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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: gran1061@wlu.ca
E. Geva
Ontario Institute for Studies in Education, University of Toronto, Toronto, ON, Canada
123
Read Writ (2012) 25:1899–1928
DOI 10.1007/s11145-012-9370-y
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.
123
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
123
(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
1902 A. Grant et al.
123
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
Measures of reading comprehension 1903
123
(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
1904 A. Grant et al.
123
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
Measures of reading comprehension 1905
123
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
1906 A. Grant et al.
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).
Measures of reading comprehension 1907
123
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1908 A. Grant et al.
123
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
Measures of reading comprehension 1909
123
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).
1910 A. Grant et al.
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
Measures of reading comprehension 1911
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
1912 A. Grant et al.
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).
Measures of reading comprehension 1913
123
Ta
ble
3P
ears
on
corr
elat
ion
sb
etw
een
var
iab
les
wit
hP
ort
ug
ues
eb
elo
wth
ed
iago
nal
and
Sp
anis
hab
ov
eth
ed
iago
nal
12
34
56
78
91
01
11
21
3
1.
PP
VT
–.4
9*
*.3
8*
*.4
0**
.30
*.3
7*
*-
.28
*-
.23
.40
**
.50
**
.50
**
.50
**
.28
*
2.
Wo
rdID
.56
**
–.8
3*
*.8
8**
.54
**
.65
**
-.6
9*
*-
.60
**
.66
**
.83
**
.83
**
.83
**
.34
*
3.
Wo
rdat
tack
.44
**
.88
**
–.7
2**
.63
**
.65
**
-.6
2*
*-
.52
**
.58
**
.77
**
.76
**
.70
*.2
7*
4.
TO
WR
Ew
ord
s.4
8**
.91
**
.84
**
–.6
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.57
**
-.6
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*-
.57
**
.52
**
.85
**
.88
**
.81
**
.24
5.
TO
WR
En
on
word
s.3
8**
.79
**
.82
**
.83
**
–.3
8*
*-
.50
**
-.3
8**
.46
**
.61
**
.62
**
.40
**
.13
6.
PA
.38
**
.73
**
.76
**
.68
**
.66
**
–-
.58
**
-.4
7**
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**
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**
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**
.32
*
7.
RA
Nle
tter
s-
.33
**
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**
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6*
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**
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**
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8
8.
RA
Nd
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s-
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**
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6*
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2*
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8*
*-
.53
**
-.1
4
9.
GO
RT
RC
.47
**
.61
**
.53
**
.61
**
.48
**
.46
**
-.4
0*
*-
.29
*–
.55
**
.56
**
.59
**
.21
10
.G
OR
Tac
cura
cy.4
6**
.88
**
.85
**
.85
**
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**
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**
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9*
*-
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–.9
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11
.G
OR
Tfl
uen
cy.4
6**
.87
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–.7
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12
.W
LP
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C.4
9**
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**
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**
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–.2
9*
13
.M
AT
.44
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2.2
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1914 A. Grant et al.
123
Ta
ble
4P
ears
on
corr
elat
ions
bet
wee
nvar
iable
sfo
rC
anto
nes
esp
eaker
s
12
34
56
78
91
01
11
21
3
1.
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VT
–
2.
Wo
rdID
.66
**
–
3.
Wo
rdat
tack
.54
**
.86
**
–
4.
TO
WR
Ew
ord
s.5
8*
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3*
*.7
3*
*–
5.
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En
on
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s.5
2*
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**
.71
**
.71
**
.55
**
.62
**
–
7.
RA
Nle
tter
s-
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-.3
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**
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8.
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igit
s-
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**
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*-
.31
*-
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**
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9*
.62
**
–
9.
GO
RT
RC
.62
**
.67
**
.58
**
.71
**
.79
**
.49
**
-.1
3-
.22
–
10
.G
OR
Tac
cura
cy.6
1*
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–
11
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–
12
.W
LP
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–
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.M
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Measures of reading comprehension 1915
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.
1916 A. Grant et al.
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
Measures of reading comprehension 1917
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
1918 A. Grant et al.
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
Measures of reading comprehension 1919
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.
1920 A. Grant et al.
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,
Measures of reading comprehension 1921
123
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.
1922 A. Grant et al.
123
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.
Measures of reading comprehension 1923
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.
1924 A. Grant et al.
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.
Measures of reading comprehension 1925
123
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.
1926 A. Grant et al.
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.
Measures of reading comprehension 1927
123
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.
1928 A. Grant et al.
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