Sociology | Statistics for Sociology
S371 | 10910 | Bartley
This course is designed to develop your quantitative analytic skills
by teaching you how to understand, apply, and interpret basic
statistical principles. The course is organized in two main parts.
The first part covers descriptive statistics and deals with
techniques for summarizing data in a sample. We will start by
developing tools for describing a single variable-that is, some
aspect of the social world that varies from case to case or over
time. We will then start looking at relationships between two
variables, in order to understand how one part of the social world
shapes, influences, or causes another.
The second part of the course covers inferential statistics-that is,
a set of methods for using data from a sample to determine the
unknown characteristics of populations. The goal here is to figure
out how we can make claims about an entire population based on
observing only a small part of that population (a sample). Once we
develop the tools for making inferences, we will use those to extend
the material from the first part of the course, in order to make
sense of large-scale social outcomes and their possible causes.
In addition to covering the logic of statistics and developing your
skills at interpreting quantitative data, this course will provide
you with practical experience working with SPSS (Statistical Package
for the Social Sciences). This computer program is used in a
variety of academic, business, and non-profit settings, and the
skills you develop at processing and interpreting data with SPSS may
prove highly useful further down the line.
I assume no prior knowledge of statistics and the course is not
particularly math-intensive. Instead, the course emphasizes the
logic of describing variation, making comparisons, moving from
samples to populations, and developing substantive interpretations
of quantitative analyses. However, we will work with a number of
simple formulas and graphical techniques, so a solid understanding
of algebra is absolutely necessary.