On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Intelligence ◽  
2003 ◽  
Vol 31 (6) ◽  
pp. 543-566 ◽  
Author(s):  
G Lubke
Keyword(s):  
Measurement Invariance ◽  
Factor Model ◽  
Common Factor ◽  
Group Differences ◽  
Common Factor Model ◽  
The Common ◽  
The Relationship

Redundancy in Five Psychological Tests

1976 ◽  
Vol 39 (3_suppl) ◽  
pp. 1072-1074
Author(s):  
A. B. Silverstein
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Psychological Tests ◽  
Adult Intelligence Scale ◽  
Intelligence Scale ◽  
Wechsler Adult Intelligence Scale ◽  
Common Factor Model ◽  
Developmental Test ◽  
The Common ◽  
Wechsler Adult Intelligence

Indices of redundancy were obtained for five psychological tests, using the full component model, and the results were compared with those previously obtained by use of the common factor model. The three indices employed appeared to yield essentially equivalent information as to the ranking of the five tests: redundancy was generally lowest in the Developmental Test of Visual Perception and highest in the Wechsler Adult Intelligence Scale. The findings provide an empirical standard for evaluating the redundancy in other tests.

scholarly journals On the cyclicality of real wages and wage differentials

2017 ◽  
Vol 19 (1) ◽  
Author(s):  
Christopher Otrok ◽  
Panayiotis M. Pourpourides
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Empirical Literature ◽  
Real Wages ◽  
Wage Cyclicality ◽  
Common Cycles ◽  
The Common ◽  
The Business Cycle ◽  
Wage Variation ◽  
The Relationship

Abstract Previous empirical literature suggests that estimated wage cyclicality depends on the structure of the relationship between real wages and an observed indicator of the business cycle that econometric models impose prior to estimation. This paper, alleviates the problem of imposing such structure by searching directly for the largest common cycles in longitudinal microdata using a Bayesian dynamic latent factor model. We find that the comovement of real wages is related to a common factor that exhibits a significant but imperfect correlation with the national unemployment rate. Among others, our findings indicate that the common factor explains, on average, no more than 9% of wage variation, it accounts for 20% or less of the wage variability for 88% of the workers in the sample and roughly half of the wages move procyclically while half move countercyclically. These facts are inconsistent with claims of a strong systematic relationship between real wages and business cycles.

scholarly journals Toward a Causal Interpretation of the Common Factor Model

Disputatio ◽  
2017 ◽  
Vol 9 (47) ◽  
pp. 581-601 ◽  
Author(s):  
Riet Van Bork ◽  
Lisa D. Wijsen ◽  
Mijke Rhemtulla
Keyword(s):  
Latent Variable ◽  
Factor Model ◽  
Causal Structure ◽  
Common Factor ◽  
Statistical Interpretation ◽  
Causal Interpretation ◽  
Psychological Constructs ◽  
Common Factor Model ◽  
The Common ◽  
Research Questions

Abstract Psychological constructs such as personality dimensions or cognitive traits are typically unobserved and are therefore measured by observing so-called indicators of the latent construct (e.g., responses to questionnaire items or observed behavior). The Common Factor Model (CFM) models the relations between the observed indicators and the latent variable. In this article we argue in favor of interpreting the CFM as a causal model rather than merely a statistical model, in which common factors are only descriptions of the indicators. When there is sufficient reason to hypothesize that the underlying causal structure of the data is a common cause structure, a causal interpretation of the CFM has several benefits over a merely statistical interpretation of the model. We argue that (1) a causal interpretation conforms with most research questions in which the goal is to explain the correlations between indicators rather than merely summarizing them; (2) a causal interpretation of the factor model legitimizes the focus on shared, rather than unique variance of the indicators; and (3) a causal interpretation of the factor model legitimizes the assumption of local independence.

Common Factors and Causal Networks

2012 ◽  
Vol 26 (4) ◽  
pp. 441-442 ◽  
Author(s):  
James J. Lee
Keyword(s):  
Cognitive Abilities ◽  
Factor Model ◽  
Common Factor ◽  
Common Factors ◽  
Personality Psychology ◽  
Common Factor Model ◽  
Target Article ◽  
Essential Questions ◽  
The Common ◽  
Causal Structures

The target article touches upon some of the most difficult and essential questions in personality psychology. Questioning the notion of a common factor as an as–yet–unobserved common cause of a behaviour domain's exemplars, the authors propose using graphical representations to inspire hypotheses of more complex causal structures. I do not find the case for the de–emphasis of the common factor model to be compelling for those behaviour domains (cognitive abilities) with which I am most familiar. It behoves all personality psychologists, however, to question the foundations of their favoured tools. Copyright © 2012 John Wiley & Sons, Ltd.

From Bi-Dimensionality to Uni-Dimensionality in Self-Report Questionnaires

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Bjarne Schmalbach ◽  
Markus Zenger ◽  
Michalis P. Michaelides ◽  
Karin Schermelleh-Engel ◽  
Andreas Hinz ◽  
...  
Keyword(s):  
Factor Model ◽  
Common Factor ◽  
Self Regulation ◽  
Model Fit ◽  
Self Report ◽  
External Criterion ◽  
Common Factor Model ◽  
Random Intercept ◽  
Using Data ◽  
Core Self

Abstract. The common factor model – by far the most widely used model for factor analysis – assumes equal item intercepts across respondents. Due to idiosyncratic ways of understanding and answering items of a questionnaire, this assumption is often violated, leading to an underestimation of model fit. Maydeu-Olivares and Coffman (2006) suggested the introduction of a random intercept into the model to address this concern. The present study applies this method to six established instruments (measuring depression, procrastination, optimism, self-esteem, core self-evaluations, and self-regulation) with ambiguous factor structures, using data from representative general population samples. In testing and comparing three alternative factor models (one-factor model, two-factor model, and one-factor model with a random intercept) and analyzing differential correlational patterns with an external criterion, we empirically demonstrate the random intercept model’s merit, and clarify the factor structure for the above-mentioned questionnaires. In sum, we recommend the random intercept model for cases in which acquiescence is suspected to affect response behavior.

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