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Nov 29, 2024
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PSY 707 - Intermediate Statistics II (3 units)
Theory and application of statistical inference with special emphasis on multivariate models, including multiple and partial regression, factor analysis, path analysis and discriminant function analysis.
Units of Lecture: 3
Offered: Every Spring
Student Learning Outcomes:
Upon completion of this course:
1. Students will be able to demonstrate the ability to account for the relevant measurement issues in planning research projects that will be appropriate for regression-bases analyses in discussion and written work.
2. Students will be able to perform and write the results of an a priori sample size analysis for research design and post-hoc power analysis and evaluate the implications of these analyses for study designs and interpretation of statistical procurers covered in this class.
3. Students will be able to demonstrate the ability to apply the assumptions underlying regression modeling in their work by using the procedures to check that the assumptions are met and/or how one might compensate if the assumptions are not met in homework assignments and discussion. This includes demonstrating the ability to evaluate the implications of these analyses in relation to study designs and interpretation of statistical analyses covering in this class in homework and written assignments.
4. Students will be able to demonstrate the ability to apply the principles behind procedures for dealing with missing data in discussions and written work and be able to use basic missing data procedures in data analysis.
5. Students will be able to use a regression approach to analyze the relationship between multiple categorical and/or continuous independent variables and a dependent continuous, count, categorical, or dichotomous variable.
6. Students will be able to demonstrate the ability to accurately perform, interpret, and write the results of the following analyses in homework, discussion and written work; moderation, mediation, path modeling, basic nested data analysis and exertions of this approach to longitudinal data analysis.
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