Meta-Analytic Review of Responsiveness-To-Intervention Research: Examining Field-Based and Research Implemented Models

NICHCY’s Structured Abstract 70 describes the following:

Title | Meta-Analytic Review of Responsiveness-to-Intervention Research: Examining Field-Based and Research Implemented Models

Authors | Burns, M.K., Appleton, J.J., & Stehouwer, J.D.

Source | Journal of Psychoeducational Assessment, 23(4), 381-394.

Year Published | 2005

This meta-analysis considered the relationship between Response-to-Intervention (RTI) and systemic and student achievement outcomes. Four existing large-scale RTI models were analyzed alongside RTI models implemented within a research context. Twenty-four effect sizes* and unbiased estimates of effect (UEE)* were computed. The UEE for student achievement and systemic outcomes both exceeded 1.0. The UEE for systemic outcomes among large-scale RTI models was significantly greater than those implemented within a research context.

Responsiveness-to-intervention (RTI) is a systemic approach to identifying student needs and meeting those needs through early intervention and sustained support. Current legislation encourages the use of processes such as RTI for the identification of students eligible to receive special education and related services. There are two primary RTI models:

  • Problem Solving Model – educational teams make decisions regarding intervention s3l3ction, instruction, and special education referral.
  • Standard Protocol Model – generally involves implementing research-based and standardized interventions within a controlled context.

Building upon research of RTI-related topics such as behavioral and academic interventions, progress monitoring*, and problem solving models, this meta-analytic review of the research investigated four existing large-scale RTI models (problem solving models) and those developed for research (standard protocol models).

Research Questions

  1. How effective are large-scale RTI models currently in practice as compared to those developed and studied in research settings?
  2. Does RTI lead to improved systemic and student outcomes?
  3. On average, what percentage of the student population was determined to have a disability under RTI?

Systemic outcomes, as defined by the authors, included:

  • referrals to and placements within special education,
  • student time in special education, and
  • retention in grade.

Student outcomes included:

  • assessment of academic skill,
  • estimates of growth in a particular skill, and
  • observations of time on task and task completion related to academic intervention.

Research Design

  • Number of Studies Included | 24
  • Number of Subjects | N/A
  • Years Spanned| N/A

Research Subjects
Elementary school students

Age/Grade of Subjects
Elementary school

Specified Disability
Children experiencing academic difficulties or identified as having a learning disability.


Duration of Intervention

Results found a larger positive effect for studies of existing RTI models than those implemented by university faculty for research, but both were strong. The positive effects for both systemic outcomes and student achievement were large. More specifically:

  • the impact on systemic outcomes among field-based RTI models was nearly twice as large as for student outcomes.
  • the opposite was true for researcher-initiated RTI models where effects for student outcomes and were more than twice as much as those for systemic outcomes.

The percentage of students whose achievement did not improve averaged 19.8% across the studies. An average, 1.68% of students were placed into special education.

Combined Effects Size
Twenty-four effect sizes* and unbiased estimates of effect (UEE)* were computed. Results found a larger UEE for studies of existing RTI models than those implemented by university faculty for research, but both were strong. Specifically, these effect sizes ranged from 0.18 to 6.71, with a mean ES of 1.49 (SD=1.43) and a median effect size of 1.09.

Several general conclusions emerged from this meta-analysis:

  • Field-based efforts consistently demonstrated stronger effects that university-based efforts.
  • Sites implementing RTI demonstrated both improved systemic and student outcomes.
  • On average, less than 2% of the student population was identified as LD* with the existing RTI model.
  • Areas for future research outlined by the authors include conducting randomized control trails to look at the effect of RTI on systemic and student outcomes.
  • The authors also suggest investigating the fidelity of RTI implementation.


* Terms Defined

Effect size (ES or d) | A statistical calculation, often represented as ES or d, that measures the impact of an intervention. An effect size below d = 0.20 suggests that a treatment did not have a significant effect. An effect size of d = 0.20 is considered small or low; an effect size of d = 0.50 is considered moderate; an effect size of d = 0.80 or above is large.

Meta-analysis | A widely-used research method in which (1) a systematic and reproducible search strategy is used to find as many studies as possible that address a given topic; (2) clear criterion are presented for inclusion/exclusion of individual studies into a larger analysis; and (3) results of included studies are statistically combined to determine an overall effect (effect size) of one variable on another.

Progress monitoring | Progress monitoring is a systemic approach to student assessment. To implement progress monitoring, the student’s current levels of performance are determined and goals are identified for learning that will take place over time. The student’s academic performance is measured on a regular basis (weekly or monthly). Progress toward meeting the student’s goals is measured by comparing expected and actual rates of learning. Based on these measurements, teaching is adjusted as needed. Thus, the student’s progression of achievement is monitored and instructional techniques are adjusted to meet the individual students learning needs. (Center for Progress Monitoring)

Unbiased Estimates of Effect (UEE) | A UEE is a weighted estimator of effect using d and the sample size for each individual study.

Back to top