This volume provides a practical introduction to the method of
maximum likelihood as used in social science research. Ward and
Ahlquist focus on applied computation in R and use real social
science data from actual, published research. Unique among books at
this level, it develops simulation-based tools for model evaluation
and selection alongside statistical inference. The book covers
standard models for categorical data as well as counts, duration
data, and strategies for dealing with data missingness. By working
through examples, math, and code, the authors build an
understanding about the contexts in which maximum likelihood
methods are useful and develop skills in translating mathematical
statements into executable computer code. Readers will not only be
taught to use likelihood-based tools and generate meaningful
interpretations, but they will also acquire a solid foundation for
continued study of more advanced statistical techniques.
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