Predicting Expository Text Processing

In this investigation, we examine the contribution of intrinsic content density (ICD) to measures of expository text processing. In Studies 1 and 2, the factor structure of select text density metrics was examined and refined using two text samples (Ns = 150) randomly selected from an expository text corpus. Scores on the ICD measure based on the entire text sample (N = 300) explained unique variance in readability and text easability. In Study 3, ICD predicted adults’ text ratings of interest and ease of comprehension above and beyond established easability measures. Participants’ text familiarity moderated the relation between ICD and ease of comprehension, revealing a density-facilitative effect for participants more familiar with the text content. Finally, in Study 4, measures of text difficulty, processing, and comprehension were obtained from adult readers using 10 researcher-constructed science texts; evidence of descriptive density effects on each measure was obtained. Implications for future research are discussed.


  • Follmer_et_al_2021.docx

    size: 149 KB | mime_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document | date: 2021-11-16


Work Title Predicting Expository Text Processing
Subtitle Causal Content Density as a Critical Expository Text Metric
Open Access
  1. D. Jake Follmer
  2. Ping Li
  3. Roy Clariana
License In Copyright (Rights Reserved)
Work Type Article
  1. Reading Psychology
Publication Date May 17, 2021
Publisher Identifier (DOI)
Deposited November 16, 2021




This resource is currently not in any collection.

Work History

Version 1

  • Created
  • Added Follmer_et_al_2021.docx
  • Added Creator D. Jake Follmer
  • Added Creator Ping Li
  • Added Creator Roy Clariana
  • Published
  • Updated
  • Updated
  • Updated