Consistency in single-case ABAB phase designs: A systematic review

Tanious, René; De, Tamal Kumar; Michiels, Bart; Noortgate, Wim Van den; Onghena, Patrick

doi:10.31234/osf.io/62t7w

Cited by 4 publications

(12 citation statements)

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“…However, due to the noticeable difference in central tendency, the CONDAP for this comparison is the highest found in all example data sets; as such, a difference in level is a sign of inconsistency. Based on a systematic review of applied A-B-A-B phase designs published over the past 50 years (Tanious, De, Michiels, Van den Noortgate, & Onghena, 2018), we offer the following guidelines for interpreting the amount of consistency: very high, 0 ≤ CONDAP ≤ 0.5; high, 0.5 < CONDAP ≤ 1; medium, 1 < CONDAP < 1.5; low, 1.5 < CONDAP ≤ 2; very low, CONDAP > 2.…”

Section: Interpretation Of Condapmentioning

confidence: 99%

Assessing Consistency in Single-Case A-B-A-B Phase Designs

Tanious

Michiels

et al. 2019

Behav Modif

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Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research.

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Section: Interpretation Of Condapmentioning

confidence: 99%

Assessing Consistency in Single-Case A-B-A-B Phase Designs

Tanious

Michiels

et al. 2019

Behav Modif

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“…Thus, the focus of the current text (namely, consistency of effects) is novel and it complements previous research. Moreover, the focus on consistency is well-aligned with recent research on the topic (Tanious, De, Michiels, et al, 2019;Tanious, Manolov et al, 2019). As a strength of the proposal made here, this assessment of consistency can be performed using a free user-friendly website and it can be easily represented visually.…”

Section: Discussionmentioning

confidence: 88%

“…When dealing with direct replication, one of the relevant concepts is consistency (Lane et al, 2017;Ledford, 2018). Although consistency has been highlighted especially in the context of visual analysis (What Works Clearinghouse, 2020), there have also been recent proposals for its quantification (Tanious, De, Michiels, et al, 2019;Tanious, Manolov, et al, 2019). Specifically, both visually and quantitatively, two types of consistency can be distinguished: consistency of measurements from similar phases and consistency of effects (e.g., when comparing data points from adjacent phases).…”

Section: Replication and Consistencymentioning

confidence: 99%

“…More Closely Related Antecedents: Quantifications of Consistency Tanious, De, Michiels, et al (2019) propose a quantification of consistency of effects, called CONEFF, referring to five data aspects, as present in the What Works Clearinghouse (2020)…”

Section: More Loosely Related Antecedents: a Review Of Quantifications Of Heterogeneitymentioning

confidence: 99%

“…A quantification of consistency of data in similar phases, called CONDAP has been suggested for several SCEDs (Tanious, De, Michiels, et al, 2019;Tanious, Manolov, et al, 2019). CONDAP can be accompanied by a randomization test in case randomization is present in the design .…”

Section: More Loosely Related Antecedents: a Review Of Quantifications Of Heterogeneitymentioning

confidence: 99%

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Assessing consistency of effects when applying multilevel models to single-case data

Manolov

Ferron

2020

Behav Res

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In the context of single-case experimental designs, replication is crucial. On the one hand, the replication of the basic effect within a study is necessary for demonstrating experimental control.On the other hand, replication across studies is required for establishing the generality of the intervention effect. Moreover, the "replicability crisis" presents a more general context further emphasizing the need for assessing consistency in replications. In the current text, we focus on replication of effects within a study and we specifically discuss the consistency of effects. Our proposal for assessing the consistency of effects refers to one of the promising data analytical techniques: multilevel models, also known as hierarchical linear models or mixed effects models.One option is to check, for each case in a multiple-baseline design, whether the confidence interval for the individual treatment effect excludes zero. This is relevant for assessing whether the effect is replicated as being non-null. However, we consider that it is more relevant and informative to assess, for each case, whether the confidence interval for the random effects includes zero (i.e., whether the fixed effect estimate is a plausible value for each individual effect). This is relevant for assessing whether the effect is consistent in size, with the additional requirement that the fixed effect itself is different from zero. The proposal for assessing consistency is illustrated with real data and it is implemented in free user-friendly software.

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Defining and assessing immediacy in single‐case experimental designs

Manolov

Onghena

2022

J Exper Analysis Behavior

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Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be accounted for when visually analyzing single‐case data. Given that it is one of the aspects that has received considerably less attention than other data aspects, the current text offers a review of the proposed conceptual definitions of immediacy (i.e., what it refers to) and also of the suggested operational definitions (i.e., how exactly is it assessed and/or quantified). Provided that a variety of conceptual and operational definitions is identified, we propose following a sensitivity analysis using a randomization test for assessing immediate effects in single‐case experimental designs, by identifying when changes were most clear. In such a sensitivity analysis, the immediate effects are tested for multiple possible intervention points and for different possible operational definitions. Robust immediate effects can be detected if the results for the different operational definitions converge.

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Consistency in single-case ABAB phase designs: A systematic review

Cited by 4 publications

References 0 publications

Assessing Consistency in Single-Case A-B-A-B Phase Designs

Assessing Consistency in Single-Case A-B-A-B Phase Designs

Assessing consistency of effects when applying multilevel models to single-case data

Defining and assessing immediacy in single‐case experimental designs

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