Treatment noncompliance in randomized experiments: Statistical approaches and design issues.

2014 ◽  
Vol 19 (3) ◽  
pp. 317-333 ◽  
Author(s):  
Brad J. Sagarin ◽  
Stephen G. West ◽  
Alexander Ratnikov ◽  
William K. Homan ◽  
Timothy D. Ritchie ◽  
...  
Keyword(s):  
Randomized Experiments ◽  
Design Issues ◽  
Statistical Approaches ◽  
Treatment Noncompliance

The Effects of Questionnaire Length and Behavioral Consequences on Careless Responding

2020 ◽  
Vol 36 (2) ◽  
pp. 410-420 ◽  
Author(s):  
Anthony M. Gibson ◽  
Nathan A. Bowling
Keyword(s):  
Self Report ◽  
Randomized Experiments ◽  
High Quality ◽  
Buffering Effect ◽  
Careless Responding ◽  
Main Effect ◽  
Limited Support ◽  
Report Data ◽  
Self Report Data ◽  
Consistent Support

Abstract. The current paper reports the results of two randomized experiments designed to test the effects of questionnaire length on careless responding (CR). Both experiments also examined whether the presence of a behavioral consequence (i.e., a reward or a punishment) designed to encourage careful responding buffers the effects of questionnaire length on CR. Collectively, our two studies found (a) some support for the main effect of questionnaire length, (b) consistent support for the main effect of the consequence manipulations, and (c) very limited support for the buffering effect of the consequence manipulations. Because the advancement of many subfields of psychology rests on the availability of high-quality self-report data, further research should examine the causes and prevention of CR.

Optimal Experimental Design With Nesting of Persons in Organizations

2013 ◽  
Vol 221 (3) ◽  
pp. 145-159 ◽  
Author(s):  
Gerard J. P. van Breukelen
Keyword(s):  
Repeated Measures ◽  
Optimal Number ◽  
Health Centers ◽  
Randomized Experiments ◽  
Optimal Sample ◽  
Treatment Conditions ◽  
Sampling Cost ◽  
Nested Designs ◽  
Simple Equations ◽  
Number Of Individuals

This paper introduces optimal design of randomized experiments where individuals are nested within organizations, such as schools, health centers, or companies. The focus is on nested designs with two levels (organization, individual) and two treatment conditions (treated, control), with treatment assignment to organizations, or to individuals within organizations. For each type of assignment, a multilevel model is first presented for the analysis of a quantitative dependent variable or outcome. Simple equations are then given for the optimal sample size per level (number of organizations, number of individuals) as a function of the sampling cost and outcome variance at each level, with realistic examples. Next, it is explained how the equations can be applied if the dependent variable is dichotomous, or if there are covariates in the model, or if the effects of two treatment factors are studied in a factorial nested design, or if the dependent variable is repeatedly measured. Designs with three levels of nesting and the optimal number of repeated measures are briefly discussed, and the paper ends with a short discussion of robust design.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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