scholarly journals Cross-Temporal Meta-Analysis: A Conceptual and Empirical Critique

2018 ◽  
Author(s):  
Cort Rudolph ◽  
David Costanza ◽  
Charlotte Wright ◽  
Hannes Zacher
Keyword(s):  
Monte Carlo ◽  
Meta Analysis ◽  
Monte Carlo Study ◽  
Cohort Effects ◽  
Social Phenomena ◽  
Period Effects ◽  
Intractable Problems ◽  
Research Questions ◽  
Proper Estimation

The proper estimation of age, period, and cohort (APC) effects is a pervasive concern for the study of a variety of psychological and social phenomena, inside and outside of organizations. One analytic technique that has been used to estimate APC effects is cross-temporal meta- analysis (CTMA). While CTMA has some appealing qualities (e.g., ease of interpretability), it has also been criticized on theoretical and methodological grounds. Furthermore, CTMA makes strong assumptions about the nature and operation of cohort effects relative to age and period effects that have not been empirically tested. Accordingly, the goal of this paper was to explore CTMA, its history, and these assumptions. Using a Monte Carlo study, we demonstrate that in many cases, cohort effects are misestimated (i.e., systematically over- or underestimated) by CTMA. This work provides further evidence that APC effects pose intractable problems for research questions where APC effects are of interest.

scholarly journals Consequences of effect size heterogeneity for meta-analysis: a Monte Carlo study

2009 ◽  
Vol 19 (2) ◽  
pp. 217-236 ◽  
Author(s):  
Mark J. Koetse ◽  
Raymond J. G. M. Florax ◽  
Henri L. F. de Groot
Keyword(s):  
Monte Carlo ◽  
Effect Size ◽  
Meta Analysis ◽  
Monte Carlo Study ◽  
Size Heterogeneity

A Meta-Analysis of Interviews and Cognitive Ability

2013 ◽  
Vol 12 (4) ◽  
pp. 157-169 ◽  
Author(s):  
Philip L. Roth ◽  
Allen I. Huffcutt
Keyword(s):  
Cognitive Ability ◽  
Meta Analysis ◽  
Incremental Validity ◽  
Employment Interviews ◽  
Moderator Analysis ◽  
The Future ◽  
Research Questions ◽  
Meta Analyses ◽  
The Relationship

The topic of what interviews measure has received a great deal of attention over the years. One line of research has investigated the relationship between interviews and the construct of cognitive ability. A previous meta-analysis reported an overall corrected correlation of .40 ( Huffcutt, Roth, & McDaniel, 1996 ). A more recent meta-analysis reported a noticeably lower corrected correlation of .27 ( Berry, Sackett, & Landers, 2007 ). After reviewing both meta-analyses, it appears that the two studies posed different research questions. Further, there were a number of coding judgments in Berry et al. that merit review, and there was no moderator analysis for educational versus employment interviews. As a result, we reanalyzed the work by Berry et al. and found a corrected correlation of .42 for employment interviews (.15 higher than Berry et al., a 56% increase). Further, educational interviews were associated with a corrected correlation of .21, supporting their influence as a moderator. We suggest a better estimate of the correlation between employment interviews and cognitive ability is .42, and this takes us “back to the future” in that the better overall estimate of the employment interviews – cognitive ability relationship is roughly .40. This difference has implications for what is being measured by interviews and their incremental validity.

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study

Monte Carlo study of 50 nm-long single and dual-gate MODFETs: suppression of short-channel effects

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Short Channel Effects ◽  
Short Channel ◽  
Channel Effects ◽  
Dual Gate
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