Determining the Efficacy of Intervention: The Use of Effect Sizes for Data Analysis in Single-Subject Research

Kromrey, Jeffrey D.; Foster‐Johnson, Lynn

doi:10.1080/00220973.1996.9943464

Cited by 107 publications

(75 citation statements)

References 31 publications

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“…Applying inferential statistics that make assumptions about the distributional properties of the parent population is also problematic because single-subject data are inherently autocorrelated. In other words, repeated measures within the same subject are clearly not independent of one another, thus limiting the choice of appropriate statistical analyses (Kromrey & Foster-Johnson, 1996;Robey, Schultz, Crawford, & Sinner, 1999). …”

Section: Introductionmentioning

confidence: 99%

Evaluating Single-Subject Treatment Research: Lessons Learned from the Aphasia Literature

2006

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The mandate for evidence-based practice has prompted careful consideration of the weight of the scientific evidence regarding the therapeutic value of various clinical treatments. In the field of aphasia, a large number of single-subject research studies have been conducted, providing clinical outcome data that are potentially useful for clinicians and researchers; however, it has been difficult to discern the relative potency of these treatments in a standardized manner. In this paper we describe an approach to quantify treatment outcomes for single-subject research studies using effect sizes. These values provide a means to compare treatment outcomes within and between individuals, as well as to compare the relative strength of various treatments. Effect sizes also can be aggregated in order to conduct meta-analyses of specific treatment approaches. Consideration is given to optimizing research designs and providing adequate data so that the value of treatment research is maximized.

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Section: Introductionmentioning

confidence: 99%

Evaluating Single-Subject Treatment Research: Lessons Learned from the Aphasia Literature

2006

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“…These data assumptions are common to simulation studies on N 1 designs (see, e.g., Brossart et al, 2006;Huitema & McKean, 2007a, 2007bParker & Brossart, 2003). Thus, future studies may explore the performance of PNCD for ABAB designs with curvilinear trends, computing the percentage for each change in the condition, as was suggested by Kromrey and Foster-Johnson (1996). Additionally, comparative studies such as the present one, which center on finding the technique that performs better, need to be complemented by precision studies in order to identify techniques that perform well-that is, yield accurate estimates of the effect sizes simulated.…”

Section: Discussionmentioning

confidence: 99%

Percentage of nonoverlapping corrected data

Manolov

Solanas

2009

Behavior Research Methods

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“…Repeated probe data were analyzed in terms of effect sizes (ESs) [29], comparing mean scores in the four posttreatment probes to mean scores at baseline relative to baseline SDs as follows: ES = (Mean posttreatment -Meanbaseline )/SD baseline . In the event where baselines had 0 SD, a pooled ES was calculated using the following formula: d2 = (Mean posttreatment -Mean baseline )/SD pooled.…”

Section: Discussionmentioning

confidence: 99%

Anomia treatment platform as a behavioral engine for use in research on physiological adjuvants to neurorehabilitation

Kendall¹,

Raymer²,

Rose³

et al. 2014

J Rehabil Res Dev

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Abstract-The purpose of this study was to create a "behavioral treatment engine" for future use in research on physiological adjuvants in aphasia rehabilitation. We chose the behavioral target anomia, which is a feature displayed by many persons who have aphasia. Further, we wished to saturate the treatment approach with many strategies and cues that have been empirically reported to have a positive influence on aphasia outcome, with the goal being to optimize the potential for positive response in most participants. A single-subject multiple baseline design with replication across eight participants was employed. Four men and four women, with an average age of 62 yr and an average of 63.13 mo poststroke onset, served as participants. Word-retrieval treatment was administered 3 d/wk, 1 h/d for a total of 20 treatment hours (6-7 wk). Positive acquisition effects were evident in all eight participants (d effect size [ES] = 5.40). Treatment effects were maintained 3 mo after treatment termination for five participants (d ES = 2.94). Within and across semantic category, generalization was minimal (d ES = 0.43 within and 1.09 across). This study demonstrates that this behavioral treatment engine provides a solid platform on which to base future studies whereby various treatment conditions are manipulated and pharmacologic support is added.

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Determining the Efficacy of Intervention: The Use of Effect Sizes for Data Analysis in Single-Subject Research

Cited by 107 publications

References 31 publications

Evaluating Single-Subject Treatment Research: Lessons Learned from the Aphasia Literature

Evaluating Single-Subject Treatment Research: Lessons Learned from the Aphasia Literature

Percentage of nonoverlapping corrected data

Anomia treatment platform as a behavioral engine for use in research on physiological adjuvants to neurorehabilitation

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