Hierarchical Linear Models: Applications and Data Analysis Methods.

Much social and behavioural research involves hierarchical data structures. Recent developments in the statistical theory of hierarchical linear models now afford an integrated set of methods for such applications. This introductory text explicates the theory and use of hierarchical linear models (HLM) through rich, illustrative examples and lucid explanations. The presentation remains reasonably nontechnical by focusing on three general research purposes—improved estimation of effects within an individual unit, estimating and testing hypotheses about cross-level effects, and partitioning of variance and covariance components among levels. This innovative volume describes use of both two and three level models in organizational research, studies of individual development and meta-analysis applications, and concludes with a formal derivation of the statistical methods used in the book In order to assist researchers, advanced graduate students and testing professionals, examples are used frequently and conceptual issues are stressed. New material includes model determination in log-linear smoothing, in-depth presentation of chained linear and equipercentile equating, equating criteria, test scoring and a new section on scores for mixed-format tests. In the third edition, each chapter contains a reference list, rather than having a single reference list at the end of the volume The themes of the third edition include:  the purposes of equating, scaling and linking and their practical context; data collection designs; statistical methodology; designing reasonable and useful equating, scaling, and linking studies; importance of test development and quality control processes to equating; equating error, and the underlying statistical assumptions for equating.
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