# HW1solns - Solutions Homework#1 Chapter 1 1 Question 6 What...

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Solutions - Homework #1 Chapter 1 1. Question 6: What are the differences between statistical and practical significance? Is one a prerequisite for the other? Statistical significance means that the results are unlikely due to chance alone. Practical significance means that the results are useful or interesting from a real world (economic, scientific, personal) perspective. Statistical significance is prerequisite for practical significance, because without statistical significance it’s unclear that any observed effect is not due to merely chance. 2. Question 7: What are the implications of low statistical power? How can power be improved if it is deemed too low? The implication of low power is that the researcher may fail to find significance when it actually exists. This has implications for reproducibility, even in experiments where significance is found. If investigators reject the null hypothesis in an experiment with low power, there’s a good chance that other researchers attempting to reproduce the results will fail to reject, due to low power. Power can be improved by increasing the sample size or controlling for more variation in the experimental design. 3. Question 8: Detail the model-building approach to multivariate analysis, focusing on the major issues at each step. Stage One: Define the research problem, objectives, and multivariate technique to be used. The starting point for any analysis is to define the research problem and objectives in conceptual terms before specifying any variables or measures. This will lead to an understanding of the appropriate type of techniques, dependence or interdependence, needed to achieve the desired objectives. Then based on the nature of the variables involved a specific technique may be chosen. Stage Two: Develop the analysis plan. A plan must be developed that addresses the particular needs of the chosen multivariate technique. These issues include sample size, type of variables (metric vs. nonmetric), and special characteristics of the technique. Stage Three: Evaluate the assumptions underlying the multivariate technique. If the technique requires assumptions of normality, homogeneity of variance, and linearity these assumptions must be checked and validated. Stage Four: Estimate the multivariate model and assess overall model fit. Once the assumptions are validated the model is fit to the data and the goodness-of-fit evaluated. Any corrections that need to be made are made and the fit reassessed.

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