No Randomization? No Problem

2017 ◽  
Vol 39 (1) ◽  
pp. 71-90 ◽  
Author(s):  
Jennifer R. Ledford
Keyword(s):  
Single Case ◽  
Internal Validity ◽  
Experimental Conditions ◽  
Treatment Groups ◽  
Case Research ◽  
Logic Control ◽  
Large Numbers ◽  
Research Designs ◽  
Single Case Research ◽  
Single Case Designs

Randomization of large number of participants to different treatment groups is often not a feasible or preferable way to answer questions of immediate interest to professional practice. Single case designs (SCDs) are a class of research designs that are experimental in nature but require only a few participants, all of whom receive the treatment(s) of interest. SCDs are particularly relevant when a dependent variable of interest can be measured repeatedly over time across two conditions (e.g., baseline and intervention). Rather than using randomization of large numbers of participants, SCD researchers use careful and prescribed ordering of experimental conditions, which allow researchers to improve internal validity by ruling out alternative explanations for behavior change. This article describes SCD logic, control of threats to internal validity, the use of randomization and counterbalancing, and data analysis in the context of single case research.

Using data collection apps and single-case designs to research transformative learning in adults

2017 ◽  
Vol 23 (2) ◽  
pp. 206-225 ◽  
Author(s):  
Kevin M Roessger ◽  
Arie Greenleaf ◽  
Chad Hoggan
Keyword(s):  
Data Collection ◽  
Transformative Learning ◽  
Single Case ◽  
Internal Validity ◽  
Research Approach ◽  
Case Research ◽  
Research Designs ◽  
Single Case Research ◽  
Using Data ◽  
Single Case Designs

To overcome situational hurdles when researching transformative learning in adults, we outline a research approach using single-case research designs and smartphone data collection apps. This approach allows researchers to better understand learners’ current lived experiences and determine the effects of transformative learning interventions on demonstrable outcomes. We first discuss data collection apps and their features. We then describe how they can be integrated into single-case research designs to make causal inferences about a learning intervention’s effects when limited by researcher access and learner retrospective reporting. Design controls for internal validity threats and visual and statistical data analysis are then discussed. Throughout, we highlight applications to transformative learning and conclude by discussing the approach’s potential limitations.

A Legacy Neglected: Restating the Case for Single-case Research in Cognitive-Behaviour Therapy

1999 ◽  
Vol 16 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Neville M. Blampied
Keyword(s):  
Cognitive Behaviour Therapy ◽  
Single Case ◽  
Behaviour Therapy ◽  
Treatment Groups ◽  
Case Research ◽  
Cognitive Behaviour ◽  
Single Case Research ◽  
Mean Differences ◽  
Single Case Designs ◽  
Scientist Practitioner Model

AbstractThe dominant research tradition in psychology, including much of cognitive-behaviour therapy, requires that large numbers of subjects be allocated randomly to form treatment groups. Treatment effects typically are assessed by testing a null hypothesis about group mean differences. This paradigm seriously thwarts the development of a science of individual behaviour, inhibits the implementation of the scientist–practitioner model, stifles innovation, impairs accountability, and precludes the scientific investigation of the exceptional or novel case. Single-case research designs, first systematically expounded by Sidman (1960), make it possible to draw scientifically valid conclusions from the investigation and treatment of individuals. Criticisms of the between-groups research paradigm are summarised, the key elements of single-case designs are outlined, and their consistent adoption strongly recommended.

Perspectives on Pitfalls and Hassles in Single-Subject Research

1988 ◽  
Vol 13 (3) ◽  
pp. 147-154 ◽  
Author(s):  
Thomas R. Kratochwill ◽  
Bonnie L. Williams
Keyword(s):  
Single Case ◽  
Social Science Research ◽  
Science Research ◽  
Single Subject ◽  
Single Case Design ◽  
Single Subject Research ◽  
Case Research ◽  
Research Designs ◽  
Single Case Research ◽  
Single Case Designs

Single-case research designs have emerged as a major methodological strategy in some areas of social science research. Yet, single-subject research is plagued by a number of methodological, conceptual, and philosophical problems that have a bearing on the efficacy of these strategies in applied settings. In this article we review briefly some methodological issues in single-case design, including factors such as baseline trends, variability in the data, and duration of phases. In addition, conceptual issues include the range of outcome questions that single-case designs are designed to address as well as establishing the generalizability of findings. Beyond these issues, a number of pitfalls and hassles are likely to emerge, including philosophical objections to single-case research design, replication problems, measurement paradigms that seem incompatible with design assumptions, and the integration of single-case designs into practice.

A Primer on Single-Case Research Designs: Contemporary Use and Analysis

2019 ◽  
Vol 124 (1) ◽  
pp. 35-56 ◽  
Author(s):  
Jennifer R. Ledford ◽  
Erin E. Barton ◽  
Katherine E. Severini ◽  
Kathleen N. Zimmerman
Keyword(s):  
Single Case ◽  
Internal Validity ◽  
Positive Outcomes ◽  
Case Research ◽  
Functional Relations ◽  
Research Designs ◽  
Single Case Research ◽  
Research Questions ◽  
Data Analytic ◽  
Selection Of

Abstract The overarching purpose of this article is to provide an introduction to the use of rigorous single-case research designs (SCRDs) in special education and related fields. Authors first discuss basic design types and research questions that can be answered with SCRDs, examine threats to internal validity and potential ways to control for and detect common threats, and provide guidelines for selection of specific designs. Following, contemporary standards regarding rigor, measurement, description, and outcomes are presented. Then, authors discuss data analytic techniques, differentiating rigor, positive outcomes, functional relations, and magnitude of effects.

Single-Case Data Analysis: A Practitioner Guide for Accurate and Reliable Decisions

2019 ◽  
pp. 014544551986705
Author(s):  
Jennifer Ninci
Keyword(s):  
Progress Monitoring ◽  
Visual Analysis ◽  
Single Case ◽  
Internal Validity ◽  
Case Research ◽  
Analysis Process ◽  
Single Case Research ◽  
Single Case Designs ◽  
Case Data ◽  
Behavioral Programming

Practitioners frequently use single-case data for decision-making related to behavioral programming and progress monitoring. Visual analysis is an important and primary tool for reporting results of graphed single-case data because it provides immediate, contextualized information. Criticisms exist concerning the objectivity and reliability of the visual analysis process. When practitioners are equipped with knowledge about single-case designs, including threats and safeguards to internal validity, they can make technically accurate conclusions and reliable data-based decisions with relative ease. This paper summarizes single-case experimental design and considerations for professionals to improve the accuracy and reliability of judgments made from single-case data. This paper can also help practitioners to appropriately incorporate single-case research design applications in their practice.

Testing incontinence pads using single-case research designs

1994 ◽  
Vol 3 (7) ◽  
pp. 316-324 ◽  
Author(s):  
Linda M Proudfoot ◽  
Elizabeth S Farmer ◽  
Jean B McIntosh
Keyword(s):  
Single Case ◽  
Case Research ◽  
Research Designs ◽  
Incontinence Pads ◽  
Single Case Research

Comparing the Effects of Two Reading Interventions Using a Randomized Alternating Treatment Design

2019 ◽  
Vol 86 (4) ◽  
pp. 355-373
Author(s):  
Youjia Hua ◽  
Michelle Hinzman ◽  
Chengan Yuan ◽  
Kinga Balint Langel
Keyword(s):  
Visual Analysis ◽  
Reading Interventions ◽  
Single Case ◽  
Vocabulary Instruction ◽  
Internal Validity ◽  
Randomization Tests ◽  
Research Designs ◽  
Single Case Research ◽  
Alternating Treatment Design ◽  
Treatment Design

An emerging body of research suggests that incorporating randomization schemes in single-case research designs strengthens study internal validity and data evaluation. The purpose of this study was to test the utility and feasibility of a randomized alternating-treatment design in an investigation that compared the combined effects of vocabulary instruction and the paraphrasing strategies on expository comprehension of six students with reading difficulties. We analyzed the data using three types of randomization tests as well as visual analysis. The visual analysis and randomization tests confirmed the additional benefit of vocabulary instruction on expository comprehension for one student. However, the effects were not replicated across the other five students. We found that proper randomization schemes can improve both internal validity and data analysis strategies of the alternating-treatment design.

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