A Decade Review of Two Potential Analysis Altering Variables in Graph Construction

Peltier, Corey; Muharib, Reem; Haas, April; Dowdy, Art

doi:10.1007/s10803-021-04959-0

Cited by 16 publications

(19 citation statements)

References 24 publications

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“…These results mean a majority of the SCRD studies included may have had analysis altering decisions and results influenced by the visual analysis. Results for standardized x:y parallel prior reviews (Kubina et al, 2017;Ledford et al, 2019;Peltier et al, 2021;2022a;2022b), which identified more than 90% of graphs were not within the standardized x:y recommendations. However, substantially more graphs in the counseling literature displayed a truncated ordinate compared to prior reviews.…”

Section: Discussionsupporting

confidence: 64%

“…Woo et al (2016) substantially diverges from other design options (i.e., group design approaches) through the collection of time series data and visual analysis of the graphed data to illustrate changes and treatment effectivness, hallmark characteristics of SCRD. Recent work has found large variability in the characteristics of time-series graphs reported in single-case designs (Kubina et al, 2017;Kubina et al, 2022;Ledford, et al, 2019;Peltier et al, 2021;Peltier et al, 2022a;Peltier et al, 2022b).…”

Section: A Graph Paints 1000 Words But Are They Valid?mentioning

confidence: 99%

“…We consulted previous research (Kubina et al, 2017;Ledford et al, 2019;Peltier et al, 2021;Peltier et al, 2022a;Peltier et al, 2022b) to develop a coding guide. The coding guide focused on capturing variables related to source information, experimental design characteristics, and graph characteristics.…”

Section: Data Extractionmentioning

confidence: 99%

See 2 more Smart Citations

A Graph Paints 1,000 Words But are they Valid?: A Systematic Review of SCRD Graphs

Brown¹,

Peltier²,

Lee³

et al. 2022

Preprint

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i. Objective: Single-case research designs are a useful methodology to investigate the effects of treatments within counseling. A core component of the design is the presentation of data collected repeatedly on a time-series graph. Research in the fields of special education and behavior analysis suggests most graphs do not adhere to reporting guidelines and specific graphical characteristics can impact decisions made by visual analysts. ii. Method: In this pre-registered (https://doi.org/10.17605/OSF.IO/FU7GV) project we conducted a systematic review of 42 journals in the field of counseling. A total of 50 single-case studies, including 272 graphs were included in our analysis. iii. Results: Most of the graphs did not meet recommended guidelines for graph construction. iv. Conclusions: We provide a discussion on how graph construction may impact visual analysis and suggestions for graph construction to enhance interpretability.

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Section: Discussionsupporting

confidence: 64%

Section: A Graph Paints 1000 Words But Are They Valid?mentioning

confidence: 99%

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A Graph Paints 1,000 Words But are they Valid?: A Systematic Review of SCRD Graphs

Brown¹,

Peltier²,

Lee³

et al. 2022

Preprint

Self Cite

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“…Similarly, if the aim was to merely check whether there is an improvement for all A-B comparisons, this would be visually evident because of the diagonal line representing lack of change, regardless of the x:y ratio and the data points per x:y ratio. A great variety in these two ratios has been found in time-series line graphs, leading to potential distortions when performing visual analysis (Kubina et al, 2017;Ledford, Barton, Severini, Zimmerman, & Pokorski, 2019;Peltier, McKenna, et al, 2022;Peltier et al, 2021;Peltier, Muharib, et al, 2022). Third, the confounding between slope and scale (Kinney, 2022) is also not likely for modified Brinley plots, given that they are square.…”

Section: Advantages Of the Use Of The Modified Brinley Plotmentioning

confidence: 99%

A proposal for the assessment of replication of effects in single‐case experimental designs

Manolov

Tanious

Fernández-Castilla

2022

J of App Behav Analysis

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In science in general and in the context of single-case experimental designs, replication of the effects of the intervention within and/or across participants or experiments is crucial for establishing causality and for assessing the generality of the intervention effect. Specific developments and proposals for assessing whether an effect has been replicated or not (or to what extent) are scarce, in the general context of behavioral sciences, and practically null in the singlecase experimental designs context. We propose an extension of the modified Brinley plot for assessing how many of the effects replicate. To make this assessment possible, a definition of replication is suggested, on the basis of expert judgment, rather than on statistical criteria. The definition of replication and its graphical representation are justified, presenting their strengths and limitations, and illustrated with real data. A user-friendly software is made available for obtaining automatically the graphical representation.

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“…Furthermore, authors reported only 9.2% of graphs fit between recommended ratios. Last, three reviews evaluated a decade worth of SCRD graphs published in journals focusing on students with emotional and behavioral disorders (Peltier, McKenna, et al, 2022), learning disabilities (Peltier et al, 2021), and autism (Peltier, Muharib, et al, 2022). The authors reported variability of y to x axis ratio within design types and few graphs met recommendations for y to x axis ratio: (a) 13% in emotional and behavioral disorders journals, (b) 3.1% in learning disability journals, and (c) 4% in autism journals.…”

Section: Prior Reviews Of Graph Constructionmentioning

confidence: 99%

Investigation of Two Preliminary Analysis-Altering Elements: Ordinate Scaling and DPPXYR

Peltier¹,

McKenna²,

Dijk³

2022

Preprint

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The purpose of this pre-registered study (https://doi.org/10.17605/OSF.IO/BD8ZQ) was to conduct a conceptual replication to investigate if the manipulation of ordinate scaling and DPPXYR impacted visual analysis. A total of 27 expert visual analysts evaluated eight data sets reporting null (n = 2), small (n = 2), moderate (n = 2), and large (n = 2) effects. Each data set was modified by manipulating the ordinate scale and DPPXYR to create 6 graphs per set, resulting in 48 ABAB graphs. We estimated two separate three-level mixed effect models with ratings nested in datasets nested in participants to evaluate impact of graphical characteristics for (1) confidence in determining a functional relation and (2) estimated magnitude of treatment effect. We included ordinate scaling and DPPXYR at level 1 and graph effect size at level 2, including all interactions. Results suggest ordinate scaling and DPPXYR did not impact participants confidence in determining a functional relation or estimate of the magnitude of treatment effect. Findings will be couched in current literature and recommendations for graph construction and future research will be discussed.

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A Decade Review of Two Potential Analysis Altering Variables in Graph Construction

Cited by 16 publications

References 24 publications

A Graph Paints 1,000 Words But are they Valid?: A Systematic Review of SCRD Graphs

A Graph Paints 1,000 Words But are they Valid?: A Systematic Review of SCRD Graphs

A proposal for the assessment of replication of effects in single‐case experimental designs

Investigation of Two Preliminary Analysis-Altering Elements: Ordinate Scaling and DPPXYR

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