We will be using a open source statistical programming language, Python, to do data analysis in this class. Python is free and you can install it on your own computer (Windows, Mac, Linux) if you wish, or you can run it online using Google’s Colaboratory. In this class, we’ll use Colab, and I will share with you a Colab notebook for each lesson and you will share your Colab notebook with me for the module exercises.
The course is structured as a series of units that are organized in a consistent way. Each class meeting will have INPUTS and OUTPUTS. You should read and take notes on the input material before the date of the class meeting. Each unit will have one or more laboratory assignments in which you will do some data analysis with Python and write about what you’ve learned. The units will incorporate in-class meetings and activities as well as outside-of-class assignments; these are the outputs.
Topics
(0.1) INPUT: Introduction to the course. What do we want sociology to be? Letter to sociology students, Letter from Contemporary Theory students. OUTPUT: Reflection #1.
Unit 1. Concepts and Tools
(1.1) INPUT: Introduction to quantitative analysis; An Example with Data. OUTPUT: What is a sociological question?; Reflection #2.
(1.2) INPUT: Sociological research involves some methodological concepts: Measurement, and Generalization. OUTPUT: Reflection #3; Lab 1. Python syntax.
(1.3) INPUT: Data ethics, part 1. OUTPUT: Reflecting on data sources.
(1.4) INPUT: Data ethics, part 2. OUTPUT: Applying data feminism; Reflection #3.
(1.5) INPUT: Review of finding and accessing data. OUTPUT: Lab 1. Getting Data; Reflection #4
(1.6) INPUT: Probability. OUTPUT: Probability Exercise; Lab 2. Probability.
(1.7) INPUT: Bayes’s Theorem. OUTPUT: Using Bayes’s Theorem.
(1.8) INPUT: Probability Distributions. OUTPUT: More Probability Exercises.
Unit 2. The Research Project
(2.1) INPUT: Discussion of research questions. OUTPUT: Selecting a research question.
(2.2) INPUT: Finding our data. OUTPUT: Lab 3. Getting our data.
Unit 3. Describing Our Sample
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(3.1) INPUT: Descriptive statistics, central tendency; Central Tendency. OUTPUT: Computing central tendency; Reflection #5.
(3.2) INPUT: Descriptive statistics, variability; Variability. OUTPUT: Computing variability.
(3.3) INPUT: Research project overview. OUTPUT: Reflection #7; Lab 3. Descriptive statistics.
Unit 4. Inference with Means and Percentages
(4.1) INPUT: The normal curve, sampling distributions. OUTPUT: Normal curve.
(4.2) INPUT: The confidence interval. OUTPUT: Reflection #8; Lab 4. The confidence interval.
(4.3) INPUT: Introduction to hypothesis testing; Introduction to Hypothesis Testing. OUTPUT: hypothesis testing.
(4.4) INPUT: Hypothesis testing in Python (Two groups); Hypothesis testing with more than 2 groups. OUTPUT: hypothesis testing with more than two groups; Reflection #9; Lab 5. The t-test.
(4.5) INPUT: Factorial analysis of variance. OUTPUT: ANOVA.
(4.6) INPUT: Review of analysis of variance. OUTPUT: Reflection #10; Lab 6. The F-test.
(4.7) INPUT: Reviewing hypothesis testing.
(4.8) INPUT: Reviewing hypothesis testing. OUTPUT: Reflection #11
(4.9) INPUT: Hypothesis testing with Percentage Tables (we’ve seen this before in unit 2). OUTPUT: Computing a hypothesis test with percentages.
(4.10) INPUT: More hypotheses with percentages. OUTPUT: Reflection #12; Lab 7. Crosstabulations and odds
Unit 5. The Linear Model
(5.1) INPUT: Linear (bivariate) regression. OUTPUT: Computing the linear model, part 1.
(5.4) INPUT: Introduction to the linear model. OUTPUT: Reflection #13
(5.5) INPUT: More on the linear model. OUTPUT: More on causal modeling.
(5.6) INPUT: Review of linear regression. OUTPUT: Reflection #14; Lab 8. The linear model.
(5.7) Review and discussion of the research projects.
Research paper due: Wednesday, December 21. This is the final exam for this course. Grades posted by Tuesday, December 27.
