Read, Understand, Learn, & Excel (RULE): Development and Feasibility of a Reading Comprehension Measure for Postsecondary Learners Purpose There is a lack of quick, reliable, and valid standardized reading comprehension assessments appropriate for postsecondary readers. We attempted to address this gap by designing Read, Understand, Learn, & Excel (RULE), a reading comprehension measure that employs sentence verification and recall tasks to assess reading comprehension. This article describes ... Research Article
Newly Published
Research Article  |   September 11, 2018
Read, Understand, Learn, & Excel (RULE): Development and Feasibility of a Reading Comprehension Measure for Postsecondary Learners
 
Author Affiliations & Notes
  • Priya Kucheria
    University of Oregon, Eugene
  • McKay Moore Sohlberg
    University of Oregon, Eugene
  • HyeonJin Yoon
    University of Oregon, Eugene
  • Stephen Fickas
    University of Oregon, Eugene
  • Jason Prideaux
    University of Oregon, Eugene
  • Disclosure: The authors have declared that no competing interests existed at the time of publication.
    Disclosure: The authors have declared that no competing interests existed at the time of publication. ×
  • Correspondence to Priya Kucheria: priyak@uoregon.edu
  • Editor-in-Chief: Julie Barkmeier-Kraemer
    Editor-in-Chief: Julie Barkmeier-Kraemer×
  • Editor: Kristie Spencer
    Editor: Kristie Spencer×
Article Information
Research Issues, Methods & Evidence-Based Practice / Attention, Memory & Executive Functions / Newly Published / Research Article
Research Article   |   September 11, 2018
Read, Understand, Learn, & Excel (RULE): Development and Feasibility of a Reading Comprehension Measure for Postsecondary Learners
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0221
History: Received December 18, 2017 , Revised March 15, 2018 , Accepted April 16, 2018
 
American Journal of Speech-Language Pathology, Newly Published. doi:10.1044/2018_AJSLP-17-0221
History: Received December 18, 2017; Revised March 15, 2018; Accepted April 16, 2018

Purpose There is a lack of quick, reliable, and valid standardized reading comprehension assessments appropriate for postsecondary readers. We attempted to address this gap by designing Read, Understand, Learn, & Excel (RULE), a reading comprehension measure that employs sentence verification and recall tasks to assess reading comprehension. This article describes the exploratory study undertaken to construct RULE and then examines the preliminary concurrent validity and alternate form reliability of this measure.

Method The RULE measure was first developed by designing reading stimuli, test items for the sentence verification task, and directions for the recall test for 2 forms based on previous work (Griffiths, Sohlberg, Kirk, Fickas, & Biancarosa, 2016). Thirty undergraduate students who identified themselves as typical readers were administered the RULE measure as well as the Nelson–Denny Reading Test (Brown, Fishco, & Hanna, 1993). Students also completed questionnaires and participated in informal interviews to provide information regarding study and learning habits and academic background.

Results There was preliminary evidence of alternate form reliability between the sentence verification task sets of 2 chapters of RULE (r = .38, p < .05). Preliminary evidence for concurrent validity between RULE and the Nelson–Denny Reading Test was provided by correlation coefficients in the low to moderate range (.03–.38).

Conclusion RULE design and preliminary findings of concurrent validity and alternate form reliability provide “proof of concept” for an ecologically valid testing format that assesses comprehension skills appropriate for the postsecondary level. Suggestions for strengthening validity and reliability of the tool are provided, and clinical contributions of RULE are discussed.

Supplemental Material https://doi.org/10.23641/asha.6987371

Acknowledgments
This study was supported by the National Science Foundation PFI-AIR TT Grant 1640492 awarded to McKay Moore Sohlberg at the University of Oregon.
The authors would like to thank all the participants who contributed to the study, as well as Kayla Davis, Haley Hash, Elise Heater, Rachel Lacewell, Kelsey Lewey, and Garrett Porter, who acted as research assistants.
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