Predicting Expository Text Processing: Causal Content Density as a Critical Expository Text Metric

D. Jake Follmer, Ping Li, Roy Clariana

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)625-662
Number of pages38
JournalReading Psychology
Volume42
Issue number6
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Education
  • Developmental and Educational Psychology
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'Predicting Expository Text Processing: Causal Content Density as a Critical Expository Text Metric'. Together they form a unique fingerprint.

Cite this