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Course ID: | ERSH 8320. 3 hours. | Course Title: | Applied Correlation and Regression Methods in Education | Course Description: | Nonexperimental and quasi-experimental research studies, including simple and multiple regression techniques, nonorthogonal analysis of variances, correlation techniques, and analysis of covariance. | Oasis Title: | App Corr and Regres Mth Educ | Duplicate Credit: | Not open to students with credit in ERSH 8320E | Prerequisite: | ERSH 8310 | Semester Course Offered: | Offered every year. | Grading System: | A-F (Traditional) |
| Course Objectives: | On successfully completing the course students should be capable of designing
studies and analyzing data sets using regression techniques. Furthermore, students
should feel comfortable reading journal articles which have used basic regression
techniques in analyzing data sets. A word of caution however is needed. Students
are not likely to fully understand all research studies using regression
techniques. Regression analysis is a very powerful technique which can be
appropriately used to analyze very complex problems. This course is designed to
introduce students to the major concepts of regression analysis. For more thorough
understanding of the analysis procedure further study will be necessary. | Topical Outline: | Correlation and Simple Linear Regression
Representing the relationship between two variables
Identifying the best fitting straight line
Estimation and hypothesis test
Appropriateness of the model
Prediction
Summarizing relations with correlations
Regression analysis with a qualitative independent variables
Developing and interpreting a qualitative model
Regression analysis with two or more quantitative explanatory variables
Overview
Meaning of the model
Assumptions
Multiple Correlation Coefficient
Testing the overall model
Partial F-test
R_increase (part correlation squared)
Partial Correlation Coefficient
Testing for an interaction
Identifying the "best" subset of independent variables
Regression analysis with one continuous and one categorical independent variable
Overview
Identifying separate regression equations.
Testing for the equality of the slopes
Johnson-Neyman Technique
Testing for the relationship between the continuous and dependent variables
Testing for differences in the intercepts
Identifying specific differences in treatments
Regression Analysis with two categorical variables independent variables
Factorial ANOVA
Weighted and Unweighted marginal means
Hierarchical vs Unique solutions | |
Syllabus:
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