From types to typological thinking: a reply to Asendorpf

2006 ◽  
Vol 20 (1) ◽  
pp. 49-51 ◽  
Author(s):  
Robert R. McCrae ◽  
Antonio Terracciano ◽  
Paul T. Costa ◽  
Daniel J. Ozer
Keyword(s):  
Individual Differences ◽  
Factor Model ◽  
Five Factor Model ◽  
Factor Analyses ◽  
Model Based ◽  
Personality Research ◽  
Typological Thinking ◽  
Q Sort

We continue to disagree with Asendorpf (2006) on the best way to analyse Q‐sort data and on our priorities for personality research. We believe on statistical grounds that the large first factor found in inverse factor analyses of raw CAQ items tells us much about response norms, but little or nothing about individual differences. These emerge more clearly in analyses of standardised items, which show the familiar dimensions of the Five‐Factor Model. Based on our research on types and the mixed results reported by other researchers, we do not believe that replicable empirical types are likely to be found, and suggest that a more profitable line of research would focus on the heuristics of types and the configural interpretation of traits. Published in 2006 by John Wiley & Sons, Ltd.

scholarly journals Using the 16PF to Test the Differentiation of Personality by Intelligence Hypothesis

2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
Julie Aitken Schermer ◽  
Georg Krammer ◽  
Richard D. Goffin ◽  
Michael D. Biderman
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Factor Model ◽  
Five Factor Model ◽  
Consistent Pattern ◽  
Factor Analyses ◽  
Personality Measure ◽  
G Factor ◽  
Configural Invariance ◽  
Job Candidates

The differentiation of personality by intelligence hypothesis suggests that there will be greater individual differences in personality traits for those individuals who are more intelligent. Conversely, less intelligent individuals will be more similar to each other in their personality traits. The hypothesis was tested with a large sample of managerial job candidates who completed an omnibus personality measure with 16 scales and five intelligence measures (used to generate an intelligence g-factor). Based on the g-factor composite, the sample was split using the median to conduct factor analyses within each half. A five-factor model was tested for both the lower and higher intelligence halves and were found to have configural invariance but not metric or scalar invariance. In general, the results provide little support for the differentiation hypothesis as there was no clear and consistent pattern of lower inter-scale correlations for the more intelligent individuals.

Development of a Measure of Calculative Mindset (CM): Establishing a Nomological Net and Predictive Utility of the CM Measure

2021 ◽  
pp. 003329412110102
Author(s):  
Joongseo Kim ◽  
I. M. Jawahar ◽  
Brigitte Steinheider ◽  
Thomas Stone ◽  
Brandon Ferrell
Keyword(s):  
Organizational Citizenship ◽  
Factor Model ◽  
Five Factor Model ◽  
Experimental Studies ◽  
Field Research ◽  
Performance Outcomes ◽  
Work Behavior ◽  
Hierarchical Regression ◽  
Organizational Contexts ◽  
Personality Research

A calculative mindset (CM) describes the tendency to analyze and convert qualitative social values into numeric or monetary metrics and is a predisposition that shapes behaviors and actions of the employee. CM has been manipulated in experimental studies, but it has not been investigated in field research due to the absence of a scale to measure CM. In study 1, we followed Hinkin’s scale development protocol to conceptualize, develop, and validate a measure of CM to facilirate research in organizational contexts. In Study 2, we examined the relationship between CM and measures of performance, counterproductive work behavior (CWB), organizational citizenship behaviors (OCB), and in role-performance (IRP). Results from hierarchical regression analyses indicate that CM is related to these performance outcomes and explains incremental variance over established measures of the Five-Factor Model of personality. Implications for personality research, selection of human resources, and facilitation of an ethical workplace are discussed.

scholarly journals A-12 The Dimensionality of Symptoms Before and After Sport-Related Concussion

2020 ◽  
Vol 35 (6) ◽  
pp. 785-785
Author(s):  
J Karr ◽  
G Iverson
Keyword(s):  
Measurement Invariance ◽  
Factor Model ◽  
Five Factor Model ◽  
Fit Indices ◽  
Factor Analyses ◽  
Post Injury ◽  
Specific Symptom ◽  
Bifactor Models ◽  
Before And After ◽  
Sleep Arousal

Abstract Objective Multiple factor analyses have examined the dimensionality of physical, emotional, and cognitive symptoms both before and after a sport-related concussion. The current study compared model fit and measurement invariance of five candidate factor models, including a one-factor model, original four-factor model (cognitive-sensory, vestibular-somatic, sleep-arousal, and affective), alternative four-factor model (cognitive, physical, sleep-arousal, and affective), five-factor model (cognitive-sensory separated), and bifactor model. Method Student athletes (N = 1,554; 56.7% boys; age: M = 16.1 ± 1.2) completed the Post-Concussion Symptoms Scale (PCSS) at preseason baseline and after a suspected concussion. Confirmatory factor analyses were conducted at both time points, with pre-injury to post-injury measurement invariance models (configural, weak, strong, and strict) also examined. Model results were assessed via fit indices (CFI ≥ .90/RMSEA≤.08) and change-in-fit indices (∆CFI ≤ -.01). Results All models other than the one-factor model showed excellent fit before and after concussion (CFIs>.95/RMSEAs < .06). Based on pre-injury to post-injury invariance analyses, full weak invariance was established for both four-factor and the bifactor models, and partial strict invariance was established for each of these models following modifications. Conclusions Support for partial strict invariance indicates that meaningful comparisons can be made between factor means before and after concussion for the four-factor and bifactor models, evidencing the validity of a total symptom score and specific symptom subscales before and after concussion. The alternative four-factor model may offer an improved conceptual framework compared to the original four-factor model, which included a non-intuitive cognitive-sensory factor. These findings could support the development of normative scores for PCSS subscales for use in research and clinical practice.

Brief Bipolar Markers for the Five Factor Model of Personality

1999 ◽  
Vol 84 (3_suppl) ◽  
pp. 1173-1179 ◽  
Author(s):  
Alan B. Shafer
Keyword(s):  
Internal Consistency ◽  
Rating Scale ◽  
Factor Model ◽  
Five Factor Model ◽  
Factor V ◽  
Factor Analyses ◽  
Initial Development ◽  
Total N ◽  
Factor Loadings

The initial development of a brief 30-item bipolar rating scale designed to measure the Five Factor Model of personality is presented. This scale assesses Factor V as a variant of Openness rather than Intellect. Factor analyses across five samples (Total N = 898) indicated that the trait-term pairs used to construct the scales exhibited relatively high univocal factor loadings ( M = .62, SD=.13) and acceptable values of internal consistency ( M α=.79, SD= 07)

scholarly journals Age Differences in the Variance of Personality Characteristics

2016 ◽  
Vol 30 (1) ◽  
pp. 4-11 ◽  
Author(s):  
René Mõttus ◽  
Jüri Allik ◽  
Martina Hřebíčková ◽  
Liisi Kööts–Ausmees ◽  
Anu Realo
Keyword(s):  
Individual Differences ◽  
Age Differences ◽  
Factor Model ◽  
Five Factor Model ◽  
Personality Development ◽  
Personality Characteristics ◽  
Group Differences ◽  
Age Group ◽  
Personality Psychology ◽  
Model Domain

In contrast to mean–level comparisons, age group differences in personality trait variance have received only passing research interest. This may seem surprising because individual differences in personality characteristics are exactly what most of personality psychology is about. Because different proposed mechanisms of personality development may entail either increases or decreases in variance over time, the current study is exploratory in nature. Age differences in variance were tested by comparing the standard deviations of the five–factor model domain and facet scales across two age groups (20 to 30 years old versus 50 to 60 years old). Samples from three cultures (Estonia, the Czech Republic and Russia) were employed, and two methods (self–reports and informant–reports) were used. The results showed modest convergence across samples and methods. Age group differences were significant for 11 of 150 facet–level comparisons but never consistently for the same facets. No significant age group differences were observed for the five–factor model domain variance. Therefore, there is little evidence for individual differences in personality characteristics being systematically smaller or larger in older as opposed to younger people. We discuss the implications of these findings for understanding personality development. Copyright © 2015 European Association of Personality Psychology

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