Benjamin Nagengast

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.

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.

Longitudinal tests of competing factor structures for the Rosenberg Self-Esteem Scale: traits, ephemeral artifacts, and stable response styles.

Self-esteem, typically measured by the Rosenberg Self-Esteem Scale (RSE), is one of the most widely studied constructs in psychology. Nevertheless, there is broad agreement that a simple unidimensional factor model, consistent with the original design and typical application in applied research, does not provide an adequate explanation of RSE responses. However, there is no clear agreement about what alternative model is most appropriate-or even a clear rationale for how to test competing interpretations. Three alternative interpretations exist: (a) 2 substantively important trait factors (positive and negative self-esteem), (b) 1 trait factor and ephemeral method artifacts associated with positively or negatively worded items, or (c) 1 trait factor and stable response-style method factors associated with item wording. We have posited 8 alternative models and structural equation model tests based on longitudinal data (4 waves of data across 8 years with a large, representative sample of adolescents). Longitudinal models provide no support for the unidimensional model, undermine support for the 2-factor model, and clearly refute claims that wording effects are ephemeral, but they provide good support for models positing 1 substantive (self-esteem) factor and response-style method factors that are stable over time. This longitudinal methodological approach has not only resolved these long-standing issues in self-esteem research but also has broad applicability to most psychological assessments based on self-reports with a mix of positively and negatively worded items.

Measurement invariance of big-five factors over the life span : ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects

This substantive-methodological synergy applies evolving approaches to factor analysis to substantively important developmental issues of how five-factor-approach (FFA) personality measures vary with gender, age, and their interaction. Confirmatory factor analyses (CFAs) conducted at the item level often do not support a priori FFA structures, due in part to the overly restrictive assumptions of CFA models. Here we demonstrate that exploratory structural equation modeling (ESEM), an integration of CFA and exploratory factor analysis, overcomes these problems with the 15-item Big Five Inventory administered as part of the nationally representative British Household Panel Study (N = 14,021; age: 15-99 years, Mage = 47.1). ESEM fitted the data substantially better and resulted in much more differentiated (less correlated) factors than did CFA. Methodologically, we extended ESEM (introducing ESEM-within-CFA models and a hybrid of multiple groups and multiple indicators multiple causes models), evaluating full measurement invariance and latent mean differences over age, gender, and their interaction. Substantively the results showed that women had higher latent scores for all Big Five factors except for Openness and that these gender differences were consistent over the entire life span. Substantial nonlinear age effects led to the rejection of the plaster hypothesis and the maturity principle but did support a newly proposed la dolce vita effect in old age. In later years, individuals become happier (more agreeable and less neurotic), more self-content and self-centered (less extroverted and open), more laid back and satisfied with what they have (less conscientious, open, outgoing and extroverted), and less preoccupied with productivity.

Why item parcels are (almost) never appropriate : Two wrongs do not make a right-camouflaging misspecification with item parcels in CFA models

The present investigation has a dual focus: to evaluate problematic practice in the use of item parcels and to suggest exploratory structural equation models (ESEMs) as a viable alternative to the traditional independent clusters confirmatory factor analysis (ICM-CFA) model (with no cross-loadings, subsidiary factors, or correlated uniquenesses). Typically, it is ill-advised to (a) use item parcels when ICM-CFA models do not fit the data, and (b) retain ICM-CFA models when items cross-load on multiple factors. However, the combined use of (a) and (b) is widespread and often provides such misleadingly good fit indexes that applied researchers might believe that misspecification problems are resolved--that 2 wrongs really do make a right. Taking a pragmatist perspective, in 4 studies we demonstrate with responses to the Rosenberg Self-Esteem Inventory (Rosenberg, 1965), Big Five personality factors, and simulated data that even small cross-loadings seriously distort relations among ICM-CFA constructs or even decisions on the number of factors; although obvious in item-level analyses, this is camouflaged by the use of parcels. ESEMs provide a viable alternative to ICM-CFAs and a test for the appropriateness of parcels. The use of parcels with an ICM-CFA model is most justifiable when the fit of both ICM-CFA and ESEM models is acceptable and equally good, and when substantively important interpretations are similar. However, if the ESEM model fits the data better than the ICM-CFA model, then the use of parcels with an ICM-CFA model typically is ill-advised--particularly in studies that are also interested in scale development, latent means, and measurement invariance.

Evaluative conditioning in social psychology: Facts and speculations

The aim of the present paper is to examine the contribution of evaluative conditioning (EC) to attitude formation theory in social psychology. This aim is pursued on two fronts. First, evaluative conditioning is analysed for its relevance to social psychological research. We show that conditioned attitudes can be acquired through simple co‐occurrences of a neutral and a valenced stimulus. Moreover, we argue that conditioned attitudes are not confined to direct contact with a valenced stimulus, but can be formed and dynamically reformed indirectly, through association chains. Second, social research is examined in an effort to identify evaluative learning mechanisms. We suggest that several important phenomena in social psychology (e.g., ingroup favouritism, prejudice, name letter effect) are at least partly due to simple mechanisms of evaluative learning. The implications for attitude formation theory and for applied settings are discussed.

Who Took the “×” out of Expectancy-Value Theory? A Psychological Mystery, a Substantive-Methodological Synergy, and a Cross-National Generalization

Expectancy-value theory (EVT) is a dominant theory of human motivation. Historically, the Expectancy × Value interaction, in which motivation is high only if both expectancy and value are high, was central to EVT. However, the Expectancy × Value interaction mysteriously disappeared from published research more than 25 years ago. Using large representative samples of 15-year-olds (N = 398,750) from 57 diverse countries, we attempted to solve this mystery by testing Expectancy × Value interactions using latent-variable models with interactions. Expectancy (science self-concept), value (enjoyment of science), and the Expectancy × Value interaction all had statistically significant positive effects on both engagement in science activities and intentions of pursuing scientific careers; these results were similar for the total sample and for nearly all of the 57 countries considered separately. This study, apparently the strongest cross-national test of EVT ever undertaken, supports the generalizability of EVT predictions—including the “lost” Expectancy × Value interaction.

Methodological Measurement Fruitfulness of Exploratory Structural Equation Modeling (ESEM): New Approaches to Key Substantive Issues in Motivation and Engagement.

The most popular measures of multidimensional constructs typically fail to meet standards of good measurement: goodness of fit, measurement invariance, lack of differential item functioning, and well-differentiated factors that are not so highly correlated as to detract from their discriminant validity. Part of the problem, the authors argue, is undue reliance on overly restrictive independent cluster models of confirmatory factor analysis (ICM-CFA) in which each item loads on one, and only one, factor. Here the authors demonstrate exploratory structural equation modeling (ESEM), an integration of the best aspects of CFA and traditional exploratory factor analyses (EFA). On the basis of responses to the 11-factor Motivation and Engagement Scale (n = 7,420, M age = 14.22), we demonstrate that ESEM fits the data much better and results in substantially more differentiated (less correlated) factors than corresponding CFA models. Guided by a 13-model taxonomy of ESEM full-measurement (mean structure) invariance, the authors then demonstrate invariance of factor loadings, item intercepts, item uniquenesses, and factor variancescovariances, across gender and over time. ESEM has broad applicability to other areas of research that cannot be appropriately addressed with either traditional EFA or CFA and should become a standard tool for use in psychometric tests of psychological assessment instruments.

General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations

Substantively, this study investigates potential heterogeneity in the developmental trajectories of anxiety in adolescence. Methodologically, this study demonstrates the usefulness of general growth mixture analysis (GGMA) in addressing these issues and illustrates the impact of untested invariance assumptions on substantive interpretations. This study relied on data from the Montreal Adolescent Depression Development Project (MADDP), a 4-year follow-up of more than 1,000 adolescents who completed the Beck Anxiety Inventory each year. GGMA models relying on different invariance assumptions were empirically compared. Each of these models converged on a 5-class solution, but yielded different substantive results. The model with class-varying variance–covariance matrices was retained as providing a better fit to the data. These results showed that although elevated levels of anxiety might fluctuate over time, they clearly do not represent a transient phenomenon. This model was then validated in relation to multiple predictors (mostly related to school violence) and outcomes (grade-point average, school dropout, depression, loneliness, and drug-related problems).

Evaluative conditioning and the awareness issue : Assessing contingency awareness with the four- picture recognition test

An experiment is described that tested the moderating influence of contingency awareness on evaluative conditioning. After participants were conditioned within the picture-picture paradigm, contingency awareness was assessed by means of a recognition test (i.e., the 4-picture recognition test). Results indicate an inverse relationship between the conditioned affective reaction and contingency awareness: Only participants classified as unaware in the recognition test showed significant effects of evaluative learning. A closer inspection indicates that aware individuals stored not only the valence but also the nominal stimulus in mind.