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Voelkle, Manuel C.; Oud, Johan H. L.; Davidov, Eldad; Schmidt, Peter – Psychological Methods, 2012
Panel studies, in which the same subjects are repeatedly observed at multiple time points, are among the most popular longitudinal designs in psychology. Meanwhile, there exists a wide range of different methods to analyze such data, with autoregressive and cross-lagged models being 2 of the most well known representatives. Unfortunately, in these…
Descriptors: Authoritarianism, Intervals, Structural Equation Models, Correlation
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Yuan, Ke-Hai; Hayashi, Kentaro – Psychological Methods, 2010
This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…
Descriptors: Structural Equation Models, Data Analysis, Visual Aids
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Bauer, Daniel J.; Sterba, Sonya K. – Psychological Methods, 2011
Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ…
Descriptors: Item Response Theory, Models, Computation, Research
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Bird, Kevin D. – Psychological Methods, 2011
Any set of confidence interval inferences on J - 1 linearly independent contrasts on J means, such as the two comparisons [mu][subscript 1] - [mu][subscript 2] and [mu][subscript 2] - [mu][subscript 3] on 3 means, provides a basis for the deduction of interval inferences on all other contrasts, such as the redundant comparison [mu][subscript 1] -…
Descriptors: Intervals, Statistical Analysis, Inferences, Comparative Analysis
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Wang, Lijuan; Hamaker, Ellen; Bergeman, C. S. – Psychological Methods, 2012
Intra-individual variability over a short period of time may contain important information about how individuals differ from each other. In this article we begin by discussing diverse indicators for quantifying intra-individual variability and indicate their advantages and disadvantages. Then we propose an alternative method that models…
Descriptors: Evaluation Methods, Data Analysis, Individual Differences, Models
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Geiser, Christian; Lockhart, Ginger – Psychological Methods, 2012
Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…
Descriptors: Psychological Studies, Simulation, Measurement, Error of Measurement
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Oravecz, Zita; Tuerlinckx, Francis; Vandekerckhove, Joachim – Psychological Methods, 2011
In this article a continuous-time stochastic model (the Ornstein-Uhlenbeck process) is presented to model the perpetually altering states of the core affect, which is a 2-dimensional concept underlying all our affective experiences. The process model that we propose can account for the temporal changes in core affect on the latent level. The key…
Descriptors: Individual Differences, Calculus, Models, Investigations
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Cooper, Harris; Patall, Erika A. – Psychological Methods, 2009
The authors describe the relative benefits of conducting meta-analyses with (a) individual participant data (IPD) gathered from the constituent studies and (b) aggregated data (AD), or the group-level statistics (in particular, effect sizes) that appear in reports of a study's results. Given that both IPD and AD are equally available,…
Descriptors: Data Analysis, Meta Analysis, Databases, Coordination
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Steinley, Douglas – Psychological Methods, 2006
Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate…
Descriptors: Diagnostic Tests, Sample Size, Multivariate Analysis, Scaling