Statistics for Demographic Data 2
DEM 5283/7283
Spring 2022
Mondays 6-8:30
Instructor: Dr. Corey S. Sparks
Office hours: Monday afternoons, preferably by appointment
[email protected]
This course represents an in-depth coverage of the general linear model framework, including alternative logistic regression models, count-data regression and multi-level modeling. Model fit, model comparison and regression diagnostics for each method are covered. In addition to these topics, students are also introduced to techniques for variable reduction and analysis of data from complex surveys. All methods will be illustrated with an appropriate demographic survey data set.
We will be using R. R is the language in which most research statisticians work and current methodological developments are being made, it is also free and can be used on any operating system.
Please update your R version (4.1) to the latest release and update all your packages.
We will use R through Rstudio (https://www.rstudio.com/products/rstudio/download/) and we will use Rpubs for turning in all assignments.
You should also install the version 4.0 install of Rtools if you are a windows user. https://cran.r-project.org/bin/windows/Rtools/ For those of you interested in R, I have published all of the examples from this class, and my other classes to my Rpubs site: http://rpubs.com/corey_sparks, so you can follow along with that stuff if you choose.
I will also post data and other resources to my Github repository https://github.com/coreysparks/DEM7093
Copy and paste the following command in Rstudio to install the packages we will use in this class:
source("https://raw.githubusercontent.com/coreysparks/Rcode/master/install_first_7283.R")
In Rstudio. Please have these programs downloaded and installed prior to class.
| Title | Author | ISBN | Status |
|---|---|---|---|
| Extending the linear model with R, 2nd ed | Faraway (F) | 9781498720960 | Recommended |
| Statistical Methods for Categorical Data Analysis, 2nd | Powers and Xie (PX) | 9780123725622 | Required |
| Multilevel Modeling | Luke (L) | 9781412985147 | Recommended |
| Analyzing Complex Survey Data | Lee and Forthofer (LF) | 9780761930389 | Recommended |
| Data analysis using regression and multilevel models | Gelman and Hill (GH) | 9780521686891 | Recommended |
| Missing data | Allison (A) | 9780761916727 | Recommended |
| --- | --- | --- |
| Week | Date | Topic | Suggested Reading - Letters refer to numbering of textbooks |
|---|---|---|---|
| 1 | 1/24 | Course Introduction and introduction to datasets to be used | PX ch 2; GH ch 3 |
| 2 | 1/31 | Survey data analysis | LF |
| 3 | 2/7 | Logistic/Probit Models | AL ch 2,3; PX ch 3 |
| 4 | 2/14 | Logistic regression for prediction Paper topic must be identified! | AL ch 5,6; PX ch 7, 8 |
| 5 | 2/21 | Logistic regression for Ordinal/Multinomial outcomes Blog post 1 due | 2 – ch 2 |
| 6 | 2/28 | Count Data Regression | AL ch 9; PX ch 4; GH ch 6 |
| 7 | 3/7 | Data Reduction/Principal Components | TBA |
| 8 | 3/14 | No Class** Spring Break** | |
| 9 | 3/21 | Multiple Imputation Blog post 2 due | Allison |
| 10 | 3/28 | Longitudinal models | F ch 11; AL ch 8 |
| 11 | 4/4 | Spline Regression | F ch 14, 15 |
| 12 | 4/11 | Multilevel Models | GH ch 11, 12; L ch 1, 2 |
| 13 | 4/18 | Multilevel Models Blog post 3 due | GH 11, 12; F ch 13 |
| 14 | 4/25 | Regression Trees | F ch 16 |
| 15 | 5/2 | TBA | |
| 16 | 5/19 | PhD students paper due All Blog posts finalized (Blog post 4 due) |