Hierarchical linear modeling : guide and applications / G. David Garson, editor.
Material type:
TextPublication details: Thousand Oaks, Calif. : SAGE Publications, c2013.Description: xix, 371 p. : ill. ; cmISBN: - 9781412998857 (pbk.)
- 003.74 GAR
- QA279 .H54 2013
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Reference
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Indian Institute Of Management, Shillong | 003.74 GAR (Browse shelf(Opens below)) | Not for loan | 0012417 |
Includes bibliographical references and index.
Preface -- Fundamentals of hierarchical linear (multilevel) modeling / G. David Garson -- Preparing to analyze multilevel data / G. David Garson -- Introductory guide to hlm with hlm6 software / G. David Garson -- Introductory guide to hlm with sas software / G. David Garson -- Introductory guide to hlm with spss software / G. David Garson -- Introductory and intermediate applications -- A random intercepts model of gpa and sat scores using spss / Forrest C. Lane, Kim F. Nimon, & J. Kyle Roberts -- A random intercept regression model using hlm : cohort analysis of a mathematics curriculum for mathematically promising students / Carissa L. Shafto & Jill L. Adelson -- Random coefficients modeling with hlm : assessment practices and the achievement gap schools / Gregory J. Palardy -- Emotional reactivity to daily stressors using a random coefficients model with sas proc mixed : a repeated measures analysis / Shevaun Neupert -- Hierarchical linear modeling of growth curve trajectories using hlm / David F. Greenberg & Julie A. Phillips -- A piecewise growth model using hlm to examine change in teaching practices following a science teacher professional development intervention / Jaime L. Maerten-Rivera -- Studying reaction to repeated life events with discontinuous change models using hlm / Maike Luhmann & Michael Eid -- A cross-classified multilevel model for first-year college natural science performance using sas / Brian F. Patterson -- Cross-classified multilevel models using stata: how important are schools and neighbourhoods for children's educational attainment? / George Leckie -- Predicting future events from longitudinal data with multivariate hierarchical models and bayes' theorem using sas / Larry J. Brant & Shan L. Sheng -- Index.
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