Performance criteria‐based effect size (<scp>PCES</scp>) measurement of single‐case experimental designs: A real‐world data study

Aydın, Orhan; Tanious, René

doi:10.1002/jaba.928

Cited by 10 publications

(19 citation statements)

References 101 publications

(188 reference statements)

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“…To identify missing data characteristics in published data, the present study included SCEDs articles derived from issues within 5 years of six journals. These were also used in the field test of performance criterion-based effect size (PCES) methods, which were previously proposed indexes (PCES, PCES trend , and PCES immediate ) to calculate effect size by considering the performance criterion in behavior (cf., Aydin & Tanious, 2022). Figure 1 shows the process of accessing data sources and the included journals.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Aydin

2023

Behav Modif

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Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

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Section: Methodsmentioning

confidence: 99%

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Aydin

2023

Behav Modif

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“…In addition to the visual analysis, an effect size analysis (performance criteria‐based effect size [PCES], Aydin & Tanious, 2022) was utilized to investigate the intervention's effect size. Given that none of the conditions had a baseline trend, only PCES without baseline trend (PCES) was manually calculated for baseline, intervention, fading, first and second maintenance data sets, and generalization.…”

Section: Methodsmentioning

confidence: 99%

Using self‐monitoring to increase on‐task behaviors of students with attention deficit hyperactivity disorders (ADHD) in inclusive classrooms in Turkey (Türkiye)

Sulu

Martella

Kiyak

2023

Behavioral Interventions

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Self-monitoring interventions have been found to be effective in improving the on-task behaviors of students with a wide range of disabilities. However, there are ongoing limitations in the literature, including the (a) lack of planning in generalization and maintenance, (b) interventions are predominantly conducted in segregated settings, and (c) inadequate number of data points collected in each condition. In the current study, self-monitoring was used to improve the on-task behavior of three elementary school students with ADHD in inclusive classrooms in Turkey. A concurrent multiple-baseline across-participants design was used. Generalization and maintenance were programmed via sequential modification, partial withdrawal, and continuous data collection. Additionally, classroom teachers rated the students' overall classroom behaviors in each data collection session. Based on the visual and the effect size analyses (i.e., performance criteria-based effect size [PCES]), the intervention was effective in improving the students' on-task behaviors. The self-monitoring intervention had 1.18 (high effect), 1.06 (effective) in generalization, and 1.14 (effective) in the first maintenance set and 1.03 (effective) in the second maintenance data set. The teacher ratings aligned with the increased on-task behaviors of the students. Implications for practice are discussed.

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“…Percentage of nonoverlap data (PND; Scruggs et al, 1987), Tau-U (Parker et al, 2011), and performance criteria-based effect size (PCES; Aydin & Tanious, 2022) methods were used to calculate the effect sizes of intervention. PND was preferred due to the most used index in the literature (Jamshidi et al, 2018(Jamshidi et al, , 2022.…”

Section: Selected Effect Size Methods and Analysesmentioning

confidence: 99%

“…PCES also is a new index that differs from nonoverlap-based methods (i.e., PND and Tau-U). PCES gives an effect size result by calculating the mean degree of behavior acquisition and taking into account mastery criteria or acceptable performance criteria of behavior (Aydin & Tanious, 2022). Hence, ,3,4 11,14,17 2,3,4 7,8,9 7,11,14 17,18,19 2,8,11 20,25,29 Note.…”

Section: Selected Effect Size Methods and Analysesmentioning

confidence: 99%

A Description of Missing Data in Single-Case Experimental Designs Studies and an Evaluation of Single Imputation Methods

Aydin

2024

Behav Modif

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Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

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Performance criteria‐based effect size (PCES) measurement of single‐case experimental designs: A real‐world data study

Cited by 10 publications

References 101 publications

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Using self‐monitoring to increase on‐task behaviors of students with attention deficit hyperactivity disorders (ADHD) in inclusive classrooms in Turkey (Türkiye)

A Description of Missing Data in Single-Case Experimental Designs Studies and an Evaluation of Single Imputation Methods

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