Defining and assessing immediacy in single‐case experimental designs

Manolov, Rumen; Onghena, Patrick

doi:10.1002/jeab.799

Cited by 4 publications

(1 citation statement)

References 179 publications

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“…The response functions represent a general framework that allows, for instance, including the study of immediate effects (see Manolov & Onghena, 2022 ) as a special case. Response functions can be defined to study both (a) different number of values per phase being compared; and (b) different latencies of effect; and (c) different focal data aspects, whose immediate or delayed appearance is analyzed.…”

Section: Discussionmentioning

confidence: 99%

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework

Manolov,

Onghena

2023

Behav Res

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Randomization tests represent a class of significance tests to assess the statistical significance of treatment effects in randomized single-case experiments. Most applications of single-case randomization tests concern simple treatment effects: immediate, abrupt, and permanent changes in the level of the outcome variable. However, researchers are confronted with delayed, gradual, and temporary treatment effects; in general, with “response functions” that are markedly different from single-step functions. We here introduce a general framework that allows specifying a test statistic for a randomization test based on predicted response functions that is sensitive to a wide variety of data patterns beyond immediate and sustained changes in level: different latencies (degrees of delay) of effect, abrupt versus gradual effects, and different durations of the effect (permanent or temporary). There may be reasonable expectations regarding the kind of effect (abrupt or gradual), entailing a different focal data feature (e.g., level or slope). However, the exact amount of latency and the exact duration of a temporary effect may not be known a priori, justifying an exploratory approach studying the effect of specifying different latencies or delayed effects and different durations for temporary effects. We provide illustrations of the proposal with real data, and we present a user-friendly freely available web application implementing it.

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Section: Discussionmentioning

confidence: 99%

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework

Manolov,

Onghena

2023

Behav Res

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Assessing Nonoverlap in Single-Case Data: Strengths, Challenges, and Recommendations

Manolov,

Tanious

2024

J Behav Educ

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Overlap is one of the data aspects that are expected to be assessed when visually inspecting single-case experimental designs (SCED) data. A frequently used quantification of overlap is the Nonoverlap of All Pairs (NAP). The current article reviews the main strengths and challenges when using this index, as compared to other nonoverlap indices such as Tau and the Percentage of data points exceeding the median. Four challenges are reviewed: the difficulty in representing NAP graphically, the presence of a ceiling effect, the disregard of trend, and the limitations in using p-values associated with NAP. Given the importance of complementing quantitative analysis and visual inspection of graphed data, straightforward quantifications and new graphical elements for the time-series plot are proposed as options for addressing the first three challenges. The suggestions for graphical representations (representing within-phase monotonic trend and across-phases overlaps) and additional numerical summaries (quantifying the degree of separation in case of complete nonoverlap or the proportion of data points in the overlap zone) are illustrated with two multiple-baseline data sets. To make it easier to obtain the plots and quantifications, the recommendations are implemented in a freely available user-friendly website. Educational researchers can use this article to inform their use and application of NAP to meaningfully interpret this quantification in the context of SCEDs.

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How to assess and take into account trend in single-case experimental design data

Manolov¹,

Lebrault

Krasny-Pacini

2023

Neuropsychological Rehabilitation

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One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend, under the premise that there is not a single correct answer to each of these questions. First, there are multiple ways in which even linear trend can be operatively defined. Second, different criteria can be followed when deciding whether the trend observed needs to be taken into account when assessing intervention effectiveness or, in contrast, its magnitude is sufficiently small to be considered inconsequential. If a decision is made to take trend into account, there are different ways in which this can be done. Finally, a challenge arises when there is a trend for some of the comparisons between conditions, but not for others. Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.

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Defining and assessing immediacy in single‐case experimental designs

Cited by 4 publications

References 179 publications

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework

Assessing Nonoverlap in Single-Case Data: Strengths, Challenges, and Recommendations

How to assess and take into account trend in single-case experimental design data

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