Cognitive Predictors of Two Distinct Reading Comprehension Tasks in Lower and Upper Grades

Abstract

Reading disorders, including reading comprehension disorders, are among the most common referrals for evaluation in schools. If that evaluation involves individually administered tests of reading, the examiner is faced with selecting at least one reading comprehension subtest that is inherently associated with specific task demands, such as a cloze procedure or a story retell procedure. This study explored the correlates of performance of these two types of reading comprehension measures in lower grades (i.e., grades 1–5) and upper grades (i.e., grades 6–12). Results revealed a moderate correlation between these two tasks and evidence that students with reading disorders may perform poorer on cloze reading comprehension measures than story retell measures in both grade groupings. Regression analyses revealed that variance in each reading comprehension task is associated with a unique grouping of predictor variables that are associated with the Big Five of Reading and short-term/working memory. Discussion focuses on the implications of these findings for the evaluation of students with suspected disabilities and research.

Keywords

reading comprehension standardized assessment education assessment intelligence/cognition learning disability exceptionalities/disabilities

Introduction

Reading is crucial for success throughout one’s lifespan as ample research supports the contributions of literacy to both academic and vocational achievement. Regarding academic performance, early reading success is predictive of higher reading performance in later grades and increased academic self-esteem, school enjoyment, and motivation to learn (Cunningham & Stanovich, 1997; Kaniuka, 2010; Reschly, 2014). Students with higher reading achievement are more likely to take advanced placement classes in high school and attend and graduate from college than lower-achieving peers (Lesnick et al., 2010; Mahony, 1994). Alternatively, lower reading achievement is associated with high school dropout (Reschly, 2010). Higher levels of education and adult literacy are associated with increased job opportunities, occupational status, and earnings across the lifespan (Kutner et al., 2007; Smart et al., 2017). Higher literacy rates have also been linked to increased civic and community engagement and positive parenting practices such as reading to young children and helping with homework (Kutner et al., 2007).

The most recent data from the Nation’s Report Card, a periodic assessment conducted by the National Center for Education Statistics (NCES), reported notably high levels of struggling readers across the United States. In 2022, only about 33% of fourth graders and 31% of eighth graders could read at or above the proficient level (NCES, 2022). Of note, for the first time since the Nation’s Report Card survey was introduced in 1990, scores decreased across the board from their previous results in 2019. Reading proficiency levels fell from 36% and 34% for fourth- and eighth-grade students, respectively. At both grade levels assessed, the 2022 average reading scores were lower than all scores reported since 2005 and did not significantly differ from the initial scores reported in 1992 (NCES, 2022). These data support the need for continued development of reading assessment procedures to identify struggling readers and the implementation of evidence-based reading interventions.

The Big Five of Reading

In 1997, U.S. Congress convened the National Reading Panel (NRP) to review decades of scientific research in order to make recommendations about the most effective methods to teach children to read. The Panel’s comprehensive report identified five components essential for reading instruction which came to be known as the “Big Five” (NRP, 2000). The Big Five included phonemic awareness, the alphabetic principle (phonological processing), vocabulary, reading fluency, and reading comprehension. Since the time of the NRP report, these have become the foundation for numerous reading instructional programs, intervention strategies, and considerable scientific reading research (e.g., Pikulski & Chard, 2005; Rayner et al., 2001; Torgesen & Mathes, 2000; Utchell et al., 2016).

Reading comprehension, or making meaning of written text, is the ultimate purpose of reading. Therefore, regarding the Big Five, the four components other than reading comprehension can be understood as inter-dependent and necessary building blocks required for the development of reading comprehension. Phonemic awareness, broadly defined as the ability to hear, differentiate, and manipulate the smallest sounds within spoken language, is an early reading skill that begins to develop well before formal schooling starts (Joseph, 2015; Torgesen & Mathes, 2000). Examples of phonemic awareness activities include rhyming, blending, and identifying syllables in spoken words.

The alphabetic principle, or the ability to understand relationships between written letters/letter groups and their sounds, is necessary to begin reading at the word level (i.e., decoding). The alphabetic principle requires one to understand that words are made up of individual letters which represent sounds, and that those letters can be re-coded to form new words. Once this principle is understood, one is able to read (i.e., de-code) words using letter–sound correspondences (Conrad & Deacon, 2016; Joseph, 2015). Teaching the alphabetic principle requires systematic phonics instruction that incorporates numerous and repeated practice opportunities, as well as careful monitoring of student progress in response to instruction.

Reading fluency is the ability to read text with speed, accuracy, and prosody (Schreiber, 1980). LaBerge and Samuels (1974) provided an automaticity theory of passage reading, suggesting that humans have a limited cognitive capacity. Dysfluent readers must devote these limited resources to the time-consuming and difficult task of decoding, leaving resources depleted when it comes time to make meaning of what they’ve read. There has been much research support for the theory of automaticity, including numerous studies reporting that readers who are able to decode passages automatically and effortlessly (i.e., fluent readers) have better reading comprehension and better overall academic outcome measures than dysfluent readers (Daane et al., 2005; Hudson et al., 2020; Rasinski et al., 2005).

In addition to phonemic awareness, the alphabetic principle, and reading fluency, the National Reading Panel identified vocabulary as a crucial component of effective reading instruction (2000). Several types of vocabulary, including written, reading, and oral vocabulary, are important for the development of reading comprehension. Students must understand the words that they read in order to make meaning of them. Much research, both correlational and experimental, has suggested that individuals with more sophisticated lexical knowledge have better reading comprehension (e.g., Elleman et al., 2009; Moghadam & Ghaderpour, 2012; Oslund et al., 2016; Spencer et al., 2019). Although the link between vocabulary and reading comprehension is well-established, researchers and educators disagree about the cause behind the association (see Nagy, 2005, for a discussion). However, there is some consensus that reading comprehension involves readers integrating their previously learned information, including that of vocabulary words, with the text they are reading in order to comprehend that text (Elleman et al., 2009; Perfetti & Stafura, 2014).

Cognitive Predictors of Reading Comprehension

Various models have been proposed to explain the specific cognitive skills that contribute to the complex human process of reading comprehension. The predictor variables selected for inclusion in these models have differed somewhat depending on the ages and developmental levels of the participants under investigation (Cao & Kim, 2021). For example, researchers studying younger children and less skilled readers have typically included so-called “lower skills,” such as phonological awareness, rapid automatized naming, letter–sound knowledge, decoding, and oral vocabulary to attempt to explain differences in reading comprehension among individuals (Adlof et al., 2010; Schatschneider et al., 2004). When investigating the reading comprehension of older and more skilled readers, researchers have more frequently included “higher-level” constructs such as vocabulary, inferential understanding, and background knowledge (Cromley & Azevedo, 2007; Landi, 2010).

Researchers tend to use theoretical models to make predictions about behavior, including the cognitive correlates of reading comprehension. For instance, Floyd et al. (2012) used the Cattell–Horn–Carroll (CHC; Schneider & McGrew, 2018) theory of cognitive development to try to explain differences in the reading comprehension of a large sample of children and young adults from the standardization sample of the Woodcock–Johnson III (WJ-III; Woodcock, McGrew, & Mather, 2001). Alternatively, the NRP’s Big Five of Reading could be used as a model to select the predictor variables in a similar investigation (2000). The Big Five may have particular value because that schema is composed of achievement-oriented constructs familiar to educators working in schools (i.e., teachers, reading specialists, and special educators). Therefore, findings from such an investigation may have more far-reaching impacts for practitioners.

Reading Comprehension Tasks Selected for the Present Investigation

A variety of activities with differing cognitive task demands have been used in cognitive predictor studies to measure reading comprehension (Cao & Kim, 2021). These include silent or oral reading followed by comprehension questions, retell tasks, and passage completion tasks like maze and cloze. For the present study, story retell and cloze were investigated as two reading comprehension tasks represented in the Woodcock–Johnson-IV Tests of Cognitive Abilities, Oral Language and Achievement (WJ-IV; McGrew et al., 2014), a commonly used standardized test battery in schools.

Retell tasks require students to write down or tell an examiner everything they can recall from a story they have just read. Because there are no limits on student responding, the retell task is thought to assess both higher (e.g., deep connections with prior knowledge) and lower (e.g., direct recall of events from story) reading processes. Additionally, germane to this study, this task requires short-term/working memory skills to hold the story in one’s mind in order to repeat it. The cloze task requires one to complete passages that are missing approximately every seventh word by providing a word that makes sense in the context from memory. Although the retell and cloze tasks differ in format, administration procedures, and test characteristics, they both purport to measure the same basic construct: reading comprehension.

Some research has been conducted to try to understand if different tests of reading comprehension do actually measure the same construct (Francis et al., 2005; Nation & Snowling, 1997) and if they can reliably be used to identify children with reading disabilities (Cutting & Scarborough, 2006). For instance, research has suggested that retell tasks are only moderately correlated with other measures of reading comprehension, with the association stronger in younger students (Shapiro et al., 2014). Furthermore, a recent meta-analysis found that retell correlated more strongly with cloze and maze tasks than with multiple-choice tasks, even after controlling for grade level (Cao & Kim, 2021).

Keenan et al. (2008) compared reading comprehension subtests from widely used standardized achievement tests in order to measure their intercorrelations. Results indicated that different subtests ultimately measured different underlying reading skills. For instance, some subtests like the maze task were more influenced by decoding skills while others were more influenced by language comprehension. The researchers suggested that differences in subtest length and format may contribute to performance differences. For instance, in a reading task involving only a single sentence, there are few available contextual clues to aid a poor decoder in correctly responding. With tasks involving lengthier reading passages, the same student is more likely to come across familiar words or phrases, or they may rely on prior knowledge to respond. In summary, Keenan et al. (2008) emphasized the importance of understanding the task demands of different reading comprehension subtests because they may not all measure the same construct. The authors further underscored that subtest choice can resultantly impact eligibility and other high-stakes instructional decisions.

Does Subtest Selection Matter During Evaluation?

Understanding what the various tests of reading comprehension actually measure, and how they differ from one another, is important from research and practical perspectives. In schools, an array of tests of reading comprehension are used as tools with which to make high-stakes decisions about identifying children with disabilities. Therefore, school psychologists and others involved in assessment and team decision-making should be informed about subtest strengths and limitations when making selections for psycho-educational evaluations.

Currently, three methods may be used by schools to evaluate children for the special education service category of specific learning disability (SLD), including reading specific learning disabilities: response to intervention (RtI), the ability–achievement discrepancy, and pattern of strengths and weaknesses (PSW; Maki & Adams, 2020). Although methods all have empirical criteria intended to reduce the subjectivity of decision-making, research has called into question the consistency with which eligibility decisions are actually made by school psychologists. For instance, Maki et al. (2017) demonstrated that the overall consistency rate of identification of SLD was relatively low (73%) among a sample of 376 school psychology practitioners across 22 states.

Furthermore, research has demonstrated that a student’s eligibility for special education services is influenced by the method used for the evaluation (Maki & Adams, 2020; Miciak et al., 2014). The method selected can be dependent upon several factors including state requirements, school district preferences, and the clinical judgment of the school psychologist. Additionally, as there is great disparity in subtests available for use within a psychoeducational evaluation (recall the variety of reading comprehension subtests discussed previously), subtest choice has the potential to affect eligibility results. Miciak et al. (2015) demonstrated this in an investigation of the consistency of SLD eligibility decisions of school psychologists using two test batteries purported to measure the same constructs within a processing strengths and weaknesses (PSW) framework. Researchers found that using the different test batteries that measure the same constructs resulted in poor consistency in eligibility decisions (only 65% agreement), and agreement was particularly poor regarding the academic domains of identification. For instance, a student identified with a basic reading skills deficit using one test battery might be identified with a reading comprehension deficit using the other. There was agreement on the academic domain of deficit for only 23% of participants identified as LD by at least one of the test batteries (Miciak et al., 2015). Taking these findings together, subtest selection can seemingly influence eligibility decisions for special services, which can hinge on a single numerical point. This paper informs how performance on two distinct reading comprehension tasks may differ, and in turn influence eligibility decisions when classification is dependent on statistical differences between indicators of cognitive ability and reading comprehension achievement.

Purpose of the Study

This study sought to learn about the cognitive correlates of two different types of reading comprehension measures commonly used in the evaluation of reading problems and to learn how students with and without identified reading disorders perform on these two tasks. As a result, this investigation aimed to answer the following research questions. First, what are the correlations among measures of phonemic awareness, alphabetic principle, reading fluency, vocabulary, short-term/working memory, reading retell as an approach to measure reading comprehension, and a maze task as a measure of reading comprehension in a lower grade group (i.e., aggregate data from grades 1–5) and upper grade group (i.e., aggregate data from grades 6–12)? The construct of short-term/working memory was included as a variable in this study, in addition to indicators of the Big Five of Reading, as the construct is logically required to successfully complete a retell comprehension task. We hypothesized that the correlations between the cloze task and retell task would be moderate, rather than high, given differing task demands. Consistent with this, we also hypothesized that the correlation between the measure of vocabulary would be greater stronger with the cloze task (i.e., Passage Comprehension) than the story retell task (i.e., Reading Recall). In turn, we hypothesized that the correlation between the measures of short-term/working memory would be greater with the story retell task than the maze task. Regarding students with and without reading disabilities, respectively, the second research question asked, does performance on a maze comprehension task differ from performance on a reading retell comprehension task? We hypothesized that in both groups students may perform poorer on Reading Recall as compared to Passage Comprehension due to comparatively heavier short-term/working memory demands inherent in the former subtest. The third research question asked, to what extent do phonemic awareness, alphabetic principle, reading fluency, vocabulary, and short-term/working memory predict performances on cloze (i.e., Passage Comprehension) and reading retell (i.e., Reading Recall) comprehension tasks in lower and upper grades of the standardization sample, respectively? We hypothesized that vocabulary skills would emerge to be a greater predictor of Passage Comprehension than Reading Recall, whereas short-term/working memory skills would be a greater predictor of Reading Recall than Passage Comprehension.

Methods

Participants

The primary participants of this study were composed of children and adolescents in grades 1 through 12 that participated in the standardization of the Woodcock–Johnson-IV Tests of Cognitive Abilities, Oral Language, and Achievement (McGrew et al., 2014). Only standardization sample participants with complete data and without extreme outlying achievement scores below standard score of 40 were included in respective analyses (n = 3891; McGrew et al., 2014). The approximately 10 cases with achievement standard scores below 40 were excluded due to largely missing data. Research question 2 sought to learn about the reading comprehension performance of students with reading disorders. The Woodcock–Johnson-IV technical manual states that to be part of the Learning Disability (LD)-Reading clinical validity study, the child must have a documented disability in reading [subtype not specified], Reading LD [subtype not specified] must be the child’s primary diagnosis or eligibility category, and that the child must currently receive special education services under the specific learning disability category. Students who participated in the reading disorder clinical validity study (n = 76; McGrew et al., 2014) were not administered the full complement of subtests needed to explore research questions 1 and 3. Therefore, students with reading disorder were only included in the analyses for the second research question. Prior to conducting analyses, an a priori decision was made to create two groups of reading disorder students to inform the performance of these students in the elementary school years (i.e., “lower grades” 1–5 when students are more likely to receive an initial psychoeducational evaluation) and in middle/high school years (i.e., “upper grades 6–12”). This resulted in two reading disorder participant groups of relatively equal sizes (see Table 1). Once this was established, and prior to conducting analyses, two groupings of standardization sample participants were formed using the same grade range parameters (i.e., lower grades 1–5 and upper grades 6–12).

Table 1.

Participant Demographics.

	Lower grades	Upper grades
Standardization sample
N	1176	1995
Gender
Male (%)	594 (49.5)	949 (48.5)
Female (%)	582 (50.5)	1006 (51.5)
Age (M/SD)	9.78 (1.11)	14.84 (1.97)
Race (%)
American Indian	9 (.8)	12 (.6)
Asian/Pacific Islander	62 (5.3)	89 (4.6)
Black	148 (12.6)	296 (15.1)
White	923 (78.5)	1517 (77.6)
Other	34 (2.9)	41 (2.1)
Ethnicity (%)
Hispanic	214 (18.2)	358 (18.3)
Non-Hispanic	962 (81.8)	1597 (81.7)
Reading disorder sample
N	35	42
Gender
Male (%)	22 (62.9)	20 (47.6)
Female (%)	13 (37.1)	22 (52.4)
Age (M/SD)	9.87 (1.76)	14.08 (1.56)
Race (percent)
American Indian	—	1 (2.4)
Asian/Pacific Islander	1 (2.9)	—
Black	12 (34.3)	7 (16.7)
White	21 (60.0)	28 (66.7)
Other	1 (2.9)	6 (14.3)
Ethnicity (%)
Hispanic	5 (14.3)	8 (19.0)
Non-Hispanic	30 (85.7)	34 (81.0)

Measures

The Woodcock–Johnson-IV Tests of Cognitive Abilities, Oral Language, and Achievement is a commonly administered family of instruments used in the comprehensive psychoeducational evaluation of students with learning difficulties. Constructed in congruence with the Cattell–Horn–Carroll (CHC) theory of cognitive abilities (see Schneider & McGrew, 2018, for a contemporary review), select subtests and composites of this instrument—which have strong evidence of reliability and validity (Mather & Wendling, 2014; McGrew et al., 2014)—align with constructs of the Big Five of Reading. Evidence of reliability will be provided below from these sources, with coefficients of 0.75 and above being considered evidence of excellent reliability (Cicchetti, 1994). Evidence of validity of the measures is most directly ascertained by examination of clinical validity studies that document poorer performance on these subtests by those with learning difficulties compared to controls (Mather & Wendling, 2014; McGrew et al., 2014).

The WJ-IV Achievement battery includes two different reading comprehension measures with unique task demands that make the study of cognitive correlates of distinct reading comprehension tasks possible. The specific dependent/criterion variables for this study included the Passage Comprehension and Reading Recall subtests of WJ-IV Achievement battery. The Passage Comprehension subtest is a cloze procedure task that requires the examinee to generate a word that could be inserted into a blank space within a brief passage so that the text then makes sense. Reading Recall is a subtest that requires one to read a passage and then retell elements of the passage from memory. Median reliability coefficients of these subtests are both approximately .90.

In order to explore the cognitive predictors of the aforementioned reading comprehension subtests, the WJ test batteries were reviewed for composites and/or subtests that may be used to represent the constructs of the Big Five of Reading and short-term/working memory. The Phonetic Coding composite, which is composed of the Segmentation and Sound Blending subtests of the WJ-IV Oral Language, was selected to represent the construct of phonemic awareness because both only require manipulation of sounds and no analysis of print. Both median reliability coefficients are approximately .90.

Alphabetic principle in this study was represented by the Basic Reading Skills composite of the WJ-IV Achievement. This composite is composed of the Letter–Word Identification (real word reading) and Word Attack (nonsense word reading) subtests. The reported median reliability coefficients of these subtests are .94 and .79, respectively.

The construct of reading fluency was represented by the Reading Fluency Composite of the WJ-IV Achievement. This composite is composed of the Sentence Reading Fluency subtest and the Oral Reading subtest. The Sentence Reading Fluency subtest requires an examinee to quickly read individual sentences and then determine if the statement is true or false under timed conditions. In contrast, the Oral Reading subtest requires the reading of passages, rather than sentences. The median reliability coefficients of these subtests are approximately .95.

The vocabulary construct was represented by the Oral Vocabulary subtest of the WJ-IV Cognitive. Lexical knowledge is demonstrated by two different tasks within the subtest. One requires the examinee to produce synonyms and the other antonyms, to target words. This subtest has a median reliability coefficient of .89.

Finally, the construct of short-term/working memory was represented by the Short-Term/Working Memory composite of the WJ-IV-Cognitive. This composed of two distinct subtests. First, Verbal Attention requires an examinee to hold combinations of numbers and animal names in memory and then answer a question from the examiner, like “tell me the number between dog and cat.” The median reliability coefficient of this subtest is .86. Finally, Numbers Reversed requires an examinee to listen to a series of numbers and then repeat them in reverse order. This subtest has a reported median reliability coefficient of .88.

Data Acquisition Procedure

Data for this study were obtained from the standardization of the Woodcock–Johnson-IV Tests of Cognitive Abilities, Oral Language, and Achievement (see McGrew et al., 2014, for details regarding standardization procedures). After IRB approval was obtained, a data request with supporting rationale was submitted to and approved by the test publisher. A deidentified data was then supplied to the researchers for analyses.

Results

Preliminary analyses were conducted to ensure that all data met the required statistical assumptions for each analysis (e.g., normal distribution). Tables 2 and 3 display descriptive statistics and correlation analyses regarding study variables in lower grades and upper grades, respectively. Values of skewness and kurtosis were examined ahead of conducting regression analyses to verify normal distribution of data. Each fell within the accepted range of negative ten to positive ten for skewness (i.e., data curves were found to be symmetrical) and negative three to positive three for kurtosis (i.e., the tails of the distribution were not heavy in relation to the normal distribution of data) (Field, 2018; Griffin & Steinbrecher, 2013).

Table 2.

Descriptive Statistics and Correlations for Lower Grades.

Variable	N	M	SD	Skewness	Kurtosis	1	2	3	4	5	6	7
1 Phonetic Coding	1176	100.53	15.54	.008	.035	—	.370***	.269***	.376***	.231***	.416***	.373***
2 Basic Reading Skills	1211	99.30	15.81	−.187	.262		—	.758***	.603***	.629***	.766***	.514***
3 Reading Fluency	1209	98.98	16.39	−.128	.521			—	.565***	.595***	.700***	.482***
4 Oral Vocabulary	1211	99.90	15.60	−.025	.088				—	.431***	.626***	.476***
5 Reading Recall	1211	100.38	16.46	−.177	.709					—	.654***	.400***
6 Passage Comprehension	1211	99.90	16.75	−.059	.898						—	.435***
7 STWM	1176	100.22	15.51	−.113	.404							—

Note. STWM = Short-Term Working Memory; *p < .05. **p < .01. ***p < .001.

Table 3.

Descriptive Statistics and Correlations for Upper Grades.

Variable	N	M	SD	Skewness	Kurtosis	1	2	3	4	5	6	7
1 Phonetic Coding	1955	100.13	15.81	−.116	.115	—	.467***	.323***	.444***	.219***	.499***	.405***
2 Basic Reading Skills	1997	99.63	16.66	−.057	.698		—	.701***	.598***	.521***	.721***	.511***
3 Reading Fluency	1997	99.64	16.25	−.245	.689			—	.559***	.550***	.671***	.463***
4 Oral Vocabulary	1997	100.20	15.90	.071	.145				—	.330***	.656***	.499***
5 Reading Recall	1996	98.74	16.85	−.153	.666					—	.555***	.316***
6 Passage Comprehension	1997	99.33	16.76	−.127	.440						—	.458***
7 STWM	1955	100.75	15.59	.047	−.103							—

Note. STWM = Short-Term Working Memory; *p < .05. **p < .01. ***p < .001.

The first research question explored the bivariate correlations among study variables in the lower and upper grades, respectively (see Tables 2 and 3). As hypothesized, the correlations between Passage Comprehension and Reading Recall were moderate in both the lower (r = .65) and upper (r = .56) grade groupings. This moderate correlation suggests that despite both being measures of reading comprehension, performance on the respective task demands may be characterized by unique cognitive predictors of the respective subtests. As hypothesized, in the lower grade group Oral Vocabulary did have a greater relationship with Passage Comprehension (r = .63; moderate correlation) than Reading Recall (r = .43; low correlation). This same pattern was true in the upper grades (r = .66 [moderate correlation] and r = .33 [low correlation], respectively. We also hypothesized that Short-Term/Working Memory would have a greater correlation with Reading Recall than with Passage Comprehension due to the inherent difference in task demands, but that was not the case in either grade grouping. All correlations did fall in the moderate range, however (see Tables 2 and 3).

The second research question sought to learn if the performance of students in the standardization sample and if performance of the students in reading disorders group differ on these two subtests that have distinct task demands. Paired samples t-tests were used to explore this research question. Data displayed in Table 4 reveal that for the lower grade grouping, there was no statistically significant difference in performance between the two subtests with the standardization same group or the reading disorder group. Because sample size of the reading disorder group was relatively small (n = 35), the statistical power necessary to detect group differences between the two subtests was limited (Passage Comprehension M = 81.57, SD = 12.05; Reading Recall M = 84.71, SD = 17.60). Examination of the effect size between these two subtests in this group (ES = .29) may be considered small but meaningful (Gignac & Szodorai, 2016; Schumn et al., 2013). With respect to the upper grade grouping, there was a significant difference in performance between the two subtests in the standardization sample, though the effect size was negligible (ES = .05). However, regarding students with reading disorder in the upper grade group, the Passage Comprehension subtest (M = 78.53; SD = 11.84) proved significantly more difficult than the Reading Recall subtest (M = 84.23; SD = 12.35) with the difference being characteristic of a moderate effect size (ES = .54).

Table 4.

Reading Comprehension Subtest Differences by Grade Grouping and Sample.

	M	SD	t	Cohen’s d	M	SD	t	Cohen’s d
Lower grades	Standardization sample (N = 1176)				Reading disability (N = 35)
Reading Recall	100.85	16.21	.990	.029	84.71	17.60	1.688	.285
Passage Comprehension	100.45	16.56			81.57	12.05
Upper grades	Standardization sample (N = 1955)				Reading disability (N = 41)
Reading Recall	99.05	16.80	−2.10*	−.047	84.23	12.35	3.442**	.538
Passage Comprehension	99.80	16.51			78.53	11.84

Note. *p < .05, **p < .01.

The final research question continued to use the Big Five of Reading, with the addition of the construct of short-term/working memory, to understand the cognitive predictors of cloze reading comprehension tasks (i.e., Passage Comprehension) and story retell reading comprehension tasks (i.e., Reading Recall). To address this research question, and to control for the contributions of vocabulary and short-term working memory in regression analyses, forward entry models were attempted. However, due to unacceptably high multicollinearity values in those attempts (VIF > 1.5; Tabachnick et al., 2013), stepwise regression procedures were ultimately used because of the desire to understand which variables of interest would account for the most variance in the respective outcome variable. It additionally solved an issue of multicollinearity between predictor variables because VIF values associated with the forward stepwise procedures were below 1.5 (Tabachnick et al., 2013). Four distinct regression procedures were conducted. Table 5 and Table 6 display the extent to which Phonetic Coding (phonemic awareness), Basic Reading Skills (alphabetic principle), Reading Fluency (reading fluency), Oral Vocabulary (vocabulary), and Short-term/Working Memory explain unique variance in Passage Comprehension and Reading Recall, respectively, in the lower and upper grades.

Table 5.

Lower Grade Stepwise Regression Analyses by Reading Comprehension Subtest.

		β	SE β	t	R ²	∆R ²
Reading Recall
Model 1					.378	.378***
	Basic Reading Skills	.64	.61	26.70***
Model 2					.412	.034***
	Basic Reading Skills	.43	.41	12.39***
	Reading Fluency	.28	.27	8.18***
Model 3					.416	.004**
	Basic Reading Skills	.40	.39	11.20***
	Reading Fluency	.26	.25	7.55***
	STWM	.08	.08	2.96**

Passage Comprehension
Model 1					.568	.568***
	Basic Reading Skills	.81	.75	39.26***
Model 2					.613	.045***
	Basic Reading Skills	.64	.60	26.60***
	Oral Vocabulary	.28	.26	11.66***
Model 3					.634	.021***
	Basic Reading Skills	.49	.46	16.50***
	Oral Vocabulary	.24	.22	9.96***
	Reading Fluency	.23	.22	8.19***
Model 4					.647	.013***
	Basic Reading Skills	.45	.42	15.27***
	Oral Vocabulary	.20	.19	8.50***
	Reading Fluency	.24	.23	8.67***
	Phonetic Coding	.13	.12	6.50***
Model 5					.648	.002**
	Basic Reading Skills	.46	.43	15.46***
	Oral Vocabulary	.21	.20	8.78***
	Reading Fluency	.25	.24	8.91***
	Phonetic Coding	.14	.13	6.82***
	STWM	−.05	−.05	−2.24**

Note. STWM = Short-Term Working Memory; *p value < .05, **p value < .01, ***p value < .001.

Table 6.

Upper Grade Stepwise Regression Analyses by Reading Comprehension Subtest.

		β	SE β	t	R ²	∆R ²
Reading Recall
Model 1					.293	.293***
	Reading Fluency	.58	.54	28.44***
Model 2					.330	.037***
	Reading Fluency	.38	.36	14.31***
	Basic Reading Skills	.27	.26	10.41***
Model 3					.332	.002**
	Reading Fluency	.40	.38	14.42***
	Basic Reading Skills	.29	.28	10.53***
	Oral Vocabulary	−.05	−.05	−2.189**
Passage Comprehension
Model 1					.502	.502***
	Basic Reading Skills	.72	.71	44.33***
Model 2					.584	.083***
	Basic Reading Skills	.51	.50	27.85***
	Oral Vocabulary	.37	.35	19.74***
Model 3					.613	.029***
	Basic Reading Skills	.38	.37	18.03***
	Oral Vocabulary	.32	.30	16.80***
	Reading Fluency	.25	.24	12.06***
Model 4					.631	.018***
	Basic Reading Skills	.32	.31	15.12***
	Oral Vocabulary	.27	.26	14.26***
	Reading Fluency	.26	.25	12.88***
	Phonetic Coding	.16	.16	9.80***

Note. *p value < .05, **p value < .01, ***p value < .001.

Table 5 reveals that in the lower grades, Basic Reading Skills accounted for 38% of variance in Reading Recall (R² = .378, F (1, 1172) = 712.64, p < .001). Reading Fluency contributed an additional 3.4% of variance beyond Basic Reading Skills (R²_Change = .034, F (1, 1171) = 67, p < .001). Short-Term/Working Memory then contributed an additional .4% of variance (R²_Change = .004, F (1, 1170) = 8.80, p < .05), resulting in a final model whereby Basic Reading Skills, Reading Fluency, and Short-Term/Working Memory together explained 41.6% of the variance in Reading Recall, a passage retell comprehension measure (R² = .416, F (3, 1170) = 278, p < .001).

Table 5 also displays that in the lower grades, Basic Reading Skills explained 57% of the variance in Passage Comprehension (R² = .568, F (1, 1172) = 1541.52, p < .001). Oral Vocabulary accounted for 4.5% of additional variance in this criterion measure (R²_Change = .045, F (1, 1171) = 135.91, p < .001). In the next model, Reading fluency explained another 2.1% of variance (R²_Change = .021, F (1, 1170) = 67.12, p < .001) in Passage Comprehension. From there, Phonetic Coding explained an additional 1.3% of variance in the criterion variable (R²_Change = .013, F (1, 1169) = 42.27, p < .001), followed by Short-Term/Working Memory which accounted for a further .2% of variance (R²_Change = .002, F (1, 1168) = 5.00, p = .025). After this progression of regression analyses, these five predictor variables accounted for 64.8% of the variance in Passage Comprehension, a cloze comprehension measure (R² = .648, F (5, 1168) = 430.60, p < .001).

With respect to the upper grades, Table 6 reveals Reading Fluency accounted for 29.3% of the variance in Reading Recall (R² = .293, F (1, 1953) = 809, p < .001). Basic Reading Skills explained an additional 3.7% variance in the criterion variable (R²_Change = .037, F (1, 1952) = 108.30, p < .001). Finally, Oral Vocabulary accounted for .2% of additional variance in Reading Recall (R²_Change = .002, F (1, 1951) = 4.80, p = .029). Taken together, Reading Fluency, Basic Reading Skills, and Oral Vocabulary explained 33.2% of the variance in Reading Recall, as an exemplar passage retell comprehension measure (R² = .332, F (3, 1951) = 322.80, p < .001).

Finally, Table 6 exhibits that Basic Reading Skills accounted for 50% of the variance in Passage Comprehension (R² = .502, F (1, 1953) = 1964.93, p < .001). Oral Vocabulary then contributed to 8% of additional variance in the criterion measure (R²_Change = .083, F (1, 1952) = 389.72, p < .001). Reading Fluency explained an additional 3% variance (R²_Change = .029, F (1, 1951) = 145.54, p < .001), followed by Phonetic Coding which in turn explained another 2% of variance in Passage Comprehension (R²_Change = .018, F (1, 1950) = 95.91, p < .001). Review of the final model reveals that considered together, Basic Reading Skills, Oral Vocabulary, Reading Fluency, and Phonetic Coding account for 63.1% of the variance in Passage Comprehension, a cloze task.

Discussion

One of the most evaluated concerns in schools is reading comprehension difficulties (Catts et al., 2012; Cortelia & Horowitz, 2014). This is because adequate reading comprehension is linked to positive long-term educational and occupational outcomes for students (Cunningham & Stanovich, 1997; Kaniuka, 2010; Kutner et al., 2007; Reschly, 2014; Smart et al., 2017). As part of a psychoeducational evaluation, an examiner must choose at least one individually administered subtest to represent the construct of reading comprehension. Well known to school psychologists and educators is that a variety of reading comprehension measures, with unique task demands, are available for use. The purpose of this study was to better understand student performance on cloze and story retell tasks in lower and upper grades by using standardization and clinical validity data available through the Woodcock–Johnson Tests of Cognitive Abilities, Oral Language, and Achievement. This family of tests was selected because the achievement battery offers a cloze task (Passage Comprehension) and a story retell task (Story Recall) which made exploration of our research questions possible.

How Related Are the Reading Recall and Passage Comprehension Subtests?

The first research question examined the relationship between Passage Comprehension (cloze task) and Reading Recall (story retell task). Interestingly, although both subtests are measures of reading comprehension, the performances associated with each of these subtests were only moderately correlated in the standardization sample and were less correlated in the upper grade grouping than in the lower grade grouping. These findings are consistent with Shapiro and colleagues (2014) who also found moderate correlations between a reading retell task and other reading comprehension tasks. The fact that the correlation between these two reading comprehension tasks is only moderate suggests that there are unique cognitive predictors associated with each of the procedures.

In both the lower and upper grade groupings, there was a greater relationship between Oral Vocabulary and Passage Comprehension than there was between Short-Term/Working Memory and Reading Recall. As expected, Oral Vocabulary had a greater correlation with Passage Comprehension than Story Recall. However, despite a hypothesis that Short-Term/Working Memory would have a greater relationship with Reading Recall than with Passage Comprehension, this was not the case. In both the lower and upper grades, the correlation between the construct of short-term/working memory was greater with the cloze task than the task requiring the repeating of a previously read story. Relatively greater short-term/working memory skills apparently are required to hold a brief passage of text in mind, access one’s lexical store, and then select a missing word that makes sense in a passage.

Are There Performance Differences on These Subtests Among Groups of Students?

Regarding the second research question, we investigated whether the performances of students in the standardization sample and the reading disorder clinical validity group differed on these two subtests due to their different task demands. In the lower grade groupings (both in standardization and the reading disorder samples), there was little meaningful difference in the performance in Reading Recall and Passage Comprehension. It is necessary to consider, however, that the sample size of the reading disorder group from this study was relatively small and thus rendered limited statistical power.

In the upper grade grouping, while there was a significant difference in performance between the two subtests in the standardization sample, this difference was likely influenced by the large sample size, as the effect size was negligible and not particularly clinically meaningful. However, in students with reading disorders in the upper grade group, the Passage Comprehension subtest was significantly more difficult than the Reading Recall subtest (5.7 standard score difference), which was associated with a moderate effect size. This finding is consistent with Keenan et al. (2008) who found that students with reading disorders might be expected to exhibit reading performance differences based on the nature of the reading comprehension task. This suggests that there are different cognitive processes involved in these task demands in students with reading disorders as students age, and these variations may be used to understand performance differences on these tasks.

What Are the Cognitive Predictors of Cloze and Retell Reading Comprehension Tasks?

With respect to the third research question, we drew from the principles of the Big Five of Reading, and included the construct of short-term/working memory, to better understand the cognitive predictors of cloze reading comprehension tasks (Passage Comprehension) and story retell reading comprehension tasks (Reading Recall). Because of the multicollinearity among the predictor variables, we used stepwise regression, employing four distinct regression procedures.

In the lower grade grouping, Basic Reading Skills accounted for 38% of the variance in Reading Recall, followed by 3.4% of Reading Fluency, and .4% of Short-Term/Working Memory. This combination produced a final model in which these three variables (Basic Reading Skills, Reading Fluency, and Short-Term/Working Memory) explained 41.6% of the variance in Reading Recall, a reading retell comprehension measure. Short-Term/Working Memory was less important in understanding reading retell performance than we anticipated given the intuitive memory demands required to hold a story that was just read in memory and then retell it. However, Short-Term/Working memory did explain variance in this task beyond foundational reading skills which are well known to be great predictors of comprehension (Floyd et al., 2012; Rasinski et al., 2005; Utchell et al., 2016).

Also in the lower grade grouping, Basic Reading Skills explained 57% of the variance in Passage Comprehension, followed by 4.5% from Oral Vocabulary, 2.1% from Reading Fluency, 1.3% from Phonetic Coding (phonemic awareness), and .2% from Short-Term/Working Memory. Altogether, these five predictor variables (Basic Reading Skills, Oral Vocabulary, Reading Fluency, Phonetic Coding, and Short-Term/Working Memory) accounted for 64.8% of the variance in Passage Comprehension, a cloze comprehension measure. Consistent with our expectations, vocabulary skills—known to be critically related to reading comprehension (Moghadam & Ghaderpour, 2012; Spencer et al., 2019)—were found to play a role in completing the task demands of a cloze task.

Turning to the upper grade grouping, reading fluency skills took on greater importance as compared to the lower grade grouping with respect to Reading Recall. This is perhaps not surprising given findings that reading fluency takes on even greater importance for reading comprehension in upper grades (Rasinski et al., 2005). Reading Fluency accounted for 29.3% of the variance in Reading Recall, with Basic Reading Skills explaining an additional 3.7%, and Oral Vocabulary accounting for .2% of additional variance in Reading Recall. Again, the combination of these three predictor variables (Reading Fluency, Basic Reading Skills, and Oral Vocabulary) explained 33.2% of the variance in Reading Recall, as an exemplar of a reading retell measure. Counter to our hypothesis, Short-Term/Working Memory did not explain variance in the reading retell procedure. That said, reading efficiency did account for the greatest variance in reading retell of all the foundational reading skills, leading one to question if efficient reading lessens the demand on memory systems in the completion of this task when older grade students are faced with more lengthy passages.

Similarly, in the upper grade grouping, Basic Reading Skills accounted for 50% of the variance in Passage Comprehension, within an additional 8% from Oral Vocabulary, 3% from Reading Fluency, and 2% from Phonetic Coding. Altogether, these four predictor variables (Basic Reading Skills, Oral Vocabulary, Reading Fluency, and Phonetic Coding) account for 63.1% of the variance in Passage Comprehension. As in the lower grade grouping, lexical skills remain important when completing a task that requires generation of a word that makes sense within a passage.

Implications for Practice

This study provides evaluating psychologists and educators, like reading specialists, with clinical validity data that suggest cloze (Passage Comprehension) and reading retell (Reading Recall) procedures both prove difficult for students in lower and upper grades with a reading disorder. As such, both may reasonably appear as part of a comprehensive evaluation in response to a reading referral. That said, Passage Comprehension (cloze) appears to be a relatively more difficult task as compared to Story Recall (story retell) for students with a reading disorder, and that is notably true in the upper grades. Within the upper grades, Passage Comprehension resulted in a 5.7 standard score poorer performance as compared to Story Recall among students with a reading disorder. Based on present findings, one might surmise that the task demands of Passage Comprehension that require retrieval of lexical knowledge make this type of reading comprehension measure more cognitively complex and thereby difficult. Applying this finding to practice, if an evaluator wished to explore the possible impact of a language impairment on reading comprehension, a cloze task like Passage Comprehension that requires an examinee to access their available (and perhaps limited) lexical store might provide valuable information.

Within an environment of special service eligibility decisions being determined by mathematical formulae (i.e., ability–achievement discrepancy or processing strengths and weaknesses calculations) and the fact that students may or may not qualify for special services by one point, reading comprehension subtest selection could then impact service delivery decisions. This claim is echoed by the findings of Miciak et al. (2015) who found that learning disorder eligibility decisions differed by subtests selected to represent reading constructs within a processing strengths and weaknesses schema. That is not to say that either cloze procedures or story retell procedures are more valid or somehow flawed. Their differing task demands and uniquely associated cognitive correlates simply have implications for performance (Miller & Maricle, 2013) which may in turn impact special service decision-making to the extent mathematical formulae are involved.

Limitations and Future Research

The findings of this study must be considered within the context of the task demands of the subtests that were used as variables. This is not only true of Passage Comprehension as a cloze task and Reading Recall as a reading retell task but also of the predictor variables used to represent the Big Five of Reading and short-term/working memory. The subtest task demands associated with the constructs to be measured also have performance implications that might impact the results of this, or any other study (McGrew & Wendling, 2010). Another study limitation regards the relatively small sample size of the reading disorder groups in the lower grades and upper grades. Replicating this study with larger group sizes, and increased statistical power, is warranted. With increased sample size in the reading disorder group, greater power would be available to break the student groups into more than two grade ranges. For example, younger elementary, older elementary, middle school, and high school grouping would be possible to explore for performance differences. This might be important as the grade of the students proved to be a salient distinguishing factor between two subtests that measure reading comprehension in an analogous, but not identical, manner.

This study used the Big Five of Reading, with the inclusion of short-term/working memory, to understand the cognitive predictors of these two reading comprehension tasks. Future studies might adopt a different model of cognitive predictors (e.g., subtests related to CHC theory in its totality). Likewise, future studies might include the third main reading comprehension task, passage reading, and then answering questions posed by an examiner, as a dependent variable for analyses as conducted in this study. Finally, this study aimed to learn about performance differences among students with a reading disorder, though future studies might also explore the performances of students with other disabilities, like speech–language impairment, attention-deficit/hyperactivity disorder, or neurological disorders. Possible is that students with lexical or attention/executive function deficits arising from other disorders might also display differential performances on these two reading comprehension tasks.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Ara J. Schmitt

Laura M. Crothers

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