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Survey Analysis Methods and Effect Sizes in Research
The chapter explores survey analysis processes from data cleaning to exploratory and confirmatory factor analysis, emphasizing achieving a high signal-to-noise ratio. It discusses the shift to smaller hypothesis-driven models in structural equation modeling, the significance of effect sizes, and challenges in determining practical effect sizes. The conversation covers Bayesian frameworks, creating benchmarks for qualitative data, and robust analytical approaches for accurate results.