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Multivariate Behavioral Research

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https://www.readbyqxmd.com/read/28098494/constant-and-variable-time-delays-of-synchrony-of-facial-expressions-in-dyadic-conversations
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
https://www.readbyqxmd.com/read/27982700/-re-evaluating-the-implications-of-the-autoregressive-latent-trajectory-model-through-likelihood-ratio-tests-of-its-initial-conditions
#16
Lu Ou, Sy-Miin Chow, Linying Ji, Peter C M Molenaar
The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model...
December 16, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/27925836/centering-predictor-variables-in-three-level-contextual-models
#17
Ahnalee M Brincks, Craig K Enders, Maria M Llabre, Rebecca J Bulotsky-Shearer, Guillermo Prado, Daniel J Feaster
Hierarchical data are becoming increasingly complex, often involving more than two levels. Centering decisions in multilevel models are closely tied to substantive hypotheses and require researchers to be clear and cautious about their choices. This study investigated the implications of group mean centering (i.e., centering within context; CWC) and grand mean centering (CGM) of predictor variables in three-level contextual models. The goals were to (a) determine equivalencies in the means and variances across the centering options and (b) use the algebraic relationships between the centering choices to clarify the interpretation of the estimated parameters...
December 7, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/27925768/unsupervised-classification-during-time-series-model-building
#18
Kathleen M Gates, Stephanie T Lane, E Varangis, K Giovanello, K Guiskewicz
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects...
December 7, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/27911083/nonlinear-growth-models-as-measurement-models-a-second-order-growth-curve-model-for-measuring-potential
#19
Daniel McNeish, Denis Dumas
Recent methodological work has highlighted the promise of nonlinear growth models for addressing substantive questions in the behavioral sciences. In this article, we outline a second-order nonlinear growth model in order to measure a critical notion in development and education: potential. Here, potential is conceptualized as having three components-ability, capacity, and availability-where ability is the amount of skill a student is estimated to have at a given timepoint, capacity is the maximum amount of ability a student is predicted to be able to develop asymptotically, and availability is the difference between capacity and ability at any particular timepoint...
December 2, 2016: Multivariate Behavioral Research
https://www.readbyqxmd.com/read/27897456/estimating-between-person-and-within-person-subscore-reliability-with-profile-analysis
#20
Okan Bulut, Mark L Davison, Michael C Rodriguez
Subscores are of increasing interest in educational and psychological testing due to their diagnostic function for evaluating examinees' strengths and weaknesses within particular domains of knowledge. Previous studies about the utility of subscores have mostly focused on the overall reliability of individual subscores and ignored the fact that subscores should be distinct and have added value over the total score. This study introduces a profile reliability approach that partitions the overall subscore reliability into within-person and between-person subscore reliability...
November 29, 2016: Multivariate Behavioral Research
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