This groundbreaking text explores causal questions in social and biomedical sciences, presenting statistical methods for analysis. It discusses potential outcomes and the challenges of causal inference, emphasizing randomized experiments and observational studies. The authors detail key analysis methods and provide practical applications for researchers.
A Correlational Approach
Focused on enhancing the understanding of contrast analysis, this volume introduces innovative concepts, measures, and indices that broaden its application in research. Aimed at researchers, educators, and graduate students, it explores correlational effect size estimates and their relevance across diverse contexts, including two-group comparisons, one-way ANOVA, factorial designs, and repeated measures. By consistently referencing these correlations, the authors provide a comprehensive guide to effectively utilizing contrast analysis in various research scenarios.
Featuring a blend of classic theoretical studies and practical applications, this collection showcases Donald B. Rubin's significant contributions to the field of matched sampling. It highlights innovative methodologies and their impact on statistical analysis, making it a valuable resource for researchers and practitioners interested in advanced sampling techniques and their real-world implications.