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Multivariate Behavioral Research

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https://www.readbyqxmd.com/read/28207288/equivalent-dynamic-models
#1
Peter C M Molenaar
Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results...
February 16, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28207283/modeling-information-content-via-dirichlet-multinomial-regression-analysis
#2
Alberto Ferrari
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions...
February 16, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28156139/investigating-reliabilities-of-intraindividual-variability-indicators-with-autocorrelated-longitudinal-data
#3
Han Du, Lijuan Wang
No abstract text is available yet for this article.
February 3, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28128999/heteroscedasticity-as-a-basis-of-direction-dependence-in-reversible-linear-regression-models
#4
Wolfgang Wiedermann, Richard Artner, Alexander von Eye
Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e...
January 27, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28121163/power-and-sample-size-calculations-for-contrast-analysis-in-ancova
#5
Gwowen Shieh
Analysis of covariance (ANCOVA) is commonly used in behavioral and educational research to reduce the error variance and improve the power of analysis of variance by adjusting the covariate effects. For planning and evaluating randomized ANCOVA designs, a simple sample-size formula has been proposed to account for the variance deflation factor in the comparison of two treatment groups. The objective of this article is to highlight an overlooked and potential problem of the exiting approximation and to provide an alternative and exact solution of power and sample size assessments for testing treatment contrasts...
January 25, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098494/constant-and-variable-time-delays-of-synchrony-of-facial-expressions-in-dyadic-conversations
#6
M Joseph Meyer
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098493/evaluating-the-use-of-the-automated-unified-structural-equation-model-for-daily-diary-data
#7
Stephanie T Lane, Kathleen M Gates
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098490/three-attitudinal-item-characteristics-that-affect-survey-responses
#8
Elisabeth M Pyburn, Deborah L Bandalos
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098489/a-gradient-boosting-machine-for-hierarchically-clustered-data
#9
Patrick J Miller, Daniel B McArtor, Gitta H Lubke
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098487/evaluating-the-performance-of-cart-based-missing-data-methods-under-a-missing-not-at-random-mechanism
#10
Timothy Hayes, John J McArdle
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098482/decomposing-the-causes-of-the-socioeconomic-status-health-gradient-with-biometrical-modeling
#11
S Mason Garrison, Joseph Lee Rodgers
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098481/adapting-latent-profile-analysis-to-take-into-account-measurement-noninvariance
#12
Veronica T Cole
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28098480/regression-spline-mixed-models-for-testing-meaningful-landmarks-in-time-series-data
#13
Karen E Nielsen, Richard Gonzalez
No abstract text is available yet for this article.
January 18, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28059559/a-general-measure-of-effect-size-for-indirect-effects-in-mediation-analysis
#14
Mark J Lachowicz
No abstract text is available yet for this article.
January 6, 2017: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28026994/sensitivity-and-specificity-ratios-as-indices-of-measurement-invariance
#15
Jordan Campbell Brace
No abstract text is available yet for this article.
December 27, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28026978/mediation-analysis-with-a-survival-mediator
#16
Hanjoe Kim
No abstract text is available yet for this article.
December 27, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28010127/accommodating-small-sample-sizes-in-three-level-models-when-the-third-level-is-incidental
#17
Daniel McNeish, Kathryn R Wentzel
Small samples sizes are a pervasive problem when modeling clustered data. In two-level models, this problem has been well studied, and several resources provide guidance for modeling such data. However, a recent review of small-sample clustered data methods has noted that no studies have investigated methods for modeling three-level data with small sample sizes. Furthermore, strategies for two-level models do not necessarily translate to the three-level context. Moreover, three-level models are prone to small samples because the "small sample" designation is primarily based on the sample size of the highest level, and large samples are increasingly difficult to amass as one progresses up a hierarchy...
December 23, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28010115/a-bayesian-synthesis-approach-to-data-fusion-using-data-dependent-priors
#18
Katerina M Marcoulides
No abstract text is available yet for this article.
December 23, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/28010114/heterogeneity-coefficients-for-mahalanobis-d-as-a-multivariate-effect-size
#19
Marco Del Giudice
The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution...
December 23, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/27997223/estimating-incremental-validity-under-missing-data
#20
Dustin A Fife, Jorge L Mendoza, Christopher M Berry
A common form of missing data is caused by selection on an observed variable (e.g., Z). If the selection variable was measured and is available, the data are regarded as missing at random (MAR). Selection biases correlation, reliability, and effect size estimates when these estimates are computed on listwise deleted (LD) data sets. On the other hand, maximum likelihood (ML) estimates are generally unbiased and outperform LD in most situations, at least when the data are MAR. The exception is when we estimate the partial correlation...
December 20, 2016: Multivariate Behavioral Research
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