Refine
Has Fulltext
- no (17)
Document Type
- Article (17) (remove)
Language
- English (17) (remove)
Is part of the Bibliography
- yes (17)
Keywords
- intelligence (3)
- ability differentiation (2)
- academic self-concept (2)
- age differentiation (2)
- childhood (2)
- Aptitude (1)
- Assessment (1)
- Attribution theory (1)
- Bayesian reasoning (1)
- Domain differences (1)
- Dynamic Structural Equation Modeling (DSEM) (1)
- ESEM (1)
- Generalizability (1)
- Helplessness (1)
- Instruction (1)
- Linda problem (1)
- Literature review (1)
- Monty Hall (1)
- Primary and secondary education (1)
- Professional development (1)
- Professional identity (1)
- Programme for International Student (1)
- SLODR (1)
- School effectiveness (1)
- Situation (1)
- Stability (1)
- Student perception (1)
- Teacher beliefs (1)
- Teacher education (1)
- Teacher educator (1)
- Teacher effectiveness (1)
- Teacher learning (1)
- Value-added modeling (1)
- Wason task (1)
- academic achievement (1)
- adolescence (1)
- affect (1)
- autoregressive wage growth (1)
- cognitive illusion (1)
- cohort differences (1)
- comparison (1)
- complex survey designs (1)
- cumulative advantage (CA) (1)
- design parameters (1)
- dimensional comparisons (1)
- educational large-scale assessments (1)
- elementary school students (1)
- factor analysis (1)
- frame of reference (1)
- grade point average (1)
- hospital problem (1)
- human capital theory (1)
- individual (1)
- instructional quality (1)
- internal/external frame-of-reference model (1)
- intraclass correlation (1)
- large-scale assessment (1)
- late (1)
- learning styles (1)
- life span research (1)
- lifespan (1)
- logical thinking (1)
- longitudinal data (1)
- machine learning (1)
- mathematics (1)
- measurement invariance (1)
- meta-analysis (1)
- model (1)
- motivation (1)
- multilevel models (1)
- nonlinear (1)
- nonlinear relations (1)
- participant data (1)
- personality traits (1)
- problem (1)
- reading (1)
- school composition (1)
- school effectiveness (1)
- school quality (1)
- socioeconomic status (1)
- statistical reasoning (1)
- student achievement (1)
- value-added modeling (1)
- wage dynamics (1)
Institute
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.
In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the "heuristics and biases" research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically explaining people's faulty thinking, or proposing and experimentally implementing measures to foster insight and to make these problems accessible to the human mind. Yet these problems have thus far usually been empirically analyzed on an individual-item level only (e.g., by experimentally comparing participants' performance on various versions of one of these problems). In this paper, by contrast, we examine these illusions as a group and look at the ability to solve them as a psychological construct. Based on an sample of N = 2,643 Luxembourgian school students of age 16-18 we investigate the internal psychometric structure of these illusions (i.e., Are they substantially correlated? Do they form a reflexive or a formative construct?), their connection to related constructs (e.g., Are they distinguishable from intelligence or mathematical competence in a confirmatory factor analysis?), and the question of which of a person's abilities can predict the correct solution of these brain teasers (by means of a regression analysis).
It is well-documented that academic achievement is associated with students' self-perceptions of their academic abilities, that is, their academic self-concepts. However, low-achieving students may apply self-protective strategies to maintain a favorable academic self-concept when evaluating their academic abilities. Consequently, the relation between achievement and academic self-concept might not be linear across the entire achievement continuum. Capitalizing on representative data from three large-scale assessments (i.e., TIMSS, PIRLS, PISA; N = 470,804), we conducted an integrative data analysis to address nonlinear trends in the relations between achievement and the corresponding self-concepts in mathematics and the verbal domain across 13 countries and 2 age groups (i.e., elementary and secondary school students). Polynomial and interrupted regression analyses showed nonlinear relations in secondary school students, demonstrating that the relations between achievement and the corresponding self-concepts were weaker for lower achieving students than for higher achieving students. Nonlinear effects were also present in younger students, but the pattern of results was rather heterogeneous. We discuss implications for theory as well as for the assessment and interpretation of self-concept.
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.
Personality is a relevant predictor for important life outcomes across the entire lifespan. Although previous studies have suggested the comparability of the measurement of the Big Five personality traits across adulthood, the generalizability to childhood is largely unknown. The present study investigated the structure of the Big Five personality traits assessed with the Big Five Inventory-SOEP Version (BFI-S; SOEP = Socio-Economic Panel) across a broad age range spanning 11-84 years. We used two samples of N = 1,090 children (52% female, M-age = 11.87) and N = 18,789 adults (53% female, M-age = 51.09), estimating a multigroup CFA analysis across four age groups (late childhood: 11-14 years; early adulthood: 17-30 years; middle adulthood: 31-60 years; late adulthood: 61-84 years). Our results indicated the comparability of the personality trait metric in terms of general factor structure, loading patterns, and the majority of intercepts across all age groups. Therefore, the findings suggest both a reliable assessment of the Big Five personality traits with the BFI-S even in late childhood and a vastly comparable metric across age groups.
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.
The aim of educational policy should be to provide a good education to all students. Thus, a key question arises regarding the extent to which key characteristics of school composition (proportion of students with migration background, socioeconomic status [SES], prior school achievement, and achievement heterogeneity), instructional quality, school quality, and later school achievement are interrelated. The present study addressed this research question by examining school inspection data, official school statistics, and large-scale achievement data from all primary schools in Berlin, Germany (N = 343). The results of correlation and path analyses showed that school composition (average SES, average prior school achievement) predicted components of instructional quality (SES: classroom management, cognitive activation; achievement: cognitive activation, individual learning support). The relation between school composition characteristics and most components of school quality was close to zero. Contrary to our expectations, only the effect of school SES on later achievement was mediated by instructional quality.
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.
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.
We assessed teacher educators? task perception and investigated its relationship with components of their professional identity and their teaching practice. Using data from 145 teacher educators, two different task perceptions were found: transmitters and facilitators. Teacher educators who were categorized as facilitator tend to demonstrate higher levels of self-efficacy, job satisfaction, constructivist beliefs about teaching and learning and use more effective teaching strategies. The findings demonstrate that teaching practices of teacher educators are rooted in their professional identity. ? 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).