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Tor Georg Jakobsen is a Professor in Political Science at the NTNU Business School. He is co-author (with Mehmet Mehmetoglu) of Applied Statistics using Stata: A Guide for the Social Sciences (Sage 2016) and has authored and co-authored numerous articles in, among others, European Sociological Review, Work, Employment & Society, Regional Studies, and Conflict Management & Peace Science. His research interest includes political behavior, sports economics, and statistical methods. Jakobsen is also winner of the 2020 Bernard Brodie Prize (together with Jo Jakobsen). He has taught quantitative courses at all levels.

Course Content
This course covers regression analysis, both with continuous, ordinal, and categorical dependent variables. The focus will be on applied regression analysis, yet we will also deal with related topic like data treatment in Stata, interpretations, and how to test regression assumptions. The course includes ordinary least squares regression, logistic, ordinal, and multinomial regression, how to model and interpret non-linear effects as well as different types of statistical interactions. We will also focus on how to deal with breaches of assumptions.

Part 1: Introduction to Stata, Research and Statistics
1. Research and statistics
2. Introduction to Stata

Part 2: Regression analysis
3. Bivariate regression
4. Multiple regression
5. Dummy-variable regression
6. Interaction effects
7. Assumptions and diagnostics
8. Logistic regression

Part 3: Advanced topics
9. Critical issues
10. Advanced topics

Course Objectives
The course will enable the students to perform a range of regression models, to work with data treatment, and to be able to critically evaluate and interpret different types of regression models.

Course Prerequisites
The students should have some knowledge about basic descriptive statistics, measures of central tendency and spread. It is also good to have some knowledge about the statistical software Stata. However, instruction and notes will be made available, so it is possible for everyone to follow (though you must work harder if you do not have previous knowledge). The course will, to a large degree, follow the structure of the book Applied Statistics using Stata. In the reading list are also included short Sage books from the series Quantitative Application in the Social Sciences that dwell deeper into the topics of logistic regression and missing data (note that these are not Stata-books but deals more with statistical theory). In the recommended readings section, I have included to Stata introduction books for those who are not very comfortable with this software (you only need one of these), as well as a book on dummy variables and some additional chapters in our main book.

Representative Background Reading
Mehmetoglu, Mehmet & Tor G. Jakobsen (2016). Applied Statistics using Stata: A Guide for the Social Sciences. Thousand Oaks, CA: Sage. Chapters: 2 & 3.

Required Texts
Allison, Paul D. (2002). Missing Data. Thousand Oaks, CA: Sage.
Pampel, Fred C. (2000). Logistic Regression: A Primer. Thousand Oaks, CA: Sage.
Mehmetoglu, Mehmet & Tor G. Jakobsen (2016). Applied Statistics using Stata: A Guide for the Social Sciences. Thousand Oaks, CA: Sage. Chapters: 1, 2, 3, 4, 5, 6, 7, 8 & 13.

Recommended Readings
Acock, Alan C. (2018). A Gentle Introduction to Stata, 6th ed. College Station, TX: Stata Press.
Hardy, Melissa A. (1993). Regression with Dummy Variables. Thousand Oaks, CA: Sage.
Mehmetoglu, Mehmet & Tor G. Jakobsen (2016). Applied Statistics using Stata: A Guide for the Social Sciences. Thousand Oaks, CA: Sage. Chapters: 9 &10

Background knowledge required
Statistics
OLS = elementary

Computer Background
Stata = elementary

Day 1) Research and statistics
– Methodology
– Positivism
– Statistical method
– Regression analysis,
– Normal distribution
– Central limit theorem
– T-distribution
– Degrees of freedom (df)

Day 2) Introduction to Stata
– Commands and windows
– Generating and recoding
– Graphs
– Do-file
– Scale, OLS, logistic
– Punching your own data

Day 3) Bivariate regression
– Regression analysis
– Bivariate regression
– Error term
– Regression analysis/OLS
– Predicting in linear regression
– Standard error
– Hypothesis testing
– Confidence intervals
– R-squared

Day 4) Multiple regression
– Multiple regression
– F-test
– Adjusted R²
– Partial slope coefficients
– Standardization and relative importance

Day 5) Dummy variable regression
– Dichotomous variable
– Dummy set

Day 6) Interaction effects
– Product-term approach
– Continuous predictor and dummy moderator
– Continuous predictor and polytomous moderator
– Dummy predictor and dummy moderator
– Continuous predictor and continuous moderator

Day 7) Assumptions and diagnostics
– The assumptions
– Correct specification of the model
– Assumptions about the residuals
– Influential observations

Day 8) Logistic regression
– What is logistic regression?
– Assumptions of logistic regression
– Example of logistic regression
– Diagnostics
– Multinomial logistic regression
– Ordered logit regression

Day 9) Critical issues
– Skewness and kurtosis
– Transformation of variables
– Weighting cases

Day 10) Advcanced topics
– Interaction with non-linear predictor
– Missing data