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Multivariate Behavioral Research

Lara Fontanella, Sara Fontanella, Pasquale Valentini, Nickolay Trendafilov
In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy...
November 7, 2018: Multivariate Behavioral Research
Carla Ferrara, Francesca Martella, Maurizio Vichi
One of the most relevant problems in principal component analysis and factor analysis is the interpretation of the components/factors. In this paper, disjoint principal component analysis model is extended in a maximum-likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets.
November 7, 2018: Multivariate Behavioral Research
Philippe Rast, Emilio Ferrer
We present a mixed-effects location scale model (MELSM) for examining the daily dynamics of affect in dyads. The MELSM includes person and time-varying variables to predict the location, or individual means, and the scale, or within-person variances. It also incorporates a submodel to account for between-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components, and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scale are also estimated...
November 5, 2018: Multivariate Behavioral Research
Edward H Ip, Michelle F Magee, Gretchen A Youssef, Shyh-Huei Chen
The Don't Know (DK) response - taking the form of an omitted response or not-reached at the end of a cognitive test, or explicitly presented as a response option in a social survey - contains important information that is often overlooked. Direct psychometric modeling efforts for DK responses are few and far between. In this article, the linear logistic test model (LLTM) is proposed for delineating the impacts of cognitive operations for a test that contains DK responses. We assume that the DK response is a valid response...
October 31, 2018: Multivariate Behavioral Research
H Felix Fischer, Matthias Rose
There are a growing number of item response theory (IRT) studies that calibrate different patient-reported outcome (PRO) measures, such as anxiety, depression, physical function, and pain, on common, instrument-independent metrics. In the case of depression, it has been reported that there are considerable mean score differences when scoring on a common metric from different, previously linked instruments. Ideally, those estimates should be the same. We investigated to what extent those differences are influenced by different scoring methods that take into account several levels of uncertainty, such as measurement error (through plausible value imputation) and item parameter uncertainty (through full Bayesian IRT modeling)...
September 20, 2018: 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
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...
July 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...
July 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...
July 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...
July 2018: Multivariate Behavioral Research
D Angus Clark, Ryan P Bowles
In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items...
July 2018: Multivariate Behavioral Research
Gabriela Stegmann, Ross Jacobucci, Sarfaraz Serang, Kevin J Grimm
In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node...
July 2018: Multivariate Behavioral Research
Jonathan L Helm, Jonas G Miller, Sarah Kahle, Natalie R Troxel, Paul D Hastings
Physiological synchrony within a dyad, or the degree of temporal correspondence between two individuals' physiological systems, has become a focal area of psychological research. Multiple methods have been used for measuring and modeling physiological synchrony. Each method extracts and analyzes different types of physiological synchrony, where 'type' refers to a specific manner through which two different physiological signals may correlate. Yet, to our knowledge, there is no documentation of the different methods, how each method corresponds to a specific type of synchrony, and the statistical assumptions embedded within each method...
July 2018: Multivariate Behavioral Research
Sacha Epskamp, Lourens J Waldorp, René Mõttus, Denny Borsboom
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e...
July 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
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
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