16%) were classified as English Learners (EL). Males accounted for 56% of the

population and females accounted for 44%. Forty-five percent of the students were

white; 45% were Hispanic; 3% were African American; and 3% were Asian. The

remaining 4% were American Indian, Pacific Islander, and Filipino. Two hundred

ninety-six students (45%) participated in the National School Lunch Program. Seven

percent of parents had graduate degrees, 61% of the parents attended or graduated

from college, 18% graduated from high school and 13% did not graduate from high

school. The kindergarten cohort in 2008–2009 was representative of the total school

population.

When the students from the first cohort completed third grade, the school’s

2009 Growth Academic Performance Index (API) score was 824, up 20 points from

the 2008 Base API score of 804. For 2008, the school met 22 of 25 Adequate Yearly

Progress (AYP) targets (California Department of Education, 2012). The Students

with Disabilities subgroup missed three targets: 95% participation rate, and the

“percent proficient” targets in English language arts and Mathematics. When the

students from the second cohort completed third grade, the school’s 2012 Growth

API score was 838, a 7-point gain from the 2011 Base API score. For 2012, the

school met 20 of 23 AYP targets (California Department of Education, 2012). All

participation rate and English language arts targets were met. In Mathematics, three

subgroups did not meet “percent proficient” targets: school-wide, white, and English

learners.

56

Once historical data for each student were entered into a spreadsheet, student

names were removed to protect confidentiality. No individual student names or other

identifiers were used to report results for this study.

Demographic Data Collection

For Research Question 1, demographic data collected as part of the regular

practices of the school and district were examined. Students’ gender, birthdate,

ethnicity, and parent education level were reported by parents or guardians at the time

of registration and recorded in the school’s student information system.

Socioeconomic status was determined by participation in the National School Lunch

Program (NSLP), which provides free and reduced-priced meals for students living in

households with incomes below 130% of the federal poverty level (USDA, 2010).

According to federal guidelines, parents enroll their children in NSLP annually by

submitting a form with income and other household information, claiming homeless

status, or providing documentation that a student is in the foster care system.

English learner status was determined by an initial assessment of English

proficiency using the California English Language Development Test (CELDT) in the

fall of kindergarten. All students whose parents indicated a language other than

English on the Home Language Survey as part of the registration process take the

CELDT within 30 days of enrollment. Based on the results of the CELDT, students

are assigned to one of five English proficiency levels: Beginning, Early Intermediate,

Intermediate, Early Advanced or Advanced. Students who score Early Advanced or

57

Advanced on the initial CELDT may be classified as Initially Fluent English

Proficient (IFEP).

Achievement Data Collection

To answer Research Questions 2 and 3, historical data were examined for this

study, and were originally collected by teachers and the school principal for the total

school population over the normal course of multiple academic school years. There

were four testing windows included during each academic year: (a) beginning of

school, (b) end of first trimester, (c) end of second trimester, and (d) end of year. For

each testing window, students participated in one or more formative assessments,

with additional tests administered as determined by a district assessment protocol. To

be included in this study, students were required to have valid beginning-of-year and

end-of-year kindergarten and third grade district reading assessment scores. For third

grade, valid total English language arts scores on the California Standards Test (CST)

or California Modified Assessment (CMA) were also required.

District Reading Achievement Data Collection Background

In the summer of 2000, the school joined several other schools and districts in

Sacramento County to participate in RESULTS, at that time a new program

developed by the California Reading and Literature Project (CRLP). The goal of the

RESULTS project was to improve reading instruction for students in kindergarten

and first grade. Teachers and administrators participated in intensive staff

development about teaching reading, including a week-long summer institute with a

focus on the key components of reading: phonemic awareness, phonics, fluency,

vocabulary, and comprehension. Teachers received training in both the

58

administration of reading assessments and individual and team data analysis

processes to understand the results of the assessments. The process included

developing interrater reliability, for which a 95% goal was set. As part of the

process, teachers agreed to assess the reading progress of all students in their classes

and report the data to CRLP for additional analysis. Additionally, ongoing

professional development was provided by CRLP throughout the school year.

By the 2004–2005 school year, when the first cohort began kindergarten, all

kindergarten through third grade teachers at the school had received the training and

were actively participating in RESULTS, along with hundreds of other teachers

throughout California. Other elementary schools in the district were also

participating in RESULTS and the district had adopted the CRLP assessment battery

as the district assessment for kindergarten and first grade.

By the time the first cohort entered third grade in 2007–2008, the CRLP

assessments were well established as part of the school and district assessment battery

from kindergarten through sixth grade. Teachers received training in the district

reading assessment protocol each fall from a reading specialist or curriculum coach.

Although they no longer reported reading assessment data to CRLP, teachers

submitted individual student scores on each assessment to the principal and reading

specialist or curriculum coach at the conclusion of each testing window.

Instrumentation

Ex post facto reading achievement data were reviewed to explore research

question two. The kindergarten assessment data examined for this study were

gathered from six assessments that were given to all students in the sample population

59

during their kindergarten year. The first two tests—Letter Names (LN) and Letter

Sounds (LS)—were administered at the beginning of the year to all students and then

administered at each subsequent testing window until a student met the benchmark.

For LN and LS, the benchmark was set at 24 correct of 26 possible. Students were

considered at risk if they scored below the Approaching Benchmark score at the end

of the first trimester; for LN, the score was 20 correct of 26 possible and for LS, the

cut score was 17 correct of 26 possible.

Oral Blending (OB) and Oral Segmenting (OS) were administered starting at

the end of the first trimester, after a few weeks of phonemic awareness and phonics

instruction. Again, the assessments were administered to all students, with

reassessment occurring at the following testing period if a student did not meet

benchmark. End of year benchmarks were set as follows: Oral Blending - 9 correct of

10 possible and Oral Segmenting - 7 correct of 10 possible. Approaching

Benchmark scores were: OB 6 of 10 words blended correctly and OS 5 of 10 correct

responses. Students scoring below these scores were considered at risk readers.

The final two tests, Blending Consonant-Vowel-Consonant Words (CVC) and

High Frequency Word Reading (HFW), were administered at the end of the second

trimester to all students and again at the end of the year to any student not yet meeting

the benchmark. The benchmark for CVC was 8 correct of 10 possible and the

benchmark for HFW was 18 correct of 20 possible. The Approaching Benchmark cut

score for CVC was 6 of 10 correct responses and for HFW 16 of 20 correct responses.

Again, students scoring below Approaching Benchmark were considered at risk. The

kindergarten assessments and benchmarks remained the same during both years of the

60

sample. A reliability analysis conducted on the six measures as one assessment

resulted in a coefficient alpha of .93.

Third grade end-of-year assessment data for the sample were also ex post

facto and collected as part of the regular practices of the school. Two of the third

grade assessments examined were also developed by CRLP, the Basic Phonics Skills

Test (BPST) and grade-level-appropriate reading passages. A coefficient alpha was

computed to find the internal consistency of the BPST. The coefficient alpha was

.90. These results supported the conclusion that the BPST is reliable. The third grade

reading passage was the same for both cohorts. It was examined using the Lexile®

Measure for Reading. The Lexile® scale ranges from 0 to 2000, and ranks the

difficulty of a text (MetaMetrics, 2008, Lexile.com). The passage had a Lexile score

of 660L, well within the Common Core State Standards text complexity band for

second and third grade, which spans from 450L to 725L (CCSS, Appendix A, 2010).

As was the case with the kindergarten assessments, 95% interrater reliability was

achieved prior to administration of assessments.

State Reading Achievement Measures

The California Standards Test (CST) is a criterion-referenced test used in the

California state assessment system and is also used by the federal accountability

system to determine Adequate Yearly Progress (AYP) as defined by No Child Left

Behind (NCLB). The test uses a multiple-choice format to assess students’

proficiency with grade level California academic content standards in the area of

English language arts. The California Department of Education (CDE) reported a

reliability coefficient alpha of .93 for the third grade CST (CDE, 2012). According to

61

the CDE CST Blueprint (2002), all five areas of English language arts achievement

measured by CST are included in third grade: Word Analysis and Vocabulary,

Reading Comprehension, Literary Response and Analysis, Written English Language

Conventions, and Writing Strategies. The number of items and the percentage of the

test for each section and grade level are reported in Table 1.

Table 1 Number and Percentage of Test Items for Each English Language Arts Subtest on the California Standards Test

Third grade

# of items % of test

Word analysis and vocabulary 20 31 Reading comprehension 15 23 Literary response and analysis 8 12 Written English language conventions 13 20 Writing strategies 9 14

The state of California reports scores for each student on each subtest. These

scores are reported as a percentage correct score. For purposes of this study, the

results of the Word Analysis and Vocabulary, Reading Comprehension, and Literary

Response and Analysis subtests were examined individually and as a contribution to

students’ overall ELA CST score. These three subtests accounted for 66% of the

CST in third grade (CDE, 2012). Student scores for these subtests were reported as

“percent correct.”

In addition to reporting students’ subtest scores, an overall ELA CST

performance level is assigned to each student. These performance levels are

determined by scale scores ranging from a low of 150 to a high of 600 and do not

62

change from year to year. The five levels are far below basic (1), below basic (2),

basic (3), proficient (4), and advanced (5). Table 2 summarizes the score ranges for

each performance level (CDE, 2012).

Table 2 Score Ranges for Third Grade Performance Levels on English Language Arts California Standards Test

Performance level Score range

Far below basic 150–258 Below basic 259–299 Basic 300–349 Proficient 350–401 Advanced 402–600

For students with learning disabilities who receive special education services

on an Individualized Education Program (IEP), the State of California allows use of

the California Modified Assessment (CMA) in place of the CST. The CMA is a

shorter version of the CST, with one fewer answer choice per question, shorter grade

level reading passages, and more white space on the page. The intent of the CMA is

to assess grade level content standards in a more accessible format for students with

disabilities. In 2006, Educational Testing Service (ETS), the company that

administers the California state testing system, piloted the CMA with over 16,000

students in grades three through eight and conducted statistical analyses on the results

(Smith & Chard, 2007). Smith and Chard reported that the CMA test items used in

the pilot were written using the same procedures as the CST test items and were

similarly rigorous. Three factors were studied in the pilot: passage length, passage

delivery mode, and stem/answer choice-options delivery mode. By examining the

63

proportion correct for the variations of the three factors, the researchers were able to

determine there was strong evidence that the CMA test items were more accessible

for students with special needs. The California Department of Education (CDE)

reported a reliability coefficient alpha of .87 for the third grade CMA (CDE, 2010).

CMA performance levels are identical to the CST performance levels, but the score

ranges are slightly different (CDE, 2012). The CMA score ranges are summarized in

Table 3.

Table 3 Score Ranges for Performance Levels on Third grade English Language Arts California Modified Assessment

Performance level Score range

Far below basic 150–227 Below basic 228–299 Basic 300–349 Proficient 350–396 Advanced 397–600

Identifying Factors that Contributed to Student Performance in Specific

Interventions

To answer Research Question 3, ex post facto intervention data were reviewed

for students who participated in a variety of reading intervention programs from

kindergarten through second grade. Reading achievement was examined before and

after participation in specific interventions including Read Naturally, Systematic

Instruction in Phoneme Awareness, Phonics and Sight Words (SIPPS), and teacher-

designed after school tutoring programs, to determine if any of the interventions had

64

an impact on student reading achievement, over and above demographic factors that

contributed to student performance.

Data Analysis

Vogt (2007) explained that correlation analyses show the strength of the

relationships between variables, but they do not imply a causal relationship.

However, regression analyses can be used to determine causation. As described by

Green and Salkind (2011), multiple regression analyses can be used in

nonexperimental designs to measure the strengths of a variety of variables or

predictors. These predictors may be categorical or scale. By measuring sets of

predictors, the multiple regression analysis can show the strength of the relationship

between the factors and of all factors in combination. Vogt (2007) discussed the risk

of overestimating the effects of some variables if all important variables are not

included; this design error is sometimes called left-out variable error, or LOVE. For

this study, both socioeconomic status and parent educational level were included in

the initial analyses, even though many studies have shown that the two variables are

highly interrelated. If any two predictors were highly correlated (r > .70), one of

them was omitted from the model.

The first two research questions in this study were examined using multiple

regression analyses to explore the effects of a set of independent variables, on a

dependent variable. For Research Question 1, student data were entered into the

Statistical Package for the Social Sciences, Version 18 (SPSS 18) and the software

was used to conduct step-wise multiple regression analyses. For each dependent

variable—the five third grade District Benchmark Tests (DBT), third grade California

65

Standards Test/California Modified Assessment (CST/CMA), and the three

CST/CMA subtests—two multiple regression analyses were conducted. The first

included the kindergarten demographic factors (ethnicity, SES or parent education

level, gender, age upon kindergarten entry, and English language level in

kindergarten) as a set of predictors while the second examined multitiered

intervention program participation to determine how much each contributed to third

grade reading achievement data. For each analysis, a multiple correlation (R) ranging

from 0 to 1 was computed, with R > .50 considered a strong correlation.

Additionally, a squared multiple correlation (R2) was also computed to indicate the

“percent of criterion variance accounted for by the linear combination of the

predictors” (Green & Salkind, 2011, p. 289). Examination of the zero-order

correlations provided insight to the relationship between each predictor and the

dependent variable. An rp value was reported through an examination of partial

correlations to identify the effects of each predictor when controlling for all other

predictors, opening the possibilitiy for mediator or moderator variables to be

revealed. Significance was set at the .05 level. To control for Type I error, a

Bonferroni approach was used.

To investigate Research Question 2, student data were entered into the

Statistical Package for the Social Sciences, Version 18 (SPSS 18) and the software

was used to conduct step-wise multiple regression analyses. For each dependent

variable—the five third grade District Benchmark Tests (DBT), third grade California

Standards Test/California Modified Assessment (CST/CMA), and the three

CST/CMA subtests—two multiple regression analyses were conducted. The first

66

included kindergarten reading readiness scores (Letter Names, Letter Sounds, Oral

Blending, Oral Segmenting, CVC Word reading and High Frequency Words). If any

two predictors were highly correlated (r > .70), one of them was omitted from the

model. The second set included multitiered intervention program participation

examined as a separate variable. The analyses determined how much each predictor

contributed to third grade reading achievement data. For each analysis, a multiple

correlation (R) ranging from 0 to 1 was computed, with R > .50 considered a strong

correlation. Additionally, a squared multiple correlation (R2) was also computed to

indicate the “percent of criterion variance accounted for by the linear combination of

the predictors” (p. 289, Green & Salkind, 2011). Examination of the zero-order

correlations provided insight to the relationship between each predictor and the

dependent variable. An rp value was reported through an examination of partial

correlations to identify the effects of each predictor when controlling for all other

predictors. Possible mediator or moderator variables may be revealed. Significance

was be set at the .05 level. To control for Type I error, a Bonferroni approach was

used.

While Research Questions 1 and 2 examined the effects of participation in a

multitiered intervention model, Research Question 3 investigated the role of

demographic factors for students who participated in one or more of the programs

that comprised the multitiered reading intervention system in kindergarten through

second grade at the school. In kindergarten, students participated in many fluid

interventions based on their initial scores on six reading readiness assessments. As

students made gains with each readiness factor, they were dismissed from that

67

intervention. Because the kindergarten interventions were short-term and item

specific, they were not included in the analyses conducted for Research Question 3.

In first and second grades, however, students participated in more formal

multitiered SIPPS interventions based on their scores on the Basic Phonic Skills Test

(BPST), given before and after participation in the intervention program. For the

Reads Naturally intervention, second grade students’ reading rate (correct words read

per minute) was assessed using the fluency subtest of the District Benchmark Test

before and after participation in the program. For teacher-created interventions, first

grade students were assessed with the BPST and second grade students were assessed

with the comprehension subtest of the District Benchmark Test.

To examine the effectiveness of each intervention on specific student groups,

student data were entered into SPSS 18 and the software was used to conduct separate

multiple regression analyses for each intervention program: SIPPS, Reads Naturally,

and teacher-created intervention. For each regression, the post-test served as the

dependent variable. An examination of the posttest scores while controlling for the

kindergarten demographic factors (ethnicity, SES, parent education level, gender, age

upon kindergarten entry, and English language level in kindergarten) provided

information about the effectiveness of the intervention. Therefore, the demographic

factors were entered as one set. For each factor, a multiple correlation (R) ranging

from 0 to 1 was computed, with R > .50 considered a strong correlation.

Additionally, a squared multiple correlation (R2) was also computed. Zero order and

partial correlations were considered and an rp value was reported to examine the

68

effect of possible mediator or moderator variables. Significance was set at the .05

level. To control for Type I error, a Bonferroni approach was used.

Summary

Chapter III included a restatement of the problem, research questions and

hypotheses to be tested in this study. This chapter also contained a description of the

sample population, data collection procedures, and data analyses to be performed.

Chapter IV will present the results of the data analyses.

69

CHAPTER IV

RESULTS

This study examined factors that influence third grade reading achievement,

specifically the predictability of demographic factors and reading readiness at the

beginning of kindergarten on students’ subsequent reading performance at the end of

third grade when a multitiered response to intervention model of instruction was

utilized.

This study was conducted at one elementary school located in the California

Central Valley. A total of 117 cases were included in the study, 55 of which

participated in a multitiered intervention model and 62 who did not. Data were

collected about student demographic factors and academic performance as part of

regular practices of the school, through the course of several academic calendar years.

The data were then coded and entered into the Statistical Package for the Social

Sciences, Version 18 (SPSS 18). All statistical analyses were conducted using SPSS

18 and the results are discussed here.

Research Question 1




70

Hypothesis 1.1

Participation in a multitiered reading intervention model explains more about

third grade reading achievement on the California Standards Test or California

Modified Assessment than ethnicity, socioeconomic status, parent education level,

gender, age upon kindergarten entry, and English language level in kindergarten.

Descriptive statistics were calculated using SPSS 18 to help provide context

for interpreting the results of Research Question 1. The mean score and standard

deviation for the CST/CMA and each subtest are listed by demographic subgroup in

Table 4.

A multiple regression analysis using ordered sets of predictors was conducted

to explain how much participation in a multitiered intervention program contributed

to third grade reading achievement on the California Standards Test (CST) or

California Modified Assessment (CMA) after controlling for demographic factors.

The first set of predictors included the six demographic factors of ethnicity,

socioeconomic status, parent education level, gender, age upon kindergarten entry,

and English language level in kindergarten. The second set was participation in a

multitiered intervention model as a single variable. Correlation coefficients were

compared across the variables to ensure that no two variables were so strongly related

that they might overinflate the relationships in the model. A Bonferroni approach

was applied to control for Type I error (.05/15 = .003). Although three demographic

variable pairs had significant moderate to strong positive relationships (SES and

CELDT level, r = .37, p <.001; SES and Parent Ed level, r = .47, p <.001; Parent Ed

level and CELDT level, r = .54, p <.001) and one pair had a significant moderate

71

Table 4 Descriptive Statistics by Demographics for CST/CMA and Subtests

Demographic subgroup (N)

CST/CMA WAV RC LRA

M SD M SD M SD M SD

White (55) 360.29 61.65 80.18 16.55 71.47 18.65 70.95 24.35 Hispanic (47) 337.32 65.14 71.91 22.06 65.30 20.74 64.06 25.00 Other (15) 350.85 65.40 75.00 25.78 73.27 23.61 76.00 24.87 Beginning English level in K (9) 305.44 66.69 58.89 25.35 60.67 24.91 51.67 28.33 Early intermediate English level in K (6) 300.83 31.47 55.83 18.82 56.50 15.19 50.17 7.91 Intermediate English level in K (6) 342.17 78.89 70.83 24.78 68.83 23.32 62.67 27.30 Initially fluent English proficient in K (7) 362.00 44.50 85.00 10.80 71.29 19.81 80.71 14.09 English only in K (89) 358.52 65.06 78.99 18.76 70.81 19.81 71.30 24.67 Economically disadvantaged (43) 319.02 62.81 65.47 24.80 60.28 22.89 58.19 26.17 Not economically disadvantaged (74) 369.34 59.86 85.43 14.12 74.42 16.63 75.01 21.98 Parent not a high school graduate (7) 299.71 64.96 56.43 25.12 54.86 25.28 46.57 23.83 Parent high school graduate (22) 337.59 62.47 71.59 24.07 64.27 17.94 63.23 28.73 Parent some college (48) 341.77 58.07 75.21 19.38 66.27 21.08 68.83 24.05 Parent college graduate (33) 384.76 65.21 85.15 12.28 80.39 14.39 78.18 19.71 Parent graduate degree (4) 388.00 48.06 90.00 9.13 76.75 16.17 81.50 16.01 Male (71) 340.73 63.47 73.24 21.42 65.96 20.14 65.59 24.24 Female (46) 366.46 65.94 80.76 17.98 74.26 19.60 73.83 25.25 Younger than 59.9 months at K entry (34) 354.94 70.99 75.74 22.06 69.94 21.46 68.50 26.71 60–62.9 months at K entry (31) 338.77 66.84 75.65 22.54 63.65 21.45 67.61 24.24 63–65.9 months at K entry (24) 340.33 62.14 71.25 22.23 68.96 20.46 68.13 25.31 66–69.9 months at K entry (22) 370.05 45.22 82.05 11.30 75.18 14.56 73.55 22.19 70 months or older at K entry (6) 361.67 97.47 80.00 17.03 73.17 23.16 62.50 30.62 Participated in multitiered intervention model 369.64 70.85 81.55 20.00 72.67 20.22 73.45 24.90 Did not participate in multitiered intervention model 334.18 55.58 71.45 19.70 66.16 19.96 64.73 24.30

72

negative relationships (Ethnicity and CELDT level, r = -.30, p = .001), no pair-wise

correlation had an r > .70, so there was no need to omit any variables. The other

relationships were not significant. Table 5 summarizes the pair-wise correlations.

Table 5 Pair-Wise Correlations for Kindergarten Demographic Factors Included in Multiple Regression Analyses

1 2 3 4 5 6

1. Ethnicity — 2. English level in K .30* — 3. Socioeconomic status −.20 .37* — 4. Parent education level −.15 .54* .47 — 5. Gender −.007 −.09 .03* .03 — 6. Age at K entry .14 .04 .03 .07 −.19 —

7.

*p < .001

The linear combination of the six demographic variables and participation in a

multitiered intervention model were significantly related to third grade performance

on the CST/CMA [R = .51, R2 = .26, R2adj = .21, F(7, 109) = 5.46, p < .001],

accounting for about 26% of the variance in CST/CMA scores. The demographic

factors significantly explained performance on CST over and above participation in a

multitiered intervention model [R2change = .23, F(6, 110) = 5.59, p < .001].

Participation in a multitiered intervention model did not significantly explain

performance on CST/CMA over and above the demographic factors [R2change = .03,

F(1, 109) = 3.84, p = .06]. It appears that participation in a multitiered intervention

model did not offer much additional predictive information about third grade

CST/CMA performance beyond what could be ascertained from the kindergarten

73

demographic factors. While the six demographic factors had significant predictive

power as a set, examination of the zero-order and partial correlations revealed that

none of them was an individually significant predictor of third grade CST/CMA

performance. Table 6 summarizes the relative strength of each predictor.

Table 6 Bivariate and Partial Correlations of the Demographic Predictors With CST/CMA

Predictor

Controls

None All other predictors

Ethnicity .08 .03 English level in K .27 .05 Socioeconomic status .37 .22 Parent education level .38 .19 Gender .19 .21 Age at K entry .07 .05 Participation in a multitiered intervention model .27 .19

Note. None of the correlations were significant.

Because the California Standards Test and California Modified Test are made

up of subtests that assess different areas of English language arts, achievement on the

three subtests most closely related to reading proficiency was examined as well. A

multiple regression analysis using ordered sets of predictors was conducted to explain

how much participation in a multitiered intervention program contributed to third

grade reading achievement on the first CST/CMA subtest: Word Analysis and

Vocabulary (WAV), after controlling for demographic factors. As in the previous

analysis, the first set of predictors included the six demographic factors while the

74

second set was participation in a multitiered intervention model as a single variable.

A Bonferroni approach was applied to control for Type I error (.05/15 = .003).



on the WAV subtest [R = .53, R2 = .28, R2adj = .24, F(7, 109) = 6.19, p < .001],

accounting for approximately 28% of the variance on the WAV subtest. The

demographic factors significantly explained performance on WAV over and above

participation in a multitiered intervention model [R2change = .27, F(6, 110) = 6.74, p <

.001]. Participation in a multitiered intervention model did not significantly explain

performance on WAV over and above the demographic factors [R2change = .01, F(1,

109) = 2.00, p = .16]. It appears that participation in a multitiered intervention model

does not offer much additional predictive information about third grade WAV subtest

performance beyond what could be ascertained from the kindergarten demographic

factors. While the six demographic factors had significant predictive power as a set,

examination of the zero-order and partial correlations revealed that none of them was

an individually significant predictor of third grade Word Analysis and Vocabulary

subtest performance. Table 7 summarizes the relative strength of each predictor.



to third grade reading achievement on the second CST/CMA subtest: Reading

Comprehension (RC), after controlling for demographic factors. As in the previous

analysis, the first set of predictors included the six demographic factors, while the

75



Table 7 Bivariate and Partial Correlations of the Demographic Predictors With Word Analysis and Vocabulary Subtest

Predictor

Controls


Ethnicity −.14 −.04 English level in K .33 .14 Socioeconomic status .40 .24 Parent education level .40 .17 Gender .18 .22 Age at K entry .08 .08 Participation in a multitiered intervention model .25 .13




on the RC subtest [R = .47, R2 = .22, R2adj = .17, F(7, 109) = 4.39, p < .001],

accounting for approximately 22% of the variance on the RC subtest. The

demographic factors significantly explained performance on the RC subtest over and

above participation in a multitiered intervention model [R2change = .22, F(6, 110) =

5.05, p < .001]. Participation in a multitiered intervention model did not significantly

explain performance on the RC subtest over and above the demographic factors

[R2change = .004, F(1, 109) = .55, p = .46]. It appears that participation in a multitiered

intervention model did not offer much additional predictive information about third

grade RC subtest performance beyond what could be ascertained from the

76

kindergarten demographic factors. As in the previous analyses, the six demographic

factors had significant predictive power as a set but examination of the zero-order and

partial correlations revealed that none of them was an individually significant

predictor of third grade Reading Comprehension subtest performance. Table 8

summarizes the relative strength of each predictor.

Table 8 Bivariate and Partial Correlations of the Demographic Predictors With Reading Comprehension Subtest

Predictor

Controls


Ethnicity −.04 .03 English level in K .18 −.03 Socioeconomic status .34 .20 Parent education level .36 .22 Gender .20 .22 Age at K entry .10 .11 Participation in a multitiered intervention model .16 .07




to third grade reading achievement on the third CST/CMA subtest: Literary Response

and Analysis (LRA), after controlling for demographic factors. As in the previous

analysis, the first set of predictors included the six demographic factors, while the



77



on the LRA subtest [R = .45, R2 = .21, R2adj = .16, F(7, 109) = 4.03, p = .001],

accounting for approximately 21% of the variance on the LRA subtest. The

demographic factors significantly explained performance on the LRA subtest over

and above participation in a multitiered intervention model [R2change = .20, F(6, 110) =


explain performance on the LRA subtest over and above the demographic factors

[R2change = .006, F(1, 109) = .35, p = .35]. It appears that participation in a multitiered


grade LRA subtest performance beyond what could be ascertained from the

kindergarten demographic factors. As in the previous analyses, the six demographic

factors had significant predictive power as a set but examination of the zero-order and


predictor of third grade Literary Response and Analysis subtest performance. Table 9


Hypothesis 1.2


third grade reading achievement on the District Benchmark Tests than ethnicity,

socioeconomic status, parent education level, gender, age upon kindergarten entry, or


78

Table 9 Bivariate and Partial Correlations of the Demographic Predictors With Literary Response and Analysis Subtest

Predictor

Controls


Ethnicity −.01 .11 English level in K .26 .08 Socioeconomic status .33 .18 Parent education level .36 .19 Gender .16 .17 Age at K entry .03 .01 Participation in a multitiered intervention model .18 .09


Descriptive statistics were calculated using SPSS 18 to help provide context

for interpreting the results of Research Question 1. The mean score and standard

deviation for each District Benchmark Test subtest is listed by demographic subgroup

in Table 10.




California Modified Assessment (CMA) after controlling for demographic factors.

The first set of predictors included the six demographic factors of ethnicity,

socioeconomic status, parent education level, gender, age upon kindergarten entry,

and English language level in kindergarten. The second set was participation in a


compared across the demographic variables to ensure that no two variables were so

79

Table 10 Descriptive Statistics by Demographics for District Benchmark Test Subtests

BPST SC OTA OTF OTC

Demographic subgroup (N) M SD M M M SD M SD M SD

White (55) 89.69 1.57 8.51 1.60 98.55 3.11 113.11 23.37 4.85 .36 Hispanic (47) 87.66 5.72 7.57 2.32 98.04 3.60 101.43 30.44 4.15 1.47 Other (15) 88.53 5.24 8.73 1.58 98.93 .88 106.20 28.56 4.80 .56 Beginning English level in K (9) 84.22 7.23 5.89 2.42 96.33 6.95 75.89 36.44 3.56 1.51 Early intermediate English level in K (6) 87.00 6.54 7.17 2.56 98.00 .89 102.17 37.65 3.33 1.97 Intermediate English level in K (6) 88.50 2.59 7.00 2.61 98.33 2.25 101.17 26.02 4.17 2.04 Initially fluent English proficient in K (7) 88.86 1.77 8.43 1.40 99.14 1.07 100.57 4.61 4.86 .38 English only in K (89) 89.30 3.71 8.52 1.69 98.57 2.76 112.07 25.00 4.75 .70 Economically disadvantaged (43) 86.51 6.34 7.26 2.38 97.28 4.84 92.40 29.69 4.07 1.52 Not economically disadvantaged (74) 90.01 1.21 8.69 1.45 99.04 .99 116.32 21.96 4.85 .40 Parent not a high school graduate (7) 84.29 11.31 5.86 2.73 98.00 2.00 86.00 31.97 3.43 1.90 Parent high school graduate (22) 87.00 5.61 7.91 2.05 98.09 2.39 100.45 33.04 4.45 1.22 Parent some college (48) 89.40 1.67 8.02 1.78 98.50 3.35 107.87 22.04 4.73 .79 Parent college graduate (33) 90.27 .91 9.15 1.15 99.00 .94 118.85 24.14 4.79 .49 Parent graduate degree (4) 89.25 2.36 9.00 1.41 99.25 1.5 103.50 14.39 4.75 .50 Male (71) 88.79 3.11 8.08 1.80 98.34 3.05 107.39 29.60 4.58 .87 Female (46) 88.63 5.68 8.28 2.21 98.48 3.30 107.74 24.24 4.54 1.26 Younger than 59.9 months at K entry (34) 88.03 6.25 8.32 2.42 98.15 4.14 105.06 27.97 4.56 1.24 60–62.9 months at K entry (31) 88.65 3.90 7.61 1.87 98.06 3.94 106.48 24.23 4.48 1.03 63–65.9 months at K entry (24) 89.04 3.46 8.21 1.89 98.46 1.67 103.04 28.84 4.38 1.21 66–69.9 months at K entry (22) 89.50 1.57 8.55 1.30 99.00 1.11 116.95 26.98 4.86 .35 70 months or older at K entry (6) 89.00 2.19 8.50 1.87 99.00 .89 110.33 37.98 4.67 .82 Participated in multitiered intervention model 89.27 3.00 8.60 1.77 98.22 3.39 108.42 28.58 4.73 .80 Did not participate in multitiered intervention model 88.24 5.14 7.77 2.06 98.55 2.91 106.74 26.73 4.42 1.20

80

strongly related that they might overinflate the relationships in the model. A

Bonferroni approach was applied to control for Type I error (.05/15 = .003). Although

three demographic variable pairs had significant moderate to strong positive

relationships (SES and CELDT level, r = .37, p <.001; SES and Parent Ed level, r =

.47, p <.001; Parent Ed level and CELDT level, r = .54, p <.001) and one pair had a

significant moderate negative relationships (Ethnicity and CELDT level, r = -.30, p =

.001), no pair-wise correlation had an r > .70, so there was no need to omit any

variables. The other relationships were not significant. See Table 4 for a summary of

the pair-wise correlations.



on the first subtest of the DBT: the Basic Phonics Skills Test (BPST) [R = .48, R2 =

.23, R2adj = .19, F(7, 109) = 4.68, p < .001], accounting for about 23% of the variance

in BPST performance. The demographic factors significantly explained performance

on the BPST over and above participation in a multitiered intervention model [R2change

= .23, F(6, 110) = 5.51, p < .001]. Participation in a multitiered intervention model

did not significantly explain performance on the BPST over and above the

demographic factors [R2change <.001, F(1, 109) = .01, p = .91]. It appears that

participation in a multitiered intervention model did not offer any additional

predictive information about third grade BPST performance beyond what could be

ascertained from the kindergarten demographic factors. While the six demographic

factors had significant predictive power as a set, examination of the zero-order and


81

predictor of third grade BPST performance. Table 11 summarizes the relative

strength of each predictor.

Table 11 Bivariate and Partial Correlations of the Demographic Predictors With Basic Phonics Skills Test Subtest

Predictor

Controls


Ethnicity −.16 −.08 English level in K .31 .08 Socioeconomic status .40 .24 Parent education level .39 .19 Gender −.02 −.01 Age at K entry .11 .11 Participation in a multitiered intervention model .12 −.01




to third grade reading achievement on the second DBT: Silent Comprehension (SC),

after controlling for demographic factors. As in the previous analysis, the first set of

predictors included the six demographic factors while the second set was participation

in a multitiered intervention model as a single variable. A Bonferroni approach was

applied to control for Type I error (.05/15 = .003).



on the SC subtest [R = .50, R2 = .25, R2adj = .20, F(7, 109) = 5.24, p < .001],

accounting for approximately 25% of the variance in SC performance. The

82

demographic factors significantly explained performance on the SC subtest over and



explain performance on the SC subtest over and above the demographic factors

[R2change < .01, F(1, 109) = .96, p = .007]. It appears that participation in a multitiered


grade SC subtest performance beyond what could be ascertained from the

kindergarten demographic factors. While the six demographic factors had significant

predictive power as a set, examination of the zero-order and partial correlations

revealed that none of them was an individually significant predictor of third grade

Silent Comprehension subtest performance. Table 12 summarizes the relative strength

of each predictor.

Table 12 Bivariate and Partial Correlations of the Demographic Predictors With Silent Comprehension Subtest

Predictor

Controls


Ethnicity −.07 .07 English level in K .39 .19 Socioeconomic status .35 .16 Parent education level .42 .20 Gender .05 .07 Age at K entry .07 .03 Participation in a multitiered intervention model .21 .09


83



to third grade reading achievement on the third DBT subtest: Oral Text Accuracy

(OTA), after controlling for demographic factors. As in the previous analysis, the

first set of predictors included the six demographic factors while the second set was

participation in a multitiered intervention model as a single variable. A Bonferroni

approach was applied to control for Type I error (.05/15 = .003).


multitiered intervention model were not significantly related to third grade

performance on the OTA subtest [R = .36, R2 = .13, R2adj = .07, F(7, 109) = 2.26, p =

.04], accounting for approximately 13% of the variance in OTA performance. The

demographic factors alone also did not significantly explain performance on the OTA

subtest over and above participation in a multitiered intervention model [R2change =

.11, F(6, 110) = 2.18, p = .51]. Participation in a multitiered intervention model also

did not significantly explain performance on the OTA subtest over and above the

demographic factors [R2change = .02, F(1, 109) = 2.51, p = .12]. It appears that neither

participation in a multitiered intervention model, nor the kindergarten demographic

factors offer much predictive information about third grade OTA subtest

performance. Examination of the zero-order and partial correlations revealed very

weak correlations (r = .01 - .26) between individual predictors and third grade Oral

Text Accuracy subtest performance. Table 13 summarizes the relative strength of

each predictor.

84

Table 13 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Accuracy Subtest

Predictor

Controls


Ethnicity −.01 .05 English level in K .17 .05 Socioeconomic status .27 .19 Parent education level .26 .12 Gender .02 .04 Age at K entry .11 .12 Participation in a multitiered intervention model −.05 −.15




to third grade reading achievement on the fourth DBT subtest: Oral Text Fluency

(OTF), after controlling for demographic factors. As in the previous analysis, the first

set of predictors included the six demographic factors while the second set was

participation in a multitiered intervention model as a single variable. A Bonferroni

approach was applied to control for Type I error (.05/15 = .003).



on the OTF subtest [R = .49, R2 = .24, R2adj = .19, F(7, 109) = 4.94, p < .001],

accounting for approximately 24% of the variance in OTF performance. The

demographic factors significantly explained performance on the OTF subtest over and


85


explain performance on the OTF subtest over and above the demographic factors

[R2change = .01, F(1, 109) = 1.87, p = .18]. It appears that participation in a multitiered


grade OTF subtest performance beyond what could be ascertained from the

kindergarten demographic factors. Examination of the zero-order and partial

correlations revealed that socioeconomic status was significantly positively related to

third grade Oral Text Fluency subtest performance (r = .42, p < .001, rp = .31), after

controlling for the effects of all other variables. Table 14 summarizes the relative


Table 14 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Fluency Subtest

Predictor

Controls


Ethnicity −.15 −.05 English level in K .34 .19 Socioeconomic status .42* .31* Parent education level .30 .03 Gender .01 .04 Age at K entry .11 .14 Participation in a multitiered intervention model .03 −.13

*p < .001.



to third grade reading achievement on the fifth DBT subtest: Oral Text

86

Comprehension (OTC), after controlling for demographic factors. As in the previous

analysis, the first set of predictors included the six demographic factors while the





on the OTC subtest [R = .48, R2 = .23, R2adj = .18, F(7, 109) = 4.58, p < .001],

accounting for approximately 23% of the variance in OTC performance. The

demographic factors alone significantly explained performance on the OTC subtest

over and above participation in a multitiered intervention model [R2change = .23, F(6,

110) = 5.39, p < .001]. Participation in a multitiered intervention model did not

significantly explain performance on the OTC subtest over and above the

demographic factors [R2change < .001, F(1, 109) = .02, p = .90]. It appears that

participation in a multitiered intervention model did not offer much additional

predictive information about third grade OTC subtest performance beyond what could

be ascertained from the kindergarten demographic factors. While the six

demographic factors had significant predictive power as a set, examination of the

zero-order and partial correlations revealed that none of them was an individually

significant predictor of third grade Oral Text Comprehension subtest performance.

Table 15 summarizes the relative strength of each predictor.

87

Table 15 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Comprehension Subtest

Predictor

Controls


Ethnicity −.15 .02 English level in K .40 .23 Socioeconomic status .37 .20 Parent education level .35 .10 Gender −.02 .01 Age at K entry .07 .05 Participation in a multitiered intervention model .15 .01


Research Question 2




Hypothesis 2.1


third grade reading achievement on the California Standards Test or California

Modified Assessment than kindergarten scores on letter names, letter sounds, oral

blending, oral segmenting, and sight word tasks.




California Modified Assessment (CMA), after controlling for the kindergarten

88

reading readiness factors of Letter Names, Letter Sounds, Oral Blending, Oral

Segmenting, Consonant-Vowel-Consonant (CVC) Word Reading, and High

Frequency Word (HFW) Reading. The first set of predictors included the six

kindergarten reading readiness factors while the second was participation in a


compared across the first set of variables to ensure that no two variables were so

strongly related that they might overinflate the relationships in the model. A

Bonferroni approach was applied to control for Type I error (.05/15 = .003). All of

the kindergarten reading readiness factors had statistically significant positive pair-

wise correlations, with r values in the moderate to strong ranges (.38 to .65, all p <

.001). However, the pair-wise correlation for Letter Names and Letter Sounds was r

= .78 (p < .001), too strong of a relationship to keep both variables in the equation.

Based on evidence from the review of the related literature, the Letter Names variable

was omitted. As shown in Table 16, the model was rerun with the remaining five

kindergarten reading readiness variables, resulting in no pair-wise correlations greater

than .70. A new Bonferroni adjustment was calculated to control for Type I error

(.05/10 = .005).

The linear combination of the five kindergarten reading readiness variables

and participation in a multitiered intervention model were significantly related to

third grade performance on the CST/CMA [R = .70, R2 = .49, R2adj = .46, F(6, 110) =

17.42, p < .001], accounting for approximately 50% of the variance in CST/CMA

performance. The reading readiness factors significantly explained performance on

89

Table 16

Pair-Wise Correlations for Kindergarten Reading Readiness Factors Included in

Multiple Regression Analyses

1 2 3 4 5

1. Letter sounds — 2. Oral blending .62* — 3. Oral segmenting .52* .49* — 4. CVC word reading .53* .64* .60* — 5. High-frequency words .58* .47* .51* .65* —

6.

Note. While Letter Names was included in the original correlational analysis, it was omitted from the regression model due to a very strong correlation with Letter Sounds (r = .78, p <.001). *p < .001

CST/CMA over and above participation in a multitiered intervention model [R2change =

.41, F(5, 111) = 15.21, p < .001]. Participation in a multitiered intervention model

also significantly explained performance on CST/CMA over and above the reading

readiness factors [R2change = .08, F(1, 110) = 17.31, p < .001]. It appears that

participation in a multitiered intervention model offers a small amount of additional

predictive information about third grade CST/CMA performance beyond what could

be ascertained from the kindergarten reading readiness factors. Examination of the

zero-order and partial correlations revealed two kindergarten reading readiness

variables were significantly positively related to third grade CST/CMA performance:

Letter Sounds (r = .55, p < .001 and rp = .33) and High Frequency Word Reading (r =

.55, p = .001 and rp = .31), after controlling for the effects of all other variables.

Participation in a multitiered intervention model had a significant moderate positive

correlation with CST/CMA performance (r = .27, p < .001 and rp = .37), after

90

controlling for the effects of all other variables. Table 17 summarizes the relative


Table 17 Bivariate and Partial Correlations of the Reading Readiness Predictors With the California Standards Test/California Modified Test

Predictor

Controls


Letter sounds .55* .33* Oral blending .39 −.01 Oral segmenting .48 −.02 CVC word reading .42 .06 High-frequency words .55* .31* Participation in a multitiered intervention model .27* .37*

*p < .001.

Because the California Standards Test and California Modified Test are made

up of subtests that assess different areas of English language arts, the three subtests

most closely related to reading proficiency were examined as well. A multiple

regression analysis using ordered sets of predictors was conducted to explain how

much participation in a multitiered intervention program contributed to third grade

reading achievement on the first CST/CMA subtest: Word Analysis and Vocabulary

(WAV), after controlling for the kindergarten reading readiness factors. As in the

previous analysis, the first set of predictors included the five kindergarten reading

readiness factors while the second was participation in a multitiered intervention

model as a single variable. A Bonferroni approach was applied to control for Type I

error (.05/10 = .005).

91



third grade performance on the WAV subtest [R = .67, R2 = .45, R2adj = .42, F(6, 110)

= 15.14, p < .001], accounting for approximately 45% of the variance in WAV


the Word Analysis and Vocabulary subtest over and above participation in a


Participation in a multitiered intervention model also significantly explained

performance on the WAV subtest over and above the reading readiness factors

[R2change = .07, F(1, 110) = 13.22, p < .001]. It appears that participation in a

multitiered intervention model offers some additional predictive information about

third grade WAV subtest performance beyond what could be ascertained from the

kindergarten reading readiness variables. Examination of the zero-order and partial

correlations revealed that the kindergarten reading readiness variable High Frequency

Word Reading had a significant strong positive correlation to third grade WAV

subtest performance (r = .59, p < .001 and rp = .41), after controlling for the effects

of all other variables. Participation in a multitiered intervention model had a

significant moderate positive correlation with Word Analysis and Vocabulary subtest

performance (r = .25, p < .001 and rp = .33), after controlling for the effects of all

other variables. Table 18 summarizes the relative strength of each predictor.



to third grade reading achievement on the second CST/CMA subtest: Reading

92

Comprehension (RC), after controlling for the kindergarten reading readiness factors.

As in the previous analysis, the first set of predictors included the five kindergarten

reading readiness factors while the second was participation in a multitiered

intervention model as a single variable. A Bonferroni approach was applied to

control for Type I error (.05/10 = .005).

Table 18 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Word Analysis and Vocabulary Subtest

Predictor

Controls


Letter sounds .45 .21 Oral blending .31 −.07 Oral segmenting .44 −.01 CVC word reading .40 .05 High-frequency words .59* .41* Participation in a multitiered intervention model .25* .33*

*p < .001



third grade performance on the RC subtest [R = .62, R2 = .38, R2adj = .35, F(6, 110) =

11.37, p < .001], accounting for approximately 38% of the variance on RC

performance. The kindergarten reading readiness significantly explained

performance on the RC subtest over and above participation in a multitiered

intervention model [R2change = .35, F(5, 111) = 12.00, p < .001]. Participation in a

multitiered intervention model did not significantly explain performance on the

93

Reading Comprehension subtest over and above the reading readiness factors [R2change

= .03, F(1, 110) = 5.70, p = .02]. It appears that participation in a multitiered


grade Reading Comprehension subtest performance beyond what could be

ascertained from the kindergarten reading readiness factors. Examination of the zero-

order and partial correlations revealed that only the kindergarten reading readiness

variable High Frequency Word Reading had a significant positive correlation to third

grade Reading Comprehension subtest performance (r = .54, p = .002 and rp = .29),

after controlling for the effects of all other variables. Table 19 summarizes the

relative strength of each predictor.

Table 19 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Reading Comprehension Subtest

Predictor

Controls


Letter sounds .48 .21 Oral blending .35 −.03 Oral segmenting .44 .01 CVC word reading .44 .01 High-frequency words .54* .29* Participation in a multitiered intervention model .16 .22

*p < .01



to third grade reading achievement on the third CST/CMA subtest: Literary Response

94

and Analysis (LRA), after controlling for the kindergarten reading readiness factors.

As in the previous analysis, the first set of predictors included the five kindergarten

reading readiness factors while the second was participation in a multitiered

intervention model as a single variable. A Bonferroni approach was applied to control

for Type I error (.05/10 = .005).



third grade performance on the LRA subtest [R = .61, R2 = .37, R2adj = .34, F(6, 110)

= 10.96, p < .001], accounting for approximately 37% of the variance in LRA

performance. The kindergarten reading readiness variables significantly explained

performance on the Literary Response and Analysis subtest over and above

participation in a multitiered intervention model [R2change = .36, F(5, 111) = 12.62, p

< .001]. Participation in a multitiered intervention model did not significantly explain

performance on the Literary Response and Analysis subtest over and above the

reading readiness factors [R2change = .01, F(1, 110) = 2.05, p = .16]. It appears that


predictive information about third grade LRA subtest performance beyond what could

be ascertained from the kindergarten reading readiness variables. Examination of the

zero-order and partial correlations revealed that the kindergarten reading readiness

variable Letter Sounds had a significant moderate positive correlation to third grade

Literary Response and Analysis subtest performance (r = .49, p = .003 and rp = .28),

after controlling for the effects of all other variables. Table 20 summarizes the


95

Table 20 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Literary Response and Analysis Subtest

Predictor

Controls


Letter sounds .49* .28* Oral blending .30 −.12 Oral segmenting .53 .22 CVC word reading .40 .07 High-frequency words .43 .10 Participation in a multitiered intervention model .18 .14

*p < .01

Hypothesis 2.2


third grade reading achievement on District Benchmark Tests than kindergarten

scores on letter names, letter sounds, oral blending, oral segmenting, and sight word

tasks.



to third grade reading achievement on each subtest of the District Benchmark Test

(DBT), after controlling for the kindergarten reading readiness factors. The first set

of predictors included the six kindergarten reading readiness factos, while the second

was participation in a multitiered intervention model as a single variable. Correlation

coefficients were compared across the first set of variables to ensure that no two

variables were so strongly related that they might over-inflate the relationships in the

model. A Bonferroni approach was applied to control for Type I error (.05/15 =

96

.003). All of the kindergarten reading readiness factors had statistically significant

positive pair-wise correlations, with r values in the moderate to strong ranges (.38 to

.65, all p < .001). However, the pair-wise correlation for Letter Names and Letter

Sounds was r = .78 (p < .001), too strong of a relationship to keep both variables in

the equation. Based on evidence from the review of the related literature, the Letter

Names variable was omitted. The model was rerun with the remaining five

kindergarten reading readiness variables, resulting in no pair-wise correlations greater

than r = .70. A new Bonferroni adjustment was calculated to control for Type I error

(.05/10 = .005). See Table 16 for a summary of pair-wise comparisons.



third grade performance on the first subtest of the DBT: the Basic Phonics Skills Test

(BPST) [R = .68, R2 = .45, R2adj = .43, F(6, 110) = 15.39, p < .001], accounting for

45% of the variance in BPST performance. The kindergarten reading readiness

factors significantly explained performance on the BPST over and above participation

in a multitiered intervention model [R2change = .45, F(5, 111) = 17.97, p < .001].


performance on the BPST over and above the reading readiness factors [R2change = .01,

F(1, 110) = .01, p = .18]. It appears that participation in a multitiered intervention

model did not offer much additional predictive information about third grade BPST

performance beyond what could be ascertained from the kindergarten reading

readiness variables. Examination of the zero-order and partial correlations revealed

that the kindergarten reading readiness variable High Frequency Word Reading had a

97

significant strong positive correlation to third grade BPST subtest performance (r =

.60, p < .001 and rp = .38), after controlling for the effects of all other variables.


Table 21 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Basic Phonics Skills Test

Predictor

Controls


Letter sounds .34 −.05 Oral blending .28 −.15 Oral segmenting .50 .14 CVC word reading .54 .25 High-frequency words .60* .38* Participation in a multitiered intervention model .12 .13

*p < .001



to third grade reading achievement on the second DBT: Silent Comprehension (SC),

after controlling for the kindergarten reading readiness factors. As in the previous

analysis, the first set of predictors included the five kindergarten reading readiness

factors while the second was participation in a multitiered intervention model as a

single variable. A Bonferroni approach was applied to control for Type I error

(.05/10 = .005).



98

third grade performance on the SC subtest [R = .66, R2 = .44, R2adj = .40, F(6, 110) =

14.13, p < .001], accounting for approximately 44% of the variance in SC

performance. The kindergarten reading readiness factors significantly explained

performance on the SC subtest over and above participation in a multitiered

intervention model [R2change = .41, F(5, 111) = 15.33, p < .001]. Participation in a

multitiered intervention model did not significantly explain performance on the SC

subtest over and above the reading readiness factors [R2change = .03, F(1, 110) = 5.20,

p = .03]. It appears that participation in a multitiered intervention model did not offer

much additional predictive information about third grade SC subtest performance

beyond what could be ascertained from the kindergarten reading readiness factors.

While the five reading readiness factors were significant as a set, examination of the

zero-order correlations revealed that none of them was an individually significant

predictor of third grade SC subtest performance. Examination of the zero-order and

partial correlations revealed that the kindergarten reading readiness variable High

Frequency Word Reading had a significant moderately strong positive correlation to

third grade Silent Comprehension subtest performance (r = .56, p < .001), and rp =

.33, after controlling for the effects of all other variables. Table 22 summarizes the




to third grade reading achievement on the third DBT subtest: Oral Text Accuracy

(OTA), after controlling for the kindergarten reading readiness factors. As in the


99



error (.05/10 = .005).

Table 22 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Silent Comprehension Subtest

Predictor

Controls


Letter sounds .47 .19 Oral blending .32 −.09 Oral segmenting .53 .15 CVC word reading .43 .04 High-frequency words .56* .33* Participation in a multitiered intervention model .21 .21

*p < .001

The linear combination of the five reading readiness variables and

participation in a multitiered intervention model was significantly related to third

grade performance on the OTA subtest [R = .60, R2 = .36, R2adj = .32, F(6, 110) =

10.16, p < .001], accounting for approximately 36% of the variance in OTA

performance. The reading readiness variables significantly explained performance on

the OTA subtest over and above participation in a multitiered intervention model

[R2change = .36, F(5, 111) = 12.24, p < .001]. Participation in a multitiered

intervention model did not significantly explain performance on the OTA subtest over

and above the reading readiness variables [R2change = .001, F(1, 110) = .19, p = .66].

It appears that participation in a multitiered intervention model did not offer much

100

additional predictive information about third grade OTA subtest performance beyond

what could be ascertained from the kindergarten reading readiness factors.

Examination of the zero-order and partial correlations revealed that the kindergarten

reading readiness variable High Frequency Word Reading had a significant

moderately strong positive correlation to third grade Oral Text Accuracy subtest

performance (r = .59, p < .001), and rp = .46, after controlling for the effects of all

other variables. Table 23 summarizes the relative strength of each predictor.

Table 23 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Accuracy Subtest

Predictor

Controls


Letter sounds .29 −.05 Oral blending .20 −.11 Oral segmenting .30 .04 CVC word reading .39 .06 High-frequency words .59* .46* Participation in a multitiered intervention model −.05 −.04

*p < .001



to third grade reading achievement on the fourth DBT subtest: Oral Text Fluency

(OTF), after controlling for the kindergarten reading readiness factors. As in the



101


error (.05/10 = .005).


participation in a multitiered intervention model was significantly related to third

grade performance on the OTF subtest [R = .55, R2 = .30, R2adj = .26, F(6, 110) =

7.92, p < .001], accounting for approximately 30% of the variance in OTF


the OTF subtest over and above participation in a multitiered intervention model

[R2change = .30, F(5, 111) = 9.35, p < .001]. Participation in a multitiered intervention

model did not significantly explain performance on the OTF subtest over and above

the reading readiness factors [R2change = .01, F(1, 110) = .84, p = .36]. It appears that


predictive information about third grade OTF subtest performance beyond what could

be ascertained from the kindergarten reading readiness factors. While the five

reading readiness factors were a significant as a set, examination of the zero-order

and partial correlations revealed that none of them was an individually significant

predictor of third grade Oral Text Fluency subtest performance. Table 24




to third grade reading achievement on the fifth DBT subtest: Oral Text

102

Table 24 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Fluency Subtest

Predictor

Controls


Letter sounds .41 .11 Oral blending .37 .02 Oral segmenting .38 −.01 CVC word reading .49 .19 High-frequency words .48 .19 Participation in a multitiered intervention model .03 .09


Comprehension (OTC), after controlling for the kindergarten reading readiness

factors. As in the previous analysis, the first set of predictors included the five

kindergarten reading readiness factors while the second was participation in a

multitiered intervention model as a single variable. A Bonferroni approach was

applied to control for Type I error (.05/10 = .005).


participation in a multitiered intervention model were significantly related to third

grade performance on the OTC subtest [R = .54, R2 = .29, R2adj = .25, F(6, 110) =

7.40, p < .001], accounting for approximately 29% of the variance in OTC

performance. The kindergarten reading readiness factors alone significantly

explained performance on the OTC subtest over and above participation in a



performance on the Oral Text Comprehension subtest over and above the reading

103

readiness factors [R2change = .01, F(1, 110) = 1.53, p = .22]. It appears that


predictive information about third grade Oral Text Comprehension subtest

performance beyond what could be ascertained from the kindergarten reading

readiness factors. Examination of the zero-order and partial correlations revealed that

the kindergarten reading readiness variable High Frequency Word Reading had a

significant moderate positive correlation to third grade Oral Text Comprehension

subtest performance (r = .48, p = .001), and rp = .30, after controlling for the effects

of all other variables. Table 25 summarizes the relative strength of each predictor.

Table 25 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Oral Text Comprehension Subtest

Predictor

Controls


Letter sounds .30 −.03 Oral blending .28 .01 Oral segmenting .42 .13 CVC word reading .38 .04 High-frequency words .48* .30* Participation in a multitiered intervention model .15 .12

*p < .01

Research Question 3

The third research question was, how much do demographic factors and

participation in multitiered Systematic Instruction in Phonemic Awareness, Phonics

104

and SIPPS explain student reading achievement on the end of year BPST? Five

hypotheses were posed for this question.

Hypothesis 3.1

For first grade students, participation in multitiered SIPPS intervention

explains more about end of first grade reading achievement on the BPST than the




to explain how much participation in a multitiered SIPPS intervention program

contributed to end of first grade reading achievement on the BPST, after controlling

for the demographic factors of socioeconomic status, parent education level,

ethnicity, and English language level in kindergarten. The first set of predictors

included the four demographic factors and the second was participation in the first

grade multitiered SIPPS intervention as a single variable. Correlation coefficients

were compared across the variables to ensure that no two variables were so strongly

related that they might overinflate the relationships in the model. No pair-wise

correlations greater than .70 were found. A Bonferroni approach was applied to


The linear combination of the four demographic variables and participation in

a multitiered intervention model were significantly related to first grade performance

on the end of year BPST [R = .71, R2 = .50, R2adj = .48, F(5, 111) = 22.32, p < .001],

accounting for approximately 50% of the BPST performance. The demographic

factors significantly explained performance on the BPST over and above participation

105

in a multitiered intervention model [R2change = .41, F(4, 112) = 19.92, p < .001].

Participation in a multitiered intervention model also significantly explained

performance on the BPST over and above the demographic factors [R2change = .09,

F(1, 111) = 19.07, p < .001]. It appears that participation in a multitiered

intervention model offers some additional predictive information about first grade

end-of-year BPST performance beyond what could be ascertained from the

demographic factors. Examination of the zero-order and partial correlations revealed

two demographic variables had significant moderate to strong positive correlations to

first grade BPST performance: English level in kindergarten (r = .59, p < .001 and rp

= .41), and SES (r = .46, p = .004) and rp = .27, after controlling for the effects of all

other variables. Participation in a multitiered SIPPS intervention model had a

significant moderate positive correlation with first grade BPST performance (r = .46,

p < .001) and rp = .38, after controlling for the effects of all other variables. Table 26


Table 26 Bivariate and Partial Correlations of the Demographic Predictors With First grade Basic Phonics Skills Test Performance

Predictor

Controls


Ethnicity −.17 .05 English level in K .59** .41** Socioeconomic status .46* .27* Parent education level .44 .07 Participation in a multitiered SIPPS intervention model .46** .38**

*p < .01, **p < .001

106

Hypothesis 3.2

For second grade students, participation in multitiered SIPPS intervention

explains more about end of second grade reading achievement on the BPST than the

demographic factors of socioeconomic status, parent education level, ethnicity, and




to end-of-second grade reading achievement on the BPST, after controlling for the

demographic factors of socioeconomic status, parent education level, ethnicity, and

English language level in kindergarten. The first set of predictors included the four

demographic factors and the second was participation in the first grade multitiered

SIPPS intervention as a single variable. Correlation coefficients were compared

across the first set of variables to ensure that no two variables were so strongly related

that they might overinflate the relationships in the model. No pair-wise correlations

greater than .70 were found. A Bonferroni approach was applied to control for Type I

error (.05/6 = .008).


a multitiered intervention model were significantly related to second grade

performance on the end of year BPST [R = .69, R2 = .46, R2adj = .44, F(5, 111) =

18.86, p < .001], accounting for 46% of the variance in BPST performance. The

demographic factors significantly explained performance on the BPST over and


12.66, p < .001]. Participation in a multitiered intervention model also significantly

107

explained performance on the BPST over and above the demographic factors [R2change

= .15, F(1, 111) = 30.38, p < .001]. It appears that participation in a multitiered

intervention model offers some additional predictive information about second grade

end-of-year BPST performance beyond what could be ascertained from the

demographic factors. Examination of the zero-order and partial correlations revealed

English level in kindergarten was significantly positively related to second grade

BPST performance (r = .50, p < .001 and rp = .28), after controlling for the effects of

all other variables. Participation in a multitiered SIPPS intervention model had a

significant moderate positive correlation with second grade BPST performance (r =

.52, p < .001) and rp = .46, after controlling for the effects of all other variables.

Table 4.24 summarizes the relative strength of each predictor.

Table 4.24 Bivariate and Partial Correlations of the Demographic Predictors With Second grade Basic Phonics Skills Test Performance

Predictor

Controls


Ethnicity −.17 .01 English level in K .50* .28* Socioeconomic status .40 .20 Parent education level .41 .12 Participation in a multitiered SIPPS intervention model .52** .46**

*p < .01. **p < .001

Hypothesis 3.3

For second grade students, participation in the Reads Naturally intervention

explains more about second grade reading achievement on the end-of-intervention

108

fluency test than the kindergarten demographic factors of socioeconomic status,

parent education level, ethnicity, and English language level in kindergarten.


to explain how much participation in the Reads Naturally intervention program

contributed to second grade reading achievement on the end-of-intervention fluency

test, after controlling for the demographic factors of socioeconomic status, parent

education level, ethnicity, and English language level in kindergarten. The first set of

predictors included the four demographic factors and the second was participation in

the Reads Naturally intervention as a single variable. Correlation coefficients were

compared across the first set of variables to ensure that no two variables were so

strongly related that they might overinflate the relationships in the model. No pair-

wise correlations greater than .70 were found. A Bonferroni approach was applied to



the Reads Naturally intervention was significantly related to second grade

performance on the end-of-intervention fluency test [R = .61, R2 = .38, R2adj = .35,

F(5, 111) = 13.43, p < .001]. The demographic factors significantly explained

performance on the end-of-intervention fluency test over and above participation in

the Reads Naturally intervention [R2change = .20, F(4, 112) = 6.89, p < .001].

Participation in the Reads Naturally intervention also significantly explained

performance on the end-of-intervention fluency test over and above the demographic

factors [R2change = .18, F(1, 111) = 31.94, p < .001]. It appears that participation in

the Reads Naturally intervention offers some additional predictive information about

109

end-of-intervention fluency test performance beyond what could be ascertained from

the demographic factors. Examination of the zero-order and partial correlations

revealed that no demographic variables were significantly individually related to end-

of-intervention fluency test performance. Participation in the Reads Naturally

intervention had a significant moderate positive correlation with end-of-intervention

fluency test performance (r = .39, p < .001) and rp = .47, after controlling for the

effects of all other variables. Table 28 summarizes the relative strength of each

predictor.

Table 28 Bivariate and Partial Correlations of the Demographic Predictors With Second grade End-of-Intervention Fluency Test Performance

Predictor

Controls


Ethnicity .001 .15 English level in K .33 .21 Socioeconomic status .24 .21 Parent education level .36 .21 Participation in a multitiered SIPPS intervention model .39* .47*

*p < .001

Hypothesis 3.4

For first grade students, participation in a teacher-created intervention

explains more about first grade reading achievement on the end-of-intervention BPST

than the kindergarten demographic factors of socioeconomic status, parent education

level, ethnicity, and English language level in kindergarten.

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to explain how much participation in a teacher-created intervention program

contributed to first grade reading achievement on the end-of-intervention BPST, after

controlling for the demographic factors of socioeconomic status, parent education

level, ethnicity, and English language level in kindergarten. The first set of predictors

included the four demographic factors and the second was participation in a teacher-

created intervention as a single variable. Correlation coefficients were compared

across the first set of variables to ensure that no two variables were so strongly related

that they might overinflate the relationships in the model. No pair-wise correlations

greater than .70 were found. A Bonferroni approach was applied to control for Type I

error (.05/6 = .008).


a teacher-created intervention was significantly related to performance on the end-of-

intervention BPST [R = .65, R2 = .42, R2adj = .39, F(5, 111) = 15.83, p < .001],

accounting for approximately 42% of the variance in student performance. The

demographic factors significantly explained performance on the end-of-intervention

BPST over and above participation in a teacher-created intervention [R2change = .42,

F(4, 112) = 19.92, p < .001]. Participation in a teacher-created intervention did not

significantly explained performance on the end-of-intervention BPST over and above

the demographic factors [R2change = .001, F(1, 111) = .10, p = .76]. It appears that

participation in a teacher-created intervention did not offer additional predictive

information about first grade end-of-intervention BPST performance beyond what

could be ascertained from the demographic factors. Examination of the zero-order

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and partial correlations revealed that two demographic variables were significantly

and positively related to end-of-intervention BPST performance: English level in

kindergarten (r = .59, p < .001 and rp = .44), and SES level (r = .46, p < .001) and rp =

.28, after controlling for the effects of all other variables. Table 29 summarizes the


Table 29 Bivariate and Partial Correlations of the Demographic Predictors With First grade End-of Intervention Basic Phonics Skills Test Performance

Predictor

Controls


Ethnicity −.17 .04 English level in K .59** .44** Socioeconomic status .46* .30* Parent education level .44 .07 Participation in a multitiered SIPPS intervention model .12 .03

*p < .01, **p < .001

Hypothesis 3.5

For second grade students, participation in a teacher-created intervention

explains more about second grade reading achievement on the end-of-intervention

comprehension test than the kindergarten demographic factors of socioeconomic

status, parent education level, ethnicity, and English language level in kindergarten.


to explain how much participation in a teacher-created intervention contributed to the

second grade end-of-intervention comprehension test, after controlling for the

kindergarten demographic factors of socioeconomic status, parent education level,

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ethnicity, and English language level. The first set of predictors included the four

demographic factors and the second was participation in teacher-created intervention

as a single variable. Correlation coefficients were compared across the first set of

variables to ensure that no two variables were so strongly related that they might

overinflate the relationships in the model. No pair-wise correlations greater than .70

were found. A Bonferroni approach was applied to control for Type I error (.05/6 =

.008).


a teacher-created intervention was significantly related to performance on the end-of-

intervention comprehension test [R = .45, R2 = .20, R2adj = .17, F(5, 111) = 5.61, p <

.001], accounting for approximately 20% of the variance in student performance. The

demographic factors significantly explained performance on the end-of-intervention

comprehension test over and above participation in a teacher-created intervention

[R2change = .20, F(4, 112) = 6.89, p < .001]. Participation in a teacher-created

intervention did not significantly explain performance on the end-of-intervention

comprehension test over and above the demographic factors [R2change = .004, F(1,

111) = .59, p = .45]. It appears that participation in a teacher-created intervention did

not offer additional predictive information about end-of-intervention comprehension

test performance beyond what could be ascertained from the demographic factors.

Examination of the zero-order and partial correlations revealed that no demographic

variable was significantly related to end-of-intervention comprehension performance.


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Table 30 Bivariate and Partial Correlations of the Demographic Predictors With Second grade End-of-Intervention Comprehension Test Performance

Predictor

Controls


Ethnicity −.14 .02 English level in K .36 .21 Socioeconomic status .37 .24 Parent education level .31 .06 Participation in a multitiered SIPPS intervention model .001 .07


Summary

Chapter IV included the results of 18 statistical analyses conducted to

examine the impact of participation in a multitiered intervention model, demographic

factors, and kindergarten reading readiness factors on third grade reading

achievement. In addition, five statistical analyses were conducted to examine the

effects of specific interventions on first and second grade reading achievement.

Generally, participation in a multitiered intervention model made only small

contributions to reading achievement, over and above kindergarten demographic and

reading readiness factors. Chapter V provides a discussion of the findings and

implications of this study, as well as recommendations for further study.

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CHAPTER V

DISCUSSION, IMPLICATIONS, AND CONCLUSIONS

This study was designed to examine predictors of third grade reading

performance, including demographic factors, kindergarten reading readiness factors,

and participation in a multitiered intervention model. Data were collected from two

cohorts of students at one school in the California Central Valley and statistical

analyses were conducted to determine the effects of the predictors on reading

achievement. Chapter V discusses the findings and implications of the study and

identifies areas for further study.

Summary of the Study

This study examined factors that influence end of third grade reading

achievement as measured by state and local assessments. Kindergarten demographic

factors, kindergarten reading readiness factors, and participation in a multitiered

Response to Intervention model were analyzed to determine their contribution to third

grade reading achievement. Additionally, three types of intervention programs used

within a kindergarten through second grade multitiered intervention model were

evaluated to determine their impact on first- and second grade reading assessments.

Summary of the Methods

This study was conducted at one elementary school located in the California

Central Valley. A total of 117 cases were included in this study. The first cohort of

students began kindergarten in 2004–2005 and consisted of 128 students, 62 of which

remained at the school through the end of third grade. Teachers in grades

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kindergarten through grade 2 did not use a multitiered intervention model; that is,

they instructed their assigned students within their own classroom and provided

uniform reading instruction to all students in the class. Each student received one

small group reading lesson per day, and the lowest students may have received

additional help of some sort. The second cohort of students began kindergarten in

2008–2009, and consisted of 82 students, 55 of which remained at the school through

third grade. These students all participated in a multitiered intervention model where

the teachers delivered one to three doses of small group, leveled reading instruction to

all students per day, based on how students performed on regularly administered

formative assessments. Student reading groups were fluid and flexible, meaning that

the amount of reading instruction a student received could change every few weeks.

Demographic factors included in the study were ethnicity, socioeconomic

status, parent education level, gender, age upon kindergarten entry, and English

language level in kindergarten as measured by the California English Language

Development Test (CELDT). Kindergarten reading readiness factors included in the

study were letter sounds, oral blending, oral segmenting, consonant-vowel-consonant

(CVC) reading, and high frequency word reading. Student demographic data were

collected about at the time of enrollment in kindergarten and academic performance

indicators were collected as part of the regular practices of the school, through the

course of several academic calendar years. The data were then coded and entered

into the Statistical Package for the Social Sciences, Version 18 (SPSS 18).

Multiple regression analyses were conducted using SPSS 18 to identify the

contribution of each kindergarten demographic and academic variable on several

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different third grade reading achievement measures: California Standards Test (CST)

or California Modified Assessment (CMA); three subtests from the CST/CMA: Word

Analysis and Vocabulary (WAV), Reading Comprehension (RC), and Literary

Response and Analysis (LRA); and five subtests that made up the District Benchmark

Test (DBT): the Basic Phonics Skills Test (BPST), Silent Comprehension (SC), Oral

Text Accuracy (OTA), Oral Text Fluency (OTF), and Oral Text Comprehension

(OTC).

For the examination of specific first and second grade intervention programs,

pre- and posttest data were collected as part of the regular practices of the school,

over multiple academic years. Three types of interventions were examined:

multitiered Systematic Instruction in Phonemic Awareness, Phonics and Sight Words

(SIPPS), Reads Naturally, and teacher-created interventions. Pre- and posttest

measures included the BPST and the fluency and comprehension subtests of the

District Benchmark Tests in first and second grade.

Discussion

Throughout this section of the study, the results of the statistical analyses,

both significant and nonsignificant, are discussed according to the individual research

questions which guided the study.

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Research Question 1

How well do demographic factors and participation in a multitiered reading

intervention model explain third grade reading achievement?

In light of recent education reforms such as No Child Left Behind (NCLB)

that require students in all subgroups to reach proficiency on standardized tests by

2014, the examination of the contribution of demographic factors to students’ third

grade reading achievement is worth considering. The intent of this study was to

determine how well demographic factors explained third grade reading achievement

and if participation in a multitiered intervention model contributed to third grade

reading achievement over and above the demographic factors.

Based on the results of the analyses, the demographic factors as a set were

statistically predictive of each standardized test measure examined in the study. For

the CST/CMA measure, the demographic factors together accounted for about 23% of

the variance in performance [Rchange = .23, F(6, 110) = 5.59, p < .001]. For the WAV

subtest, the demographic factor set accounted for approximately 27% of the variance

in performance [Rchange = .27, F(6, 110) = 6.74, p < .001]. For the RC subtest, the

demographic factors accounted for about 22% of the variance in performance [Rchange

= .22, F(6, 110) = 5.05, p < .001], and for the LRA subtest, the demographic factors

together accounted for approximately 20% of the variance in performance [Rchange =

.20, F(6, 110) = 4.56, p < .001]. However, when examined separately, no individual

demographic factor contributed significantly to third grade reading achievement on

the standardized tests.

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This study also examined how well demographic factors explained

performance on the District Benchmark Tests (DBT). As a set, they were significant

contributors to each DBT subtest. For the BPST subtest, the demographic factors


.23, F(6, 110) = 5.51, p < .001]. For the SC subtest, the demographic factor set

accounted for approximately 25% of the variance in performance [Rchange = .25, F(6,

110) = 6.74, p < .001]. For the OTA subtest, the demographic factors accounted for

approximately 13% of the variance in performance [Rchange = .22, F(6, 110) = 5.05, p

< .001], and for the OTF subtest, the demographic factors together accounted for

approximately 23% of the variance in performance [Rchange = .23, F(6, 110) = 4.41, p

< .001]. On the OTC subtest, the demographic factors accounted for approximately

13% of the variance in performance [Rchange = .23, F(6, 110) = 5.39, p < .001].

Examining the demographic factors individually and controlling for all other factors,

revealed that only socioeconomic status had a significant positive correlation on one

subtest, Oral Text Fluency (rp = .31, p < .001].

These findings are important for two reasons. First, if individual demographic

factors do not contribute significantly to reading achievement, then educators should

not base their predictions of students’ reading ability on demographic factors.

Second, if demographic factors as a set do impact students’ reading achievement, then

teachers should be proactive for those students who are identified in multiple

demographic subgroups. This would necessitate a delicate balance of identifying the

demographics of a student and then suspending judgment of preconceived notions

about the effects of those demographics. For example, a teacher may say that

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students are at risk of lower reading achievement because they are economically

disadvantaged, but the results of the analyses suggest that if that is the only

demographic “risk factor” that applies, then it will not contribute significantly to

reading achievement. On the other hand, if students are economically disadvantaged,

their parents are not well educated, and they came to kindergarten speaking very little

English, the combination of those factors will have a larger effect on their reading

achievement.

The other variable examined as part of Research Question 1 was the impact of

participation in a multitiered intervention program. As Response to Intervention

models are implemented across America as part of educational reform efforts, an

examination of the effects of such models seems a worthy effort. The results of the

statistical analyses revealed that they explain a very small (often nonsignificant)

portion of the variance over and above demographic factors as a set in both the

standardized measures and the local District Benchmark Tests. Participation in a

multitiered intervention model did not significantly contribute to CST/CMA

performance for the overall test or any of the subtests, nor to any of the DBT subtests,

over and above the demographic factors.

Examining participation in a multitiered intervention model while controlling

for all other factors revealed very weak correlations with the DBT subtests (Rp ranges

from .09 to -.15). Partial correlations with the CST/CMA and its subtests were also

weak with Rp ranges from .07 to .19. The results seem to suggest that participation in

a kindergarten through second grade multitiered intervention model does not

contribute much to third grade reading achievement. This finding is important

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because considerable amounts of funding are spent on interventions each year and if

the interventions do not result in academic achievement gains, schools and districts

should reevaluate the design, content, and delivery of their interventions. Many

interventions are given by schools to help students from various demographic groups

such as those who are economically disadvantaged or who do not speak English, yet

if they are not more powerful than the latent effects of the demographic, then the

interventions should be reviewed and revamped, or the interventions should be

administered before kindergarten to those most at risk.

Another possible explanation for the small contribution to third grade reading

performance by participation in a multitiered intervention program is that the

kindergarten through second grade interventions may address foundational skills that

are not specifically measured by third grade reading achievement assessments. For

example, kindergarten and first grade interventions may focus more on phonemic

awareness, which is not directly assessed by any third grade measure. Also, it is

possible that a multitiered intervention model might provide too much scaffolding for

students; that is, students are given so much assistance that they are not able to

adequately perform the skills independently.

One other possible explanation is that once students are behind, it is hard to

shrink the gap between their performance and that of their peers who continue to

grow. This study did not measure change in performance from kindergarten through

grade 3, but it is possible that students who were most targeted by the interventions

experienced at least as much growth as their peers. These explanations are explored

further in the discussion of Research Question 2.

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Research Question 2

How well do reading readiness factors and participation in a multitiered

reading intervention model explain third grade reading achievement?

The review of the literature revealed that while demographic factors explained

some part of students’ reading achievement, early reading readiness skills

consistently contributed more than demographics to subsequent reading performance.

Research Question 2 examined how much kindergarten reading readiness factors

contributed to third grade reading achievement on both standardized tests and local

measures such as the DBT subtests. The study initially included six kindergarten

reading readiness factors, but one factor (Letter Names) was eliminated from the

study after a pair-wise correlational analysis showed a significant very strong positive

correlation with another factor (Letter Sounds). The two-tailed correlation was .78, p

< .001. Letter Names was the variable omitted based on evidence in the literature

review that indicated that Letter Sounds was a better predictor of reading readiness.

The combination of the five remaining reading readiness factors was a

significant predictor of third grade reading achievement, over and above participation

in a multitiered intervention model, for every measure included in the analyses. For

the CST/CMA measure, the reading readiness factors together accounted for about

41% of the variance in performance [Rchange = .41, F(5, 111) = 15.21, p < .001]. For

the WAV subtest, the reading readiness set accounted for approximately 39% of the

variance in performance [Rchange = .39, F(5, 111) = 13.98, p < .001]. For the RC

subtest, the reading readiness factors accounted for about 35% of the variance in

performance [Rchange = .35, F(5, 111) = 12.00, p < .001], and for the LRA subtest, the

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reading readiness factors together accounted for approximately 36% of the variance

in performance [Rchange = .36, F(5, 111) = 12.62, p < .001].

When examined separately, a few kindergarten reading readiness factors

individually contributed significantly to third grade reading achievement on the

standardized tests. For the CST/CMA, two factors were significant when controlling

for all other factors: Letter Sounds (rp = .33, p < .001) and High Frequency Words (rp

= .31, p < .001). For the WAV and RC subtests, High Frequency Words was the only

significant variable when controlling for all other factors: (rp = .41, and .29

respectively, p < .001). For the LRA subtest, Letter Sounds was the only significant

variable when controlling for all other factors: (rp = .28, p < .001).

For the District Benchmark Tests, kindergarten reading readiness factors were

significant contributors to each DBT subtest, over and above participation in a

multitiered intervention model. For the BPST subtest, the reading readiness factors


.45, F(5, 111) = 17.97, p < .001]. For the SC subtest, the reading readiness factor set


111) = 15.33, p < .001]. For the OTA subtest, the reading readiness factors accounted

for approximately 36% of the variance in performance [Rchange = .36, F(5, 111) =

12.24, p < .001], and for the OTF subtest, the reading readiness factors together


111) = 9.35, p < .001]. On the OTC subtest, the reading readiness factors accounted

for approximately 28% of the variance in performance [Rchange = .28, F(5, 111) =

8.53, p < .001]. Examining the reading readiness factors individually and controlling

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for all other factors revealed that only High Frequency Words had a significant

moderate positive correlation on four DBT subtests: BPST, SC, OTA, and OTC (Rp

= .38, .33, .46, and .30, respectively; p < .001).

The findings about kindergarten reading readiness factors have several

implications for parents and educators. First, the reading readiness factors as a set

can predict a large amount of the variance in performance on the third grade reading

measures. For the standardized state tests, the range of variance was 35 to 41%. For

the local measures, the range of variance was 28 to 45%. This is related to the second

implication—early reading skills matter. It appears that the efficacy of these skills as

a set is evident as early as the 10th week of kindergarten. Teachers and school

administrators can use the results of the analyses to educate parents on the impact of

these early reading readiness indicators and help parents work with their children

before kindergarten entry. Another avenue for improving students’ performance on

these early reading factors would be to work with area preschools and childcare

programs.

It seems to follow logic that early identification of these reading readiness

factors allows time for teachers to intervene and remediate any areas of difficulty for

students. The second part of this research question examined the effects of

participation in a multitiered intervention program over and above what the reading

readiness factors contributed. The results of the statistical analyses revealed that

participation in a multitiered intervention program explains a small portion of the

variance over and above kindergarten reading readiness factors as a set—sometimes

statistically significant, but often not—in both the standardized measures and the

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local District Benchmark Tests. For the CST/CMA measure, participation in a

multitiered intervention model accounted for about 8% of the variance in

performance [Rchange = .08, F(1, 110) = 17.31, p < .001]. For the WAV subtest,

participation in a multitiered intervention model set accounted for approximately 7%

of the variance in performance [Rchange = .07, F(1, 110) = 13.22, p < .001]. For the

RC subtest, participation in a multitiered intervention model accounted for about 3%

of the variance in performance [Rchange = .03, F(1, 110) = 5.70, p = .02].

For the DBT subtests, the effects of participation in a multitiered intervention

model, over and above the kindergarten reading readiness factors, remained small and

nonsignificant. This seems to indicate that participation in the interventions did not

significantly contribute to student performance on any of the DBT subtests.

Examining participation in a multitiered intervention model while controlling for all

other factors, revealed two significant moderate correlations, one with CST/CMA (rp

= .37, p < .001) and the other with the WAV subtest (rp = .33, p < .001). The other

partial correlations were weak and nonsignficant (rp range = .09 to .22). These results

seem to indicate that participation in a multitiered intervention model contributes

somewhat to third grade reading achievement as measured by standardized tests, but

is less predictive of achievement on local measures.

As discussed with the analysis of Research Question 1, educators can use the

information derived from these analyses to consider if the interventions they have

designed are contributing to the growth of skills needs to show proficiency in reading

in third grade and beyond. If kindergarten through second grade instruction and

intervention are largely focused on decoding skills, attention to comprehension

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strategies may not be adequate to build the skills students need to show proficiency in

reading at the third grade level.

Also, an examination of the level of scaffolding provided to students in a

multitiered intervention model seems to be a worthwhile endeavor. Ensuring that

students are not given so much support that they are unable to perform reading tasks

independently is an important part of the design of a multitiered intervention

program. Input from third grade teachers about the types of tasks students will be

expected to perform, both independently and with support, may be beneficial to the

design of the early interventions in kindergarten through second grade. The final

research question for this study examines more immediate effects of interventions

used by the school where the study was conducted.

Research Question 3

How much do kindergarten demographic factors and participation in

multitiered interventions explain student reading achievement on the end-of-

intervention assessment?

Three types of interventions were examined for Research Question 3. The

first is a multitiered SIPPS intervention used in first and second grades, in which

students receive more frequent, longer, slower paced, or smaller group size lessons,

determined by the students’ performance on regularly administered formative

assessments. Based on the knowledge that demographic factors as a set contribute

significantly to third grade reading performance, this study examined the contribution

of four of the demographic factors with higher correlations to third grade reading

(socioeconomic status, parent education level, ethnicity, and English language level

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in kindergarten) on a first grade reading measure, the BPST, after students

participated in a multitiered SIPPS intervention. The statistical analysis revealed that

the demographic factors significantly contributed about 41% of the variance, and

participation in the multitiered intervention model contributed about 9% over and

above the demographic factors [R = .71, R2 = .50, R2adj = .48, F(5, 111) = 22.32, p <

.001; demographics R2change = .41, F(4, 112) = 19.92, p < .001; intervention R2

change =

.09, F(1,111) = 19.07, p < .001].

The same statistical analysis was conducted on the multitiered SIPPS

intervention in second grade. The results of that analysis revealed that the

demographic factors significantly contributed about 31% of the variance, and

participation in the multitiered intervention model contributed about 15% over and

above the demographic factors [R = .69, R2 = .46, R2adj = .44, F(5, 111) = 18.86, p <

.001; demographics R2change = .31, F(4, 112) = 12.66, p < .001; intervention R2

change =

.15, F(1,111) = 30.38, p < .001]. When examining the contributions of the

multitiered SIPPS intervention, controlling for all other factors, the results are

significant moderate positive correlations (1st grade Rp = .38, and 2nd grade Rp = .46,

both p < .001).

The results of these analyses indicate that the multitiered SIPPS model of

instruction do contribute significantly to first and second grade reading achievement,

perhaps because the intervention closely matched the assessment. Both the SIPPS

intervention and the BPST assessment are firmly rooted in what the Common Core

State Standards call the Foundational Skills and what the National Reading Panel

called Alphabetics. While the BPST was also given as a third grade assessment,

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students who did very well on the BPST by the end of second grade did not have to

make much growth to attain the top score on the assessment in third grade.

The second type of intervention examined was Reads Naturally, a

commercially published fluency program used with second graders, based on fluency

scores from the District Benchmark Test. The intervention was delivered as

prescribed by the publisher by trained parent helpers and paraeducators in one-on-one

settings. Again the influence of demographic factors was included to account for

their predictive nature as a set. The results of the analyses showed that the

demographic factors contributed significantly, but in this case, participation in the

intervention contributed nearly as much as all of the demographic factors combined

[R = .61, R2 = .38, R2adj = .35, F(5, 111) = 13.43, p < .001; demographics R2

change =

.20, F(4, 112) = 6.89, p < .001; intervention R2change = .18, F(1,111) = 31.94, p <

.001]. Additionally, participation in the Reads Naturally intervention, after

controlling for all other factors, had a significant moderate positive correlation to the

end of intervention fluency test (Rp = .47, p < .001). Again, the intervention and the

assessment seemed well aligned on a specific skill set, reading fluently.

The last type of intervention examined in this study was teacher-created

interventions. These interventions are more difficult to define because teachers often

select students they perceive to need additional support, whether or not the data

indicate a specific need. The selected students are often struggling academically

overall and teachers want to provide help that includes affective types of support such

as motivation and encouragement. Nearly all teachers in first and second grades

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offered this type of intervention to a small group of students. Once again, the

contributions of demographic factors were considered in the analysis.

The results of the statistical analyses were surprising. In both first and second

grade the teacher-created interventions contributed very little to the posttest

performance of the students. In first grade, demographic factors accounted for about

42% of the variance in the post-test and participation in the teacher-created

intervention added less than 1% over and above the demographics[R = .65, R2 = .42,

R2adj = .40, F(4, 112) = 15.83, p < .001; demographics R2

change = .42, F(4, 112) =

19.92, p < .001; intervention R2change = .001, F(1,111) = .10, p < .76]. In second

grade, demographic factors accounted for about 20% of the variance in the posttest

and participation in the teacher-created intervention added less than 1% over and

above the demographics [R = .45, R2 = .20, R2adj = .17, F(5, 111) = 5.61, p < .001;

demographics R2change = .20, F(4, 112) = 6.89, p < .001; intervention R2

change = .004,

F(1,111) = .59, p < .45].

It seems that the post-tests were not well related to the skills on which the

teachers actually worked. First grade teachers worked on phonics, but they also

reviewed high frequency words, played games, read aloud, gave students time to

practice reading, and did other activities. The BPST only assessed phonics

achievement. In second grade, teachers worked on comprehension, but they also

worked on spelling/writing and some phonics skills. Their post-test was a

comprehension assessment, but it only reflected a portion of what teachers delivered

during the intervention.

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What could not be measured through the post-tests administered were the

affective benefits of the teacher-created interventions. If the students left the

interventions more motivated or encouraged, or if the teacher saw the student in a

different light after working with them closely in a small group setting without the

distractions of the regular school day, that might benefit them greatly in a way that

the school was not prepared to measure. While the results of the analyses for the

teacher-created interventions were discouraging, these types of interventions should

not be quickly dismissed. Finding a way to measure how affective interventions

support student reading achievement may be an area of future study.

Implications

The implications of this study are trifold. First, the results of the statistical

analyses confirm many of the findings in the current literature and contribute new

insight to the existing body of empirical evidence. Demographic factors such as SES

have been cited as significant contributors to student reading achievement (Butler,

Marsh, Shepard & Shepard, 1982; Noble, Farah, and McCandliss, 2006). The present

study suggested that demographic factors together can have a significant effect on

reading achievement. However, few demographic variables had significant

individual predictive information about third grade reading achievement on state or

local measures.

Additionally, reading readiness factors were discussed extensively in the

review of the literature. Early phonemic awareness and phonics skills such as

knowing letter names and sounds, oral blending, reading consonant-vowel-consonant

words, and recognizing high frequency words were all found to be important

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predictors of subsequent reading achievement (Adlof, Catts, & Lee, 2010). The

present study suggests that high frequency word knowledge is a significant moderate

predictor to third grade reading achievement.

Because the kindergarten reading readiness factors as a set contributed

significantly to third grade reading achievement, educators may wish to consider how

they work with parents of incoming kindergarten students. If kindergarteners are

screened in the spring prior to kindergarten entry, it seems to be a good opportunity to

provide parents with information about reading readiness factors that contribute

significantly to third grade reading achievement. In addition, schools should provide

tools that parents can use to increase their children’s reading readiness skills.

Educating local preschool and daycare providers could also strengthen the skills with

which students enter kindergarten.

Second, participation in a kindergarten through second grade multitiered

intervention model alone does not contribute significantly to third grade end-of-year

reading achievement. Although Chard (2008) found that early reading interventions

mitigated the effects of early reading achievement factors, the present study did not

fully confirm those findings. While the effects of participation in a multitiered

intervention program were partially evident, they were small overall and mostly

nonsignificant. The design and delivery of a multitiered intervention model must be

carefully considered and monitored. Simmons, Coyne, Kwok, McDonagh, Harn and

Kame’enui advised, “RtI by design, requires dynamic, well-orchestrated use of

measures and intervention to optimize student performance” (2008, p. 171). The

present study suggests that the kindergarten through second grade intervention model

131

used in the sample school contributes small effects to third grade reading

achievement.

While not all of the contributions of the multitiered intervention model were

statistically significant, they may be considered practically significant to some

educators. For example, if participation in a multitiered model adds a small amount

of growth for students who are struggling with reading, teachers might consider it a

worthwhile endeavor. Teachers often seem to see instruction and intervention results

on a per student basis. If a program helps one or two students, they are often willing

to continue using it. Administrators, on the other hand, may have to make decisions

about overall program effectiveness. If a program or instruction and intervention

model does not have significant large-scale effects, an administrator might be less

likely to fund that program or more likely to demand that changes are made to

increase its efficiency.

The present study does seem to indicate that two specific interventions used in

first and second grade make significant moderate contributions to student

achievement at the conclusion of the intervention: multitiered SIPPS instruction and

Reads Naturally. Both of these programs contributed significantly over and above

the effects of student demographic factors. This seems to indicate that the programs

are beneficial to student reading achievement, regardless of students’ demographics.

By using the results of this study, the school can do several things to increase

the efficacy of its intervention model as a contribution to third grade reading

achievement. Possible improvements to the intervention model include closely

examining the reading tasks that third grade students are asked to perform at the end

132

of the year, soliciting input from third grade teachers about the design of the

kindergarten through second grade intervention model, expanding multitiered SIPPS

instruction to third grade, and designing interventions that work on specific skills for

periods of time. Teacher-created interventions should be approached with careful

thought and planning. While there may have been affective benefits such as

increased motivation or a stronger personal relationship between teacher and student,

those effects are not easy to measure. Finding a way to quantify the benefits of these

types of interventions will help teachers and school administrators justify the time and

resources spent on them.

Third, the pressure and demands of educational reform efforts such as NCLB,

RtI, and the Common Core State Standards (CCSS) are likely to increase in the

future. Although NCLB is set to expire in the near future, the reauthorization of the

Elementary and Secondary Education Act is inevitable. It will surely contain an

accountability system meant to ensure that students of all demographic groups are not

forgotten or ignored. Response to Intervention systems are becoming more and more

common, but ensuring a high-quality model requires extensive planning, teacher

training, and evaluation. Districts in California are expected to fully implement the

CCSS in 2014–2015 and many states across the nation have fully implemented them

already. The CCSS include specific reading comprehension standards for both

literature and informational text in kindergarten through twelfth grade, and standards

for foundational skills for kindergarten through fifth grade. The CCSS design seems

to be directing teachers away from the “first we learn to read, then we read to learn”

philosophy. By focusing on both decoding and comprehension simultaneously,

133

instruction and intervention models in kindergarten through second grade may be

more aligned to the reading tasks required of third grade students. The results of this

study may be helpful with the planning, delivery, and evaluation of all of these reform

efforts.

Limitations and Delimitations

This study was limited to one school in the California Central Valley. Only

interventions offered at each grade level in the sample school were examined.

Because of the fluid nature of the multitiered intervention system, it is uncertain if

some of the interventions are replicable. Caution should be used in generalizing the

findings to other schools.

For the purposes of this study, changes in the district-adopted curriculum and

changes in the faculty at the kindergarten through second grade levels were not taken

into consideration. The state-approved curriculum for both cohorts included the key

components of reading instruction as defined by the National Reading Panel, and

changes in the faculty at each grade level were minimal over the course of the study.

Recommendations for Further Study

Further study in the area of reading achievement and multitiered intervention

models is recommended as follows:

A similar study with a larger sample across multiple schools may mitigate

any teacher effect seen in this study.

A qualitative analysis should be conducted to examine the effectiveness of

kindergarten interventions on end-of-year kindergarten reading readiness

factors.

134

A quantitative analysis should be conducted to survey parents of incoming

kindergarten students about the skills they have worked on with their

children. These skills could be correlated with students’ performance on

the kindergarten reading readiness factors to determine areas where parent

education could be most effective.

A qualitative analysis should be conducted to determine the affective

benefits of teacher-created interventions that focus on motivation,

relationship building, and encouragement.

A mixed-methods study should be conducted to examine the reading

achievement of students who are considered to be at-risk of reading

difficulties, including a quantitative analysis of the academic factors

contributing to their performance and a qualitative study of affective

factors contributing to their performance. Interviews with students might

lead to new insights about motivation and hidden effects of demographic

factors.

Conclusion

Reading proficiency is critical for both students who need strong reading

skills to be successful in college and career and schools that need to meet the

accountability requirements of educational reform initiatives. Previous literature

identified several early demographic and academic factors that helped explain

subsequent reading achievement. This study did not find an individual statistically

significant demographic factor that could consistently explain third grade reading

achievement. Additionally, only two kindergarten reading readiness factors

135

explained third grade reading achievement on more than one measure: Letter Sounds

and High Frequency Word reading. Participation in a kindergarten through second

grade multitiered intervention model accounted for very small percentages of the

variance in third grade reading achievement, over and above what the demographic

and reading readiness factors contributed. When examining intervention types within

the multitiered model, two were moderately effective: SIPPS and Reads Naturally.

In the ever-changing world of the 21st century, students will need to be able to

proficiently navigate complex text and make sense of both the word and the world

(Friere, 1983). Schools must continue to pursue effective and efficient methods of

attaining student proficiency in reading achievement.

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137

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Documents

THE EFFECTS OF KINDERGARTEN THROUGH SECOND GRADE READING INSTRUCTION AND