Course Descriptions

The Course Descriptions catalog describes all undergraduate and graduate courses offered by Michigan State University. The searches below only return course versions Fall 2000 and forward. Please refer to the Archived Course Descriptions for additional information.

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Course Descriptions: Search Results

SOC 882 Analysis of Social Data II

Description:
Applications of multiple regression, path analysis, and factor analysis to social science data. Interpretation, strengths, and weaknesses of commonly used statistical practices.
Effective Dates:
FS95 - US04

SOC 882 Analysis of Social Data II

Semester:
Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:2   Lab Hours: 2
Recommended Background:
SOC 881
Restrictions:
Open only to graduate students in the College of Social Science.
Description:
Applications of multiple regression, path analysis, and factor analysis to social science data. Interpretation, strengths, and weaknesses of commonly used statistical practices.
Effective Dates:
FS04 - FS06

SOC 882 Analysis of Social Data II

Semester:
Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:2   Lab Hours: 2
Recommended Background:
SOC 881
Restrictions:
Open to graduate students in the Department of Sociology.
Description:
Applications of multiple regression, path analysis, and factor analysis to social science data. Interpretation, strengths, and weaknesses of commonly used statistical practices.
Effective Dates:
SS07 - US26

SOC 882 Analysis of Social Data II

Semester:
Spring of every year
Credits:
Total Credits: Credits: 3   Lecture/Recitation/Discussion Hours:2   Lab Hours: 2
Recommended Background:
SOC 881
Restrictions:
Open to graduate students in the Department of Sociology.
Description:
Applications of multiple regression, path analysis, and factor analysis to social science data. Interpretation, strengths, and weaknesses of commonly used statistical practices. Consideration of sociodemographic characteristics (e.g., race, ethnicity, sex, gender, class, nationality, age, weight/size, disability, sexuality) in the analysis of social science data.
Effective Dates:
FS26 - Open