The capacity to exercise control over the nature and quality of one's life is the essence of humanness. Human agency is characterized by a number of core features that operate through phenomenal and functional consciousness. These include the temporal extension of agency through intentionality and forethought, self-regulation by self-reactive influence, and self-reflectiveness about one's capabilities, quality of functioning, and the meaning and purpose of one's life pursuits. Personal agency operates within a broad network of sociostructural influences. In these agentic transactions, people are producers as well as products of social systems. Social cognitive theory distinguishes among three modes of agency: direct personal agency, proxy agency that relies on others to act on one's behest to secure desired outcomes, and collective agency exercised through socially coordinative and interdependent effort. Growing transnational embeddedness and interdependence are placing a premium on collective efficacy to exercise control over personal destinies and national life.

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Social cognitive theory: An agentic perspective

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

/pdf/missing-data-our-view-of-the-state-of-the-art-3xnwc88wmd.pdf

Missing data: Our view of the state of the art.

Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test ...

Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

In this article, I review the diverse ways in which perceived self-efficacy contributes to cognitive development and functioning. Perceived self-efficacy exerts its influence through four major processes. They include cognitive, motivational, affective, and selection processes. There are three different levels at which perceived self-efficacy operates as an important contributor to academic development. Students' beliefs in their efficacy to regulate their own learning and to master academic activities determine their aspirations, level of motivation, and academic accomplishments. Teachers' beliefs in their personal efficacy to motivate and promote learning affect the types of learning environments they create and the level of academic progress their students achieve. Faculties' beliefs in their collective instructional efficacy contribute significantly to their schools' level of academic achievement. Student body characteristics influence school-level achievement more strongly by altering faculties' beli...

https://www.itma.vt.edu/courses/tel/resources/bandura(1993)_self-efficacy.pdf

Perceived Self-Efficacy in Cognitive Development and Functioning

The establishment of measurement invariance across groups is a logical prerequisite to conducting substantive cross-group comparisons (e.g., tests of group mean differences, invariance of structura...

A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research

The present study was designed to assess selected psychometric properties of the Intrinsic Motivation Inventory (IMI) (Ryan, 1982), a multidimensional measure of subjects' experience with regard to experimental tasks. Subjects (N = 116) competed in a basketball free-throw shooting game, following which they completed the IMI. The LISREL VI computer program was employed to conduct a confirmatory factor analysis to assess the tenability of a five factor hierarchical model representing four first-order factors or dimensions and a second-order general factor representing intrinsic motivation. Indices of model acceptability tentatively suggest that the sport data adequately fit the hypothesized five factor hierarchical model. Alternative models were tested but did not result in significant improvements in the goodness-of-fit indices, suggesting the proposed model to be the most accurate of the models tested. Coefficient alphas for the four dimensions and the overall scale indicated adequate reliabilit...

Psychometric Properties of the Intrinsic Motivation Inventory in a Competitive Sport Setting: A Confirmatory Factor Analysis

Contents: Preface. Introduction. Specification of the LGM. LGM, Repeated Measures ANOVA, and the Mixed Linear Model. Multivariate Representations of Growth and Development. Analyzing Growth in Multiple Populations. Accelerated Designs. Multilevel Longitudinal Approaches. Growth Mixture Modeling. Piecewise and Pooled Interruped Time Series LGMs. Latent Growth Curve Modeling With Categorical Variables. Missing Data Models. Latent Variable Framework for LGM Power Estimation. Testing Interaction Effects in LGMs. Summary.

An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition

Although attribution theory continues to be a fertile area of social psychological research, much of the extant literature has suffered from questionable measurement of the constructs of interest. This is especially true in the case of assigning causal attributions placement in theorized dimensional space. Russell's (1982) Causal Dimension Scale represented an important development toward more precise measurement of causal dimensions; however, it has been criticized on a number of fronts. The present report presents the rationale for and initial psychometric properties of a revised version of the scale, the CDSII. Employing data from four studies, a confirmatory factor analysis is reported examining the goodness of fit of the hypothesized four factor oblique structure to the data. The results are discussed in terms of possible applications of the CDSII and the need for further validity testing.

Measuring Causal Attributions: The Revised Causal Dimension Scale (CDSII)

Adolescent problem behavior: the influence of parents and peers.

The developmental model of adolescent antisocial behavior advanced by Patterson and colleagues (e.g., Patterson, Reid, & Dishion, 1992) appears to generalize the development of a diverse set of problem behaviors. Structural equation modeling methods were applied to 18-month longitudinal data from 523 adolescents. The problem behavior construct included substance use, antisocial behavior, academic failure, and risky sexual behavior. Families with high levels of conflict were less likely to have high levels of parent–child involvement. Such family conditions resulted in less adequate parental monitoring of adolescent behavior, making associations with deviant peers more likely. Poor parental monitoring and associations with deviant peers were strong predictors of engagement in problem behavior. These constructs accounted for 46% of the variance in problem behavior. Although association with deviant peers was the most proximal social influence on problem behavior, parental monitoring and family factors (conflict and involvement) were key parenting practices that influenced this developmental process.

Terry E. Duncan

Papers

Psychometric Properties of the Intrinsic Motivation Inventory in a Competitive Sport Setting: A Confirmatory Factor Analysis

An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition

Measuring Causal Attributions: The Revised Causal Dimension Scale (CDSII)

Adolescent problem behavior: the influence of parents and peers.

Development of Adolescent Problem Behavior