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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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Cook, Thomas D.; Steiner, Peter M. – Psychological Methods, 2010
In this article, we note the many ontological, epistemological, and methodological similarities between how Campbell and Rubin conceptualize causation. We then explore 3 differences in their written emphases about individual case matching in observational studies. We contend that (a) Campbell places greater emphasis than Rubin on the special role…
Descriptors: Research Methodology, Pretests Posttests, Data Analysis, Evaluation Methods
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Dekle, Dawn J.; Leung, Denis H. Y.; Zhu, Min – Psychological Methods, 2008
Across many areas of psychology, concordance is commonly used to measure the (intragroup) agreement in ranking a number of items by a group of judges. Sometimes, however, the judges come from multiple groups, and in those situations, the interest is to measure the concordance between groups, under the assumption that there is some within-group…
Descriptors: Item Response Theory, Statistical Analysis, Psychological Studies, Evaluators
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Gonzalez, Jorge; De Boeck, Paul; Tuerlinckx, Francis – Psychological Methods, 2008
Structural equation models are commonly used to analyze 2-mode data sets, in which a set of objects is measured on a set of variables. The underlying structure within the object mode is evaluated using latent variables, which are measured by indicators coming from the variable mode. Additionally, when the objects are measured under different…
Descriptors: Structural Equation Models, Data Analysis, Evaluation Methods, Models
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Brusco, Michael J. – Psychological Methods, 2004
A number of important applications require the clustering of binary data sets. Traditional nonhierarchical cluster analysis techniques, such as the popular K-means algorithm, can often be successfully applied to these data sets. However, the presence of masking variables in a data set can impede the ability of the K-means algorithm to recover the…
Descriptors: Mathematics, Multivariate Analysis, Statistical Data, Statistical Analysis
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Lubke, Gitta H.; Muthen, Bengt – Psychological Methods, 2005
Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models…
Descriptors: Youth, Evaluation Methods, Factor Analysis, Data Analysis
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Long, Jeffrey D. – Psychological Methods, 2005
Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q[superscript 2]…
Descriptors: Simulation, Social Science Research, Error of Measurement, Least Squares Statistics