Extensions of factorial solutions

A method is developed for extending any type of factor solution to new tests. The theoretical basis for this approximating scheme is thoroughly investigated, and then a simplification in the technique is introduced for practical purposes. An example is presented which illustrates the procedure of extending a factor solution to three new tests simultaneously.     

Cumulative Advantage: Conductors and Insulators of Heavy‐Tailed Productivity Distributions and Productivity Stars

We use the metatheoretical principle of cumulative advantage as a framework to understand the presence of heavy‐tailed productivity distributions and productivity stars. We relied on 229 datasets including 633,876 productivity observations collected from approximately 625,000 individuals in occupations including research, entertainment, politics, sports, sales, and manufacturing, among others. We implemented a novel methodological approach developed in the field of physics to assess the precise shape of the productivity distribution rather than relying on a normal versus nonnormal artificial dichotomy. Results indicate that higher levels of multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity (i.e., conductors of cumulative advantage) are associated with a higher probability of an underlying power law distribution, whereas lower productivity ceilings (i.e., insulator of cumulative advantage) are associated with a lower probability. In addition, higher levels of multiplicity of productivity, monopolistic productivity, and job autonomy were associated with a greater proportion of productivity stars (i.e., productivity distributions with heavier tails), whereas lower productivity ceilings were associated with a smaller proportion of productivity stars (i.e., productivity distributions with lighter tails). Results serve as a building block for future theory development and testing efforts aimed at understanding why, when, and how the distribution of individual productivity may follow a nonnormal curve—and to what extent. We also discuss implications for organizations and management in terms of the design and implementation of human resource systems (e.g., selection, training, compensation), as well as for individuals interested in becoming productivity stars themselves.     

A Structural Equation Model for the School Reinforcement Survey Schedule: Italian and American Early Adolescents

The School Reinforcement Survey Schedule (SRSS) was administered to 2,828 boys and girls in middle schools in the United States and an Italian translation was administered to 342 boys and girls in middle schools in Northern Italy. An exploratory factor analysis using half the American data set was performed using maximum likelihood estimation with a promax rotation. This analysis produced a structural equation model with six interpretable latent variables. This analysis was confirmed by results demonstrating a good fit with the other half of the American sample and separately with the Italian sample. Scores for the six latent variables were constructed and information about the distribution of scores was obtained. Multiple comparisons of the means were performed by gender, within each national sample, for each of the six latent variables. American and Italian girls report obtaining greater enjoyment from a wider variety of school activities compared to American and Italian boys.     

The effect of induced anxiety on cognition: threat of shock enhances aversive processing in healthy individuals

Individuals with anxiety disorders demonstrate altered cognitive performance including (1) cognitive biases towards negative stimuli (affective biases) and (2) increased cognitive rigidity (e.g., impaired conflict adaptation) on affective Stroop tasks. Threat of electric shock is frequently used to induce anxiety in healthy individuals, but the extent to which this manipulation mimics the cognitive impairment seen in anxiety disorders is unclear. In this study, 31 healthy individuals completed an affective Stroop task under safe and threat-of-shock conditions. We showed that threat (1) enhanced aversive processing and abolished a positive affective bias but (2) had no effect on conflict adaptation. Threat of shock thus partially models the effects of anxiety disorders on affective Stroop tasks. We suggest that the affective state of anxiety—which is common to both threat and anxiety disorders—modulates the neural inhibition of subcortical aversive processing, whilst pathologies unique to anxiety disorders modulate conflict adaptation.