@article{BrunnerKellerStallaschetal.2022,
  author    = {Brunner, Martin and Keller, Lena and Stallasch, Sophie E. and Kretschmann, Julia and Hasl, Andrea and Preckel, Franzis and Luedtke, Oliver and Hedges, Larry},
  title     = {Meta-analyzing individual participant data from studies with complex survey designs},
  series = {Research synthesis methods},
  volume    = {14},
  journal   = {Research synthesis methods},
  number    = {1},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1759-2879},
  doi       = {10.1002/jrsm.1584},
  pages     = {5 -- 35},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{HaslVoelkleKretschmannetal.2022,
  author    = {Hasl, Andrea and Voelkle, Manuel and Kretschmann, Julia and Richter, Dirk and Brunner, Martin},
  title     = {A dynamic structural equation approach to modeling wage dynamics and cumulative advantage across the lifespan},
  series = {Multivariate Behavioral Research},
  volume    = {58},
  journal   = {Multivariate Behavioral Research},
  number    = {3},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {0027-3171},
  doi       = {10.1080/00273171.2022.2029339},
  pages     = {504 -- 525},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}