By nature or by measurement, dependent variables of interest to social and behavioral
scientists are frequently categorical. Outcomes that include several ranked or unranked, non-
continuous categories–like vote choice, social media platform preference, brand loyalty,
and/or condom use—are often of interest, with scientists expressly interested in developing
models to explain or classify variation therein. Explanatory models are process-focused, and
aim to determine the individual impact of factors that contribute to a particular outcome, often
based on a priori theory—e.g., “How does social class affect whether an individual voted for
the Conservatives in 2019?”; classification models, alternatively, are outcome-focused, and
aim to identify the set of factors that most accurately classify (or predict) a particular
outcome—e.g., “How do the Tories best use information from polls, geography, weather,
Twitter feeds, and/or social demographics to predict who voted Conservative in 2019?”
Chances are your research involves a categorical outcome—binary, ordinal, or multinomial—
and thus options thus abound for the modeling approach(es) you might take to address your
research question of interest. This course is designed to provide an overview of a number of
parametric and non-parametric approaches to exploring your outcome of interest, via both
explanatory and classification perspectives. At the end of this course, you should have a clear
understanding as to which types of models and methods are available to answer different
research questions, and also have experience applying a varied toolkit of these models.
