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Talwar, A., Tighe, E. L., & Greenberg, D. (2018). Augmenting the simple view of reading for struggling adult readers: A unique role for background knowledge. Scientific Studies of Reading22(5), 351–366.

Summary by Maryam Khan and Yusra Ahmed, Ph.D.

Study Background

Background knowledge refers to the conceptual knowledge and experiences that readers bring to a learning task. Students vary not only in cultural knowledge, but also in general academic knowledge in areas such as science, domain knowledge related to disciplinary areas such as history or biology, and prior knowledge of key vocabulary words related to specific topics (Cervetti & Wright, 2020). Students with learning disabilities may be particularly lacking in academic knowledge and are slow at activating existing knowledge. 

In studies with children and undergraduate students, background knowledge has been found to be a significant contributor to reading comprehension. In studies of adults who struggle with reading, however, the contribution of background knowledge to their reading comprehension has not been well-explored. Talwar, Tighe, and Greenberg (2018) use the simple view of reading as a framework for examining the relationship between reading comprehension and background knowledge in struggling adult readers. The simple view of reading is a framework for describing and understanding reading comprehension as the product of a person’s word reading abilities and their language competence—the ability to understand oral discourse or orally presented text (i.e., listening comprehension). This study investigated whether background knowledge should be added to word reading and general language competence as a predictor of reading comprehension in struggling adult readers. This study measured word decoding, listening comprehension, and oral vocabulary knowledge as latent variables, which means that several measures of each skill were given. Background knowledge has previously been studied in adults in relation to reading comprehension, but only in terms of general information. An additional focus of the study was to test whether academic knowledge (e.g., domain-specific knowledge in the areas of science, social studies, and humanities) and general knowledge (e.g., knowledge about common objects and situations) are separable types of knowledge in struggling adult readers and also whether these types of knowledge predict reading comprehension. Finally, the researchers broaden the definition of background knowledge by testing models in which the background knowledge construct also includes oral vocabulary knowledge, based on prior research that has found vocabulary and background knowledge to be highly correlated in secondary school students (Ahmed et al., 2016).

Purpose and Research Questions

The purpose of the study was to investigate the nature of background knowledge (i.e., what types of knowledge are separable or are really the same construct) and its relationship to components of the simple view of reading to determine whether knowledge should be incorporated into a reading comprehension model for struggling adult readers. The following research questions were addressed: 

  1. What is the underlying factor structure of background knowledge for struggling adult readers?
  2. What is an appropriate measurement model of background knowledge in conjunction with the simple view of reading components?
  3. Does background knowledge predict individual differences in reading comprehension after controlling for adults’ highest-completed grade and the components of the simple view?


Participants of the study were 222 students in adult education programs, all of whom read between third- and eighth-grade levels. Participants’ ages ranged from 16 to 71, with a mean age of 37 (SD = 15.54). All were native English speakers, and the sample was primarily African American. Reading comprehension was measured using the Woodcock-Johnson (WJ) III Normative Update Reading Comprehension assessment. Background knowledge was assessed with two measures, the WJ Academic Knowledge test and WJ General Information test. Decoding was measured through three tests, the WJ Letter-Word Identification test, the WJ Word Attack test, and the Test of Irregular Word Reading Efficiency. The WJ Story Recall and WJ Understanding Directions tests were administered to assess listening comprehension. Three measures of oral vocabulary knowledge were also included: the WJ Picture Vocabulary test, the Clinical Evaluation of Language Fundamentals (CELF) Word Classes test, and the CELF Word Definitions test. 

Based on these measures, confirmatory factor analyses were used to develop a background knowledge construct and examine its factor structure in relation to factors of oral vocabulary knowledge, listening comprehension, and decoding. Structural equation modeling was used to evaluate the relative contribution of these factors, along with highest grade completed, to reading comprehension.


Reading comprehension was found to be positively correlated with both measures of background knowledge, the WJ Academic Knowledge (r = .62, p < .001) and the WJ General Information (r = .57, p < .001). 

To address Research Question 1 (What is the underlying factor structure of background knowledge for struggling adult readers?), confirmatory factor analyses (CFAs) were used to create the factor structure of background knowledge. A single-factor CFA between background knowledge and the WJ Academic Knowledge and General Information subtests was compared with a two-factor CFA that separated the subtests into individual constructs. The very strong correlation (r = .98) between the academic and general knowledge tests indicated that they were not separable constructs. Consequently, the researchers treated background knowledge as a unidimensional factor in the subsequent models, rather than as two separable types of knowledge.

Research Question 2 (What is an appropriate measurement model of background knowledge in conjunction with the simple view of reading components?) was addressed using a four-factor CFA for latent factors of background knowledge and the simple view of reading components (decoding, oral vocabulary knowledge, and listening comprehension). Interfactor correlations between constructs were found to be moderate to strong (r = .27–.83), and the model was found to be an acceptable fit to the data (χ2 (59) = 78.01, p = .049, CFI = .986, TLI = .981, RMSEA = .038, SRMR = .042). In this model, all measures of academic and general knowledge loaded on the background knowledge factor, along with the picture vocabulary subtest of the WJ. This means that the picture vocabulary measure was comparable to the background knowledge measures and that all measures represented the same underlying construct rather than separable constructs.  Vocabulary measures from the CELF loaded on the oral vocabulary knowledge factor, indicating that these measures adequately represented the theoretical construct of oral vocabulary knowledge. 

A final analysis was run to address Research Question 3 (Does background knowledge predict individual differences in reading comprehension after controlling for the adults’ highest-completed grade and the components of the simple view?). Using the WJ PC measure as the reading comprehension outcome, background knowledge (academic + general), oral vocabulary knowledge, listening comprehension, and decoding from the final Research Question 2 model were included as the latent factors, and participants’ highest grade completed was included as a covariate. The model was found to be an excellent fit to the data (χ2 (81) = 122.13, p = .002, CFI = .974, TLI = .966, RMSEA = .048, SRMR = .044), and the predictors accounted for approximately 67% of the variance for reading comprehension. The analyses indicated that background knowledge (including both general and academic knowledge) was strongly correlated to and a significant predictor of reading comprehension. Though reading comprehension scores were positively correlated with word decoding, listening comprehension, and oral vocabulary knowledge, oral vocabulary knowledge was not found to have a significant individual contribution to reading comprehension over and above decoding, listening comprehension, and background knowledge. Highest grade completed was also not related to reading comprehension. Decoding and listening comprehension accounted for the most amount of variance, with decoding accounting for 12% and comprehension accounting for 10%. Background knowledge accounted for approximately 4% of the variance in reading comprehension. 


A limitation of the study was the use of a single reading comprehension measure that required participants to read one to three sentences and orally fill a missing word in each item instead of assessments of reading comprehension with comprehension questions or those involving retelling and summarizing to show understanding. The results may have been statistically different (although not necessarily conceptually different) had the researchers used measures more similar to those used in previous studies of reading comprehension and background knowledge or those used in high stakes literacy assessments such as state accountability or national tests of academic progress. The sample was also restricted to native English speakers, meaning that the results might not be generalizable to other populations, such as adult English learners. 


The results support that background knowledge is a significant individual contributor to reading comprehension, in addition to word decoding and listening comprehension, in adult struggling readers. These findings suggest that background knowledge may be important to measure when studying reading comprehension in adult populations with lower literacy skills. The results of this study may also have implications for further research on background knowledge as part of the reading comprehension model in younger populations, such as children who struggle with reading. 

Implications for Educators

Prominent models of reading comprehension emphasize the importance of knowledge for understanding what is read and also how reading is necessary for acquiring knowledge (Kintsch, 1988). Background knowledge also facilitates students’ ability to make inferences and form connections across different texts and from different sources (Cervetti & Wright, 2020); this is also part of the Common Core State Standards. Teachers can be instrumental in activating and building background knowledge (including academic knowledge) of children by fostering conceptual growth, but also conceptual change. Conceptual growth refers to adding to existing student knowledge, whereas conceptual change refers to revising student knowledge when students’ knowledge consists of misconceptions. For example, teachers can discuss prior knowledge and experiences and build knowledge needed to understand what is read or heard by filling in knowledge gaps using a variety of media or give students a problem to think through prior to teaching a new topic. Five concrete steps for building conceptual knowledge can be found in Bybee et al. (2016). Steps for revising student knowledge due to misconceptions can be found in Lewandowsky et al. (2020) and Lucariello and Naff (2010).


Ahmed, Y., Francis, D. J., York, M., Fletcher, J. M., Barnes, M., & Kulesz, P. (2016). Validation of the direct and inferential mediation (DIME) model of reading comprehension in grades 7 through 12. Contemporary Educational Psychology44, 68–82.

Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Powell, J. C., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins and effectiveness. BSCS.

Cervetti, G. N., & Wright, T. S. (2020). The role of knowledge in understanding and learning from text. In E. Moje, P. Afflerbach, P. Enciso, & N. Lesaux (Eds.), Handbook of reading research (Vol. 5; pp. 237–260). Routledge.  

Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review95(2), 163–182.

Lewandowsky, S., Cook, J., Ecker, U. K. H., Albarracín, D., Amazeen, M. A., Kendeou, P., Lombardi, D., Newman, E. J., Pennycook, G., Porter, E., Rand, D. G., Rapp, D. N., Reifler, J., Roozenbeek, J., Schmid, P., Seifert, C. M., Sinatra, G. M., Swire-Thompson, B., van der Linden, S., . . . Zaragoza, M. S. (2020). The debunking handbook 2020.

Lucariello, J., & Naff, D. (2010). How do I get my students over their alternative conceptions (misconceptions) for learning? American Psychological Association.