Measures of reading comprehension: do they measure ......Measures of reading comprehension: do they measure different skills for children learning English as a second language? Amy

Measures of reading comprehension: do they measuredifferent skills for children learning English as a secondlanguage?

Amy Grant • Alexandra Gottardo • Esther Geva

Published online: 30 March 2012

� Springer Science+Business Media B.V. 2012

Abstract The validity of two measures of English reading comprehension was

examined across three different groups of English language learners (ELLs; 64

Portuguese, 66 Spanish and 65 Cantonese). All three groups were achieving within

the average range in second grade. An exploratory principal components analysis of

reading skills was carried out to determine which skills were related to two com-

monly used tests of reading comprehension, the Woodcock Language Proficiency

Battery’s test of Passage Comprehension (WLPB-PC; Woodcock, 1991) and the

Gray Oral Reading Test-4 (GORT-4; Wiederholt & Bryant, 2001). The factor

solutions were different for the three language groups but showed many similarities

in that the GORT-4 and WLPB-R tests of reading comprehension fell on the same

factor within each group. Hierarchical regression analyses examining relationships

among vocabulary, decoding and reading comprehension showed that language

group membership did not significantly predict performance on either measure of

reading comprehension. Differences that arose are likely due to issues with task

validity and not ELL status. Limitations and future research are discussed.

Keywords Reading comprehension measures � Validity �English language learners (ELLs)

Introduction

Research in the field of bilingual or multilingual reading development, that is

relevant to the current study, deals with understanding the cognitive mechanisms

A. Grant (&) � A. Gottardo

Department of Psychology, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada

e-mail: [email protected]

E. Geva

Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada

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Read Writ (2012) 25:1899–1928

DOI 10.1007/s11145-012-9370-y

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behind learning to read in a second language (e.g., Carlisle, Beeman, Davis, &

Spharim, 1999; Grant, Gottardo, & Geva, 2011; Lindsey, Manis, & Bailey, 2003;

Manis, Lindsey, & Bailey, 2004; Verhoeven, 2000), making comparisons among

children with different first languages (L1) (e.g., Aarts & Verhoeven, 1999;

Bialystok, Majumder, & Martin, 2003; Droop & Verhoeven, 1998; Jongejan,

Verhoeven & Siegel, 2007; Lesaux, Rupp & Siegel, 2007; Verhoeven, 1990, 2000),

and testing models of reading comprehension in second language (L2) learners

(Aarnoutse, van Leeuwe, Voeten, & Oud, 2001; Gottardo & Mueller, 2009; Hoover

& Gough, 1990; Manis, Nakamoto, & Lindsey, 2006; Proctor, Carlo, August, &

Snow, 2005; Verhoeven, 2000). However, the development of valid and reliable

reading comprehension measures for ELLs is inextricably linked to acceptable

models of reading comprehension. Although research with monolingual English

speakers has compared within-group performance across tests (Keenan, Betjemann,

& Olson, 2008; Keenan & Betjemann, 2006) this line of research has yet to be

explored for ELLs.

Task validity and reliability are key factors in test development (Leary, 2008).

For example, a test of reading comprehension is assumed to measure skills and

underlying processes related to reading comprehension. Additionally, it is hoped

that performance accross measures of reading comprehension is highly corre-

lated. Valid measures are required to assess the skill profiles of individuals and

groups of readers, and make comparisons across groups and across studies. For

example, L2 learners are reported to have lower reading comprehension skills

than their monolingual peers (e.g., August, Carlo, Dressler, & Snow, 2005).

Therefore, in order to make definitive comparisons across groups and deter-

mine performance profiles, the reading comprehension tests being used must be

valid.

The current study examines the validity of two well-known measures of reading

comprehension, the Gray Oral Reading Test-4 (GORT-4; Wiederholt & Bryant,

2001) and the Woodcock Language Proficiency Battery’s test of Passage

Comprehension (WLPB-PC; Woodcock, 1991), in three groups of ELL’s—

Portuguese, Spanish and Cantonese-students. This study focuses on construct

validity, specifically whether these two commonly used tests of reading compre-

hension correlate with each other (convergent validity), and with measures related

to reading comprehension, in this case decoding and vocabulary (Carver, 1997;

Sinatra & Royer, 1993). If tests of reading comprehension measure the same

construct they should be correlated with each other and with the same measures of

cognitive and linguistic processing regardless of the specific L1 background of the

ELL. The study however, does not examine discriminant validity, defined as

measures not related to the construct of reading comprehension not being correlated

with test performance, because no ‘‘gold standard’’ exists for reading comprehen-

sion. The validity of reading comprehension measures for monolingual English

speakers has been examined by comparing performance across measures of reading

comprehension and variables related to these measures (e.g., Keenan et al., 2008;

Nation & Snowling, 1997). However, this research has not yet been conducted with

ELLs.

1900 A. Grant et al.

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Components of reading comprehension

In order to better understand the components of reading comprehension, commonly

tested models and variables must be considered. Two key components of reading

comprehension include linguistic comprehension and decoding (Gough & Tunmer,

1986; Hoover & Gough, 1990; Sinatra & Royer, 1993). Decoding is defined as

efficient word recognition derived from printed text. Additionally, linguistic

comprehension is defined as being able to use words and grammatical information

to comprehend printed material that has been decoded. It encompasses vocabulary

knowledge, grammatical knowledge, memory and even background knowledge.

When children first learn to read, decoding and linguistic comprehension are

typically unrelated skills in native speakers (e.g., Castles & Coltheart, 2004; Rupley,

Willson & Nichols, 1998). At this stage, decoding is highly correlated with reading

comprehension and is the limiting variable in reading comprehension (Torgesen,

Wagner, & Rashotte, 1997). However, when children become more skilled

decoders, linguistic comprehension becomes the driving force behind reading

comprehension and explains increasing amounts of variance in performance (Catts,

Hogan, & Adolf, 2005; Hoover & Tunmer, 1993). A similar pattern has been

noted in ELLs with decoding being a key factor in reading comprehension

(e.g., D’Angiulli, Siegel, & Serra, 2001; Durgunoglu, Nagy, & Hancin-Bhatt, 1993;

Geva & Siegel, 2000; Geva, Yaghoub-Zadeh, & Schuster, 2000).

However, research conducted with ELLs also has found some different patterns

of relationships between decoding and linguistic comprehension in their prediction

of reading comprehension. Some researchers found that in addition to decoding,

language proficiency and vocabulary were related to reading comprehension across

elementary grades (Gottardo & Mueller, 2009; Proctor et al., 2005, 2006;

Verhoeven, 2000). For example, even in young ELLs oral language proficiency

played a role in predicting reading comprehension and contributed unique variance

beyond decoding (Gottardo & Mueller, 2009), a pattern that differs from mono-

lingual readers of the same age.

However, measuring linguistic comprehension presents the same challenges as

measuring reading comprehension, such as controlling for the role of background

knowledge and the response format (see below for a detailed discussion of the role

of response format). In many cases, vocabulary knowledge has been used as a proxy

for linguistic comprehension because it is independently and directly related to both

listening comprehension and reading comprehension (Biemiller & Slomin, 2001;

Braze, Tabor, Shankweiler, & Mencl, 2007; Proctor et al., 2005).

Additionally, vocabulary knowledge is a key area of weakness in ELLs, with

ELLs demonstrating a vocabulary deficit in relation to their peers even after years of

instruction in English (August et al., 2005; August & Shanahan, 2006; Cummins,

1991; Farnia & Geva, 2011; Geva, 2006; Lesaux, Koda, Siegel, & Shanahan, 2006;

Proctor et al., 2005; Verhoeven, 1990, 2000; Verhoeven & Vermeer, 2006). In

contrast, although L2 students tend to show lower early vocabulary skills in

comparison to L1 children, their word reading skills are generally comparable to

L1s (Aarts & Verhoeven, 1999; Geva & Yaghoub-Zadeh, 2006; Jean & Geva, 2009;

Jongejan et al., 2007; Lesaux & Siegel, 2003), given appropriate early instruction

Measures of reading comprehension 1901

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so Word reading skills are not VOCABULARY skills?

(Gersten & Baker, 2000). Therefore for the current study, the underlying processes

that will be considered in relation to performance on the tests of reading

comprehension include decoding and vocabulary knowledge.

Another factor to consider in relation to ELLs performance on reading tasks is

their L1. Differences have been found between L1 groups based on the regularity

and consistency of the orthographic conventions of their first language, specifically

the consistency of grapheme-phoneme correspondences (Bialystok, Luk, & Kwan,

2005; Ziegler & Goswami, 2005). In the context of the current study students have

been selected based on the orthographic characteristics of their L1. Spanish has the

most consistent orthography, whereas Portuguese orthography is less consistent—

with high consistency in some sounds while others are represented by accent

markers rather than conventional graphemes. In contrast, for Chinese speakers

(Cantonese in the current study), script does not map onto the level of individual

sounds (Leong & Tamaoka, 1998). Students with a more consistent L1 tend to learn

grapheme-phoneme conventions more easily. Therefore, they can use the regular

and consistent nature of their L1 script to understand the alphabetic principle, which

can assist in reading words in a less consistent L2 such as English (Bialystok et al.,

2005; DaFontoura & Siegel, 1995; Seymour, Aro, & Erskine, 2003).

In terms of the pattern of relationships among variables and reading compre-

hension, Lindsey and colleagues (Lindsey et al., 2003; Manis et al., 2004) measured

Spanish and English phonological processing and listening comprehension skills in

kindergarten and Grade 1 in relation to English reading comprehension in Grades 1

and 2. These children received high quality decoding instruction in their L1 and

achieved approximately average levels of English (L2) word reading by the end of

first grade (Lindsey et al., 2003). Although the exact measures that were related to

reading comprehension changed slightly over time, all of the relevant measures

were tasks related to decoding and word level knowledge. Proctor et al. (2005)

examined the English reading comprehension performance of fourth grade children

who were Spanish-speakers and showed that English word level reading skills were

related to English reading comprehension. Additionally, general English listening

comprehension skills and English vocabulary knowledge were independently and

significantly related to English reading comprehension performance (Proctor et al.,

2005).

Longitudinal research with Spanish-speaking ELLs showed that early word

reading at age 4� years was more strongly related to reading comprehension at age

11 than initial vocabulary knowledge or vocabulary growth (Mancilla-Martinez &

Lesaux, 2010). However, these students showed average achievement on decoding

measures and lower scores on measures of vocabulary and reading comprehension.

In contrast, a comparison of a Canadian group of ELLs with several different L1s

and their English L1 peers in Grade 2 showed the same absolute levels of English

reading comprehension (Lesaux & Siegel, 2003). The patterns of longitudinal

relationships for the groups were somewhat different with phonological processing

and grammatical knowledge being related to reading comprehension in second

grade for the English L1 group and phonological awareness and letter identification

being related to English reading comprehension for the ELLs. Similarly, Verhoeven

(2000) found that reading comprehension was related to different variables in Dutch


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L2 learners compared to native speakers. Therefore, there are differences between

L2 learners and native speakers in the variables that are related to reading

comprehension, and how these variables are related to each other over time.

Therefore, findings of previous research examining the use and validity of reading

comprehension measures in English L1 speakers does not necessarily transfer to

ELLs. Additionally, the skill levels of the ELLs play a role in the relations among

cognitive-linguistic variables and measures of reading comprehension.

Models such as the Simple View of Reading have been used to explain reading

comprehension in both L1s and ELLs (Gough & Tunmer, 1986; Gottardo &

Mueller, 2009; Sinatra & Royer, 1993). Although commonly accepted models of

reading comprehension focus on decoding and linguistic comprehension, there has

been discussion as to the manner in which these predictors are entered into the

model (i.e., sequentially, or through cross-products) and whether additional

variables should be included when investigating factors related to reading

comprehension (e.g., Cain, Oakhill, & Bryant, 2004; Kirby & Savage, 2008;

Ouelette & Beers, 2009; Savage, 2006; Tilstra, McMaster, Van den Broek,

Kendeou, & Rapp, 2009). For example, the initial Simple View model examined the

product of decoding and linguistic comprehension. It also defined decoding as

context-free word recognition (Gough & Tunmer, 1986; Hoover & Tunmer, 1993).

However, decoding is now typically assumed to include pseudoword reading which

is believed to entail similar strategies as retrieving phonological representations of

words and mapping these onto known words (Braze et al., 2007; Hoover & Gough,

1990). Although other variables (e.g., pseudoword reading as it is related to

decoding) could be included in models of reading comprehension, models using the

Simple View of Reading framework, tend to limit variables to the basic skills of oral

language proficiency and decoding. These variables account for the majority of the

variance in reading comprehension skill while allowing the model to remain

parsimonious, ‘‘simple’’.

Potential variables that could be included in the models of reading comprehen-

sion are variables underlying decoding or listening comprehension. One of the

component skills is phonological processing. Phonological awareness is one of the

best predictors of word reading ability within the first years of school for L1 and L2

students (Gottardo, Collins, Baciu, & Gebotys, 2008; Lindsey et al., 2003; Manis

et al., 2004; Wagner, Torgesen, & Rashotte, 1994). Another key phonological

processing skill related to reading is lexical access. Rapid naming tasks are typically

used to assess lexical access. These tasks are associated with the fluency of retrieval

of verbal labels, which is a skill highly related to word reading proficiency (Wolf,

1991). Rapid naming is one skill in which ELL children tend to perform at an equal

or higher level than L1 children in the early stages of reading acquisition despite

lower performance in other areas of reading (e.g., Chiappe & Siegel, 1999; Chiappe,

Siegel, & Gottardo, 2002; Chiappe, Siegel, & Wade-Woolley, 2002; Geva et al.,

2000; Lesaux & Siegel, 2003).

These cognitive abilities (e.g., phonological awareness, speed of processing) are

related to the acquisition of reading skills in an L1 or an L2 (e.g., Geva & Wade-

Woolley, 1998). Additionally, ELLs with higher verbal abilities tend to have a

higher vocabulary in both their L1 and L2, and tend to learn to read more easily


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(Bialystok, 2007; Carlisle et al., 1999). Therefore, examining relations between

measures of reading comprehension and subcomponents of reading might be more

sensitive to variability in reading comprehension performance in ELLs.

Content and format of reading comprehension tests

One factor affecting the validity of measures of reading comprehension is whether

correctly answering the comprehension questions relies heavily on reading and

comprehending the text or can be accomplished solely using background

knowledge. Although the current study does not specifically examine the content

of these tests and the reliance on background knowledge to answer questions, this

research is relevant given the specific tests being examined and the differences in

content between them. If a reading comprehension test is truly measuring

comprehension, one should be required to have read the information and

comprehended it, in order to answer questions about that material correctly.

However, if one can rely solely on background knowledge to answer questions

correctly, the test is not validly assessing reading comprehension. Keenan and

Betjemann (2006) found that scores on the Gray Oral Reading Test (GORT;

Wiederholt & Bryant, 1992, 2001) appeared to be passage independent since

undergraduates were able to score above chance on more than 86 % of the questions

on the test, and were able to respond correctly to 57 % of the questions on the

GORT-3 without having read the passages.

Passage-less comprehension performance can be the result of the content of the

passages and/or format of the questions. Katz and Lautenschlager (2001) showed

that the content of the questions themselves, or passage-independent performance, is

more predictive of item difficulty than the passage content. For the GORT, Keenan

and Betjemann (2006) also found that undergraduate students’ performance on the

test in the absence of having read the passage accounted for more variance in

performance on the test than fluency, age, and general decoding ability. Thus, it was

not the content of the passage itself that predicts performance on this measure in

older students, but most likely prior knowledge. These researchers did not find

evidence of concurrent validity in this study, as items on the GORT that were found

to be passage independent were not significantly correlated with performance on

other commonly used tests of comprehension (Keenan & Betjemann, 2006).

In order to determine construct validity, recent research on the validity of the

reading comprehension measures has examined whether specific skills are more

strongly related to performance on various standardized reading comprehension

tests (Cutting & Scarborough, 2006; Keenan et al., 2008; Nation & Snowling,

1997). Nation and Snowling examined two tests of reading comprehension, one that

tested text comprehension, the Neale Analysis of Reading Ability (NARA; Neale,

1989), and the other which tested sentence completion, the Suffolk Reading Scale

(Hagley, 1987), to test the validity of the reading comprehension construct. These

two tests were differentially related to components of reading comprehension,

where the NARA was equally related to listening comprehension and single word

reading as it should be in 7- to 10-year-old children, and the Suffolk was more

highly related to single word decoding and nonword reading. Similarly, Cutting and


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Scarborough (2006) found that reading comprehension scores on the Wechsler

Individual Achievement Test (Wechsler, 1992), the Gates-MacGinitie Reading Test

(MacGinitie, MacGinitie, Maria, & Dreyer, 2000), and the GORT were all

significantly related to decoding and linguistic comprehension. However, the degree

to which comprehension was related to decoding and linguistic knowledge, varied

across the comprehension measures. For the GORT, approximately equal unique

variance was explained by decoding and linguistic comprehension, whereas

Wechsler more unique variance was explained by decoding than linguistic

comprehension, and the Gates-MacGinitie had more unique variance explained by

linguistic comprehension than decoding in children in Grades 1 through 10.

Additional research by Keenan et al. (2008) focused on subskills related to

reading comprehension by examining the GORT, the Qualitative Reading Inventory

(QRI; Leslie & Caldwell, 2001), the Peabody Individual Achievement Test (PIAT;

Markwardt, 1989), and the Woodcock Johnson Passage Comprehension (WJ-PC;

Woodcock, McGrew, & Mather, 2001) tests of reading comprehension in a sample

of eight to 18-year-old children. Not only were modest intercorrelations found

between the tests, but differential relationships were found with decoding and

linguistic comprehension depending on the test used. Scores on the GORT and the

QRI are based on oral reading and answering questions regarding the story’s

content, through either multiple choice questions (GORT) or re-telling and listing

the main ideas involved (QRI). Performance on these tests was more highly related

to linguistic comprehension. Meanwhile, scores on the WJ-PC and the PIAT, which

are both cloze-format tests that involve filling in a missing word or choosing a

picture that matches the story’s content, were more highly related to decoding.

Furthermore, developmental differences were found in the extent to which each test

was related to linguistic comprehension and age. Although all tests demonstrated

developmental differences in test scores, the PIAT and the WJ-PC were more

significantly affected by this interaction. That is, the relationship between reading

comprehension and linguistic comprehension increased with age to a greater extent

for these tests. Similarly, cloze-format tests have been found to show a stronger

relationship between decoding and comprehension than multiple-choice, true–false

format or open-ended tests (Francis, Fletcher, Catts, & Tomblin, 2005; Francis

et al., 2006). However, the above studies have been conducted using English

speakers as participants but have not targeted ELLs.

Current study

Previous studies with children who speak English as an L1 have highlighted some

problems with frequently used measures of reading comprehension. However, as

previously mentioned, to the authors’ knowledge, no known research exists

comparing patterns of performance on reading comprehension tests with ELLs. The

current study compares measures of reading comprehension and examines the skill

profiles related to performance on two popular measures of reading comprehension

to determine whether these measures are validly assessing reading comprehension

in ELLs based on commonly used criteria, decoding and vocabulary knowledge. It

is especially important to understand how these measures assess reading


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So grammar-->decoding VS Reading comprehension--->Linguistic awareness

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So syntactic awareness indicate more?

comprehension in ELLs, as reading comprehension and vocabulary are areas of

weakness in ELLs (August et al., 2005; Farnia & Geva, 2011). In order to make

accurate judgments about the reading ability of these students, the relationships

among component reading skills and reading comprehension in ELLs must be

further understood. One of the issues in understanding how skills are related to

comprehension, is to first test and ensure measures of comprehension are validly

assessing the intended construct. Therefore, the primary research questions

addressed in the current study are as follows:

1. How do the three groups of ELLs (Portuguese, Spanish, and Cantonese)

perform, relative to each other, on cognitive measures of language and reading?

2. To what extent are the GORT-4 (Wiederholt & Bryant, 2001) and the passage

comprehension subtest of the WLPB-R (Woodcock, 1991) related to (a) one

another, and (b) to cognitive measures of language and reading, across three

groups of ELLs (Portuguese, Spanish, Cantonese)?

3. Are the two measures of reading comprehension differentially related to

decoding and vocabulary across the three ELL groups?

Method

Participants

Students from large metropolitan areas in South-Western Ontario, Canada were

recruited to participate. The data being used in the current study are a sub-sample of

longitudinal data collected from early kindergarten to fourth grade. However, only

second grade data are reported in the current study, because in this grade students

completed the two measures of reading comprehension being examined.

The participants were 64 Portuguese-speaking ELLs (M = 93.77 months,

N = 39 females), 66 Spanish-speaking ELLs (M = 92.26 months, N = 28 females)

and 65 Cantonese-speaking ELLs (M = 92.92 months, N = 29 females). Spanish

speakers originated from Latin American countries in South, Central and North

America, while Cantonese speakers were from Hong Kong, China. Finally,

Portuguese speakers had parents and grandparents who immigrated to Canada from

the Azores. Each of these groups was learning English as a second language, and

began schooling in English in at least Grade 1, with the majority beginning school in

English in Junior Kindergarten at age four. Therefore, the students had at least

2 years of formal schooling in English by second grade, but had from 2 (Grade 1

and Grade 2) to 7 years (birth to Grade 2) of experience learning English depending

on whether they began learning English at home (simultaneous bilinguals;

DeHouwer, 2005) or at school (sequential bilinguals; Flege, 1992). However, the

specific number of years that each student had learned English was not always

available.

The Spanish and Cantonese students had relatively greater proficiency in their L1

than their L2 at the time of school entry, whereas Portuguese students had

approximately equal levels of proficiency in their L2 (English) and their L1 at the


123

time of school entry. L1 data were obtained through the larger research study. When

children began schooling in English, the majority of their day was spent conversing

in their L2 rather than their L1, though their L1 was the language still being spoken

at home for the majority of students. In addition, the students received instruction

only in English within the school system. The number of years of English

experience is a factor to be considered in interpreting the results. Additionally, L1

literacy was a criterion for inclusion in the larger study, however, data on whether

students participated in after-school literacy instruction in their L1 were not

available for all groups. To ensure that the Cantonese speakers could read at least a

few Chinese characters, all of the Cantonese speakers were recruited based on

having some L1 instruction in summer or on weekends. Chinese character

recognition must be explicitly taught. In contrast, basic literacy skills are more

easily acquired in Spanish and Portuguese, which have regular sound-symbol

correspondences.

Although all reasonable efforts were taken to ensure group comparability, some

differences among groups are possible despite efforts to recruit children from

similar areas (see Table 1 for a description of each group and related demographic

information). In the area of Canada where the data were collected, many children

from similar language backgrounds reside in the same neighbourhoods and go to the

same schools due to immigration trends. There were three main areas from which

children were recruited. However, all children attended schools with students with

many other L1s, including monolingual English speakers. Due to differences that

could have been present as a result of non-measured variables, analyses were carried

out to examine if there were area1 and school effects related to reading performance.

Although these effects are important to consider in the interpretation of results, they

will not be considered in further statistical analyses due to the small number of

children in each subsample.

Income and educational level are used interchangeably or together as the two

most popular indicators of socioeconomic status (SES) and were examined in this

sample (Ensminger & Fothergill, 2003; Entwisle & Astone, 1994). Despite living in

different communities, children from different language groups were recruited from

communities with very similar levels of SES according to data on median income

and educational level from Statistics Canada (2006). These data were examined as a

further indicator of group comparability. Please see Table 1 for a description of the

comparable income and educational levels of the three participant groups.

1 No significant differences were found for the Spanish speaking students (ps [ .14) across location.

There were two significant differences between the Portuguese speakers in two areas, where the students

from the smaller metropolitan area outperformed the students from the larger area on rapid naming of

letters, F (1, 62) = 5.34, p \ .05, and on the GORT comprehension measure, F (1, 62) = 6.34, p \ .05.

Due to the differences in sample size across these two locations (N = 10–56), it is difficult to determine

whether these effects would persist with larger, and more equal samples. Similarly, due to the large

number of schools (N = 28) and the small number of participants per school (range 1–17, median = 5)

school effects could not be statistically controlled (see ‘‘Appendix’’ for a discussion of school effects).


123

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Materials

Eight standardized measures and one non-standardized measure were administered

in English for the present study. The tests included measures of oral language

proficiency, measures of word reading, and measures of reading comprehension skill.

Oral language proficiency

Peabody Picture Vocabulary Test (PPVT-III; Dunn & Dunn, 1997)

Children’s receptive vocabulary was measured using Form B of the PPVT-III. Each

participant was presented with four pictures at a time and was required to point to

the picture he thought represented the word being given orally. The test items

became increasingly difficult and when the child failed at least eight items in a block

of twelve items, testing was discontinued. Reported reliabilities (alpha) for age

seven and eight are both .95 (Dunn & Dunn, 1997).

Decoding measures

Woodcock reading mastery test-revised (WRMT-R; Woodcock, 1987)

The Word Identification subtest was used to measure word reading accuracy. It

involved reading as many words as possible before reaching a ceiling of six

consecutive errors. The words began at an easy level (e.g., is, you, and) and became

progressively harder throughout the task (e.g., torpedo, almanac). The Word Attack

subtest, a measure of pseudoword reading, also included items that became

progressively more difficult throughout the task (e.g., dee, zirdn’t, gnouthe).

Reported reliabilities from the norms for English-speaking children in age are .94

for the word reading task and .91 for the pseudoword reading task (Woodcock,

1987).

Test of word reading efficiency (TOWRE; Wagner, Torgesen, & Rashotte, 1999)

This test involved reading lists of words and pseudowords as rapidly as possible,

which measured reading fluency. Standardized scores were calculated based on the

number of items read correctly at the 45 s cut-off. If a student finished reading the

list, the total time it took the individual to read each list was recorded. Reported

reliabilities from the norms for English-speaking children at age seven are .97 and at

age eight are .95 (Wagner et al., 1999).

Phonological processing

Phonological awareness

This experimental measure involved three parts, the first of which involved syllable

awareness (e.g., say ‘bamdaw’, now say bamdaw without saying ‘bam’). The next


123

Cleo

Highlight

part of the test involved deleting phonemes at the level of the onset or rime (e.g., say

‘vock’ without /v/). The last part involved deleting phonemes within the onset or

rime (e.g., say ‘bip’ without /p/). The maximum score on each subtest was 12, for a

combined maximum score of 36 on the three subtests. Cronbach’s alpha for this test

was calculated to be .70.

Lexical access

Rapid automatized naming (RAN; Wagner et al., 1999)

Two tests from the Comprehensive Test of Phonological Processing were used,

which involved reading a list of numbers (digits) or letters, presented in rows, as

quickly as possible. Each subtest (letters, and digits) involved two trials, with the

total time taken to name each trial being recorded in seconds. Reported reliabilities

from the norms for English-speaking children at age eight are .80 for the digit

naming, and are .72 for the letter naming task (Wagner et al., 1999).

Reading comprehension

Gray Oral Reading Test (GORT-4; Wiederholt & Bryant, 2001)

This test was used as one of the two measures of reading comprehension in Grade 2.

Form B of this test was used to assess comprehension, though it also provided scores

for rate, accuracy and fluency (a combined score adding rate and accuracy together).

This test required children to read a series of short passages aloud. During this time,

the examiner marked the number of errors the child made, and the time it took the

child to read the passage. Comprehension questions followed on a page separate

from the story the child had read. These questions were read aloud to the child by

the examiner, while the child followed along and chose the correct multiple choice

answers.

The rate score was based on the time it took the child to read the passage, while

the accuracy score was based on the number of errors the child made in decoding

the passage. Both the rate and accuracy scores are converted to a score from 0 to 5

for each passage. Comprehension is calculated based on the number of correct

answers to the set of five multiple choice questions that follow each passage.

Testing is discontinued if firstly, a child is slow and inaccurate at decoding such that

the fluency score is 2 or less, and a ceiling is reached for the decoding part of the

test. Secondly, if the comprehension score was 2 or less, such that 3 out of 5

questions were missed, then a ceiling was reached for comprehension. However, a

ceiling for both fluency and comprehension must be reached for testing to be

discontinued. For example, if a child has reached ceiling on fluency but not on

comprehension they must continue to read the passages while their data for accuracy

and rate are no longer recorded, and if they reach a ceiling on comprehension their

scores will only be recorded for data they have not yet reached ceiling on. Reported

reliabilities for English-speaking norms for reading comprehension are .96 for age

seven and age eight (Wiederholt & Bryant, 2001).


123

Woodcock language proficiency battery-revised (WLPB-R; Woodcock, 1991)

The passage comprehension subtest of the WLPB-R was the other measure used to

assess reading comprehension of the students in Grade 2. This test involved the

child reading passages silently. For easy passages, the child was instructed to pick a

picture that matched the statement being read. More difficult passages involved

providing a missing word for increasingly difficult sentences. Testing is discon-

tinued when the child has missed or provided the incorrect word for six consecutive

sentences. Reported reliabilities for English-speaking norms are .95 for age six and

.88 for age nine (internal consistency reliability coefficients; Woodcock, 1991).

Cognitive factor: nonverbal reasoning

Matrix Analogies Test (MAT-Expanded Form; Naglieri, 1989)

This test involved four subtests related to reasoning and problem solving, Pattern

Completion, Reasoning by Analogy, Serial Reasoning, and Spatial Visualization.

For each subtest, the participant was asked to choose from one of the six pictures in

order to complete the pattern. Reported reliabilities from the norms for English-

speaking children at age seven are .94 and at eight are .93 (Naglieri, 1989).

Procedure

The series of standardized and experimental measures were administered to students

in two to three individual testing sessions over a maximum period of 2 weeks. The

total testing time was approximately three to three and a half hours, as it included

several other tasks, the results of which are not reported in the current study. The

tasks varied slightly in the order that they were presented as each student was

involved in several individual testing sessions. The presentation of these tasks was

not rigid, but generally involved giving the tests in the order they were presented in

the booklets. The order typically proceeded in the following way: receptive

vocabulary, word and nonword reading, test of phonological awareness, test of word

reading efficiency, WLPB-PC, rapid naming of letters, rapid naming of digits,

nonverbal reasoning and the GORT-4 reading comprehension test.

Results

The current study focuses on three research questions involving three different

groups of ELLs. Initially, the performance of these three groups was compared on

reading and cognitive-linguistic measures. Then, a series of exploratory factor

analyses were conducted, which examined how the two measures of comprehension

are related to each other in the three groups and how the cognitive-linguistic

measures tested are related to the two measures of reading comprehension. Lastly,

hierarchical regression analyses examined how the key component skills, decoding


123

and vocabulary, were related to performance on the two reading comprehension

measures and whether language status was related to comprehension performance.

Overall performance

Although both raw scores and standard scores are displayed, raw scores were used

for comparisons across groups and all subsequent analyses (see Table 2). Standard

scores are displayed to demonstrate how the groups in the current study performed

relative to what is expected for students learning English as an L1, the group whose

performance is used to establish norms for standardized tests. These ELLs

performed around the standardized mean for the majority of language and reading

Table 2 Mean scores and group comparisons between ELs

Portuguese (1) Spanish (2) Cantonese (3) Differences

Mean SD Mean SD Mean SD

PPVT-raw 106.14 15.90 91.20 18.85 101.58 20.58 1, 3 [ 2

PPVT-SS 99.73 13.28 89.69 14.23 96.63 15.71

Word ID-raw 50.78 16.15 45.33 17.09 54.98 14.57 3 [ 2

Word ID-SS 103.42 22.58 101.08 17.60 109.36 16.35

Word attack-raw 22.53 10.35 18.80 10.28 21.43 11.48

Word attack-SS 100.78 21.86 99.83 15.76 104.26 17.98

TOWRE words-raw 47.52 16.91 43.55 18.62 54.92 15.28 3 [ 2

TOWRE words-SS 101.08 16.97 100.83 18.13 109.43 14.71

TOWRE nonwords-raw 21.71 12.78 19.64 13.90 24.23 14.74

TOWRE nonwords-SS 99.98 17.36 99.24 15.09 105.25 16.85

PA-raw 27.68 6.25 26.48 6.67 25.43 6.54

RAN letters-raw 44.75 10.67 50.14 17.66 40.18 9.26 3 [ 2

RAN digits-raw 42.95 10.53 47.21 18.08 43.03 12.29

GORT RC-raw 15.42 7.28 12.14 7.98 15.49 8.05

GORT RC-SS 9.48 3.14 8.47 3.13 9.62 2.78

GORT accuracy-raw 16.75 9.42 15.24 11.13 21.00 9.66 3 [ 2

9.81 3.98 9.33 4.23 11.42 3.33

GORT fluency-raw 32.12 43.16 28.50 21.28 42.73 18.76 3 [ 1, 2

GORT fluency-SS 9.72 5.14 8.84 3.93 11.63 3.56

WLPB PC-raw 15.87 4.94 13.99 4.78 17.40 4.71 3 [ 2

WLPB PC-SS 105.60 18.29 99.73 17.23 107.13 12.95

MAT-raw 24.82 9.30 22.27 9.38 30.08 11.37 3 [ 1, 2

MAT-SS 102.80 11.22 100.56 10.19 107.64 14.05

Differences are based on raw (-raw) scores using a series of one-way ANOVA’s and Scheffe’s post hoc

comparison tests

SS standardized score, PPVT Peabody Picture Vocabulary Test, Word ID word reading, RAN rapid

automatized naming, PA phonological awareness (maximum score of 36), TOWRE Test of Word

Reading Efficiency, GORT Gray Oral Reading Test, RC reading comprehension, WLPB-PC Woodcock

Language Proficiency Battery passage comprehension, MAT Matrix Analogies Test


123

measures. There were a few exceptions to this result. The groups performed just at

or below the mean for receptive vocabulary, with the Spanish group scoring almost

one standard deviation below the mean. Also, the Cantonese group performed in the

high average range on measures of word reading accuracy and fluency.

A series of one-way ANOVA’s was carried out to compare the performance of

the three ELL groups on the measures of interest. Variance was assessed to be

homogeneous across groups using Levene’s Test for Homogeneity of Variance. As

a result, Scheffe’s post hoc comparison test was chosen to analyze differences

across groups as it is a very conservative test and is most likely to avoid Type I

error. The main pattern of differences revealed that the Cantonese group performed

better on the majority of measures compared to the Spanish group, with few other

significant differences among the groups. The Cantonese group performed

significantly better than the Spanish group on the measure of word reading, F (2,

192) = 5.74, p \ .01, rapid naming of letters, F (2, 192) = 9.01, p \ .001, and the

test of word reading efficiency, F (2, 192) = 7.10, p = .001. Both the Portuguese

and Cantonese groups outperformed the Spanish group on the receptive vocabulary

measure, F (2, 192) = 10.86, p \ .001. On the GORT-4 measure of fluency, the

Cantonese group had a faster reading rate than both the Portuguese and the Spanish

group, F (2, 192) = 8.71, p \ .001, and the Cantonese group also outperformed the

two other groups on the nonverbal reasoning measure, F (2, 192) = 9.62, p \ .001.

With respect to differences on the two measures of reading comprehension, there

were no differences between the groups on the GORT-4 measure, while on the

WLPB-R passage comprehension measure, the Cantonese group performed better

than the Spanish group, F (2, 192) = 7.53, p = .001.

Pearson correlations were carried out for all three groups (see Tables 3, 4).

Correlations for Portuguese and Spanish groups are presented on the same table

because they are more similar in terms of L1 orthography (Table 3). In order to

verify the theoretical reasoning used to statistically compare the three language

groups, Box’s M test was carried out to statistically compare the covariance

matrices for each group. The test verified earlier analyses and reasoning, that the

three groups have statistically different patterns of correlations (Box’s M = 318.87,

p \ .001).

The differences that are important to consider in the context of the current study,

are the correlations between the two reading comprehension measures themselves

and with other reading-related variables. Firstly, the WLPB-PC and the GORT-4

reading comprehension measure were significantly related to one another in all three

groups. However, the degree of this relationship ranged from moderate to highly

correlated depending upon the group measured. The two measures were most highly

related to one another in the Cantonese group (r = .75), moderately related in the

Spanish group (r = .59) and the least related in the Portuguese group (r = .39).

These correlations for the Portuguese and Cantonese groups were found to be

significantly different from one another using Fisher’s r to z transformation (z = -

3.11, p \ .01), while the comparison of correlations between the two comprehen-

sion measures were not statistically different in the Portuguese and Spanish groups

(p = .14), or the Spanish and Cantonese groups (p = .10).


123

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123

There were no significant language group differences in the correlations between

vocabulary and the GORT-4 measure (all p [ .09) or the WLPB-PC measure (all

p [ .10), across groups, using Fisher’s r to z transformation for each independent

comparison. Vocabulary and the GORT-4 measure were moderately correlated

across the groups, r = .47 for the Portuguese group, r = .40 for the Spanish group,

and r = .62 for the Cantonese group. Similar patterns were found for the

relationship between vocabulary and the WLPB-PC measure, r = .49 for

Portuguese, r = .50 for Spanish, r = .68 for Cantonese ELLs. In comparing the

correlations between vocabulary and each measure of reading comprehension, there

were no significant differences (z = .92, ns) in the Cantonese group using Meng’s

test for comparing correlations within the same sample (Meng, Rosenthal & Rubin,

1992), where vocabulary was highly related to both the GORT-4 and WLPB-PC

measures (r = .62, r = .68, respectively). Similarly, there were no differences in

the relationship between vocabulary and each comprehension measure in the

Spanish (z = 1.0, ns) or Portuguese (z = .17, ns) group using Meng’s test.

In terms of the relationship between word reading and reading comprehension,

there were once again, no significant differences between groups with respect to the

relationship between word reading and the GORT-4 measure (all p [ .58), or

between word reading and the WLPB-PC measure (all p [ .09) using Fisher’s r to

z transformation for each independent comparison. The WLPB-PC measure was

more highly related to decoding than the GORT-4 was to decoding both in the

Spanish (z = 2.67, p \ .01), and the Cantonese group (z = 3.52, p \ .001) using

Meng’s test for group comparisons. In the Portuguese group, there was no

significant difference in the correlation between the WLPB-PC measure or the

GORT-4 measure and decoding (z = 1.47, ns).

Principal components analysis

A series of exploratory factor analyses were conducted to determine which

cognitive-linguistic skills were related to the GORT-4 and WLPB-PC measure of

reading comprehension for each group of ELLs. These analyses were conducted

separately for each ELL group in order to determine which variables were related to

each reading comprehension measure, and whether the reading comprehension

measures fell onto separate factors both within and between groups (see Table 5).

An exploratory Principal Components Analysis was carried out for each language

group, and factors with Eigen values greater than 1.0 were extracted. A Promax

Rotation with Kaiser Normalization (which is a method of oblique rotation) was

used in order to allow the factors to be correlated. Although this method of rotation

sometimes makes the results more difficult to interpret than a varimax solution, this

method of rotation was deemed theoretically essential as the majority of variables in

the current study were significantly related, especially in relation to the two

measures of reading comprehension across all three groups. The rotated pattern

matrix is presented, where factor loadings greater than .45 were considered to be

meaningful. The criterion of .50 was used, as the rotated pattern matrix contains

regression weights, which correspond to roughly half of the variance and is

equivalent to using a factor loading of greater than .70 on an unrotated matrix.


123

The principal components analysis was employed with relatively small samples.

However, sample size is only one component to consider when evaluating the

validity of this type of analysis. Different recommendations regarding acceptable

sample sizes or subject to variable ratios have been proposed (see Costell &

Osborne, 2005; Henson & Roberts, 2006 for reviews). If the communalities are

high, the number of factors is small, and model error is low, a small sample size is

not an area of great concern in factor recovery (MacCallum, Widaman, Zhang, &

Hong, 1999; Preacher & MacCallum, 2002). Within our analyses, there were few

factors and the communalities were in the acceptable range, supporting the validity

of the analysis.

A three-factor solution was found for the Portuguese group, which explained

cumulatively 81.32 % of variance. The first factor was interpreted to represent

decoding because the word and nonword reading as well as phonological awareness

fell onto this factor (see Table 5). Additionally, both the GORT-4 and WLPB

reading comprehension factors fell onto this factor, meaning that according to the

factor analyses both comprehension measures were highly related to decoding. Only

two variables fell onto the second factor, both rapid naming measures, thus, this

factor was interpreted to be a phonological retrieval factor. Receptive vocabulary

alone loaded onto the third factor, which was interpreted to be an oral languagefactor. The decoding and phonological retrieval factors were highly related

(r = .51), the decoding and language factors were moderately correlated (r = .41),

while the phonological retrieval and language factors showed a small correlation to

Table 5 Factor models for Spanish, Portuguese, Mandarin, and Cantonese EL children

Portuguese Spanish Cantonese

F1 F2 F3 F1 F2 F3 F1 F2

PPVT .022 .193 .754 .651 -.333 .453 .843 -.127

Word ID .937 -.002 -.020 .657 .277 .017 .894 .084

Word attack .905 .029 .074 .683 .289 .100 .923 -.028

TOWRE-words .859 .155 .004 .735 .273 -.051 .571 .471

TOWRE-nonwords .851 .253 -.229 .783 .030 -.286 .734 .241

PA .793 .001 -.048 .036 .635 .346 .811 -.057

RAN-letters -.107 2.875 .102 -.090 2.909 .065 .265 2.985

RAN-digits .145 2.975 -.173 .104 21.00 .079 .079 2.893

GORT-RC .748 -.264 .213 .799 -.125 .040 .846 -.136

GORT-accuracy .996 -.108 -.011 .965 -.021 -.026 .717 .300

GORT-fluency 1.00 -.046 -.045 .999 -.022 -.096 .788 .241

WLPB-PC .713 .094 .277 .598 .263 .160 .969 -.105

MAT -.002 -.068 .277 -.207 .069 .962 .704 -.426

Eigen-values 8.02 1.36 1.19 7.79 1.17 1.05 8.00 1.57

% of variance 61.69 10.46 9.17 59.91 9.05 8.12 61.56 12.07

Extraction method: principal component analysis with Eigen values C1.0; rotation method: promax with

Kaiser normalization. Rotated pattern matrix, meaningful factor loadings for each variable in bold


123

one another (r = .18). Surprisingly, nonverbal reasoning did not load onto any of

the factors in this group.

A very similar solution was found for the Spanish group, where a three factor

solution was found accounting for 77.08 % of variance. Three differences emerged

in this analysis. Vocabulary loaded most highly on the first factor, meeting the

appropriate factor loading criteria. Vocabulary also loaded onto the last factor

similar to the Portuguese group. This first factor was deemed to be a generallanguage/decoding factor. One other difference for this group was that phonological

awareness also fell onto the phonological retrieval factor which was renamed

phonological processing for this group. The other difference, which was minor, is

that nonverbal reasoning loaded onto the third factor, which was interpreted to be a

general cognitive factor due to the inclusion of vocabulary in this factor. The

decoding and phonological processing factors were highly related (r = .65), the

decoding and cognitive factors were moderately correlated (r = .40), while the

phonological processing and cognitive factors showed a small correlation to one

another (r = .28) (see Table 5).

For the Cantonese group, a two factor solution was found that accounted for

73.63 % of the variance. The first factor was interpreted to be the general language/cognitive factor, which contained both measures of reading comprehension in

addition to the decoding measures, phonological awareness, receptive vocabulary,

and nonverbal reasoning. The second factor was the phonological retrieval factor,

and contained both measures of rapid naming. The two factors showed a moderate

correlation to one another (r = .54).

Components of reading comprehension

The last research question which examined whether the two measures of reading

comprehension are differentially related to decoding and vocabulary was addressed

through three hierarchical multiple regression analyses. A model based on variables

known to be related to reading comprehension, was run for each comprehension

measure.

The first two hierarchical multiple regressions examined a full model, where

language status (type of L1—Portuguese, Spanish, or Cantonese) was entered in

addition to vocabulary, decoding, the vocabulary 9 decoding interaction, a

language 9 vocabulary interaction, and a language 9 decoding interaction (see

Table 6). Firstly, language status did not predict significant variance in either the

WLPB-PC measure of comprehension, F (1, 178) = 1.91, p = .17, or the GORT-4

measure of comprehension, F (1, 178) = 3.64, p = .06. The best model was that

which included both vocabulary and decoding, but none of the interactions (see Step

3 in Table 6), F (3, 176) = 139.74, p \ .001, in predicting the WLPB-PC, and in

predicting the GORT-4, F (3, 177) = 54.26, p \ .001.

Data were further analyzed using another hierarchical regression to compare

order of entry of the significant variables above (see Table 7). Since language status

did not significantly predict performance on either comprehension measure, data

were collapsed across the three language groups. In addition, because the interaction

between vocabulary and decoding was not significant, only the unique effects of the


123

Table 6 Hierarchical multiple regression testing the simple view of reading for two measures of reading

comprehension

Predictors WLPB-PC GORT

R2 DR2 Std. b t test R2 DR2 Std. b t test

Step 1 Language .011 -.103 -1.38 .020 -.141 -1.91

Step 2 Language .301 .290 .070 1.06 .310 .290 .011 .17

V .566 8.57*** .560 8.65***

Step 3 Language .704 .403 -.011 -.26 .479 .169 -.027 -.48

V .088 1.66 .259 3.75***

D .784 15.50*** .505 7.58***

Step 4 Language .704 .000 -.011 -.25 .488 .009 -.033 -.58

V .102 .88 .026 .18

D .806 4.87*** .138 .63

V 9 D -.033 -.14 .543 1.75

Step 5 Language .704 .000 -.025 -.10 .489 .001 -.194 -.59

V .095 .59 -.048 -.23

D .806 4.85*** .130 .59

V 9 D -.032 -.14 .555 1.78

Language 9 V .014 .06 .159 .50

Step 6 Language .704 .000 -.024 -.10 .489 .000 -.185 -.57

V .098 .58 -.015 -.07

D .798 3.73*** .041 .15

V 9 D -.029 -.12 .581 1.83

Language 9 V .004 .02 .055 .15

Language 9 D .011 .06 .119 .50

V PPVT-III, D word identification (WRMT-R), WLPB-PC Woodcock Language Proficiency Battery

passage comprehension subtest, GORT Gray Oral Reading Test

* p \ .05, ** p \ .01, *** p \ .001

Table 7 Hierarchical multiple regression testing the simple view of reading for two measures of reading

comprehension collapsed across the three EL groups

Predictors WLPB-PC GORT

R2 DR2 Std. b t test R2 DR2 Std. b t test

Model 1 1. V .296 .544 8.68*** .308 .555 8.95***

1. V .094 1.88 .264 4.01***

2. D .703 .407 .780 15.59*** .478 .170 .505 7.65***

Model 2 1. D .697 .835 20.28*** .432 .657 11.69***

1. D .780 15.59*** .505 7.65***

2. V .703 .006 .094 1.88 .478 .046 .264 4.01***

V PPVT-III, D word identification (WRMT-R), WLPB-PC Woodcock Language Proficiency Battery

passage comprehension subtest, GORT Gray Oral Reading Test

* p \ .05, ** p \ .01, *** p \ .001


123

two variables, decoding and vocabulary, were examined with respect to order of

entry. Thus, in the first model, receptive vocabulary was entered before decoding as

measured through Woodcock word reading. In the second model, decoding was

entered first followed by vocabulary to examine the unique variance that each

variable contributed to performance on the measure of reading comprehension.

Results showed that these two variables explained more variance in performance for

the WLPB-PC than the GORT-4 for this sample.

For the WLPB-PC measure of reading comprehension, decoding explained more

unique variance than vocabulary. Decoding explained 40.7 % of significant unique

variance, whereas vocabulary did not explain unique variance. Shared variance of

29.0 % was found between decoding and vocabulary. For the GORT-4 measure of

comprehension, although decoding still explained more unique variance than

vocabulary, it explained less variance than for the WLPB-PC measure. Decoding

explained 17.0 % of unique variance on the GORT-4 measure of comprehension

while vocabulary explained 4.6 % significant unique variance. With respect to

shared variance, since the models for the GORT-4 did not explain as much variance

as compared to the WLPB-PC measure, the percentage of shared variance was also

lower, with 26.2 % of the variance being shared by decoding and vocabulary.

Discussion

The results of this study focused on examining the validity of two measures of

reading comprehension for ELLs. More specifically, the primary research questions

examined the extent to which two commonly administered tests of reading

comprehension—the GORT-4 and the WLPB-R were related to each other and how

they were related to measures of language and reading across three groups of ELLs,

Portuguese, Spanish and Cantonese. The questions were designed to address

whether these measures validly assess comprehension in ELLs.

Overall, the study suggests that young ELLs who are developing word reading

skills at grade level show many similarities to native speakers on reading

comprehension. Despite learning English as an L2, the mean scores for these groups

were within the average range for word reading and reading comprehension. The

students’ scores show that these children who had been educated in English for

3–4 years were progressing well based on standardized test scores normed on native

English speakers. These findings are consistent with other research conducted with

ELLs in Canada who also began their education in English from a young age

(D’Angiulli, Siegel, & Maggi, 2004; Lesaux & Siegel, 2003; Low & Siegel, 2005).

The focus of this study was to determine the convergent and construct validity of

two commonly used tests of reading comprehension. These goals were accom-

plished by determining whether the two tests were related to each other and to skills

known to be related to reading comprehension in monolingual speakers, using three

groups of ELLs. Consequently, comparisons were conducted across tests and across

L1 groups. Firstly, the relationship between the two reading comprehension

measures was examined, convergent validity. These two measures did show

convergent validity in their correlation with each other across all three groups.


123

Across groups analyses were carried out examining this same construct. Although

the reading comprehension measures were related across groups, the degree of

relatedness was different for the groups ranging from a moderately high correlation

for the Cantonese ELLs to a moderately low relationship for the Portuguese ELLs.

To examine the question of construct validity, the relationship between cognitive

variables and the comprehension measures were examined across the three groups.

There were no significant differences in the extent to which vocabulary was related

to the two comprehension measures across groups. Vocabulary was highly related to

performance on both measures. A similar pattern was found for decoding, in that it

was related to comprehension, to a similar degree across the three groups.

Similarities in the relative relationships among variables were found across the three

groups of ELLs. However, there was a difference in the extent to which each

comprehension measure was related to vocabulary and to decoding. The WLPB-PC

measure was more highly related to decoding than to vocabulary in the Spanish and

Cantonese groups, while there was no difference in this relationship in the

Portuguese group. These differences could be due to two reasons: (1) orthographic

differences in the L1 led to differing performance on the cognitive measures and

thus the degree of relatedness between the comprehension tests or, (2) the two tests

are measuring somewhat different skills across the three groups, suggesting issues

with the validity of these comprehension tests. Due to the goals of the current study,

data from L1 measures were not examined. Thus, L1 orthographic differences

cannot be completely ruled out as a cause of differences across groups (please see

limitations for further discussion of this issue).

Construct validity was also examined using principal components analyses for

each group. Although somewhat different factor solutions were found for the three

groups, there were more similarities than differences in the results. For all three

groups, the two reading comprehension measures belonged to the same factor,

although these findings are not entirely consistent across groups in terms of the other

variables that shared that factor. For example, for the Portuguese L1 group, the two

reading comprehension measures fell onto the factor that included all the decoding

variables. The existence of this factor is consistent with the results of research

conducted on young English L1 speakers for whom reading comprehension

performance is most strongly related to decoding (Catts et al., 2005). For the

Cantonese and Spanish ELLs, reading comprehension fell into a factor that included

decoding as well as vocabulary to varying extents. This finding suggests that reading

comprehension is related to a general language factor in these groups. These results

are consistent with previous research conducted with ELLs, where vocabulary

played a stronger role in the prediction of reading comprehension in ELLs than in

English L1s (Gottardo & Mueller, 2009; Proctor et al., 2005, 2006). These findings

contrast with previous research with young native English speakers where the

Woodcock is consistently related to word level reading (Keenan et al., 2008).

However, for groups of ELLs, in the current study, Spanish and Cantonese L1

speakers, performance on the Woodcock also appears to be related to vocabulary

knowledge.

For the final test of construct validity, the two measures of reading comprehen-

sion were examined in relation to known predictors of reading comprehension,


123

Cleo

Highlight

Cleo

Sticky Note

RC1*V'=RC2*V'=RC1*V''=RC2*V''=RC1*V'''=RC2*V'''（Decoding也一样）但：在RC与V的关系和RC与D的关系上，组与组之间不同。 NOTE:RC后数字代表test序号，V(Vocab)与D（decoding）后撇代表组号

specifically decoding and vocabulary knowledge, to determine if these key variables

were highly related to performance. Both across task and across group components

were examined in these analyses. Decoding was more strongly related to reading

comprehension performance than vocabulary for both tasks. This is a developmen-

tally appropriate expectation for young readers, and is consistent with L1 research

(Catts et al., 2005). Additionally, ELL group membership did not predict

performance on either measure of reading comprehension, and there were no

significant interactions between ELL group membership and decoding or vocab-

ulary knowledge in predicting comprehension performance.

Interestingly, the patterns of relationships were more consistent across groups than

across the two comprehension measures. Thus, although the two tests are measuring

similar skills in the three groups, differences in the relationship between the

comprehension measures themselves may be due to what the comprehension tests are

measuring—i.e., an issue related to test validity. Although differences in the

correlations may be due to non-measured variables, it is likely that they are

significantly affected by what the comprehension tests are measuring (i.e., differences

in the overall construct of ‘‘reading comprehension’’). Similar to previous research

examining the validity of reading comprehension measures in monolingual English

speakers, decoding predicted more variance on the WLPB-PC measure than the

GORT measure (Keenan et al., 2008). These results once again suggest that

differences across groups are more likely due to issues with validity, in that these two

tasks are measuring slightly different skill sets regardless of the group tested.

Limitations

The current study’s goal was to examine the complex relations between two

commonly used measures of reading comprehension in three diverse groups of

ELLs. As with any research comparing different groups, many variables determine

whether the differences that exist are due to measured between-group differences, or

whether these differences are due to unmeasured variables or error. At the outset of

the study, measures of SES were not obtained for each participant, though in

hindsight having complete information on the socioeconomic and educational

background for all of the families involved would have been preferable. Basic

demographic information was obtained from Statistics Canada, which allowed for a

qualitative comparison of the SES and educational background for each language

group. The data available showed that the communities in which the children lived

were generally comparable on the two most popular indicators of SES, income and

education (Ensminger & Fothergill, 2003; Entwisle & Astone, 1994). Although data

attained from one urban geographic location may have been less generalizable and

representative of ELLs across the country, these results are most likely general-

izable to similar groups of ELLs across Canada living in urban areas. The results

may differ for countries with different levels of L1 maintenance, and with highly

disadvantaged ELLs. These data were taken from three relatively diverse groups of

ELLs. Some between-group differences in language experience might be respon-

sible for some of the findings. However, the authors consider the heterogeneity of

the ELL group to be a strength of the study.


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Cleo

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Cleo

Highlight

The inclusion of an L1 measure of reading comprehension would provide

evidence of this ability in the student’s L1. However, the use of these measures is

fraught with the same validity challenges as well as additional potential problems

with reliability when creating new measures (Shahidi, Gottardo, Farnia, &

Pasquarella, 2010).

Conclusions

The results of this study show that for some younger ELLS who are progressing

well on measures of L2 reading acquisition, there were more similarities than

differences in the relationships found between performance on measures of

language and word reading, and reading comprehension across language groups.

Specifically, there were no differences in the extent to which the two comprehension

measures were related to vocabulary and decoding across groups, though there were

differences in the extent to which the two comprehension measures were related to

one another and to decoding and vocabulary between the two measures. Similar to

the work of Keenan and colleagues (2006, 2008) decoding explained more variance

in performance on the Woodcock than on the GORT measure, as slightly more

shared variance between vocabulary and decoding predicts performance on the

GORT (Keenan & Betjemann, 2006; Keenan et al., 2008). Additionally, language

group did not predict performance on either measure of reading comprehension.

Therefore, the choice of measure should be considered when measuring reading

comprehension in ELLs. As with any measure, caution should also be taken when

using either test with different groups, given past research showing each test’s

differential reliance on decoding and vocabulary across development in monolin-

gual English speakers. As passage difficulty increases and is more reliant on

background knowledge and more complex language skills, the performance of ELLs

might resemble that of native speakers in that vocabulary knowledge would be more

strongly related to understanding passages from the GORT-4, while the Woodcock

passage comprehension measure would continue to rely on decoding skill. In terms

of clinical implications, poor reading comprehension scores in young ELLs, who

have received the majority of their education in English, are likely related to poor

word level reading in second grade. However, this study does not address the role of

vocabulary or word reading in later grades when students are ‘‘reading to learn’’

(Chall, 1996) and are relying more on vocabulary knowledge to comprehend text.

The current study found, that the relation among variables is similar for these

ELLs as for monolingual English speakers, suggesting that these measures are

equally valid for groups of readers who are achieving within the average range in

second grade regardless of L1 status. However, the two tests of comprehension are

differentially related to decoding and vocabulary, which might have an impact on

the underlying skills that they are measuring. It is important to determine if the

trajectories and relations among variables in these ELLs who are achieving at grade

level continue to mimic the known performance of English as an L1 students (Catts

et al., 2005), as the ELLs progress through school.


123

Appendix

In order to determine if there were school effects, separate analyses were carried out

for each language group. There were 28 different schools involved in the current

study with the number of students in each school ranging from 1 to 17 students. The

Portuguese students were located at nine schools (N = 1–13 per school). A series of

one-way ANOVAs revealed differences across schools on all measures (all

ps \ .03) except phonological awareness, rapid naming, and matrix analogies.

Similarly, differences were found on all measures (all ps \ .04) except receptive

vocabulary, rapid naming of letters, and GORT comprehension in the Cantonese

group over 11 schools (N = 1–17 per school). No differences were found between

the eight schools in the Spanish group (N = 1–12 per school; all ps [ .17). Because

many schools had such a low sample size, post hoc analyses could not be carried out

to determine what specific school differences there were. Thus, although there were

significant differences between schools overall, these effects are largely participant

level effects due to the small sample size for each school. There were no overall

outliers, and data were collapsed within language groups. Although it is possible

that if given sufficient samples, the cause of these effects (e.g., SES, maternal

education, neighbourhood cohesion) could be analyzed, the current study was not

designed to explore these specific influences. As such, the data may be interpreted to

represent a diverse group of ELLs, who are more likely to be representative of the

overall population for each language group than children who are recruited from one

small geographic area, and from within the same school.

References

Aarnoutse, C., van Leeuwe, J., Voeten, M., & Oud, H. (2001). Development of decoding, reading

comprehension, vocabulary and spelling during the elementary school years. Reading and Writing:An Interdisciplinary Journal, 14, 61–89.

Arts, R., & Verhoeven, L. (1999). Literacy attainment in second language submersion context. AppliedPsycholinguistics, 20, 377–393.

August, D., Carlo, M., Dressler, C., & Snow, C. (2005). The critical role of vocabulary development for

English language learners. Learning Disabilities Research & Practice, 20, 50–57.

August, D., & Shanahan, T. (2006). Developing literacy in second-language learners: A report of theNational Literacy Panel on Language-Minority Children and Youth. Mahwah, NJ: Erlbaum.

Bialystok, E. (2007). Acquisition of literacy in bilingual children: A framework for research. LanguageLearning, 57, 45–77.

Bialystok, E., Luk, G., & Kwan, E. (2005). Bilingualism, biliteracy, and learning to read: Interactions

among languages and writing systems. Scientific Studies of Reading, 9, 43–61.

Bialystok, E., Majumder, S., & Martin, M. (2003). Developing phonological awareness: Is there a

bilingual advantage? Applied Psycholinguistics, 24, 27–44.

Biemiller, A., & Slomin, N. (2001). Estimating root word vocabulary growth in normative and

advantaged populations: Evidence for a common sequence of vocabulary acquisition. Journal ofEducational Psychology, 93, 498–520.

Braze, D., Tabor, W., Shankweiler, D. P., & Mencl, W. E. (2007). Speaking up for vocabulary: Reading

skill differences in young adults. Journal of Learning Disabilities, 40, 226–243.

Cain, K., Oakhill, J., & Bryant, P. (2004). Children’s reading comprehension ability: Concurrent

prediction by working memory, verbal ability, and component skills. Journal of EducationalPsychology, 96, 31–42.


123

Carlisle, J. F., Beeman, M., Davis, L. H., & Spharim, G. (1999). Relationship of metalinguistic

capabilities and reading achievement for children who are becoming bilingual. Applied Psycho-linguistics, 20, 459–478.

Carver, R. P. (1997). Reading for one second, one minute, or one year from the perspective of rauding

theory. Scientific Studies of Reading, 1, 3–43.

Castles, A., & Coltheart, M. (2004). Is there a causal link from phonological awareness to success in

learning to read? Cognition, 91, 71–111.

Catts, H. W., Hogan, T. P., & Adolf, S. M. (2005). Developmental changes in reading and reading

disabilities. In H. W. Catts & A. G. Kamhi (Eds.), The connections between language and readingdisabilities (pp. 25–40). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Chall, J. S. (1996). Learning to read: The great debate (3rd ed.). New York, NY: McGraw-Hill.

Chiappe, P., & Siegel, L. (1999). Phonological awareness and reading acquisition in English-and Punjabi-

speaking Canadian children. Journal of Educational Psychology, 91, 20–28.

Chiappe, P., Siegel, L., & Gottardo, A. (2002a). Reading related skills of kindergarteners from diverse

linguistic backgrounds. Applied Psycholinguistics, 23, 95–116.

Chiappe, P., Siegel, L., & Wade-Woolley, L. (2002b). Linguistic diversity and the development of

reading skills: A longitudinal study. Scientific Studies of Reading, 6, 369–400.

Costell, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four

recommendations for getting the most from your analysis. Practical Assessment Research &Evaluation, 10(7). Available online: http://pareonline.net/getvn.asp?v=1-&n=7.

Cummins, J. (1991). Interdependence of first- and second-language proficiency in bilingual children. In E.

Bialystok (Ed.), Language processing in bilingual children (pp. 70–89). New York, NY: Cambridge

University Press.

Cutting, L. E., & Scarborough, H. S. (2006). Prediction of reading comprehension: Relative contributions

of word recognition, language proficiency, and other cognitive skills can depend on how

comprehension is measured. Scientific Studies of Reading, 10, 277–299.

D’Angiulli, D., Siegel, L. S., & Maggi, S. (2004). Literacy instruction, SES, and word-reading

achievement in English-language learners and children with English as a first language: A

longitudinal study. Learning Disabilities Research & Practice, 19, 202–213.

D’Angiulli, A., Siegel, L., & Serra, E. (2001). The development of reading in English and Italian in

bilingual children. Applied Psycholinguistics, 22, 479–507.

DaFontoura, H. A., & Siegel, L. S. (1995). Reading, syntactic, and working memory skills of bilingual

Portuguese-English Canadian children. Reading and Writing: An Interdisciplinary Journal, 7,

139–153.

DeHouwer, A. (2005). Early bilingual acquisition: Focus on morphosyntax and the separate development

hypothesis. In J. F. Kroll & A. M. de Groot (Eds.), Handbook of bilingualism: Psycholinguisticapproaches (pp. 30–48). New York, NY: Oxford University Press.

Droop, M., & Verhoeven, L. (1998). Language proficiency and reading ability in first- and second-

language learners. Reading Research Quarterly, 38, 78–103.

Dunn, L., & Dunn, L. (1997). The peabody-picture vocabulary test-III. Circle Pines, MN: American

Guidance Service.

Durgunoglu, A., Nagy, W., & Hancin-Bhatt, B. (1993). Cross-language transfer of phonological

awareness. Journal of Educational Psychology, 85, 453–465.

Ensminger, M. E., & Fothergill, K. (2003). A decade of measuring SES: What it tells us and where to go

from here. In M. H. Bornstein & R. H. Bradley (Eds.), Socioeconomic status, parenting and childdevelopment. Monographs in parenting series (pp. 13–27). Mahwah, NJ: Lawrence Erlbaum

Associates Publishers.

Entwisle, D. R., & Astone, N. M. (1994). Some practical guidelines for measuring youth’s race/ethnicity

and socioeconomic status. Child Development, 65, 1521–1540.

Farnia, F., & Geva, E. (2011). Cognitive correlates of vocabulary growth in English language learners.

Applied Psycholinguistics, 32, 711–738.

Flege, J. E. (1992). Speech learning in a second language. In C. Ferguson, L. Menn, & C. Stoel Gammon

(Eds.), Phonological development: Models, research, and implications (pp. 565–604). Timonium,

MD: York.

Francis, D. J., Fletcher, J. M., Catts, H. W., & Tomblin, J. B. (2005). Dimensions affecting the assessment

of reading comprehension. In S. G. Paris & S. A. Stahl (Eds.), Children’s reading comprehensionand assessment (pp. 369–394). Mahwah, NJ: Erlbaum.


123

http://pareonline.net/getvn.asp?v=1-&n=7

Francis, D. J., Snow, C. E., August, D., Carlson, C., Miller, J., & Iglesias, A. (2006). Measures of reading

comprehension: A latent variable analysis of the diagnostic assessment of reading comprehension.

Scientific Studies of Reading, 10, 301–322.

Gersten, R., & Baker, S. (2000). What we know about effective instructional practices for English

language learners. Exceptional Children, 66, 454–470.

Geva, E. (2006). Second language oral proficiency and second language literacy. In D. August & T.

Shanahan (Eds.), Developing literacy in second-language learners: A report of the NationalLiteracy Panel on Language-Minority Children and Youth. NJ: Erlbaum.

Geva, E., & Siegel, L. (2000). Orthographic and cognitive factors in the concurrent development of basic

reading skills in two languages. Reading and Writing: An Interdisciplinary Journal, 12, 1–30.

Geva, E., & Wade-Woolley, L. (1998). Component processes in becoming English-Hebrew biliterate. In

A. Y. Durgunoglu & L. Verhoeven (Eds.), Literacy development in a multilingual context. Crosscultural perspectives (pp. 85–110). Mahwah, NJ: Lawrence Erlbaum Associates.

Geva, E., & Yaghoub-Zadeh, Z. (2006). Reading efficiency in native English speaking and ESL children:

The role of oral proficiency and underlying cognitive-linguistic processes. Scientific Studies ofReading, 10, 31–57.

Geva, E., Yaghoub-Zadeh, Z., & Schuster, B. (2000). Understanding individual differences in word

recognition skills of ESL children. Annals of Dyslexia, 50, 123–153.

Gottardo, A., Collins, P., Baciu, I., & Gebotys, R. (2008). Predictors of grade 2 word reading and

vocabulary learning from grade 1 variables in Spanish-speaking children: Similarities and

differences. Learning Disabilities Research & Practice, 23, 11–24.

Gottardo, A., & Mueller, J. (2009). Are first- and second-language factors related in predicting second-

language reading comprehension? A study of Spanish-speaking children acquiring English as a

second language from first to second grade. Journal of Educational Psychology, 101, 330–344.

Gough, P. B., & Tunmer, W. (1986). Decoding, reading, and reading disability. Remedial and SpecialEducation, 7, 6–10.

Grant, A., Gottardo, A., & Geva, E. (2011). How is language acquisition background related to the

development of reading comprehension in students learning to speak English as a second language?

Learning Disabilities: Research and Practice, 26, 67–83.

Hagley, F. (1987). Suffolk Reading Scale. Windsor: NFER-Nelson.

Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research:

Common errors and some comment on improved practice. Educational and PsychologicalMeasurement, 66, 393–416.

Hoover, W., & Gough, P. (1990). The simple view of reading. Reading and Writing: An InterdisciplinaryJournal, 2, 127–160.

Hoover, W., & Tunmer, W. E. (1993). The components of reading. In G. B. Thompson, W. E. Tunmer, &

T. Nicholson (Eds.), Reading acquisition processes (pp. 1–19). Adelaide, Australia: Multilingual

Matters.

Jean, M., & Geva, E. (2009). The development of vocabulary in English as a second language children

and its role in word recognition ability. Applied Psycholinguistics, 30, 153–185.

Jongejan, W., Verhoeven, L., & Siegel, L. S. (2007). Predictors of reading and spelling abilities in first-

and second-language learners. Journal of Educational Psychology, 99, 835–851.

Katz, S., & Lautenschlager, G. J. (2001). The contribution of passage and no-passage factors to item

performance on the SAT reading task. Educational Assessment, 7, 165–176.

Keenan, J. K., & Betjemann, R. S. (2006). Comprehending the Gray Oral Reading Test without reading it:

Why comprehension tests should not include passage-independent items. Scientific Studies ofReading, 10, 363–380.

Keenan, J. K., Betjemann, R. S., & Olson, R. K. (2008). Reading comprehension tests vary in the skills

they assess: Differential dependence on decoding and oral comprehension. Scientific Studies ofReading, 12, 281–300.

Kirby, J. R., & Savage, R. S. (2008). Can the simple view deal with the complexities of reading? Literacy,42, 75–82.

Leary, M. R. (2008). Introduction to behavioural research methods. Boston, MA: Pearson Education Ltd.

Leong, C. K., & Tamaoka, K. (1998). Cognitive processing of Chinese characters, words, sentences and

Japanese kanji and kana: An introduction. Reading and Writing: An Interdisciplinary Journal, 10,

155–164.


123

Lesaux, N., Koda, K., Siegel, L., & Shanahan, T. (2006). Development of literacy. In D. August & T.

Shanahan (Eds.), Developing literacy in second-language learners: A report of the NationalLiteracy Panel on Language-Minority Children and Youth. Mahwah, NJ: Erlbaum.

Lesaux, N., Rupp, A. A., & Siegel, L. S. (2007). Growth in reading skills of children from diverse

linguistic backgrounds: Findings from a 5-year longitudinal study. Journal of EducationalPsychology, 99, 821–834.

Lesaux, N. K., & Siegel, L. S. (2003). The development of reading in children who speak English as a

second language. Developmental Psychology, 39, 1005–1019.

Leslie, L., & Caldwell, J. (2001). Qualitative Reading Inventory-3. New York, NY: Addison Wesley

Longman.

Lindsey, K. A., Manis, F. R., & Bailey, C. E. (2003). Prediction of first-grade reading in Spanish-speaking

English-language learners. Journal of Educational Psychology, 93, 482–494.

Low, P. B., & Siegel, L. S. (2005). A comparison of the cognitive processes underlying reading

comprehension in native English and ESL speakers. Written Language and Literacy: Special Issue:Literacy processes and literacy development, 8, 207–231.

MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size and factor analysis.

Psychological Methods, 4, 84–99.

MacGinitie, W. H., MacGinitie, R. K., Maria, K., & Dreyer, L. G. (2000). Gates–MacGinitie reading tests(4th ed.). Itasca, IL: Riverside.

Mancilla-Martinez, J., & Lesaux, N. (2010). Predictors of reading comprehension for struggling readers:

The case of Spanish-speaking language minority learners. Journal of Educational Psychology, 102,

701–711.

Manis, F. R., Lindsey, K. A., & Bailey, C. E. (2004). Development of reading in grades K-2 in Spanish-

speaking English-language learners. Learning Disabilities Research & Practice, 19, 214–224.

Manis, F. R., Nakamoto, J., & Lindsey, K. (2006). Growth of word decoding and reading comprehensionin English language learners. Paper presented at the 13th annual meeting for the Society for

Scientific Studies in Reading, Vancouver, BC, Canada.

Markwardt, F. C. (1989). Peabody individual achievement test-revised. Bloomington, MN: Pearson

Assessments.

Meng, X. L., Rosenthal, R., & Rubin, D. (1992). Comparing correlated correlation coefficients.

Quantitative Methods in Psychology, 111, 172–175.

Naglieri, J. (1989). Matrix Analogies Test—expanded form. San Antonio, TX: Psychological Corporation.

Nation, K., & Snowling, M. (1997). Assessing reading difficulties: The validity and utility of current

measures of reading skill. British Journal of Educational Psychology, 67, 359–370.

Neale, M. (1989). Neale analysis of reading ability. Windsor, UK: NFER-Nelson.

Ouelette, G., & Beers, A. (2009). A not-so-simple view of reading: how oral vocabulary and visual-word

recognition complicate the story. Reading and Writing, 23, 189–208.

Pasquarella, A., Gottardo, A., & Grant, A. (2012). Comparing factors related to reading comprehension in

adolescents who speak English as a first (L1) or second (L2) language. Scientific Studies of Reading.

Retrieved from http://www.tandfonline.com/loi/hssr20.

Preacher, K. J., & MacCallum, R. C. (2002). Exploratory factor analysis in behavior genetics research:

Factor recovery with small sample sizes. Behavior Genetics, 32, 153–161.

Proctor, C., August, D., Carlo, M., & Snow, C. (2006). The intriguing role of Spanish language

vocabulary knowledge in predicting English reading comprehension. Journal of EducationalPsychology, 98, 159–169.

Proctor, C., Carlo, M., August, D., & Snow, C. (2005). Native Spanish-speaking children reading in

English: Toward a model of comprehension. Journal of Educational Psychology, 97, 246–256.

Rupley, W. H., Willson, V. L., & Nichols, W. D. (1998). Exploration of the developmental components

contributing to elementary school children’s reading comprehension. Scientific Studies of Reading,2, 143–158.

Savage, R. (2006). Reading comprehension is not always the product of nonsense word decoding and

linguistic comprehension: Evidence from teenagers who are extremely poor readers. ScientificStudies of Reading, 10, 143–164.

Seymour, P. H. K., Aro, M., & Erskine, J. M. (2003). Foundation literacy acquisition in European

orthographies. British Journal of Psychology, 94, 143–174.

Shahidi, V., Gottardo, A., Farnia, F., & Pasquarella, A. (2010). Challenges in developing first languageassessments for adolescent English as second language learners: Farsi speakers in Canada.


123

http://www.tandfonline.com/loi/hssr20

Retrieved May 31, 2011 from: http://docs.cllrnet.ca/LARCIC/17_VS%20&%20AG%20-%20

LARCIC%20paper.pdf.

Sinatra, G. M., & Royer, J. M. (1993). Development of cognitive component processing skills that

support skilled reading. Journal of Educational Psychology, 85, 509–519.

Statistics Canada (2006). 2006 census. Retrieved July 28, 2010 from: http://www12.statcan.ca/census-

recensement/2006/dp-pd/prof/92-597/index.cfm?lang=E.

Tilstra, J., McMaster, K., Van den Broek, P., Kendeou, P., & Rapp, D. (2009). Simple but complex:

Components of the simple view of reading across grade levels. Journal of Research in Reading, 32,

383–401.

Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. (1997). Contributions of phonological awareness and

rapid automatic naming ability to growth of word reading skills in second-to fifth grade children.

Scientific Studies in Reading, 1, 161–185.

Verhoeven, L. (1990). Acquisition of reading in a second language. Reading Research Quarterly, 25,

90–114.

Verhoeven, L. (2000). Components in early second language reading and spelling. Scientific Studies ofReading, 4, 313–330.

Verhoeven, L., & Vermeer, A. (2006). Literacy achievement of children with intellectual disabilities and

differing linguistic backgrounds. Journal of Intellectual Disabilities Research, 50, 725–738.

Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-related phonological

processing abilities: New evidence of bidirectional causality from a latent variable longitudinal

study. Developmental Psychology, 30, 73–87.

Wagner, R., Torgesen, J., & Rashotte, C. (1999). Comprehensive test of phonological processing. Austin,

TX: Pro-ED.

Wechsler, D. L. (1992). Wechsler individual achievement test. San Antonio, TX: Psychological

Corporation.

Wiederholt, J. L., & Bryant, B. R. (1992). Gray Oral Reading Test (3rd ed.). Austin, TX: PRO-ED.

Wiederholt, J. L., & Bryant, B. R. (2001). GORT 4: Gray Oral Reading Tests examiner’s manual. Austin,

TX: PRO-ED.

Wolf, M. (1991). The word-retrieval deficit hypothesis and developmental dyslexia. Learning andIndividual Differences. Special Issue: Integrating Cognitive and Neurodevelopmental Approaches,

3, 205–223.

Woodcock, R. W. (1987). Woodcock reading mastery test, revised. Circle Pines, MN: American

Guidance Service, Inc.

Woodcock, R. W. (1991). Woodcock language proficiency battery, revised. Chicago, IL: Riverside.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III tests of achievement.Itasca, IL: Riverside.

Ziegler, J., & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading

across languages: A psycholinguistic grain size theory. Psychological Bulletin, 131, 3–29.


123

http://docs.cllrnet.ca/LARCIC/17_VS%20&%20AG%20-%20LARCIC%20paper.pdf

http://docs.cllrnet.ca/LARCIC/17_VS%20&%20AG%20-%20LARCIC%20paper.pdf

http://www12.statcan.ca/census-recensement/2006/dp-pd/prof/92-597/index.cfm?lang=E

http://www12.statcan.ca/census-recensement/2006/dp-pd/prof/92-597/index.cfm?lang=E

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