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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
Suzanne Jak, Mike W-L Cheung
Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables...
December 8, 2017: Multivariate Behavioral Research
Michael J Rovine, Paul A McDermott
The relationship between the latent growth curve and repeated measures ANOVA models is often misunderstood. Although a number of investigators have looked into the similarities and differences among these models, a cursory reading of the literature can give the impression that they are very different models. Here we show that each model represents a set of contrasts on the occasion means. We demonstrate that the fixed effects parameters of the estimated basis vector latent growth curve model are merely a transformation of the repeated measures ANOVA fixed effects parameters...
December 8, 2017: Multivariate Behavioral Research
Michaela Hoffman, Douglas Steinley, Kathleen M Gates, Mitchell J Prinstein, Michael J Brusco
Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms...
December 8, 2017: Multivariate Behavioral Research
Johan H L Oud, Manuel C Voelkle, Charles C Driver
This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample...
November 7, 2017: Multivariate Behavioral Research
M Marsman, D Borsboom, J Kruis, S Epskamp, R van Bork, L J Waldorp, H L J van der Maas, G Maris
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other...
November 7, 2017: 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
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