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To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals. In the present paper, we examine how continuous time dynamic models can be used to study nonexperimental intervention effects in longitudinal studies where measurement intervals vary between and within participants. We empirically illustrate this method by using panel data (N = 2,877) to study the effect of the transition from primary to secondary school on students' motivation. Results of a simulation study also show that the precision and recovery of the estimate of the effect improves with individual variation in time intervals. [ABSTRACT FROM AUTHOR]

Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality—that is, cumulative advantage (CA), human capital theory, and the lifespan perspective—can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework. [ABSTRACT FROM AUTHOR]

The present study examines how historical changes in the U.S. socioeconomic environment in the 20th century may have affected core assumptions of the "American Dream." Specifically, the authors examined whether such changes modulated the extent to which adolescents' intelligence (IQ), their grade point average (GPA), and their parents' socioeconomic status (SES) could predict key life outcomes in adulthood about 20 years later. The data stemmed from two representative U.S. birth cohorts of 15- and 16-year-olds who were born in the early 1960s (N = 3,040) and 1980s (N = 3,524) and who participated in the National Longitudinal Surveys of Youth (NLSY). Cohort differences were analyzed with respect to differences in average relations by means of multiple and logistic regression and for specific points in each outcome distribution by means of quantile regressions. In both cohorts, IQ, GPA, and parental SES predicted important educational, occupational, and health-related life-outcomes about 20 years later. Across historical time, the predictive utility of adolescent IQ and parental SES remained stable for the most part. Yet, the combined effects of social-ecological and socioeconomic changes may have increased the predictive utility (that is, the regression weights) of adolescent GPA for educational, occupational, and health outcomes over time for individuals who were born in the 1980s. Theoretical implications concerning adult development, aging, and late life inequality are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large‐scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta‐analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends. Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two‐stage approach to IPD meta‐analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three‐level meta‐analytic and meta‐regression models to take into account dependencies among effect sizes (Stage 2). The two‐stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles. All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies. [ABSTRACT FROM AUTHOR]