Examining Individual Differences in Interpersonal Influence: On the Psychometric Properties of the Generalized Opinion Leadership Scale (GOLS)

Opinion leadership describes an individual's tendency to informally influence others’ attitudes and overt behaviors. In contrast to contemporary views of opinion leadership as a highly domain-specific trait, this paper introduces a multi-faceted personality trait, generalized opinion leadership (GOL) that characterizes exceptionally influential individuals independent of a specific subject area. Two studies report on the psychometric properties of a scale to assess GOL. Study 1 is based on three independent samples (N = 1,575, N = 1,275, and N = 231) and demonstrates the factorial structure of the instrument and its measurement invariance across sex, age, and educational levels. Study 2 (N = 310) analyzes multitrait-multiinformant data to highlight the scale's discriminant validity with regard to innovativeness and trendsetting.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

Convergent and Discriminant Validity of Trait and Source Effects in ADHD-Inattention and Hyperactivity/Impulsivity Measures Across a 3-Month Interval

Confirmatory factor analysis was used to model a multitrait (ADHD-inattention and hyperactivity/ impulsivity) by multisource (teachers and parents) design across a 3-month interval in a sample of 360 Australian elementary school children. The purpose was to evaluate the convergent and discriminant validity of the ADHD-inattention (IN) and hyperactivity/impulsivity (H/I) measures. Although similar traits and similar sources showed stronger correlations across time than dissimilar traits and dissimilar sources, the amount of source variance in the ADHD-IN and ADHD-H/I measures was substantial and consistent across the interval (M = 59%; range = 35–84%). This large amount of source variance raises the possibility that the correlations of the IN and H/I rating scales with other constructs (e.g., social competence, conduct problems) represent mostly source rather than trait effects. Multitrait by multisource analyses provide a means to answer this question and further advance understanding of ADHD.     

An efficient algorithm for TUCKALS3 on data with large numbers of observation units

A modification of the TUCKALS3 algorithm is proposed that handles three-way arrays of order I × J × K for any I. When I is much larger than JK, the modified algorithm needs less work space to store the data during the iterative part of the algorithm than does the original algorithm. Because of this and the additional feature that execution speed is higher, the modified algorithm is highly suitable for use on personal computers. This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the first author.     

Multitrait-multimethod model of adolescent deviance,drug use,academic, and sexual behaviors

A multitrait-multimethod design was used to test Jessor and Jessor's (1977) problem behavior theory with a sample of adolescents, their parents, and peers. Behaviors included general deviance, drug use, low academic motivation, and precocious sexual behavior. While research has supported this theory, the reliance on self-report data for the majority of this work may threaten the validity of these findings. Results from hierarchical confirmatory factor analyses indicated that the convergent and discriminant validity of the seven behavior constructs was high and method effects were low. A two-factor second-order model, representing General Drug Use and Other Problem Behavior, accounted for a larger proportion of variance in the lower order factors compared to a single-factor model.This research was supported by Grant DA 03706 from the National Institute on Drug Abuse.     

Identification with deficient rank loading matrices in confirmatory factor analysis: Multitrait-multimethod models

This paper presents some results on identification in multitrait-multimethod (MTMM) confirmatory factor analysis (CFA) models. Some MTMM models are not identified when the (factorial-patterned) loadings matrix is of deficient column rank. For at least one other MTMM model, identification does exist despite such deficiency. It is also shown that for some MTMM CFA models, Howe's (1955) conditions sufficient for rotational uniqueness can fail, yet the model may well be identified and rotationally unique. Implications of these results for CFA models in general are discussed.     

儿童中期攻击行为测评的多质多法分析

以2695名小学三、四年级的儿童(平均年龄 10.06 ± 0.54岁)作为被试, 使用同伴评定、同伴提名和教师评定对这些儿童的三种攻击行为(身体、言语、关系)进行了测评, 采用相关分析与结构方程模型对儿童攻击行为的多质多法数据进行统计处理。多质多法模型的分析结果显示, 三种方法对儿童攻击行为的测评具有一定的会聚效度, 但区分效度较低; 同伴测评儿童攻击行为的有效性优于教师测评, 其中同伴评定的有效性好于同伴提名。     

Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony

Abstract

CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.