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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.
Differentiation hypotheses concern changes in the structural organization of cognitive abilities that depend on the level of general intelligence (ability differentiation) or age (developmental differentiation). Part 1 of this article presents a review of the literature on ability and developmental differentiation effects in children, revealing the need for studies that examine both effects simultaneously in this age group with appropriate statistical methods. Part 2 presents an empirical study in which nonlinear factor analytic models were applied to the standardization sample (N = 2,619 German elementary schoolchildren; 48% female; age: M = 8.8 years, SD = 1.2, range 6-12 years) of the THINK 1-4 intelligence test to investigate ability differentiation, developmental differentiation, and their interaction. The sample was nationally representative regarding age, gender, urbanization, and geographic location of residence but not regarding parents' education and migration background (overrepresentation of children with more educated parents, underrepresentation of children with migration background). The results showed no consistent evidence for the presence of differentiation effects or their interaction. Instead, different patterns were observed for figural, numerical, and verbal reasoning. Implications for the construction of intelligence tests, the assessment of intelligence in children, and for theories of cognitive development are discussed.
Between-School Variation in Students' Achievement, Motivation, Affect, and Learning Strategies
(2017)
To plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated.
There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.
Effects of achievement differences for internal/external frame of reference model investigations
(2018)
Background
Achievement in math and achievement in verbal school subjects are more strongly correlated than the respective academic self-concepts. The internal/external frame of reference model (I/E model; Marsh, 1986, Am. Educ. Res. J., 23, 129) explains this finding by social and dimensional comparison processes. We investigated a key assumption of the model that dimensional comparisons mainly depend on the difference in achievement between subjects. We compared correlations between subject-specific self-concepts of groups of elementary and secondary school students with or without achievement differences in the respective subjects.
Aims
The main goals were (1) to show that effects of dimensional comparisons depend to a large degree on the existence of achievement differences between subjects, (2) to demonstrate the generalizability of findings over different grade levels and self-concept scales, and (3) to test a rarely used correlation comparison approach (CCA) for the investigation of I/E model assumptions.
Samples
We analysed eight German elementary and secondary school student samples (grades 3–8) from three independent studies (Ns 326–878).
Method
Correlations between math and German self-concepts of students with identical grades in the respective subjects were compared with the correlation of self-concepts of students having different grades using Fisher's Z test for independent samples.
Results
In all samples, correlations between math self-concept and German self-concept were higher for students having identical grades than for students having different grades. Differences in median correlations had small effect sizes for elementary school students and moderate effect sizes for secondary school students.
Conclusions
Findings generalized over grades and indicated a developmental aspect in self-concept formation. The CCA complements investigations within I/E-research.
Im Schuljahr 2008/09 war Jahrgangsübergreifendes Lernen (JÜL) in der Berliner Schuleingangsphase verpflichtend eingeführt worden. Doch nicht alle Schulen übernahmen diese Reform. In dieser Studie untersuchen wir, inwiefern Schulen sich in Abhängigkeit davon, wie schnell und umfassend sie JÜL implementiert hatten, in Merkmalen ihrer Schülerschaft voneinander unterscheiden. Wir nahmen an, dass mit dem Ziel von JÜL, Heterogenität produktiv für das Lernen zu nutzen, die Reform für solche Schulen besonders attraktiv war, die eine heterogene Schülerschaft haben. Heterogenität wurde über die Anteile von Kindern mit (a) nichtdeutscher Erstsprache und (b) Lernmittelzuzahlungsbefreiung operationalisiert. Weiter wurde untersucht, ob sich die Leistungen der Kinder in Deutsch und Mathematik zwischen den Schulen unterschieden. Die Ergebnisse zeigen erwartungsgemäß, dass Schulen mit einer heterogenen Schülerschaft JÜL schnell und nachhaltig implementierten. Im zeitlichen Verlauf ließen sich, nach Kontrolle der Heterogenität der Schülerschaft, keine Leistungsunterschiede zwischen den Schulen feststellen. Die Ergebnisse werden hinsichtlich der Frage diskutiert, unter welchen Voraussetzungen Schulen Reformen implementieren und wie sich JÜL auf Bildungsergebnisse auswirken kann.
Bildungspolitische Reformen unterscheiden sich in der Breite, Tiefe und Nachhaltigkeit, mit der sie realisiert werden. Der vorliegende Beitrag beschäftigt sich mit diesem Thema am Beispiel der Umsetzung des Jahrgangsübergreifenden Lernens (JÜL) in Berlin. JÜL war eine der zentralen Innovationen bei der Neugestaltung des Schulanfangs. Vor diesem Hintergrund behandelt die erste Teilstudie, wie JÜL an Schulen in den Schuljahren 2007/08 bis 2015/16 implementiert wurde. Es wurden Daten der Berliner Schulstatistik zu einem Längsschnitt auf Schulebene zusammengefasst (N = 356). Latente Profilanalysen identifizieren sechs Implementationstypen, die sich in Zeitpunkt und Dauer der Umsetzung von JÜL unterscheiden. Hierbei diente der Anteil der JÜL-Klassen an den Klassen der Schulanfangsphase als Indikator. Die zweite Teilstudie analysiert Unterschiede in der Schul- und Unterrichtsqualität auf Grundlage von Daten aus der Berliner Schulinspektion (N = 282). Mittels Varianzanalysen (ANOVA) zeigen sich a) Unterschiede zugunsten der Schulen, die frühzeitig und dauerhaft JÜL umsetzten und b) Unterschiede zugunsten der Schulen, die in ihren JÜL-Klassen drei – im Vergleich zu zwei – Jahrgänge zusammenfassen.
Differentiation of intelligence refers to changes in the structure of intelligence that depend on individuals' level of general cognitive ability (ability differentiation hypothesis) or age (developmental differentiation hypothesis). The present article aimed to investigate ability differentiation, developmental differentiation, and their interaction with nonlinear factor analytic models in 2 studies. Study 1 was comprised of a nationally representative sample of 7,127 U.S. students (49.4% female; M-age = 14.51, SD = 1.42, range = 12.08-17.00) who completed the computerized adaptive version of the Armed Service Vocational Aptitude Battery. Study 2 analyzed the norming sample of the Berlin Intelligence Structure Test with 1,506 German students (44% female; M-age = 14.54, SD = 1.35, range = 10.00-18.42). Results of Study 1 supported the ability differentiation hypothesis but not the developmental differentiation hypothesis. Rather, the findings pointed to age-dedifferentiation (i.e., higher correlations between different abilities with increasing age). There was evidence for an interaction between age and ability differentiation, with greater ability differentiation found for older adolescents. Study 2 provided little evidence for ability differentiation but largely replicated the findings for age dedifferentiation and the interaction between age and ability differentiation. The present results provide insight into the complex dynamics underlying the development of intelligence structure during adolescence. Implications for the assessment of intelligence are discussed.
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.
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.