Composite Data Types and Operations

2003 ◽  
pp. 105-133
Author(s):  
Peter J. Ashenden ◽  
Gregory D. Peterson ◽  
Darrell A. Teegarden
Keyword(s):  
Data Types ◽  
Composite Data

Composite Data Types

1993 ◽  
pp. 245-263
Author(s):  
André Heck
Keyword(s):  
Data Types ◽  
Composite Data

Composite Data Types

1996 ◽  
pp. 293-323
Author(s):  
André Heck
Keyword(s):  
Data Types ◽  
Composite Data

Composite Data Types

2003 ◽  
pp. 289-332
Author(s):  
André Heck
Keyword(s):  
Data Types ◽  
Composite Data

ACDT: Architected Composite Data Types trading-in unfettered data access for improved execution

2014 ◽  
Author(s):  
Andres Marquez ◽  
Joseph Manzano ◽  
Shuaiwen Leon Song ◽  
Benoit Meister ◽  
Sunil Shrestha ◽  
...  
Keyword(s):  
Data Access ◽  
Data Types ◽  
Composite Data

Composite Data Types and Operations

2008 ◽  
pp. 95-135
Author(s):  
Peter J. Ashenden
Keyword(s):  
Data Types ◽  
Composite Data

Composite Data Types and Operations

2002 ◽  
pp. 85-106
Author(s):  
Peter J. Ashenden
Keyword(s):  
Data Types ◽  
Composite Data

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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