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Austin, Peter C. – Multivariate Behavioral Research, 2011
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing…
Descriptors: Probability, Scores, Statistical Analysis, Computation
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Imai, Kosuke; Jo, Booil; Stuart, Elizabeth A. – Multivariate Behavioral Research, 2011
In this commentary, we demonstrate how the potential outcomes framework can help understand the key identification assumptions underlying causal mediation analysis. We show that this framework can lead to the development of alternative research design and statistical analysis strategies applicable to the longitudinal data settings considered by…
Descriptors: Research Design, Statistical Analysis, Research Methodology, Longitudinal Studies
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Pituch, Keenan A.; Stapleton, Laura M. – Multivariate Behavioral Research, 2008
A Monte Carlo study compared the statistical performance of standard and robust multilevel mediation analysis methods to test indirect effects for a cluster randomized experimental design under various departures from normality. The performance of these methods was examined for an upper-level mediation process, where the indirect effect is a fixed…
Descriptors: Research Design, Monte Carlo Methods, Statistical Analysis, Error Patterns
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Cook, Thomas D.; Steiner, Peter M.; Pohl, Steffi – Multivariate Behavioral Research, 2009
This study uses within-study comparisons to assess the relative importance of covariate choice, unreliability in the measurement of these covariates, and whether regression or various forms of propensity score analysis are used to analyze the outcome data. Two of the within-study comparisons are of the four-arm type, and many more are of the…
Descriptors: Statistical Bias, Reliability, Data Analysis, Regression (Statistics)
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Overall, John E.; Tonidandel, Scott – Multivariate Behavioral Research, 2010
A previous Monte Carlo study examined the relative powers of several simple and more complex procedures for testing the significance of difference in mean rates of change in a controlled, longitudinal, treatment evaluation study. Results revealed that the relative powers depended on the correlation structure of the simulated repeated measurements.…
Descriptors: Monte Carlo Methods, Statistical Significance, Correlation, Depression (Psychology)
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Markus, Keith A. – Multivariate Behavioral Research, 2008
One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts…
Descriptors: Research Design, Structural Equation Models, Data Analysis, Causal Models
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Bauer, Daniel J.; Sterba, Sonya K.; Hallfors, Denise Dion – Multivariate Behavioral Research, 2008
Individually randomized treatments are often administered within a group setting. As a consequence, outcomes for treated individuals may be correlated due to provider effects, common experiences within the group, and/or informal processes of socialization. In contrast, it is often reasonable to regard outcomes for control participants as…
Descriptors: Youth Programs, High Risk Students, Behavior Disorders, Outcomes of Treatment
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Pituch, Keenan A.; Stapleton, Laura M.; Kang, Joo Youn – Multivariate Behavioral Research, 2006
A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…
Descriptors: Monte Carlo Methods, Research Design, Mediation Theory, Comparative Testing