research expertıse > advanced data analysıs
In our lab, we analyze complex datasets to convert the raw information into meaningful and actionable stories. We aim to identify the optimal analysis strategies to test the research hypotheses and promote the effective communication of results.
We map the research questions of interest onto the most appropriate set of quantitative models to fit the models to data at hand. Some examples of analytic approaches we use are:
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Program Evaluation Optimizing Interventions Methodological Innovations Advanced Data Analysis Systematic Reviews of Evidence |
Testing Two-Wave Data: A Monte Carlo Simulation Study
In this study, we compare the analysis of covariance (ANCOVA), difference score, and residual change score methods in testing the group effect for pretest–posttest data in terms of statistical power and Type I error rates using a Monte Carlo simulation. More... 
Achieving accurate confidence interval estimation for indirect effects
In this journal article, we demonstrate why normal theory confidence intervals for indirect effects are often less accurate than those obtained from the asymmetric distribution of the product or from bootstrapping. More... |
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Bayesian mediation analysis for studies with small samples
The analysis of mediated effect in prevention and intervention programs can be sometimes problematic if the sample size is small. A solution to this problem is to use the Bayesian perspective to estimate the mediated effect. More... |
Using Bayesian propensity score analysis in testing causal mechanisms
Bayesian propensity score analysis to address the issue of causal inference in testing indirect effects is illustrated. More... |