The Relationship Between Visual Depictions of Rate of Improvement and Quantitative Effect Sizes in Academic Single-Case Experimental Design Studies

Norman, Ethan R. Van; Boorse, Jaclin; Klingbeil, David A.

doi:10.1007/s10864-022-09500-6

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Studies that targeted specific reading (oral reading fluency, words read correct per minute), mathematics (computation; problems correct, problems correct per minute, digits correct per minute; percentage of problems correct), and writing (text production; total words written, correct word sequences, percentage of correct word sequences, and correct-incorrect word sequences; Wagner et al, 2019) skills and outcomes were initially included in the database and analyzed in this study. For more comprehensive information regarding database development, including specific literature search terms, the flowchart of the screening process, and coding criteria, see technical reports outlining the creation of the database see Van Norman and Klingbeil (2021). At present, the database includes records with dependent variables of oral reading fluency ( N = 91), mathematics computation ( N = 51), and text writing ( N = 68).…”

Section: Methodsmentioning

confidence: 99%

A Summary of Relevant Demographic Characteristics of Multiple-Baseline Designs Targeting Academic Skills

Van Norman,

Klingbeil,

Boorse

et al. 2023

Remedial and Special Education

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Educators are required to use evidence-based practices to support student development of academic skills. Results from single-case experimental designs are well suited for this purpose and may be more flexible for applied use than group-based designs. However, educators also require information about which interventions work for whom and under what conditions. Although previous reviews of single-case experimental designs exist, most focused on design characteristics related to internal validity. The purpose of this study was to determine the participant characteristics within multiple-baseline and multiple-probe across participant designs targeting academic skills. Representation of various demographic subgroups differ between studies targeting different academic skill areas (mathematics, reading, and writing). However, reporting was often inconsistent, with a substantial proportion of records omitting information about participants’ race/ethnicity, economic status, and English learner status. This finding was divergent from best practice recommendations. Implications for future research are discussed within the context of the study’s limitations.

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Section: Methodsmentioning

confidence: 99%

A Summary of Relevant Demographic Characteristics of Multiple-Baseline Designs Targeting Academic Skills

Van Norman,

Klingbeil,

Boorse

et al. 2023

Remedial and Special Education

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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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The Relationship Between Visual Depictions of Rate of Improvement and Quantitative Effect Sizes in Academic Single-Case Experimental Design Studies

Cited by 3 publications

References 36 publications

A Summary of Relevant Demographic Characteristics of Multiple-Baseline Designs Targeting Academic Skills

A Summary of Relevant Demographic Characteristics of Multiple-Baseline Designs Targeting Academic Skills

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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