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
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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
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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.
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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.
Hypothesis 1.2. Participation in a multitiered reading intervention model
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
participation in a multitiered reading intervention model explain third grade reading
achievement? Two hypotheses were posed for this question.
10
Hypothesis 2.1. Participation in a multitiered reading intervention model
explains more about third grade reading achievement on the California Standards Test
or California Modified Assessment than kindergarten scores on letter names, letter
sounds, oral blending, oral segmenting, consonant-vowel-consonant reading, or sight
word tasks.
Hypothesis 2.2. Participation in a multitiered reading intervention model
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
intervention BPST than the kindergarten demographic factors of socioeconomic
status, parent education level, ethnicity, or English language level in kindergarten.
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
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 3.5. For second grade students, participation in a teacher-created
intervention explains more about second grade reading achievement on the end-of-
intervention District Benchmark Comprehension Test than the kindergarten
demographic factors of socioeconomic status, parent education level, ethnicity, or
English language level in 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
Standards Test (CST) and California Modified Assessment (CMA), administered
each spring to students in Grades 2–11 (California Department of Education, 2012).
Segmenting. To “perceive the separable sounds within words” (Shanahan,
2006, p. 6).
Word Analysis and Vocabulary 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).
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
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.
Hypothesis 1.2. Participation in a multitiered reading intervention model
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
The second research question was, how well do reading readiness factors and
participation in a multitiered reading intervention model explain third grade reading
achievement? Two hypotheses were posed for this question.
Hypothesis 2.1. Participation in a multitiered reading intervention model
explains more about third grade reading achievement on the California Standards Test
or California Modified Assessment than kindergarten scores on letter names, letter
sounds, oral blending, oral segmenting, consonant-vowel-consonant reading, or sight
word tasks.
Hypothesis 2.2. Participation in a multitiered reading intervention model
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
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-
intervention BPST than the kindergarten demographic factors of socioeconomic
status, parent education level, ethnicity, or English language level in kindergarten.
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
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 3.5. For second grade students, participation in a teacher-created
intervention explains more about second grade reading achievement on the end-of-
intervention District Benchmark Comprehension Test than the kindergarten
demographic factors of socioeconomic status, parent education level, ethnicity, or
English language level in 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
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.
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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
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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).
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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.
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 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
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).
Table 7 Bivariate and Partial Correlations of the Demographic Predictors With Word Analysis and Vocabulary Subtest
Predictor
Controls
None All other predictors
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
Note. None of the correlations were significant.
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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
None All other predictors
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
Note. None of the correlations were significant.
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 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
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).
77
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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) =
4.56, p < .001]. Participation in a multitiered intervention model did not significantly
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
intervention model did not offer much additional predictive information about third
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
partial correlations revealed that none of them was an individually significant
predictor of third grade Literary Response and Analysis subtest performance. Table 9
summarizes the relative strength of each predictor.
Hypothesis 1.2
Participation in a multitiered reading intervention model 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.
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Table 9 Bivariate and Partial Correlations of the Demographic Predictors With Literary Response and Analysis Subtest
Predictor
Controls
None All other predictors
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
Note. None of the correlations were significant.
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.
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 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.
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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
partial correlations revealed that none of them was an individually significant
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
None All other predictors
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
Note. None of the correlations were significant.
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 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).
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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
above participation in a multitiered intervention model [R2change = .25, F(6, 110) =
5.95, p < .001]. Participation in a multitiered intervention model did not significantly
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
intervention model did not offer much additional predictive information about third
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
None All other predictors
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
Note. None of the correlations were significant.
83
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 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).
The linear combination of the six demographic variables and participation in a
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
None All other predictors
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
Note. None of the correlations were significant.
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 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).
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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
above participation in a multitiered intervention model [R2change = .23, F(6, 110) =
85
5.41, p < .001]. Participation in a multitiered intervention model did not significantly
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
intervention model did not offer much additional predictive information about third
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
strength of each predictor.
Table 14 Bivariate and Partial Correlations of the Demographic Predictors With Oral Text Fluency Subtest
Predictor
Controls
None All other predictors
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.
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 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
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).
The linear combination of the six demographic variables and participation in a
multitiered intervention model were significantly related to third grade performance
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
None All other predictors
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
Note. None of the correlations were significant.
Research Question 2
The second research question was, how well do reading readiness factors and
participation in a multitiered reading intervention model explain third grade reading
achievement? Two hypotheses were posed for this question.
Hypothesis 2.1
Participation in a multitiered reading intervention model explains more about
third grade reading achievement on the California Standards Test or California
Modified Assessment than kindergarten scores on letter names, letter sounds, oral
blending, oral segmenting, and sight word tasks.
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 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
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 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
strength of each predictor.
Table 17 Bivariate and Partial Correlations of the Reading Readiness Predictors With the California Standards Test/California Modified Test
Predictor
Controls
None All other predictors
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
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 WAV subtest [R = .67, R2 = .45, R2adj = .42, F(6, 110)
= 15.14, p < .001], accounting for approximately 45% of the variance in WAV
performance. The reading readiness factors significantly explained performance on
the Word Analysis and Vocabulary subtest over and above participation in a
multitiered intervention model [R2change = .39, F(5, 111) = 13.98, p < .001].
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.
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 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
None All other predictors
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
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 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
intervention model did not offer much additional predictive information about third
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
None All other predictors
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
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 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).
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 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
participation in a multitiered intervention model did not offer much additional
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
relative strength of each predictor.
95
Table 20 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Literary Response and Analysis Subtest
Predictor
Controls
None All other predictors
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
Participation in a multitiered reading intervention model explains more about
third grade reading achievement on District Benchmark Tests than kindergarten
scores on letter names, letter sounds, oral blending, oral segmenting, and sight word
tasks.
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 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.
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 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].
Participation in a multitiered intervention model did not significantly explain
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 summarizes the relative strength of each predictor.
Table 21 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Basic Phonics Skills Test
Predictor
Controls
None All other predictors
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
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 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).
The linear combination of the five kindergarten reading readiness variables
and participation in a multitiered intervention model were significantly related to
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
relative strength of each predictor.
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 third DBT subtest: Oral Text Accuracy
(OTA), after controlling for the kindergarten reading readiness factors. As in the
previous analysis, the first set of predictors included the five kindergarten reading
99
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 22 Bivariate and Partial Correlations of the Reading Readiness Predictors With the Silent Comprehension Subtest
Predictor
Controls
None All other predictors
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
None All other predictors
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
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 fourth DBT subtest: Oral Text Fluency
(OTF), 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
101
model as a single variable. A Bonferroni approach was applied to control for Type I
error (.05/10 = .005).
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 OTF subtest [R = .55, R2 = .30, R2adj = .26, F(6, 110) =
7.92, p < .001], accounting for approximately 30% of the variance in OTF
performance. The reading readiness factors significantly explained performance on
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
participation in a multitiered intervention model did not offer much additional
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
summarizes the relative strength of each predictor.
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 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
None All other predictors
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
Note. None of the correlations were significant.
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).
The linear combination of the five reading readiness variables and
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
multitiered intervention model [R2change = .28, F(5, 111) = 8.53, p < .001].
Participation in a multitiered intervention model did not significantly explain
performance on the Oral Text Comprehension subtest over and above the reading
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readiness factors [R2change = .01, F(1, 110) = 1.53, p = .22]. It appears that
participation in a multitiered intervention model did not offer much additional
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
None All other predictors
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
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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
demographic factors of socioeconomic status, parent education level, ethnicity, or
English language level in kindergarten.
A multiple regression analysis using ordered sets of predictors was conducted
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
control for Type I error (.05/15 = .003).
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
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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
summarizes the relative strength of each predictor.
Table 26 Bivariate and Partial Correlations of the Demographic Predictors With First grade Basic Phonics Skills Test Performance
Predictor
Controls
None All other predictors
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
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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
English language level in kindergarten.
A multiple regression analysis using ordered sets of predictors was conducted
to explain how much participation in a multitiered intervention program contributed
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).
The linear combination of the four demographic variables and participation in
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
above participation in a multitiered intervention model [R2change = .31, F(4, 112) =
12.66, p < .001]. Participation in a multitiered intervention model also significantly
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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
None All other predictors
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
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fluency test than the kindergarten demographic factors of socioeconomic status,
parent education level, ethnicity, and English language level in kindergarten.
A multiple regression analysis using ordered sets of predictors was conducted
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
control for Type I error (.05/6 = .008).
The linear combination of the four demographic variables and participation in
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
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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
None All other predictors
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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A multiple regression analysis using ordered sets of predictors was conducted
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).
The linear combination of the four demographic variables and participation in
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
relative strength of each predictor.
Table 29 Bivariate and Partial Correlations of the Demographic Predictors With First grade End-of Intervention Basic Phonics Skills Test Performance
Predictor
Controls
None All other predictors
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.
A multiple regression analysis using ordered sets of predictors was conducted
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).
The linear combination of the four demographic variables and participation in
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.
Table 30 summarizes the relative strength of each predictor.
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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
None All other predictors
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
Note. None of the correlations were significant.
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
together accounted for approximately 23% of the variance in performance [Rchange =
.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
together accounted for approximately 45% of the variance in performance [Rchange =
.45, F(5, 111) = 17.97, p < .001]. For the SC subtest, the reading readiness factor set
accounted for approximately 41% of the variance in performance [Rchange = .41, F(5,
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
accounted for approximately 30% of the variance in performance [Rchange = .30, F(5,
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
124
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
125
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
126
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,
127
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
128
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.
129
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
130
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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