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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...
March 5, 2018: Multivariate Behavioral Research
Steven M Boker, Mike Martin
The 10 year anniversary of the COGITO Study provides an opportunity to revisit the ideas behind the Cattell data box. Three dimensions of the persons × variables × time data box are discussed in the context of three categories of researchers each wanting to answer their own categorically different question. The example of the well-known speed-accuracy tradeoff is used to illustrate why these are three different categories of statistical question. The 200 persons by 100 variables by 100 occasions of measurement COGITO data cube presents a challenge to integrate theories and methods across the dimensions of the data box...
February 26, 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...
February 9, 2018: Multivariate Behavioral Research
Scott Monroe
This research concerns the estimation of polychoric correlations in the context of fitting structural equation models to observed ordinal variables by multistage estimation. The first main contribution of this research is to propose and evaluate a Monte Carlo estimator for the asymptotic covariance matrix (ACM) of the polychoric correlation estimates. In multistage estimation, the ACM plays a prominent role, as overall test statistics, derived fit indices, and parameter standard errors all depend on this quantity...
January 29, 2018: Multivariate Behavioral Research
Audrey J Leroux, S Natasha Beretvas
The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units...
January 15, 2018: Multivariate Behavioral Research
Rens van de Schoot, Marit Sijbrandij, Sarah Depaoli, Sonja D Winter, Miranda Olff, Nancy E van Loey
There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions...
January 11, 2018: Multivariate Behavioral Research
Jingheng Cai, Ming Ouyang, Kai Kang, Xinyuan Song
Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities...
January 11, 2018: Multivariate Behavioral Research
Sien Deng, Danielle E McCarthy, Megan E Piper, Timothy B Baker, Daniel M Bolt
Extreme response style (ERS) has the potential to bias the measurement of intra-individual variability in psychological constructs. This paper explores such bias through a multilevel extension of a latent trait model for modeling response styles applied to repeated measures rating scale data. Modeling responses to multi-item scales of positive and negative affect collected from smokers at clinic visits following a smoking cessation attempt revealed considerable ERS bias in the intra-individual sum score variances...
January 11, 2018: Multivariate Behavioral Research
Niels Waller
Kristof's Theorem (Kristof, 1970 ) describes a matrix trace inequality that can be used to solve a wide-class of least-square optimization problems without calculus. Considering its generality, it is surprising that Kristof's Theorem is rarely used in statistics and psychometric applications. The underutilization of this method likely stems, in part, from the mathematical complexity of Kristof's ( 1964 , 1970 ) writings. In this article, I describe the underlying logic of Kristof's Theorem in simple terms by reviewing four key mathematical ideas that are used in the theorem's proof...
January 11, 2018: Multivariate Behavioral Research
Guangjian Zhang
Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data...
January 11, 2018: Multivariate Behavioral Research
D Angus Clark, Amy K Nuttall, Ryan P Bowles
Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS...
January 4, 2018: Multivariate Behavioral Research
Amanda Kay Montoya
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Miao Yang, Ge Jiang
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Samantha F Anderson
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Seungwon Chung, Li Cai
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Sunyoung Park, S Natasha Beretvas
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Marilu Isiordia, Joseph E Gonzales, Emilio Ferrer
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Clifford E Hauenstein, Susan E Embretson
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Dingjing Shi, Xin Tong
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
Justin M Luningham, Gitta H Lubke
No abstract text is available yet for this article.
January 2018: Multivariate Behavioral Research
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