The utilization of logistic regression modeling has increased over the past couple
of decades as its use spread from epidemiological research to a number of other
research fields, including business management (Hosmer and Lemeshow, 2000). In
parallel, considerable effort has been spent toward researching the various statistical
aspects of this modeling process. Hosmer and Lemeshow have commented on "more
than 1,000 citations that have appeared in the 10 years since the first edition of
this book was published" (2000, p. ix).
Logistic regression has facilitated quantitative research in various aspects
of business management, including marketing, strategy, decision theory,
and organizational behavior. Whether it is used in business research, or in another
field, the goal of using this model development technique is the same; to develop a
model that best describes the relationship between a dependent variable and a set
of independent variables. The choice to use logistic regression versus linear
regression is a function of the dependent variable. If the dependent variable has a
binary outcome, logistic regression is a good choice.
The pseudo R2 in logistic regression (in contrast to R2 in linear regression) is useful when trying to determine the relative fit of various models, each using
different combinations of independent variables. Pseudo R2s are also valuable in
determining the absolute fit of logistic regression models in cases where large sample sizes
are utilized as input to the models.