Assessing Consistency in Single-Case Alternation Designs

Manolov, Rumen; Tanious, René; De, Tamal Kumar; Onghena, Patrick

doi:10.1177/0145445520923990

Cited by 6 publications

(6 citation statements)

References 59 publications

(93 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of CONDAP has subsequently been extended to multiple-baseline and changing criterion designs (Tanious, Manolov, et al, 2019). A different way of assessing consistency has been proposed for alternation designs with randomized blocks, on the basis of the way in which variability is partitioned in the analysis of variance (Manolov et al, 2020). Additionally, Tanious et al (2020) propose a quantification of the consistency of effects (changes in level, trend, variability, overlap, immediacy) when comparing across adjacent conditions.…”

Section: Existing Options For Quantifying Consistencymentioning

confidence: 99%

See 1 more Smart Citation

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

Manolov

Tanious

2020

Behav Modif

Self Cite

View full text Add to dashboard Cite

The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications.

show abstract

Section: Existing Options For Quantifying Consistencymentioning

confidence: 99%

“… 3. See Manolov et al (2020) for a proposal applicable to alternating treatment designs with block randomization. …”

mentioning

confidence: 99%

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

Manolov

Tanious

2020

Behav Modif

Self Cite

View full text Add to dashboard Cite

show abstract

“…When conducting statistical analyses of SCED research, there are several characteristics of SCEDs that can affect effect size estimates which warrant consideration. For example, autocorrelation (Barnard-Brak et al 2021), trend (Solomon 2014;Tarlow 2017), magnitude of within-case variance (Fingerhut et al 2021), data consistency (Manolov et al 2021) and study features such as sample size, design and type of recording system (Pustejovsky & Ferron 2017) can all impact effect size estimates and must be considered both when selecting a metric and interpreting results. Another variable that appears to influence estimates of effect sizes from SCED data is whether the treatment data path for the dependent variable is ascending or descending.…”

Section: Introductionmentioning

confidence: 99%

“…2021), data consistency (Manolov et al . 2021) and study features such as sample size, design and type of recording system (Pustejovsky & Ferron 2017) can all impact effect size estimates and must be considered both when selecting a metric and interpreting results. Another variable that appears to influence estimates of effect sizes from SCED data is whether the treatment data path for the dependent variable is ascending or descending.…”

Section: Introductionmentioning

confidence: 99%

Interpreting treatment effect size from single case experimental design data: a preliminary analysis of differential effects of treatments designed to increase or decrease behaviour

Richman

Barnard‐Brak

Watkins

2022

J intellect Disabil Res

View full text Add to dashboard Cite

Background Estimates of treatment effect size from single case experimental design (SCED) data may be impacted by the direction for treatment effects (i.e. ascending or descending slope for the dependent variable). Estimating effect sizes for treatments designed to decrease behaviour are potentially more restricted because the intended direction for treatment is zero (i.e. an absolute basal). Conversely, effect sizes for interventions that increase behaviour are less restricted due to a relatively unconstrained ceiling from a pure measurement standpoint (i.e. no absolute ceiling). That is, treatments that increase behaviour have a broader range of possible effect size values as the ceiling is only limited by demand characteristics and the learners' skills and motivation to exhibit the behaviour. Method The current study represents a preliminary analysis of the mean and range of SCED effect sizes for treatments designed to either increase or decrease target behaviour. A within‐case Cohen's d measure that was developed for SCED data was used to estimate treatment effect sizes. Results Results indicated that the mean and range of effect size values for treatments that increased behaviour were significantly greater compared with treatments that decreased behaviour. Conclusions Results are discussed in terms of developing standards, or best practices, specific to interpreting effect size values and meeting quality control requirements for inclusion of the data set in future SCED meta‐analytic studies estimating treatment effect size. Specifically, preliminary results suggest that benchmarks for low, medium and high SCED effect size values need to be developed separately for treatments that increase or decrease levels of the dependent variable.

show abstract

“…Another aspect that recently received attention is the assessment of consistency, focusing on the consistency of data patterns in similar phases and also on the consistency of effects (Tanious, De, Michiels, et al, 2019;Tanious et al, 2020. Other proposals for assessing consistency have focused on specific designs (see Manolov et al, 2021, dealing with alternation designs), or specific graphical representations (Manolov & Tanious, 2022). Moreover, there have been proposals for assessing consistency of effects in the context of multilevel models (Manolov & Ferron, 2020).…”

mentioning

confidence: 99%

Defining and assessing immediacy in single-case experimental designs

Manolov¹,

Onghena²

2022

Preprint

View full text Add to dashboard Cite

Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be taken into account 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 broad 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 multiverse-randomization approach for assessing immediate effects in single-case experimental designs. The multiverse element refers to checking the congruency of the results for the different possible operational definitions. The randomization element refers to the possibility to conduct multiple randomization tests to assess the immediate effects.

show abstract

Assessing Consistency in Single-Case Alternation Designs

Cited by 6 publications

References 59 publications

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

Interpreting treatment effect size from single case experimental design data: a preliminary analysis of differential effects of treatments designed to increase or decrease behaviour

Defining and assessing immediacy in single-case experimental designs

Contact Info

Product

Resources

About