Ulrich Trautwein

A new look at the Big Five factor structure through exploratory structural equation modeling

NEO instruments are widely used to assess Big Five personality factors, but confirmatory factor analyses (CFAs) conducted at the item level do not support their a priori structure due, in part, to the overly restrictive CFA assumptions. We demonstrate that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis (EFA), overcomes these problems with responses (N = 3,390) to the 60-item NEO-Five-Factor Inventory: (a) ESEM fits the data better and results in substantially more differentiated (less correlated) factors than does CFA; (b) tests of gender invariance with the 13-model ESEM taxonomy of full measurement invariance of factor loadings, factor variances-covariances, item uniquenesses, correlated uniquenesses, item intercepts, differential item functioning, and latent means show that women score higher on all NEO Big Five factors; (c) longitudinal analyses support measurement invariance over time and the maturity principle (decreases in Neuroticism and increases in Agreeableness, Openness, and Conscientiousness). Using ESEM, we addressed substantively important questions with broad applicability to personality research that could not be appropriately addressed with the traditional approaches of either EFA or CFA.

Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept

In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing a combination of profile level (high vs. low overall ASC) and profile shape (math vs. verbal self-concepts) that complemented results based on a traditional variable-centered approach. Whereas LPA groups were substantially and logically related to the set of 10 correlates, much of the predictive power of individual ASC factors was lost in the formation of groups and the inclusion of the correlates into the LPA distorted the nature of the groups. LPA issues examined include distinctions between quantitative (level) and qualitative (shape) differences in LPA profiles, goodness of fit and the determination of the number of LPA groups, appropriateness of correlates as covariates or auxiliary variables, and alternative approaches to present and interpret the results.

The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies.

In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach, in which the observed (manifest) group mean is assumed to be perfectly reliable. This article demonstrates mathematically and with simulation results that this MMC approach can result in substantially biased estimates of contextual effects and can substantially underestimate the associated standard errors, depending on the number of L1 individuals per group, the number of groups, the intraclass correlation, the sampling ratio (the percentage of cases within each group sampled), and the nature of the data. To address this pervasive problem, the authors introduce a new multilevel latent covariate (MLC) approach that corrects for unreliability at L2 and results in unbiased estimates of L2 constructs under appropriate conditions. However, under some circumstances when the sampling ratio approaches 100%, the MMC approach provides more accurate estimates. Based on 3 simulations and 2 real-data applications, the authors evaluate the MMC and MLC approaches and suggest when researchers should most appropriately use one, the other, or a combination of both approaches.

Self-Esteem, Academic Self-Concept, and Achievement: How the Learning Environment Moderates the Dynamics of Self-Concept

The authors examine the directionality of effects between global self-esteem, domain-specific academic self-concepts, and academic achievement. Special emphasis is placed on learning environments as potential moderators of the direction of these effects. According to the meritocracy principle presented here, so-called bottom-up effects (i.e., self-esteem is influenced by academic self-concept) are more pronounced in meritocratic learning environments than in ego-protective learning environments. This hypothesis was examined using a three-wave cross-lagged panel design with a large sample of 7th graders from East and West Germany, a total of 5,648 students who were tested shortly after German reunification. Reciprocal effects were found between self-esteem, academic self-concept, and academic achievement. In conformance with the meritocracy principle, support for bottom-up effects was stronger in the meritocratic learning environment.

Umgang mit fehlenden Werten in der psychologischen Forschung : Probleme und Lösungen

Zusammenfassung. Fehlende Werte stellen in der empirisch-psychologischen Forschung oftmals ein Problem dar. Haufig verwendete Verfahren wie fallweiser und paarweiser Ausschluss, Regression- und Mean-Imputation sind aus methodischer Sicht defizitar. Alternative Verfahren fur die Analyse von Datensatzen mit fehlenden Werten, die in den letzten drei Jahrzehnten entwickelt wurden, werden in der Forschungspraxis noch selten angewendet. Der vorliegende Beitrag fuhrt zunachst in die grundlegende Terminologie fehlender Werte nach Rubin (1976) ein. Im Anschluss daran wird eine Ubersicht der in der Literatur diskutierten Ansatze zum Umgang mit fehlenden Werten vorgenommen, wobei drei Typen von Verfahren unterschieden werden: klassische Verfahren (z.B. fallweiser Ausschluss), imputationsbasierte Verfahren, in denen fehlende Werte ersetzt (imputiert) werden, und modellbasierte Verfahren, in denen die Schatzung des Modells und die Behandlung der fehlenden Werte in einem Schritt vorgenommen werden. Anhand eines Datenbe...

Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error

This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2) levels and sampling error due the sampling of persons in the aggregation of L1 characteristics to form L2 constructs. We consider a set of models that are latent or manifest in relation to sampling items (measurement error) and sampling of persons (sampling error) and discuss when different models might be most useful. We demonstrate the flexibility of these 4 core models by extending them to include random slopes, latent (single-level or cross-level) interactions, and latent quadratic effects. Substantively we use these models to test the big-fish-little-pond effect (BFLPE), showing that individual student levels of academic self-concept (L1-ASC) are positively associated with individual level achievement (L1-ACH) and negatively associated with school-average achievement (L2-ACH)—a finding with important policy implications for the way schools are structured. Extending tests of the BFLPE in new directions, we show that the nonlinear effects of the L1-ACH (a latent quadratic effect) and the interaction between gender and L1-ACH (an L1 × L1 latent interaction) are not significant. Although random-slope models show no significant school-to-school variation in relations between L1-ACH and L1-ASC, the negative effects of L2-ACH (the BFLPE) do vary somewhat with individual L1-ACH. We conclude with implications for diverse applications of the set of latent contextual models, including recommendations about their implementation, effect size estimates (and confidence intervals) appropriate to multilevel models, and directions for further research in contextual effect analysis.

A Random Walk Down University Avenue: Life Paths, Life Events, and Personality Trait Change at the Transition to University Life

This longitudinal study examined the relation between continuity and change in the Big Five personality traits and life events. Approximately 2,000 German students were tracked from high school to university or to vocational training or work, with 3 assessments over 4 years. Life events were reported retrospectively at the 2nd and 3rd assessment. Latent curve analyses were used to assess change in personality traits, revealing 3 main findings. First, mean-level changes in the Big Five factors over the 4 years were in line with the maturity principle, indicating increasing psychological maturity from adolescence to young adulthood. Second, personality development was characterized by substantive individual differences relating to the life path followed; participants on a more vocationally oriented path showed higher increases in conscientiousness and lower increases in agreeableness than their peers at university. Third, initial level and change in the Big Five factors (especially Neuroticism and Extraversion) were linked to the occurrence of aggregated as well as single positive and negative life events. The analyses suggest that individual differences in personality development are associated with life transitions and individual life experiences.

The Relationship Between Homework and Achievement—Still Much of a Mystery

Despite the long history of homework and homework research, the role that homework plays in enhancing student achievement is, at best, only partly understood. In this review, we give an overview of twentieth-century homework research and discuss the reasons why the relationship between homework and achievement remains unclear. We identify the operationalization of homework and achievement and the problematic handling of hierarchically ordered data as two important factors affecting the validity of many of the studies performed over the last century. We then describe a new generation of homework studies using multilevel modeling to deal with hierarchically nested data. Finally, we argue that homework research should be more closely connected to well-founded psychological theories of learning and instruction, and we offer three potential links (theories of instruction time, self-regulation theory and expectancy-value theory, and teaching behavior).

Schulumwelten — institutionelle Bedingungen des Lehrens und Lernens

Ein strukturbildendes Merkmal der Mittelstufe des Schulwesens in Deutschland ist die Gliederung in unterschiedliche Schulformen. Die fruhe Verteilung von Schulerinnen und Schulern auf unterschiedliche Bildungsgange nach Masgabe von Eignung und Leistung ist eine Besonderheit, die Deutschland mit anderen deutschsprachigen Landern wie Osterreich und der Schweiz teilt. Osterreich etwa trennt in der 5. Jahrgangsstufe die allgemein bildenden hoheren Schulen (AHS) von den Hauptschulen. Die Schweiz differenziert — zumindest in einigen Kantonen — ebenfalls fruh unterschiedliche Bildungsgange. Andere europaische Lander kennen die Differenzierung in Schulformen auch; sie setzt jedoch erst in der 7., 8. oder 9. Jahrgangsstufe ein, wie dies in Belgien, Luxemburg, den Niederlanden oder der Tschechischen Republik der Fall ist. Mit der fruhen Verteilung auf unterschiedliche weiterfuhrende Schulformen wird bis heute eine der masgeblichen Entscheidungen uber individuelle Bildungsverlaufe in Deutschland im Alter von zehn bis elf Jahren getroffen.

Big-Fish-Little-Pond Effect : Persistent negative effects of selective high schools on self-concept after graduation

According to the big-fish-little-pond effect (BFLPE), attending academically selective high schools negatively affects academic self-concept. Does the BFLPE persist after graduation from high school? In two large, representative samples of German high school students (Study 1: 2,306 students, 147 schools; Study 2: 1,758 students, 94 schools), the predictive effects of individual achievement test scores and school grades on math self-concept are very positive, whereas the predictive effects of school-average achievement are negative (the BFLPE). Both studies showed that the BFLPE was substantial at the end of high school and was still substantial 2 years (Study 1) or 4 years (Study 2) later. In addition, because of the highly salient system of school tracks within the German education system, the authors are able to show that negative effects associated with school type (highly academically selective schools, the Gymnasium) were similar—but smaller—than the BFLPE based on school-average achievement.