Inter-individual differences in intra-individual variability in personality within and across contexts

This study focuses on intra-individual variability in personality at work, and how it relates to job performance. 288 professionals completed contextualised adjective-based personality assessments in work and non-work contexts, and a non-contextualised personality measure. Ratings of their personality were also obtained from colleagues, family members and friends. Supervisors provided performance ratings for 130 participants. Results indicate that personality is context- and source-dependent, and varies systematically within contexts intra-individually regardless of source. Whilst this variability was predictive of some performance criteria when based on other-ratings, overall predictive effects were small in number and size. This study adds to the relatively small body of research on personality variability and performance and contributes to the conceptualisation of personality as a dynamic construct.     

Kindergarten self-control mediates the gender reading achievement gap: A population-based cohort study

Could superior self-control explain the gender difference in reading achievement favoring girls? To test this idea, we drew on a unique population-based sample (N = 11,336) where self-control was measured in kindergarten using a multimethod battery of assessments. Girls showed substantially higher levels of self-control in kindergarten (β = 0.47) and outperformed boys on standardized tests of reading achievement in third/fourth grade (β = 0.20). Further, kindergarten self-control prospectively predicted reading achievement throughout elementary school (β = 0.37). Connecting these findings, our mediation analyses revealed that the female self-control advantage in kindergarten could account for subsequent gender differences in reading achievement. Our results suggest that early gender differences in self-control may represent a key pathway through which gender disparities in reading skills, vocabulary knowledge, and reading comprehension occur.     

Canonical analysis of two convex polyhedral cones and applications

Canonical analysis of two convex polyhedral cones consists in looking for two vectors (one in each cone) whose square cosine is a maximum. This paper presents new results about the properties of the optimal solution to this problem, and also discusses in detail the convergence of an alternating least squares algorithm. The set of scalings of an ordinal variable is a convex polyhedral cone, which thus plays an important role in optimal scaling methods for the analysis of ordinal data. Monotone analysis of variance, and correspondence analysis subject to an ordinal constraint on one of the factors are both canonical analyses of a convex polyhedral cone and a subspace. Optimal multiple regression of a dependent ordinal variable on a set of independent ordinal variables is a canonical analysis of two convex polyhedral cones as long as the signs of the regression coefficients are given. We discuss these three situations and illustrate them by examples.     

Factor invariance of the Millon Clinical Multiaxial Inventory

Several factor analyses of the Millon Clinical Multiaxial Inventory (MCMI) have resulted in very similar solutions. Interpretation of this consistency is hampered by the fact that the 20 scales of the inventory share items. Overlapping items cause the scales to be linearly dependent and may create structure in the interscale correlation matrix which is separate from the subject response patterns. A factor analysis was performed on the matrix of item-overlap coefficients which describes the underlying artifactual structure of the instrument. Data from two new subject samples were factor analyzed and compared to previously published studies. Similarity coefficients among factors across studies were calculated.     

Daily stress and anxiety and their relation to daily fluctuations of symptoms in asthma and chronic obstructive pulmonary disease (COPD) patients

Although stress and anxiety have long been assumed to play an exacerbatory role in asthma, no study has systematically documented that daily exacerbations of asthma symptoms are related to stress and/or anxiety. In this study, 24 airways obstruction patients (12 asthmatics and 12 chronic obstructive pulmonary disease patients) were instructed to monitor the severity of daily respiratory symptoms. In addition, subjects recorded their daily anxiety level and the number and perceived impact of daily stressors. The results showed that although there were differences between high- and low-stress days for both groups, there were no differences between groups on symptom severity or between high- and low-anxiety days, as measured by the State-Trait Anxiety Inventory (STAI). Thus, although the number and impact of daily stressors were found to be directly associated with the severity of asthma symptoms, anxiety does not appear to have a direct role in the exacerbation of asthma. The findings failed to support the anxiety theory of asthma but provided an explanation for the poor results obtained in previous treatment studies which employed anxiety management with asthmatics.     

Imputation of missing categorical data by maximizing internal consistency

This paper suggests a method to supplant missing categorical data by reasonable replacements. These replacements will maximize the consistency of the completed data as measured by Guttman's squared correlation ratio. The text outlines a solution of the optimization problem, describes relationships with the relevant psychometric theory, and studies some properties of the method in detail. The main result is that the average correlation should be at least 0.50 before the method becomes practical. At that point, the technique gives reasonable results up to 10–15% missing data.We thank Anneke Bloemhoff of NIPG-TNO for compiling and making the Dutch Life Style Survey data available to use, and Chantal Houée and Thérèse Bardaine, IUT, Vannes, France, exchange students under the COMETT program of the EC, for computational assistance. We also thank Donald Rubin, the Editors and several anonymous reviewers for constructive suggestions.     

Examining differential item functioning due to item difficulty and alternative attractiveness

A method for analyzing test item responses is proposed to examine differential item functioning (DIF) in multiple-choice items through a combination of the usual notion of DIF, for correct/incorrect responses and information about DIF contained in each of the alternatives. The proposed method uses incomplete latent class models to examine whether DIF is caused by the attractiveness of the alternatives, difficulty of the item, or both. DIF with respect to either known or unknown subgroups can be tested by a likelihood ratio test that is asymptotically distributed as a chi-square random variable.     

An automated test development of parallel tests from a seed test

Binary programming models are presented to generate parallel tests from an itembank. The parallel tests are created to match item for item an existing seed test and match user supplied taxonomic specifications. The taxonomic specifications may be either obtained from the seed test or from some other user requirement. An algorithm is presented along with computational results to indicate the overall efficiency of the process. Empirical findings based on an itembank for the Arithmetic Reasoning section of the Armed Services Vocational Aptitude Battery are given.The Office of Naval Research, Program in Cognitive Science, N00014-87-C-0696 partially supported the work of Douglas H. Jones. The Rutgers Research Resource Committee of the Graduate School of Management partially supported the work of Douglas H. Jones and Ing-Long Wu. A Thomas and Betts research fellowship partially supported the work of Ing-Long Wu. The Human Resources Laboratory, United States Air Force, partially supported the work of Ronald Armstrong. The authors benefited from conversations with Dr. Wayne Shore, Operational Technologies, San Antonio, Texas. The order of authors' names is alphabetical and denotes equal authorship.     

Factor structure of the Treatment Evaluation Inventory: Implications for measurement of treatment acceptability

The Treatment Evaluation Inventory (TEI), a frequently used measure of treatment acceptability, was used by 164 undergraduates to rate the acceptability of each of the following treatments: differential reinforcement of other behavior, exclusionary time-out, overcorrection, medical restraint, contingent electric shock, and physical restraint. TEI ratings of each treatment type were grouped separately, variance-covariance matrices were formed and compared, and data were subjected to factor analysis. The results indicated that the factor structure of the TEI varied with the treatment it was used to evaluate. Item analysis of the TEI indicated a high degree of internal consistency, although item-total correlations varied between rated treatments. The findings suggest that although the TEI is a reliable instrument, sensitive assessment of the treatment acceptability construct probably requires multidimensional measurement.     

Diathesis-stress models of psychopathology: A quantitative genetic perspective

When genetics is considered in diathesis-stress models of psychopathology, it is often assumed that heredity provides the diathesis and environmental factors are responsible for the stressor. We discuss two quantitative genetic models relevant to the diathesis-stress construct. One model focuses on genotype-environment interaction, which is the usual way in which genetic influence is represented in diathesis-stress models. A second model—genotype-environment correlation—provides an alternative that represents both genetic and environmental influences relevant to the development of psychopathology. Implications of these models for clinical research are discussed.