Using Single‐Case Research Designs to Examine the Effects of Interventions in Special Education

Abstract

Single‐case research methods provide the basis for evaluating effective instructional approaches in special education. The purpose of this article is to provide special educators an overview of single‐case research methods, with an emphasis on how these designs are used to establish whether an instructional practice relates to improved learner outcomes. Specifically, we describe (1) core principles of single‐case design (SCD) research, (2) two frequently used SCDs—withdrawal and multiple‐baseline designs, (3) how visual analysis of graphed data is used to examine functional relations in SCDs, (4) limitations to the generalizability of findings from individual SCD studies, and (5) two studies in the special education literature that use SCDs. Our take‐home message is that SCDs can be used to determine whether an instructional intervention causes improved outcomes for students, but caution is warranted when generalizing results from individual SCD studies.

Using a Systematic Review to Explore Single-Subject Design Use Within Special Education Over Time

“…Single-subject designs reflect the tenets of educational psychology that emphasise each student's individualised learning needs and allow for simple methods of data collection and analysis (Forbes et al, 2011). Single-subject designs are primarily used to examine the existence of a causal, or functional, relationship between the dependent and independent variables; they rely on the participant serving as their own control across various settings, rather than using another participant or participant group (Cakiroglu, 2012;Maggin, Cook, & Cook, 2018). The demonstration of a functional relationship in single-subject design research is particularly important when assessing whether an educational intervention should be considered evidence based (Horner & Kratochwill, 2012).…”

“…Observational data from across multiple conditions are typically represented visually (e.g., line graph) and analysed for within-participant data patterns of trend, level, and variability (Tankersley et al, 2008). Behaviour analysis through within-study replication (i.e., repeated measurement across participants/participant groups, behaviours/tasks, conditions/settings) improves the data's reliability and allows researchers to explore the intervention's effect on student behaviour and determine whether a functional relationship between the independent and dependent variables exists (Horner et al, 2005;Maggin et al, 2018). To illustrate a causal relationship between variables, single-subject research must also repeatedly and systematically introduce interventions across conditions.…”

Making Sense of Single‐Case Design Effect Sizes

“…See Maggin et al. () for additional information on how to visually analyze SCD research by considering patterns related to level, trend, and variability of performance.…”

“…Visual analysis of data patterns to determine whether, and the degree to which, an experimental effect is replicated within a study is the primary basis for analyzing SCD data and concluding whether a functional relation exists between the intervention and target outcomes. See Maggin et al (2018) for additional information on how to visually analyze SCD research by considering patterns related to level, trend, and variability of performance.…”

“…His curiosity piqued, Mr. Katz searches for resources to assist with interpreting each of these types of effect sizes. Maggin et al (2018) emphasized that the traditional approach for determining whether data in an SCD study indicate a functional relation is visual analysis, or visually examining data level, trend, and variability within and between phases. Increasingly, however, researchers are including statisticsoften referred to as effect sizes-to provide another way to describe and analyze the graphed results.…”