Measurement Error and Theory Testing in Consumer Research: An Illustration of the Importance of Construct Validation

1988 ◽  
Vol 14 (4) ◽  
pp. 579 ◽  
Author(s):  
Joseph A. Cote ◽  
M. Ronald Buckley
Keyword(s):  
Measurement Error ◽  
Consumer Research ◽  
Construct Validation ◽  
Theory Testing

scholarly journals On the value of homogeneous constructs for construct validation, theory testing, and the description of psychopathology.

2009 ◽  
Vol 21 (3) ◽  
pp. 272-284 ◽  
Author(s):  
Gregory T. Smith ◽  
Denis M. McCarthy ◽  
Tamika C. B. Zapolski
Keyword(s):  
Construct Validation ◽  
Theory Testing ◽  
Validation Theory

scholarly journals Estimating Trait, Method, and Error Variance: Generalizing across 70 Construct Validation Studies

1987 ◽  
Vol 24 (3) ◽  
pp. 315-318 ◽  
Author(s):  
Joseph A. Cote ◽  
M. Ronald Buckley
Keyword(s):  
Measurement Error ◽  
Validation Studies ◽  
Construct Validation ◽  
Error Variance ◽  
Systematic Measurement ◽  
Statistical Techniques ◽  
Published Data ◽  
Data Sets ◽  
Systematic Measurement Error

The authors examine the construct validation results of 70 published data sets. The analysis shows that, on average, traits account for less than 50% of the variance in construct measures. These findings raise questions about the application of statistical techniques that assume minimal measurement error or do not properly model systematic measurement error.

Theory Testing and Measurement Error

Intelligence ◽  
1999 ◽  
Vol 27 (3) ◽  
pp. 183-198 ◽  
Author(s):  
Frank L. Schmidt ◽  
John E. Hunter
Keyword(s):  
Measurement Error ◽  
Theory Testing ◽  
Testing And Measurement

Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke
Keyword(s):  
Measurement Error ◽  
Computerized Adaptive Testing ◽  
Test Procedure ◽  
Adaptive Testing ◽  
Parameter Estimates ◽  
Simulation Studies ◽  
Computerized Adaptive Test ◽  
Item Banks ◽  
Item Parameters ◽  
General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

But Where's the Theory Testing?

1988 ◽  
Vol 33 (12) ◽  
pp. 1074-1076
Author(s):  
John J. Furedy
Keyword(s):  
Theory Testing

Stimulating Perspectives in Conducting Consumer Research

PsycCRITIQUES ◽  
2012 ◽  
Vol 57 (32) ◽  
Author(s):  
Candice R. Hollenbeck
Keyword(s):  
Consumer Research
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