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Categorical Data Analysis

SW862, Section 001

Researchers are most commonly aware of methods that are suitable for continuous dependent
variables (e.g. mental health scores), such as the use of ordinary least squares regression.
However many outcomes of interest to social workers, and other social researchers, are
decidedly not continuous, but are dichotomous or binary in nature: entered the program versus
did not enter the program; left the program versus stayed in the program; received a particular
diagnosis; did not receive a diagnosis. Many researchers are familiar with the basics of logistic
regression, yet do not have a grounding in some of the intricacies of logistic regression, such as
generating predicted probabilities, or using interaction terms in a categorical model, which can
lead to clearer and more accurate reporting of results. Further, the basic logistic regression
model serves as the foundation for a wide variety of more advanced statistical approaches that
can help advance social work research. Study of the logistic regression model can lead to
variations of logistic regression such as logistic regression for ordered variables, or multinomial
logistic regression where are more than two categories of the outcome variable (e.g. multiple
forms of family violence). An understanding of logistic regression also helps to motivate
understanding of models for censored data, such as the tobit model (useful in studies of income
and wealth), along with models for count data such as the Poisson and negative binomial model
suitable for studying counts of events such as incidence of disease or incidence of violence.
Lastly, categorical data model serve as the foundation for event history models that are used to
study the timing of events, such as the timing of program entry, program departure, or receipt of a diagnosis.
Proposed Topics (some topics may span more than 1 week)
1) Review of ordinary least squares regression
2) Logistic and probit models
3) Ordered and multinomial logistic regression models.
4) Models for censored and truncated data
5) Models for count data
6) Event history models for the timing of events

Semester: Fall 2020
Instructor: Andrew C. Grogan-Kaylor
Category: Research
U-M Class #: 34458
Prerequisites: Doctoral Standing or permission of instructor
Time: Mon 9:00 AM - 12:00 PM
Format: ONLINE
Credits: 3 Credit Hours

Course Codes

O:On-line - course is delivered entirely on-line

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