13 classes matched your search criteria.

Spring 2025  |  SOC 8811 Section 001: Advanced Social Statistics (51413)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person
Enrollment Requirements:
Graduate Student
Times and Locations:
Regular Academic Session
 
01/21/2025 - 05/05/2025
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Enrollment Status:
Open (0 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed course information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2025
Class Description:

Many of the questions that we wish to answer in the social sciences address outcomes that are limited and fixed in their answer choices. For example, do Americans agree that Atheists share a common vision of American society? How did the Great Recession affect employment inequalities across racial groups? Who do happy people compare themselves to? Which social class does the child of a blue-collar worker end up in? How frequently do adolescents use marijuana? Questions such as these cannot be appropriately answered using linear regression models, requiring more advanced techniques which will be covered extensively in Soc8811.

This course will focus on applied statistics and primarily deal with regression models in which the dependent variable is categorical: binary, nominal, ordinal, count, etc. As a catalyst for the course, we will consider flexible methods developed for introducing nonlinearities into the linear regression framework. Specific models to be addressed include: logit, probit, generalized ordered logit, multinomial logit, Poisson, negative binomial, zero inflated, fractional response, LOWESS, kernel weighted local polynomial, and mixture models.

Throughout the course, we will address common statistical issues that require special consideration when applied to nonlinear regression models, including: the calculation of predictions, interpretation of coefficients, interaction, and mediation. We will also become familiarized with techniques developed for applied research: model fit, selection, and robustness, joint hypothesis testing, weighting, clustering, and poststratification for complex survey design, and missing data.

Soc8811 covers statistical methods for analyzing social data and is designed for graduate students in the social sciences. Students are assumed to have a background equivalent to Soc5811 and thus have familiarity with linear regression models. The course will be taught in Stata, but students will have the opportunity to instead use R if they prefer.
Learning Objectives:

1. Produce, interpret, and report results from complex statistical models

2. Understand how to apply data analysis to substantive research questions, and effectively present results to a general interest academic audience

3. Develop strategies and competency to conduct future studies of advanced techniques in quantitative methods

4. Build a robust, reproducible workflow to move from raw data to numerical and visual information placed in a final paper.

Grading:
Grading is based on 11 statistical computing assignments
Class Format:
In person, lectures, statistical computing lab.
Workload:
11 Statistical Computing Assignments
Readings include textbook and lecture notes.
Textbooks:
https://bookstores.umn.edu/course-lookup/51413/1253
Instructor Supplied Information Last Updated:
10 November 2022

Spring 2024  |  SOC 8811 Section 001: Advanced Social Statistics (51697)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person
Enrollment Requirements:
Graduate Student
Times and Locations:
Regular Academic Session
 
01/16/2024 - 04/29/2024
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Social Sciences Building 1114
Enrollment Status:
Open (10 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed course information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2024
Class Description:

Many of the questions that we wish to answer in the social sciences address outcomes that are limited and fixed in their answer choices. For example, do Americans agree that Atheists share a common vision of American society? How did the Great Recession affect employment inequalities across racial groups? Who do happy people compare themselves to? Which social class does the child of a blue-collar worker end up in? How frequently do adolescents use marijuana? Questions such as these cannot be appropriately answered using linear regression models, requiring more advanced techniques which will be covered extensively in Soc8811.

This course will focus on applied statistics and primarily deal with regression models in which the dependent variable is categorical: binary, nominal, ordinal, count, etc. As a catalyst for the course, we will consider flexible methods developed for introducing nonlinearities into the linear regression framework. Specific models to be addressed include: logit, probit, generalized ordered logit, multinomial logit, Poisson, negative binomial, zero inflated, fractional response, LOWESS, kernel weighted local polynomial, and mixture models.

Throughout the course, we will address common statistical issues that require special consideration when applied to nonlinear regression models, including: the calculation of predictions, interpretation of coefficients, interaction, and mediation. We will also become familiarized with techniques developed for applied research: model fit, selection, and robustness, joint hypothesis testing, weighting, clustering, and poststratification for complex survey design, and missing data.

Soc8811 covers statistical methods for analyzing social data and is designed for graduate students in the social sciences. Students are assumed to have a background equivalent to Soc5811 and thus have familiarity with linear regression models. The course will be taught in Stata, but students will have the opportunity to instead use R if they prefer.
Learning Objectives:

1. Produce, interpret, and report results from complex statistical models

2. Understand how to apply data analysis to substantive research questions, and effectively present results to a general interest academic audience

3. Develop strategies and competency to conduct future studies of advanced techniques in quantitative methods

4. Build a robust, reproducible workflow to move from raw data to numerical and visual information placed in a final paper.

Grading:
Grading is based on 11 statistical computing assignments
Class Format:
In person, lectures, statistical computing lab.
Workload:
11 Statistical Computing Assignments
Readings include textbook and lecture notes.
Textbooks:
https://bookstores.umn.edu/course-lookup/51697/1243
Instructor Supplied Information Last Updated:
10 November 2022

Spring 2023  |  SOC 8811 Section 001: Advanced Social Statistics (52041)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person
Enrollment Requirements:
Graduate Student
Times and Locations:
Regular Academic Session
 
01/17/2023 - 05/01/2023
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Social Sciences Building 1114
Enrollment Status:
Open (8 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed course information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2023
Class Description:

Many of the questions that we wish to answer in the social sciences address outcomes that are limited and fixed in their answer choices. For example, do Americans agree that Atheists share a common vision of American society? How did the Great Recession affect employment inequalities across racial groups? Who do happy people compare themselves to? Which social class does the child of a blue-collar worker end up in? How frequently do adolescents use marijuana? Questions such as these cannot be appropriately answered using linear regression models, requiring more advanced techniques which will be covered extensively in Soc8811.

This course will focus on applied statistics and primarily deal with regression models in which the dependent variable is categorical: binary, nominal, ordinal, count, etc. As a catalyst for the course, we will consider flexible methods developed for introducing nonlinearities into the linear regression framework. Specific models to be addressed include: logit, probit, generalized ordered logit, multinomial logit, Poisson, negative binomial, zero inflated, fractional response, LOWESS, kernel weighted local polynomial, and mixture models.

Throughout the course, we will address common statistical issues that require special consideration when applied to nonlinear regression models, including: the calculation of predictions, interpretation of coefficients, interaction, and mediation. We will also become familiarized with techniques developed for applied research: model fit, selection, and robustness, joint hypothesis testing, weighting, clustering, and poststratification for complex survey design, and missing data.

Soc8811 covers statistical methods for analyzing social data and is designed for graduate students in the social sciences. Students are assumed to have a background equivalent to Soc5811 and thus have familiarity with linear regression models. The course will be taught in Stata, but students will have the opportunity to instead use R if they prefer.
Learning Objectives:

1. Produce, interpret, and report results from complex statistical models

2. Understand how to apply data analysis to substantive research questions, and effectively present results to a general interest academic audience

3. Develop strategies and competency to conduct future studies of advanced techniques in quantitative methods

4. Build a robust, reproducible workflow to move from raw data to numerical and visual information placed in a final paper.

Grading:
Grading is based on 11 statistical computing assignments
Class Format:
In person, lectures, statistical computing lab.
Workload:
11 Statistical Computing Assignments
Readings include textbook and lecture notes.
Textbooks:
https://bookstores.umn.edu/course-lookup/52041/1233
Instructor Supplied Information Last Updated:
10 November 2022

Spring 2022  |  SOC 8811 Section 001: Advanced Social Statistics (52848)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Enrollment Requirements:
Graduate Student
Times and Locations:
Regular Academic Session
 
01/18/2022 - 05/02/2022
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Blegen Hall 210
Enrollment Status:
Open (5 of 12 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed course information: http://classinfo.umn.edu/?knoke001+SOC8811+Spring2022
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, and multilevel modeling or structural equation models.
Grading:
3 data analysis papers on the three topics, each 33.3% of the course grade.
Exam Format:
No exams
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Data Analysis Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/52848/1223
Past Syllabi:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
17 September 2018

Spring 2021  |  SOC 8811 Section 001: Advanced Social Statistics (48772)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
Partially Online
Enrollment Requirements:
Graduate Student
Times and Locations:
Regular Academic Session
 
01/19/2021 - 05/03/2021
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Blegen Hall 115
 
01/19/2021 - 05/03/2021
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
UMN ONLINE-HYB
Enrollment Status:
Closed (12 of 12 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
6 seats reserved for Sociology grad students. Some students will be physically present for this in person grad class. The rest will be online synchronous at the scheduled class times via zoom. Click this link for more detailed information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2021
Class Description:

Many of the questions that we wish to answer in the social sciences address outcomes that are limited and fixed in their answer choices. For example, do Americans agree that Atheists share a common vision of American society? How did the Great Recession affect employment inequalities across racial groups? Who do happy people compare themselves to? Which social class does the child of a blue-collar worker end up in? How frequently do adolescents use marijuana? Questions such as these cannot be appropriately answered using linear regression models, requiring more advanced techniques which will be covered extensively in Soc8811.

This course will focus on applied statistics and primarily deal with regression models in which the dependent variable is categorical: binary, nominal, ordinal, count, etc. As a catalyst for the course, we will consider flexible methods developed for introducing nonlinearities into the linear regression framework. Specific models to be addressed include: logit, probit, generalized ordered logit, multinomial logit, Poisson, negative binomial, zero inflated, fractional response, LOWESS, kernel weighted local polynomial, and mixture models.

Throughout the course, we will address common statistical issues that require special consideration when applied to nonlinear regression models, including: the calculation of predictions, interpretation of coefficients, interaction, and mediation. We will also become familiarized with techniques developed for applied research: model fit, selection, and robustness, joint hypothesis testing, weighting, clustering, and poststratification for complex survey design, and missing data.

Soc8811 covers statistical methods for analyzing social data and is designed for graduate students in the social sciences. Students are assumed to have a background equivalent to Soc5811 and thus have familiarity with linear regression models. The course will be taught in Stata, but students will have the opportunity to instead use R if they prefer.
Learning Objectives:

1. Produce, interpret, and report results from complex statistical models

2. Understand how to apply data analysis to substantive research questions, and effectively present results to a general interest academic audience

3. Develop strategies and competency to conduct future studies of advanced techniques in quantitative methods

4. Build a robust, reproducible workflow to move from raw data to numerical and visual information placed in a final paper.

Textbooks:
https://bookstores.umn.edu/course-lookup/48772/1213
Instructor Supplied Information Last Updated:
1 November 2019

Spring 2020  |  SOC 8811 Section 001: Advanced Social Statistics (52265)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/21/2020 - 05/04/2020
Tue, Thu 02:30PM - 03:45PM
UMTC, West Bank
Social Sciences Building 1114
Enrollment Status:
Closed (16 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2020
Class Description:

Many of the questions that we wish to answer in the social sciences address outcomes that are limited and fixed in their answer choices. For example, do Americans agree that Atheists share a common vision of American society? How did the Great Recession affect employment inequalities across racial groups? Who do happy people compare themselves to? Which social class does the child of a blue-collar worker end up in? How frequently do adolescents use marijuana? Questions such as these cannot be appropriately answered using linear regression models, requiring more advanced techniques which will be covered extensively in Soc8811.

This course will focus on applied statistics and primarily deal with regression models in which the dependent variable is categorical: binary, nominal, ordinal, count, etc. As a catalyst for the course, we will consider flexible methods developed for introducing nonlinearities into the linear regression framework. Specific models to be addressed include: logit, probit, generalized ordered logit, multinomial logit, Poisson, negative binomial, zero inflated, fractional response, LOWESS, kernel weighted local polynomial, and mixture models.

Throughout the course, we will address common statistical issues that require special consideration when applied to nonlinear regression models, including: the calculation of predictions, interpretation of coefficients, interaction, and mediation. We will also become familiarized with techniques developed for applied research: model fit, selection, and robustness, joint hypothesis testing, weighting, clustering, and poststratification for complex survey design, and missing data.

Soc8811 covers statistical methods for analyzing social data and is designed for graduate students in the social sciences. Students are assumed to have a background equivalent to Soc5811 and thus have familiarity with linear regression models. The course will be taught in Stata, but students will have the opportunity to instead use R if they prefer.
Learning Objectives:

1. Produce, interpret, and report results from complex statistical models

2. Understand how to apply data analysis to substantive research questions, and effectively present results to a general interest academic audience

3. Develop strategies and competency to conduct future studies of advanced techniques in quantitative methods

4. Build a robust, reproducible workflow to move from raw data to numerical and visual information placed in a final paper.

Textbooks:
https://bookstores.umn.edu/course-lookup/52265/1203
Instructor Supplied Information Last Updated:
1 November 2019

Spring 2019  |  SOC 8811 Section 001: Advanced Social Statistics (52399)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/22/2019 - 05/06/2019
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Enrollment Status:
Open (10 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed information: http://classinfo.umn.edu/?knoke001+SOC8811+Spring2019
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, and multilevel modeling or structural equation models.
Grading:
3 data analysis papers on the three topics, each 33.3% of the course grade.
Exam Format:
No exams
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Data Analysis Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/52399/1193
Past Syllabi:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
17 September 2018

Spring 2018  |  SOC 8811 Section 001: Advanced Social Statistics (49141)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/16/2018 - 05/04/2018
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Enrollment Status:
Open (12 of 15 seats filled)
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed information: http://classinfo.umn.edu/?knoke001+SOC8811+Spring2018
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, structural equation models.
Grading:
3 data analysis papers on the three topics, each 33.3% of the course grade.
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/49141/1183
Past Syllabi:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
12 October 2015

Spring 2017  |  SOC 8811 Section 001: Advanced Social Statistics (49563)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/17/2017 - 05/05/2017
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: recommend 5811 or equiv; graduate student or instr consent
Class Notes:
Click this link for more detailed information: http://classinfo.umn.edu/?knoke001+SOC8811+Spring2017
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, structural equation models.
Grading:
3 data analysis papers on the three topics, each 33.3% of the course grade.
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/49563/1173
Past Syllabi:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
12 October 2015

Spring 2016  |  SOC 8811 Section 001: Advanced Social Statistics (47551)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
Regular Academic Session
 
01/19/2016 - 05/06/2016
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: 5811 or equiv, grad soc major or instr consent
Class Notes:
Click this link for more detailed information http://classinfo.umn.edu/?knoke001+SOC8811+Spring2016
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, structural equation models.
Grading:
3 data analysis papers on the three topics, each 33.3% of the course grade.
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/47551/1163
Syllabus:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf
Instructor Supplied Information Last Updated:
12 October 2015

Spring 2015  |  SOC 8811 Section 001: Advanced Social Statistics (47392)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
Regular Academic Session
 
01/20/2015 - 05/08/2015
Mon, Wed 01:00PM - 02:15PM
UMTC, West Bank
Social Sciences Building 1114
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers. prereq: 5811 or equiv, grad soc major or instr consent
Class Description:
Statistical methods for analyzing social data. This course is designed for Sociology graduate students and assumes a background equivalent to Soc 5811 Intermediate Social Statistics. The class will be comprised primarily of introduction to modern statistical techniques such as categorical data analysis (e.g., logistic regression), time series analysis (e.g., event history analysis), and modern computational statistics (e.g., monte carlo tests). Labs are organized to help students with the data analysis required to complete the weekly exercises, develop the term paper, and to further training in statistical software used by social science researchers.
Grading:
Other Grading Information: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.
Class Format:
70% Lecture
30% Laboratory
Workload:
5-15 Pages Reading Per Week Other Workload: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.
Textbooks:
https://bookstores.umn.edu/course-lookup/47392/1153
Instructor Supplied Information Last Updated:
18 April 2013

Spring 2014  |  SOC 8811 Section 001: Advanced Social Statistics (52128)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
Regular Academic Session
 
01/21/2014 - 05/09/2014
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers.
Class Description:
Statistical methods for analyzing social data. This course is designed for Sociology graduate students and assumes a background equivalent to Soc 5811 Intermediate Social Statistics. The class will be comprised primarily of introduction to modern statistical techniques such as categorical data analysis (e.g., logistic regression), time series analysis (e.g., event history analysis), and modern computational statistics (e.g., monte carlo tests). Labs are organized to help students with the data analysis required to complete the weekly exercises, develop the term paper, and to further training in statistical software used by social science researchers.
Grading:
Other Grading Information: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.
Class Format:
70% Lecture
30% Laboratory
Workload:
5-15 Pages Reading Per Week Other Workload: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.
Textbooks:
https://bookstores.umn.edu/course-lookup/52128/1143
Instructor Supplied Information Last Updated:
18 April 2013

Spring 2013  |  SOC 8811 Section 001: Advanced Social Statistics (47131)

Instructor(s)
Class Component:
Lecture
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Class Attributes:
Delivery Medium
Times and Locations:
Regular Academic Session
 
01/22/2013 - 05/10/2013
Tue, Thu 11:15AM - 12:30PM
UMTC, West Bank
Social Sciences Building 1114
Also Offered:
Course Catalog Description:
Statistical methods for analyzing social data. Sample topics: advanced multiple regression, logistic regression, limited dependent variable analysis, analysis of variance and covariance, log-linear models, structural equations, and event history analysis. Applications to datasets using computers.
Class Description:
Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, structural equation models.
Grading:
100% Reports/Papers
Class Format:
60% Lecture
10% Discussion
30% Laboratory
Workload:
12 Pages Reading Per Week
40 Pages Writing Per Term
3 Paper(s)
Textbooks:
https://bookstores.umn.edu/course-lookup/47131/1133
Past Syllabi:
http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf (Spring 2016)
Instructor Supplied Information Last Updated:
7 November 2011

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