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Multivariate Behavioral Research

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https://read.qxmd.com/read/30755036/problems-with-rationales-for-parceling-that-fail-to-consider-parcel-allocation-variability
#1
Sonya K Sterba
In structural equation modeling applications, parcels-averages or sums of subsets of item scores-are often used as indicators of latent constructs. Parcel-allocation variability (PAV) is variability in results that arises within sample across alternative item-to-parcel allocations. PAV can manifest in all results of a parcel-level model (e.g., model fit, parameter estimates, standard errors, and inferential decisions). It is a source of uncertainty in parcel-level model results that can be investigated, reported, and accounted for...
February 12, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30744425/opportunities-and-issues-in-modeling-intensive-longitudinal-data-learning-from-the-cogito-project
#2
Stephen G West
Technological developments increasingly permit the collection of longitudinal data sets in which the data structure contains a large number of participants N and a large number of measurement occasions T. Promising new dynamical systems approaches to the analysis of large N, large T data sets have been proposed that utilize both between-subjects and within-subjects information. The COGITO project, begun over a decade ago, is an early large N = 204, large T = 100 study that collected high quality cognitive and psychosocial data...
February 12, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30736702/fungible-correlation-matrices
#3
Alexandria Hadd
No abstract text is available yet for this article.
February 8, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30732475/the-corrosive-influence-of-the-flynn-effect-on-age-normed-tests
#4
Patrick O'Keefe, Joseph Lee Rodgers
No abstract text is available yet for this article.
February 7, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30693803/time-specific-errors-in-growth-curve-modeling-type-1-error-inflation-and-a-possible-solution-with-mixed-effects-models
#5
Satoshi Usami, Kou Murayama
Growth curve modeling (GCM) has been one of the most popular statistical methods to examine participants' growth trajectories using longitudinal data. In spite of the popularity of GCM, little attention has been paid to the possible influence of time-specific errors, which influence all participants at each timepoint. In this article, we demonstrate that the failure to take into account such time-specific errors in GCM produces considerable inflation of type-1 error rates in statistical tests of fixed effects (e...
January 29, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30693802/a-comparison-of-multilevel-imputation-schemes-for-random-coefficient-models-fully-conditional-specification-and-joint-model-imputation-with-random-covariance-matrices
#6
Craig K Enders, Timothy Hayes, Han Du
Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and "reverse random coefficient" imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for random coefficient analyses. This study is apparently the first to directly compare these procedures...
January 29, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30668172/the-role-of-time-in-the-quest-for-understanding-psychological-mechanisms
#7
Manuel C Voelkle, Christian Gische, Charles C Driver, Ulman Lindenberger
The lead-lag structure of multivariate time-ordered observations and the possibility to disentangle between-person (BP) from within-person (WP) sources of variance are major assets of longitudinal (panel) data. Hence, psychologists are making increasing use of such data, often with the intent to delineate the dynamic properties of psychological mechanisms, understood as a sequence of causal effects that govern psychological functioning. However, even with longitudinal data, psychological mechanisms are not easily identified...
January 22, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663401/a-data-analysis-method-for-using-longitudinal-binary-outcome-data-from-a-smart-to-compare-adaptive-interventions
#8
John J Dziak, Jamie R T Yap, Daniel Almirall, James R McKay, Kevin G Lynch, Inbal Nahum-Shani
Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663394/mcar-mar-and-mnar-values-in-the-same-dataset-a-realistic-evaluation-of-methods-for-handling-missing-data
#9
Brenna Gomer
No abstract text is available yet for this article.
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663388/a-comparison-of-multilevel-mediation-modeling-methods-recommendations-for-applied-researchers
#10
Christina K Zigler, Feifei Ye
Multilevel structural equation modeling (MSEM) has been proposed as a valuable tool for estimating mediation in multilevel data and has known advantages over traditional multilevel modeling, including conflated and unconflated techniques (CMM & UMM). Recent methodological research has focused on comparing the three methods for 2-1-1 designs, but in regards to 1-1-1 mediation designs, there are significant gaps in the published literature that prevent applied researchers from making educated decisions regarding which model to employ in their own specific research design...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663387/poisson-multilevel-models-with-small-samples
#11
Daniel McNeish
Recent methodological studies have investigated the properties of multilevel models with small samples. Previous work has primarily focused on continuous outcomes and little attention has been paid to count outcomes. The estimation of count outcome models can be difficult because the likelihood has no closed-form solution, meaning that approximation methods are required. Although adaptive Gaussian quadrature (AGQ) is generally seen as the gold standard, its comparative performance has been investigated with larger samples...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663384/analysis-of-variance-models-with-stochastic-group-weights
#12
Axel Mayer, Felix Thoemmes
The analysis of variance (ANOVA) is still one of the most widely used statistical methods in the social sciences. This article is about stochastic group weights in ANOVA models - a neglected aspect in the literature. Stochastic group weights are present whenever the experimenter does not determine the exact group sizes before conducting the experiment. We show that classic ANOVA tests based on estimated marginal means can have an inflated type I error rate when stochastic group weights are not taken into account, even in randomized experiments...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663381/on-standardizing-within-person-effects-potential-problems-of-global-standardization
#13
Lijuan Wang, Qian Zhang, Scott E Maxwell, C S Bergeman
Person-mean centering has been recommended for disaggregating between-person and within-person effects when modeling time-varying predictors. Multilevel modeling textbooks recommended global standardization for standardizing fixed effects. An aim of this study is to evaluate whether and when person-mean centering followed by global standardization can accurately estimate fixed-effects within-person relations (the estimand of interest in this study) in multilevel modeling. We analytically derived that global standardization generally yields inconsistent (asymptotically biased) estimates for the estimand when between-person differences in within-person standard deviations exist and the average within-person relation is nonzero...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30663379/a-bootstrap-version-of-the-hausman-test-to-assess-the-impact-of-cluster-level-endogeneity-beyond-the-random-intercept-model
#14
Wouter Talloen, Tom Loeys, Beatrijs Moerkerke
In the random intercept model for clustered data, the random effect is typically assumed to be independent of predictors. Violation of this assumption due to unmeasured cluster-level confounding (endogeneity) induces bias in the estimates of effects of within-cluster predictors. Treating cluster-specific intercepts as fixed rather than random avoids this bias. The Hausman test contrasts the fixed effect estimator with the traditional random effect estimator in the random intercept model to test for the presence of cluster-level endogeneity and has a known asymptotic <mml:math xmlns:mml="http://www...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30661402/four-covariance-structure-models-for-canonical-correlation-analysis-a-cosan-modeling-approach
#15
Fei Gu, Yiu-Fai Yung, Mike M-L Cheung
The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated...
January 20, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30644764/a-new-multilevel-cart-algorithm-for-multilevel-data-with-binary-outcomes
#16
Shuqiong Lin, Wen Luo
The multilevel logistic regression model (M-logit) is the standard model for modeling multilevel data with binary outcomes. However, many assumptions and restrictions should be considered when applying this model for unbiased estimation. To overcome these limitations, we proposed a multilevel CART (M-CART) algorithm which combines the M-logit and single level CART (S-CART) within the framework of the expectation-maximization. Simulation results showed that the proposed M-CART provided substantial improvements on classification accuracy, sensitivity, and specific over the M-logit, S-CART, and single level logistic regression model when modeling multilevel data with binary outcomes...
January 15, 2019: Multivariate Behavioral Research
https://read.qxmd.com/read/30595072/the-impact-of-item-misspecification-and-dichotomization-on-class-and-parameter-recovery-in-lca-of-count-data
#17
Kathryn S Macia, Robert E Wickham
Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis...
December 31, 2018: Multivariate Behavioral Research
https://read.qxmd.com/read/30596259/the-relationship-between-the-standardized-root-mean-square-residual-and-model-misspecification-in-factor-analysis-models
#18
Dexin Shi, Alberto Maydeu-Olivares, Christine DiStefano
We argue that the definition of close fitting models should embody the notion of substantially ignorable misspecifications (SIM). A SIM model is a misspecified model that might be selected, based on parsimony, over the true model should knowledge of the true model be available. Because in applications the true model (i.e., the data generating mechanism) is unknown, we investigate the relationship between the population standardized root mean square residual (SRMR) values and various model misspecifications in factor analysis models to better understand the magnitudes of the SRMR...
December 30, 2018: Multivariate Behavioral Research
https://read.qxmd.com/read/30574796/a-note-on-the-conversion-of-item-parameters-standard-errors
#19
Chun Wang, Xue Zhang
The relations among alternative parameterizations of the binary factor analysis (FA) model and two-parameter logistic (2PL) item response theory (IRT) model have been thoroughly discussed in literature. However, the conversion formulas widely available are mainly for transforming parameter estimates from one parameterization to another. There is a lack of discussion about the standard error (SE) conversion among different parameterizations, when SEs of IRT model parameters are often of immediate interest to practitioners...
December 21, 2018: Multivariate Behavioral Research
https://read.qxmd.com/read/30569740/multidimensional-cross-recurrence-quantification-analysis-mdcrqa-a-method-for-quantifying-correlation-between-multivariate-time-series
#20
Sebastian Wallot
In this paper, Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) is introduced. It is an extension of Multidimensional Recurrence Quantification Analysis (MdRQA), which allows to quantify the (auto-)recurrence properties of a single multidimensional time-series. MdCRQA extends MdRQA to bi-variate cases to allow for the quantification of the co-evolution of two multidimensional time-series. Moreover, it is shown how a Diagonal Cross-Recurrence Profile (DCRP) can be computed from the MdCRQA output that allows to capture time-lagged coupling between two multidimensional time-series...
December 20, 2018: Multivariate Behavioral Research
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