Summarizing your data and statistical analyses: The results section.

2020 ◽  
pp. 73-108
Author(s):  
Harris Cooper
Keyword(s):  
Statistical Analyses

Structuring Situational Information

2015 ◽  
Vol 20 (3) ◽  
pp. 176-189 ◽  
Author(s):  
John F. Rauthmann
Keyword(s):  
Statistical Analyses ◽  
Current Work ◽  
Common Language ◽  
Road Map ◽  
Taxonomic Approach ◽  
Situational Information

Abstract. There is as yet no consensually agreed-upon situational taxonomy. The current work addresses this issue and reviews extant taxonomic approaches by highlighting a “road map” of six research stations that lead to the observed diversity in taxonomies: (1) theoretical and conceptual guidelines, (2) the “type” of situational information studied, (3) the general taxonomic approach taken, (4) the generation of situation pools, (5) the assessment and rating of situational information, and (6) the statistical analyses of situation data. Current situational taxonomies are difficult to integrate because they follow different paths along these six stations. Some suggestions are given on how to spur integrated taxonomies toward a unified psychology of situations that speaks a common language.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

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