Individual work role performance drives the entire economy. It is organizational psychology and organizational behavior's (OP/OB's) most crucial dependent variable. In this review, alternative specifications for the definition and latent structure of individual performance are reviewed and summarized. Setting aside differences in terminology, the alternatives are remarkably similar. The Campbell (2012) model is offered as a synthesized description of the content of the latent structure. Issues pertaining to performance dynamics are then reviewed, along with the role played by individual adaptability to changing performance requirements. Using the synthesized model of the latent content structure and dynamics of performance as a backdrop, issues pertaining to the assessment of performance are summarized. The alternative goals of performance assessment, general measurement issues, and the construct validity of specific methods (e.g., ratings, simulations) are reviewed and described. Cross-cultural issues and future research needs are noted. 47 Annu. Rev. Organ. Psychol. Organ. Behav. 2015.2:47-74. Downloaded from www.annualreviews.org Access provided by University of Minnesota -Twin Cities on 11/24/15. For personal use only.
Co-occurrence of psychiatric disorders is welldocumented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the 'general psychopathology' or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are wellsuited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data. 1 Sometimes, group factors are called "specific factors." However, "specific factor" more correctly refers to an item's reliable (non-error) variance that is not shared with other items (5).
The see package is embedded in the easystats ecosystem, a collection of R packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language (R Core Team, 2021). Most easystats packages return comprehensive numeric summaries of model parameters and performance. The see package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics. As a core pillar of easystats, the see package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting.
Purpose -Research has shown that individuals of different ages hold different environmental attitudes and perform environmental behaviors of different kinds and to varying degrees. The strength and direction of age-effects observed across studies has been inconsistent, however. This study aims to examine the relationship between age and a variety of environmental sustainability-related psychological variables using meta-analytic techniques. Design/methodology/approach -Relationships between age and environmental concern, environmental values, attitudes toward environmental behaviors, environmental awareness, environmental knowledge, environmental motives, environmental intentions, and pro-environmental behaviors were examined. Data from relevant studies between 1970 and 2010 were meta-analyzed to determine the magnitudes of relationships between age and environmental variables, and to investigate whether effects generalize across studies. Findings -Most relationships were negligibly small. Small but generalizable relationships indicated that older individuals appear to be more likely to engage with nature, avoid environmental harm, and conserve raw materials and natural resources. Originality/value -Stereotypes about age-differences in environmental sustainability are commonly held in organizations. If work and organizational psychologists are to encourage and help individuals to be more environmentally responsible at work, understanding how age affects these efforts is imperative. By meta-analytically estimating age-differences in environmental sustainability variables, the present study helps to dispel erroneous stereotypes and guide organizations to implement effective environmental interventions.
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