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Another interesting paper just “online” ...

 

Pettersson, E., Turkheimer, E., Hom, E.E., & Menatti, A.R. (2011) The general factor of personality and evaluation. European Journal of Personality (DOI: 10.1002/per.839 - http://onlinelibrary.wiley.com/doi/10.1002/per.839/abstract ), , , 0-0.

Abstract

According to the proposal of the general factor of personality (GFP), socially desirable personality traits have been selected for throughout evolution because they increase fitness. However, it remains unknown whether people high on this factor actually behave in socially desirable ways or whether they simply endorse traits of positive valence. We separated these two sources of variance by having 619 participants respond to 120 personality adjectives organised into 30 quadruples balanced for content and valence (e.g. unambitious, easy-going, driven and workaholic tapped the trait achievement-striving). An exploratory six-factor solution fit well, and the factors resembled the Big Five. We subsequently extracted a higher-order factor from this solution, which appeared similar to the GFP. A Schmid–Leiman transformation of the higher-order factor, however, revealed that it clustered items of similar valence but opposite content (e.g. at the negative pole, unambitious and workaholic), rendering it an implausible description of evolved adaptive behaviour. Isolating this evaluative factor using exploratory structural equation modelling generated factors consisting of items of similar descriptive content but different valence (e.g. driven and workaholic), and the correlations among these factors were of small magnitude, indicating that the putative GFP capitalises primarily on evaluative rather than descriptive variance. Implications are discussed. 

 

A peculiar paper in some respects – the methodology is “state of the art” but there seems to be a complete disconnection between scientific substance and the number-crunching methodology part-way through the article. However, for those who enjoy the General Factor lark, this is going to cause the protagonists some considerable stress! 

 

The paper will provide much new-publication food for rejoinders from those who are content to argue at the level of number-relations rather than be guided by issues of measurement, awkward empirical facts, and rather more coherent theory surrounding the causal basis for those behaviors which can be classified into an ad-hoc taxonomy (the Big Five).

 

For me, the whole GFP saga is another perfect exemplar to add to the examples in latest book by the late David Freedman ...

Freedman, D.A., Collier, D., Sekhon, J.S., & Stark, P.B(Eds.). (2009) Statistical Models and Causal Inference: A Dialogue with the Social Sciences. Cambridge University Press. ISBN: 978-0521123909.

http://www.amazon.com/Statistical-Models-Causal-Inference-Dialogue/dp/0521123909/ref=pd_rhf_shvl_3

 

Abstract

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a "shoe leather" methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with skepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor "low-tech" approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views.

 

A rather poignant statement is made at the end of the preface ..

This volume will not end the modeling enterprise. As Freedman wrote ‘there  will always be a desire to substitute intellectual capital for labor‘ by using statistical methods to avoid the hard work of examining problems in their full specificity and complexity.“

 

Regards .. Paul

 

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