Stats Refresher using SPSS

With the ubiquitous increase in the availability of quantitative data in political science, there is a growing need for literacy in analyzing and interpreting it. The goal of the course is to teach participants basic statistical techniques used in the field, and thus enable them to progress to more advanced techniques. The course will first go over various descriptive statistics which help organize and summarize data, and then move on to inferential statistics. The latter help answer general questions about unknown populations on the basis of limited information gathered from samples, which is the case for most of social science research.

The first part of the course will introduce the distinction between population and sample and discuss sampling error. It will then touch upon issues of measurement and scale, including the relationship between concepts and indicators, as well as validity and reliability. Next, the course reviews frequency distributions and different measures of central tendency. Furthermore, it will cover three measures of variability: the range, standard deviation and variance. Finally, it covers the foundations of inferential statistics by discussing probability distributions, the normal distribution, the central limit theorem and standard error.

The second part of the course introduces some basic inferential techniques used in social science research. It covers hypothesis testing, the difference between type I and type II errors and focuses on two types of tests for comparing groups which use sample means and mean differences to draw inferences about the corresponding population parameters: the t-test for two independent and two related samples. It finally looks at the association between categorical variables with the chi-square test for goodness of fit and the chi-square test for independence.

The final part of the course deals with associations between continuous variables, namely bivariate correlation and simple linear regression. It will discuss the principles behind these two statistical techniques, the interpretation of results, the differences between correlation and causation, and finish with model assumptions and the implications of violating these assumptions.

At the end of the course, participants will have a good understanding of the descriptive statistics used for organizing and summarizing results, as well as of the principles of inferential statistics used for establishing relationships between samples and populations. Although previous statistical knowledge is not a prerequisite, given the breadth of the material covered, participants are expected to prepare for the course by thoroughly reading the assigned materials. Participants with limited prior knowledge are particularly encouraged to do so. This should allow everyone to easily follow through the material covered throughout the class. The lab sessions will be held in SPSS and will follow closely the structure of the material presented in the first part of each day. At the end of the course, participants will have learned to enter and manipulate data, present it using tables and graphs, and conduct basic statistical analyses.

This course is designed for participants that have been exposed to statistics courses in the past and would like to freshen up basic statistical concepts in preparation for more advanced courses. However, participants with limited prior knowledge of statistics are also welcome to join the course. Everyone is strongly recommended to read the assigned materials in advance of the course.