Data-Based Decision Making in Reading Interventions: A Synthesis and Meta-Analysis of the Effects for Struggling Readers

Marissa J. Filderman, Jessica R. Toste, Lisa Anne Didion, Peng Peng, Nathan H. Clemens

Research output: Contribution to journalReview article

3 Scopus citations


For students with persistent reading difficulties, research suggests one of the most effective ways to intensify interventions is to individualize instruction through use of performance data—a process known as data-based decision making (DBDM). This article reports a synthesis and meta-analysis of studies of reading interventions containing DBDM for struggling readers, as well as the characteristics and procedures that support the efficacy of these interventions. A systematic search of peer-reviewed literature published between 1975 and 2017 was conducted, resulting in 15 studies of reading interventions that incorporated DBDM for struggling readers in Grades K–12. A comparison of students who received reading interventions with DBDM with those in business-as-usual (BAU) comparison groups yielded a weighted mean effect of g =.24, 95% confidence interval (CI) = [.01 to.46]. A subset of six studies that compared students receiving similar reading interventions with and without DBDM yielded a weighted mean effect of g =.27, 95% CI = [.07,.47]. Implications for DBDM in reading interventions for struggling readers and areas for future research are described. In particular, experimental investigation is necessary to establish DBDM as an evidence-based practice for struggling readers.

Original languageEnglish (US)
Pages (from-to)174-187
Number of pages14
JournalJournal of Special Education
Issue number3
StatePublished - Nov 1 2018



  • data-based decision making
  • data-based individualization
  • intensive intervention
  • reading

ASJC Scopus subject areas

  • Education
  • Rehabilitation

Cite this