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Harvard School of Public Health

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Spring 2010

Dr. V. S. Sankaranarayanan
5 Credits
Lectures: Two 2-hour sessions each week; One 1-hour lab each week.

This course is designed to provide doctoral students with a training experience in the concept and application of multilevel statistical modeling. Students will be motivated to think about correlated and dependent data structures that arise due to sampling design and/or are inherent in the population (such as pupils nested within schools; patients nested within clinics; individuals nested within neighborhoods and so on). The substantive motivation for analyzing such complex data structures would be to make quantitative assessments about the role of contexts (e.g., schools, clinics, neighborhoods) in predicting individual outcomes. In particular, the principles of recognizing and modeling the underlying heterogeneity in average relationships would be emphasized. Linear, non-linear, and multivariate multilevel models will be covered. Upon completion, students should be able to conceptualize multilevel modeling strategies and to undertake empirical, quantitative multilevel research. The course will be lecture-based with substantial hands-on component.
Course Activities: Data management, modeling and analysis; individual assignments; project submission and class participation.
Course Notes: SHH245 or instructor's permission required; this course is a requirement for all SHDH doctoral students. (5.06)


Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2009

Dr. V. S. Sankaranarayanan
5 Credits
Lectures: Two 2-hour sessions each week; One 1-hour lab each week.

This course is designed to provide doctoral students with a training experience in the concept and application of multilevel statistical modeling. Students will be motivated to think about correlated and dependent data structures that arise due to sampling design and/or are inherent in the population (such as pupils nested within schools; patients nested within clinics; individuals nested within neighborhoods and so on). The substantive motivation for analyzing such complex data structures would be to make quantitative assessments about the role of contexts (e.g., schools, clinics, neighborhoods) in predicting individual outcomes. In particular, the principles of recognizing and modeling the underlying heterogeneity in average relationships would be emphasized. Linear, non-linear, and multivariate multilevel models will be covered. Upon completion, students should be able to conceptualize multilevel modeling strategies and to undertake empirical, quantitative multilevel research. The course will be lecture-based with substantial hands-on component.
Course Activities: Data management, modeling and analysis; individual assignments; project submission and class participation.
Course Notes: SHH245 or instructor's permission required; this course is a requirement for all SHDH doctoral students. (5.06)


Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2008

Dr. V. S. Sankaranarayanan
5 Credits
Lectures: Two 2-hour sessions each week; One 1-hour lab each week.

This course is designed to provide doctoral students with a training experience in the concept and application of multilevel statistical modeling. Students will be motivated to think about correlated and dependent data structures that arise due to sampling design and/or are inherent in the population (such as pupils nested within schools; patients nested within clinics; individuals nested within neighborhoods and so on). The substantive motivation for analyzing such complex data structures would be to make quantitative assessments about the role of contexts (e.g., schools, clinics, neighborhoods) in predicting individual outcomes. In particular, the principles of recognizing and modeling the underlying heterogeneity in average relationships would be emphasized. Linear, non-linear, and multivariate multilevel models will be covered. Upon completion, students should be able to conceptualize multilevel modeling strategies and to undertake empirical, quantitative multilevel research. The course will be lecture-based with substantial hands-on component.
Course Activities: Data management, modeling and analysis; individual assignments; project submission and class participation.
Course Notes: SHH245 or instructor's permission required; this course is a requirement for all SHDH doctoral students. (5.06)


Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2007

Dr. V. S. Sankaranarayanan
5 Credits
Lectures: Two 2-hour sessions each week; One 1-hour lab each week.

This course is designed to provide doctoral students with a training experience in the concept and application of multilevel statistical modeling. Students will be motivated to think about correlated and dependent data structures that arise due to sampling design and/or are inherent in the population (such as pupils nested within schools; patients nested within clinics; individuals nested within neighborhoods and so on). The substantive motivation for analyzing such complex data structures would be to make quantitative assessments about the role of contexts (e.g., schools, clinics, neighborhoods) in predicting individual outcomes. In particular, the principles of recognizing and modeling the underlying heterogeneity in average relationships would be emphasized. Linear, non-linear, and multivariate multilevel models will be covered. Upon completion, students should be able to conceptualize multilevel modeling strategies and to undertake empirical, quantitative multilevel research. The course will be lecture-based with substantial hands-on component.
Course Activities: Data management, modeling and analysis; individual assignments; project submission and class participation.
Course Notes: SHH245 or instructor's permission required; this course is a requirement for all SHDH doctoral students. (5.06)


Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2006

Dr. S. V. Sankaranarayanan
5 Credits
Lectures: Two 2-hour sessions each week; One 1-hour lab each week.

This course is designed to provide doctoral students with a training experience in the concept and application of multilevel statistical modeling. Students will be motivated to think about correlated and dependent data structures that arise due to sampling design and/or are inherent in the population (such as pupils nested within schools; patients nested within clinics; individuals nested within neighborhoods and so on).. The substantive motivation for analyzing such complex data structures would be to make quantitative assessments about the role of contexts (e.g., schools, clinics, neighborhoods) in predicting individual outcomes. In particular, the principles of recognizing and modeling the underlying heterogeneity in average relationships would be emphasized. Linear, non-linear, and multivariate multilevel models will be covered. Upon completion, students should be able to conceptualize multilevel modeling strategies and to undertake empirical, quantitative multilevel research. The course will be lecture-based with substantial hands-on component.
Course Activities: Data management, modeling and analysis; individual assignments; project submission and class participation.
Course Notes: SHH245 or instructor's permission required; this course is a requirement for all SHDH doctoral students; minimum enrollment of 5 students.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

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