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A cautionary note on modeling growth trends in longitudinal data.
Random coefficient and latent growth curve modeling are currently the dominant approaches to the analysis of longitudinal data in psychology. The application of these models to longitudinal data...
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Generalized full-information item bifactor analysis.
Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single-group analysis and inflexible in the types...
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Aggregate and individual replication probability within an explicit model of...
We study a model of the research process in which the true effect size, the replication jitter due to changes in experimental procedure, and the statistical error of effect size measurement are all...
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Choosing the number of clusters in Κ-means clustering.
Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to...
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A two-mode clustering method to capture the nature of the dominant...
Profile data abound in a broad range of research settings. Often it is of considerable theoretical importance to address specific structural questions with regard to the major pattern as included in...
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Individual differences methods for randomized experiments.
Experiments allow researchers to randomly vary the key manipulation, the instruments of measurement, and the sequences of the measurements and manipulations across participants. To date, however, the...
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Sample size planning for longitudinal models: Accuracy in parameter...
Longitudinal studies are necessary to examine individual change over time, with group status often being an important variable in explaining some individual differences in change. Although sample size...
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Three Cs in measurement models: Causal indicators, composite indicators, and...
In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective)...
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Deduced inference in the analysis of experimental data.
Any set of confidence interval inferences on J − 1 linearly independent contrasts on J means, such as the two comparisons μ1 − μ2 and μ2 − μ3 on 3 means, provides a basis for the deduction of interval...
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A 2 × 2 taxonomy of multilevel latent contextual models: Accuracy–bias...
In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in...
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Bayes factor approaches for testing interval null hypotheses.
Psychological theories are statements of constraint. The role of hypothesis testing in psychology is to test whether specific theoretical constraints hold in data. Bayesian statistics is well suited to...
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Publication bias in psychological science: Prevalence, methods for...
The issue of publication bias in psychological science is one that has remained difficult to address despite decades of discussion and debate. The current article examines a sample of 91 recent...
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A hierarchical latent stochastic differential equation model for affective...
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...
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Masking misfit in confirmatory factor analysis by increasing unique...
Fit indices are widely used in order to test the model fit for structural equation models. In a highly influential study, Hu and Bentler (1999) showed that certain cutoff values for these indices could...
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Marginal mean weighting through stratification: A generalized method for...
Propensity score matching and stratification enable researchers to make statistical adjustment for a large number of observed covariates in nonexperimental data. These methods have recently become...
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Clusterwise simultaneous component analysis for analyzing structural...
Many studies yield multivariate multiblock data, that is, multiple data blocks that all involve the same set of variables (e.g., the scores of different groups of subjects on the same set of...
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Many tests of significance: New methods for controlling type I errors.
There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in...
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Fitting multilevel models with ordinal outcomes: Performance of alternative...
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...
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The design and analysis of state-trace experiments.
State-trace analysis (Bamber, 1979) addresses a question of interest in many areas of psychological research: Does 1 or more than 1 latent (i.e., not directly observed) variable mediate an interaction...
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A time-varying effect model for intensive longitudinal data.
Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are...
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