Embracing Our Visual Inspection and Analysis Tradition

2010 ◽  
Vol 33 (2) ◽  
pp. 71-77 ◽  
Author(s):  
Kathleen Artman ◽  
Mark Wolery ◽  
Paul Yoder
Keyword(s):  
Visual Inspection ◽  
Interobserver Agreement ◽  
Single Case ◽  
Internal Validity ◽  
Experimental Designs ◽  
Primary Study ◽  
Observer Drift

Most investigators using single-case experimental designs use interobserver agreement (IOA) checks to enhance the credibility of the collected data, and they report the results of those assessments using percentage of agreement estimates. An alternative is to graph both observers’ records of the measured behavior on the primary study graphs. Such graphing leads to greater transparency and is advocated for five reasons: (a) to make explicit how IOA assessments were distributed across the study, (b) to ensure agreement estimates are reported at the level of the measured behavior of interest rather than a broader observational code, (c) to detect observer drift, (d) to detect the effect of observer expectations, and (e) to put the IOA data in a more suitable context for assessing the internal validity of the study by eliminating the need for an arbitrary agreement criterion.

scholarly journals Machine Learning to Support Visual Inspection of Data: A Clinical Application

2021 ◽  
pp. 014544552110382
Author(s):  
Tessa Taylor ◽  
Marc J. Lanovaz
Keyword(s):  
Machine Learning ◽  
Visual Inspection ◽  
Single Case ◽  
Autism Spectrum ◽  
Experimental Designs ◽  
Feeding Programs ◽  
Home Based ◽  
Pediatric Feeding ◽  
Reversal Design ◽  
Additional Support

Practitioners in pediatric feeding programs often rely on single-case experimental designs and visual inspection to make treatment decisions (e.g., whether to change or keep a treatment in place). However, researchers have shown that this practice remains subjective, and there is no consensus yet on the best approach to support visual inspection results. To address this issue, we present the first application of a pediatric feeding treatment evaluation using machine learning to analyze treatment effects. A 5-year-old male with autism spectrum disorder participated in a 2-week home-based, behavior-analytic treatment program. We compared interrater agreement between machine learning and expert visual analysts on the effects of a pediatric feeding treatment within a modified reversal design. Both the visual analyst and the machine learning model generally agreed about the effectiveness of the treatment while overall agreement remained high. Overall, the results suggest that machine learning may provide additional support for the analysis of single-case experimental designs implemented in pediatric feeding treatment evaluations.

scholarly journals Single-case experimental designs: Reflections on conduct and analysis

2014 ◽  
Vol 24 (3-4) ◽  
pp. 634-660 ◽  
Author(s):  
Rumen Manolov ◽  
David L. Gast ◽  
Michael Perdices ◽  
Jonathan J. Evans
Keyword(s):  
Single Case ◽  
Experimental Designs

Experimental Designs in Business Research

2021 ◽  
Author(s):  
Heiko Breitsohl
Keyword(s):  
Internal Validity ◽  
Building Blocks ◽  
Experimental Designs ◽  
Management Research ◽  
Natural Experiments ◽  
Managerial Decisions ◽  
Sources Of Variation ◽  
Available Information ◽  
Statistical Conclusion ◽  
Business And Management

Conducting credible and trustworthy research to inform managerial decisions is arguably the primary goal of business and management research. Research design, particularly the various types of experimental designs available, are important building blocks for advancing toward this goal. Key criteria for evaluating research studies are internal validity (the ability to demonstrate causality), statistical conclusion validity (drawing correct conclusions from data), construct validity (the extent to which a study captures the phenomenon of interest), and external validity (the generalizability of results to other contexts). Perhaps most important, internal validity depends on the research design’s ability to establish that the hypothesized cause and outcome are correlated, that variation in them occurs in the correct temporal order, and that alternative explanations of that relationship can be ruled out. Research designs vary greatly, especially in their internal validity. Generally, experiments offer the strongest causal inference, because the causal variables of interest are manipulated by the researchers, and because random assignment makes subjects comparable, such that the sources of variation in the variables of interest can be well identified. Natural experiments can exhibit similar internal validity to the extent that researchers are able to exploit exogenous events creating (quasi-)randomized interventions. When randomization is not available, quasi-experiments aim at approximating experiments by making subjects as comparable as possible based on the best available information. Finally, non-experiments, which are often the only option in business and management research, can still offer useful insights, particularly when changes in the variables of interest can be modeled by adopting longitudinal designs.

Grounded procedures of separation in clinical psychology: what's to be expected?

2021 ◽  
Vol 44 ◽  
Author(s):  
Anke Haberkamp ◽  
Thomas Schmidt
Keyword(s):  
Psychiatric Disorders ◽  
Cognitive Processes ◽  
Internal Validity ◽  
Vantage Point ◽  
Experimental Designs ◽  
Obsessive Compulsive ◽  
Parametric Variation ◽  
Specific Disorders ◽  
Obsessive Compulsive Disorders ◽  
Compulsive Disorders

Abstract The hypothesis of grounded procedures of separation predicts accentuated effects in individuals with psychiatric disorders, for example, obsessive-compulsive disorders with washing compulsion. This could provide a vantage point for understanding cognitive processes related to specific disorders. However, fully exploring it requires updated experimental designs, including extensive control conditions, exclusion of alternative explanations, internal replications, and parametric variation to strengthen internal validity.

Designing Experiments

2015 ◽  
pp. 82-96
Keyword(s):  
Experimental Design ◽  
Internal Validity ◽  
Random Selection ◽  
Experimental Designs ◽  
Control Groups ◽  
Test Measurement ◽  
Post Test ◽  
Quasi Experimental ◽  
And Control

In this chapter, students will learn the process of designing experiments. The classic experimental design is presented first. Following this, three distinct quasi-experimental designs are presented. The benefits and burdens of the classic and quasi-experimental designs are discussed in depth. By the end of this chapter, students will understand concepts related to random selection, generalizability, treatment and control groups, pre- and post-test measurement of the dependent variable, and internal validity.

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