Applying this process, the chapters focus on these social problems: political extremism; global human development; violence against religious minorities; computerization of work; reform of urban schools; and the utilization and costs of health care. Because these chapters exemplify the usefulness of multilevel modeling for the quantification of effects and causal inference, they can serve as vivid exemplars for the teaching of students. This use of examples reverses the usual procedure for introducing statistical methods. Rather than beginning with a new statistical model bearing on statistical theory and searching for illustrative data, each core chapter begins with a pressing social problem. The specific problem motivates theoretical analysis, gathering of relevant data, and application of appropriate statistical procedures. Readers can use the provided data sets and syntaxes to replicate, critique, and advance the analyses, thereby developing their ability to produce future applications of multilevel modeling.
The chapters address the multilevel data structures of these social problems by grouping observations on the micro units (level-1) by more macro-units (level-2) (e.g., school children are grouped by their classroom), and by conducting multilevel statistical modeling in contextual, longitudinal, and meta-analyses. Each core chapter applies a qualitative typology to nest the variance between the macro units, thereby crafting a "mixed-methods" approach that combines qualitative attributes with quantitative measures