Abstract and Keywords
Definitions of what constitutes advanced statistical analysis often differ among social-work researchers and across disciplines. In this article, the term advanced statistical analysis refers to advanced models increasingly applied to social-work research that help address important research questions. Because contemporary statistical models fully rely on the maximum likelihood (ML) estimator, this article begins with an overview of ML that serves as a foundation for understanding advanced statistical analysis. Following the overview, the article describes six categories of analytic methods that are important in confronting a broad range of issues in social-work research (that is, hierarchical linear modeling, survival analysis, structural equation modeling, propensity score analysis, missing data imputation, and other corrective methods for causal inference such as instrumental variable approach and regression discontinuity designs). These analytic methods are used to address research issues such as generating knowledge for evidence-based practices; evaluating intervention effectiveness in studies that use an experimental or quasi-experimental design; assessing clients’ problems, well-being, and outcome changes over time; and discerning the effects of policies.
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