We propose a new measure of individual differences in reading comprehension ability that is theoretically motivated, is easy to administer, and that has high predictive power. Participants read 3-sentence paragraphs that describe the relations among a set of real and artificial terms, and then they respond to true-false statements that assess their ability to access and integrate long-term memory knowledge with text information, to make text-based inferences, and to recall text. The components of our task predict performance on a test of global reading comprehension and on a range of specific comprehension tests, each of which draws more heavily on one particular component. Our task is better at predicting reading comprehension than is a typical working memory span task and has the potential for advancing researchers' understanding and measurement of a range of linguistic and cognitive tasks.

When taking multiple-choice tests of reading comprehension such as the Scholastic Assessment Test (SAT), test takers use a range of strategies that vary in the extent to which they emphasize reading the questions versus reading the passages. Researchers have challenged the construct validity of these tests because test takers can achieve better-than-chance performance even if they do not read the passages at all. By using an individual-differences approach that compares the relative power of working memory span to predict SAT performance for different test-taking strategies, the authors show that the SAT appears to be tapping reading comprehension processes as long as test takers engage in at least some reading of the passages themselves.

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Although a considerable amount of evidence has been amassed regarding the contributions of lower‐level word processes, higher‐level processes, and working memory to reading comprehension, little is known about the relationships among these sources of individual differences or their relative contributions to reading comprehension performance. This study addresses these shortcomings by using structural equation modeling. The principal structural equation model tested in this study—called the cognitive components‐resource model of reading comprehension—proposes a set of specific relationships among lower‐level word processing, higher‐level processes, and working memory. This model is then systematically compared with a series of other models that propose alternative relationships among these three sources of individual differences. The results show that, although working memory influences higher‐level processes, speed of lower‐level word processing exerts little to no influence on higher‐level processes or working memory. The results also show that a variant of the cognitive components‐resource model of reading comprehension accounts for 62% of the variance in reading comprehension performance. Taken as a whole, the present study informs theories of reading comprehension by proposing relationships among important sources of individual differences. It also provides a foundation for future research seeking to test and compare theories of reading comprehension and other sources of individual differences.

The present study reports the development of a theoretically motivated measure that provides estimates of a preschooler's ability to recall auditory text, to make text-based inferences, to access knowledge from long-term memory, and to integrate this accessed knowledge with new information from auditory text. This new preschooler component processes task is based on measures developed by Hannon and Daneman (2001) and August, Francis, Hsu, and Snow (2006), but it uses pictures and auditory text to make it more suitable for children 4–6 years of age. The results show that the new task is suitable for understanding the contributions of higher level processes to performance on a measure of language comprehension. In fact, it appears to be a better predictor of language comprehension performance than it is a measure of working memory. In addition, its medium-knowledge integration component is a good predictor of performance on a composite measure of fluid intelligence. Finally, a factor analysis reveals 3 separate clusters of abilities: word decoding skills, higher level processes associated with text-based processing, and higher level processes associated with accessing prior knowledge.