Current issues in factor analysis.

Thurstone, L. L.

doi:10.1037/h0059402

Cited by 74 publications

(38 citation statements)

References 21 publications

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“…1 To best approximate simple structure (Thurstone, 1940), factor loadings above .4 were considered notable. Congruence coefficients (Gorsuch, 1983) were computed to determine the similarities between the obtained factor solutions and published factor solutions for each instrument.…”

Section: Discussionmentioning

confidence: 99%

Convergent and Discriminant Validity of Psychopathy Factors Assessed Via Self-Report

et al. 2005

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Psychopathy has been conceptualized as a personality disorder with distinctive interpersonalaffective and behavioral deviance features. The authors examine correlates of the factors of the Psychopathic Personality Inventory (PPI), Self-Report Psychopathy-II (SRP-II) scale, and Antisocial Process Screening Device (APSD) to understand similarities and differences among the constructs embodied in these instruments. PPI Fearless Dominance and SRP-II Factor 1 were negatively related to most personality disorder symptoms and were both predicted by high Dominance and low Neuroticism. In addition, PPI Fearless Dominance correlated positively with antisocial personality features, although SRP-II Factor 1 did not. In contrast, PPI Impulsive Antisociality, SRP-II Factor 2, and both APSD factors correlated with antisocial personality features and symptoms of nearly all personality disorders, and were predicted by low Love. Results suggest ways in which the measurement of the constructs in each instrument may be improved. KeywordsPsychopathic Personality Inventory; Self-Report Psychopathy-II scale; Antisocial Process Screening Device; psychopathy; self-report; nomological network Psychopathy is a personality disorder that is distinguished from common criminality and chronic antisociality by the presence of distinctive interpersonal-affective features (Hare, 2003;Harpur, Hare, & Hakstian, 1989;Lilienfeld, 1994;Lykken, 1995;Patrick & Lang, 1999). Recent self-report measures of psychopathy, including the Self-Report Psychopathy scale (SRP;Hare, 1985Hare, , 1991 and the Psychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996), demonstrate better convergence and coverage of both the interpersonalaffective and antisocial lifestyle features of psychopathy. Factor analytic work on these and other self-report instruments of psychopathy has revealed that many of these instruments have a two-factor structure, each of which parses the interpersonal-affective and behavioral features into separate subscales (Benning, Patrick, Hicks, Blonigen, & Krueger, 2003;Frick, O'Brien, Wootton, & McBurnett, 1994;Hare, 1991). Here, we examined in an undergraduate sample the construct validity of the two-factor structures of the PPI, the SRP-II scale, and a self-report version of the Antisocial Process Screening Device (APSD; Frick & Hare, 2001). We examined the relations of each psychopathy factor to the others, to symptoms of other personality disorders, and to normal-range personality dimensions.Correspondence may be addressed either to Stephen D. Benning, Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455 (e-mail: benn0224@umn.edu), or to Randall T. Salekin, Department of Psychology, University of Alabama, Box 870348, Tuscaloosa, AL 35487 (e-mail: rsalekin@bama.ua.edu). Cronbach and Meehl (1955) proposed the concept of the nomological network as a theoretical framework for understanding construct validity. In this framework, a network is set up between unobserved theoretical constructs ...

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Section: Discussionmentioning

confidence: 99%

Convergent and Discriminant Validity of Psychopathy Factors Assessed Via Self-Report

et al. 2005

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“…Thus, it was not reasonable of Armstrong to criticize the methodology for Swift's failure to exactly recover five dimensions when there were inherent incompatibilities between the model (linearity assumed) and the data (nonlinear). Violation of the linearity assumption does not completely compromise the chances of recovering an interpretable factor solution (e.g., Thurstone, 1940), but it may have contributed to the recovery of poor or uninterpretable factor solutions in the past, including that reported by Tom Swift.…”

Section: Violation Of the Linearity Assumptionmentioning

confidence: 99%

Repairing Tom Swift's Electric Factor Analysis Machine

Preacher

MacCallum

2003

Understanding Statistics

679

470

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Proper use of exploratory factor analysis (EFA) requires the researcher to make a series of careful decisions. Despite attempts by Floyd and Widaman (1995), Fabrigar, Wegener, MacCallum, andStrahan (1999), and others to elucidate critical issues involved in these decisions, examples of questionable use of EFA are still common in the applied factor analysis literature. Poor decisions regarding the model to be used, the criteria used to decide how many factors to retain, and the rotation method can have drastic consequences for the quality and meaningfulness of factor analytic results. One commonly used approach-principal components analysis, retention of components with eigenvalues greater than 1.0, and varimax rotation of these components-is shown to have potentially serious negative consequences. In addition, choosing arbitrary thresholds for factor loadings to be considered large, using single indicators for factors, and violating the linearity assumptions underlying EFA can have negative consequences for interpretation of results. It is demonstrated that, when decisions are carefully made, EFA can yield unambiguous and meaningful results. exploratory factor analysis, principal components, EFA, PCA Exploratory factor analysis (EFA) is a method of discovering the number and nature of latent variables that explain the variation and covariation in a set of measured variables. Because of their many useful applications, EFA methods have enjoyed widespread use in psychological research literature over the last several decades. In the process of conducting EFA, several important decisions need to be UNDERSTANDING STATISTICS, 2(1),

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“…Thurstone's multiple factor models. Thurstone's [39] approach to factor analysis was to focus on extracting and interpreting group factors. Interestingly, both Spearman and Thurstone thought that the factorial description of a measured variable should remain the same if the variable were moved to another battery of tests (i.e., invariance).…”

Section: Higher-order Modelmentioning

confidence: 99%

John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models

Beaujean

2015

J. Intell.

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The development of factor models is inextricably tied to the history of intelligence research. One of the most commonly-cited scholars in the field is John Carroll, whose three-stratum theory of cognitive ability has been one of the most influential models of cognitive ability in the past 20 years. Nonetheless, there is disagreement about how Carroll conceptualized the factors in his model. Some argue that his model is best represented through a higher-order model, while others argue that a bi-factor model is a better representation. Carroll was explicit about what he perceived the best way to represent his model, but his writings are not always easy to understand. In this article, I clarify his position by first describing the details and implications of bi-factor and higher-order models then show that Carroll's published views are better represented by a bi-factor model.

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Current issues in factor analysis.

Cited by 74 publications

References 21 publications

Convergent and Discriminant Validity of Psychopathy Factors Assessed Via Self-Report

Convergent and Discriminant Validity of Psychopathy Factors Assessed Via Self-Report

Repairing Tom Swift's Electric Factor Analysis Machine

John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models

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