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Multivariate Behavioral Research

Kenneth A Bollen
Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators...
September 17, 2018: Multivariate Behavioral Research
Urbano Lorenzo-Seva, Pere J Ferrando
This article proposes a procedure for fitting a pure exploratory bifactor solution in which the general factor is orthogonal to the group factors, but the loadings on the group factors can satisfy any orthogonal or oblique rotation criterion. The proposal combines orthogonal Procrustes rotations with analytical rotations and consists of a sequence of four steps. The basic input is a semispecified target matrix that can be (a) defined by the user, (b) obtained by using Schmid-Leiman orthogonalization, or (c) automatically built from a conventional unrestricted solution based on a prescribed number of factors...
August 30, 2018: Multivariate Behavioral Research
Qian Zhang, Beth Phillips
In the study, extending from the cross-lagged panel model (CLPM) and the 2-2-1 cross-sectional multilevel mediation model, we proposed a three-level longitudinal mediation model to evaluate the causal process among variables at different levels over time. Given the complexity of the proposed model, Bayesian estimation was used. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for the proposed model. Factors considered in the simulation study included average sample sizes of the lower-level units within each upper-level unit (or cluster size; [Formula: see text]), numbers of upper-level units (or clusters; J), numbers of time points (T), fixed direct and indirect effect sizes, and variances and covariances of upper-level random effects...
July 9, 2018: Multivariate Behavioral Research
Hansjörg Plieninger, Daniel W Heck
When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes...
May 29, 2018: Multivariate Behavioral Research
Steffen Zitzmann
Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small...
May 21, 2018: Multivariate Behavioral Research
Gabriele B Durrant, Rebecca Vassallo, Peter W F Smith
Multilevel multiple membership models account for situations where lower level units are nested within multiple higher level units from the same classification. Not accounting correctly for such multiple membership structures leads to biased results. The use of a multiple membership model requires selection of weights reflecting the hypothesized contribution of each level two unit and their relationship to the level one outcome. The Deviance Information Criterion (DIC) has been proposed to identify such weights...
May 17, 2018: Multivariate Behavioral Research
Daniel M Swan, James E Pustejovsky
Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time...
May 14, 2018: Multivariate Behavioral Research
Walter P Vispoel, Carrie A Morris, Murat Kilinc
Over the years, research in the social sciences has been dominated by reporting of reliability coefficients that fail to account for key sources of measurement error. Use of these coefficients, in turn, to correct for measurement error can hinder scientific progress by misrepresenting true relationships among the underlying constructs being investigated. In the research reported here, we addressed these issues using generalizability theory (G-theory) in both traditional and new ways to account for the three key sources of measurement error (random-response, specific-factor, and transient) that affect scores from objectively scored measures...
May 2, 2018: Multivariate Behavioral Research
Chun Wang, Shiyang Su, David J Weiss
A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model...
May 2018: Multivariate Behavioral Research
Victoria Savalei
A new type of nonnormality correction to the RMSEA has recently been developed, which has several advantages over existing corrections. In particular, the new correction adjusts the sample estimate of the RMSEA for the inflation due to nonnormality, while leaving its population value unchanged, so that established cutoff criteria can still be used to judge the degree of approximate fit. A confidence interval (CI) for the new robust RMSEA based on the mean-corrected ("Satorra-Bentler") test statistic has also been proposed...
May 2018: Multivariate Behavioral Research
Kristine D O'Laughlin, Monica J Martin, Emilio Ferrer
Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data...
May 2018: Multivariate Behavioral Research
Ryan W Walters, Lesa Hoffman, Jonathan Templin
Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses...
May 2018: Multivariate Behavioral Research
Arne C Bathke, Sarah Friedrich, Markus Pauly, Frank Konietschke, Wolfgang Staffen, Nicolas Strobl, Yvonne Höller
To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data...
May 2018: Multivariate Behavioral Research
Tom Loeys, Haeike Josephy, Marieke Dewitte
In hierarchical data, the effect of a lower-level predictor on a lower-level outcome may often be confounded by an (un)measured upper-level factor. When such confounding is left unaddressed, the effect of the lower-level predictor is estimated with bias. Separating this effect into a within- and between-component removes such bias in a linear random intercept model under a specific set of assumptions for the confounder. When the effect of the lower-level predictor is additionally moderated by another lower-level predictor, an interaction between both lower-level predictors is included into the model...
May 2018: Multivariate Behavioral Research
Paras D Mehta
A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework...
May 2018: Multivariate Behavioral Research
Laura F Bringmann, Emilio Ferrer, Ellen L Hamaker, Denny Borsboom, Francis Tuerlinckx
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R...
May 2018: Multivariate Behavioral Research
Yuelin Li, Jennifer Lord-Bessen, Mariya Shiyko, Rebecca Loeb
This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles...
May 2018: Multivariate Behavioral Research
Marco Del Giudice
In a previous paper (Del Giudice, 2017 [Heterogeneity coefficients for Mahalanobis' D as a multivariate effect size. Multivariate Behavioral Research, 52, 216-221]), I proposed two heterogeneity coefficients for Mahalanobis' D based on the Gini coefficient, labeled H and EPV. In this addendum I discuss the limitations of the original approach and note that the proposed indices may overestimate heterogeneity under certain conditions. I then describe two revised indices H2 and EPV2 , and illustrate the difference between the original and revised indices with some real-world data sets...
April 23, 2018: Multivariate Behavioral Research
Han Du, Lijuan Wang
Intraindividual variability can be measured by the intraindividual standard deviation ([Formula: see text]), intraindividual variance ([Formula: see text]), estimated hth-order autocorrelation coefficient ([Formula: see text]), and mean square successive difference ([Formula: see text]). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorrelations in the longitudinal responses; (2) the reliabilities of [Formula: see text] and [Formula: see text] have not been studied...
April 23, 2018: Multivariate Behavioral Research
Paolo Ghisletta, Emilie Joly-Burra, Stephen Aichele, Ulman Lindenberger, Florian Schmiedek
We examined adult age differences in day-to-day adjustments in speed-accuracy tradeoffs (SAT) on a figural comparison task. Data came from the COGITO study, with over 100 younger and 100 older adults, assessed for over 100 days. Participants were given explicit feedback about their completion time and accuracy each day after task completion. We applied a multivariate vector auto-regressive model of order 1 to the daily mean reaction time (RT) and daily accuracy scores together, within each age group. We expected that participants adjusted their SAT if the two cross-regressive parameters from RT (or accuracy) on day t-1 of accuracy (or RT) on day t were sizable and negative...
April 23, 2018: Multivariate Behavioral Research
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