Contemporary societal problems are complex, intractable, and
costly. Aiming to ameliorate them, social scientists formulate
policies and programs, and conduct research testing the efficacy of
the interventions. All too often the results are disappointing;
partly because the theories guiding these studies are
inappropriate, the study designs are flawed, and the empirical
databases covering their research questions are sparse. This book
confronts these problems of research by following this process:
analyze the roots of the social problem both theoretically and
empirically; formulate a study design that captures the nuances of
the problem; gather appropriate empirical data operationalizing the
study design; model these data using multilevel statistical methods
to uncover potential causes and any biases to their implied
effects; use the results by refining theory and by formulating
evidence-based policy recommendations for implementation and
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
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
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!