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.

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.

11 Statistical Computing Assignments

Readings include textbook and lecture notes.

","","","10 November 2022"
"51697","SOC","Sociology","8811","001","Advanced Social Statistics","Tom VanHeuvelen","Lecture","","","No Special Consent Required","In Person","","Graduate Student","","January 16, 2024 through April 29, 2024 Tuesday, Thursday 02:30PM - 03:45PM TCWESTBANK SocSci 1114","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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","Click this link for more detailed course information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2024
","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.

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.

11 Statistical Computing Assignments

Readings include textbook and lecture notes.

","","","10 November 2022"
"52041","SOC","Sociology","8811","001","Advanced Social Statistics","Tom VanHeuvelen","Lecture","","","No Special Consent Required","In Person","","Graduate Student","","January 17, 2023 through May 1, 2023 Tuesday, Thursday 02:30PM - 03:45PM TCWESTBANK SocSci 1114","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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","Click this link for more detailed course information: http://classinfo.umn.edu/?tvanheuv+SOC8811+Spring2023
","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.

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.

11 Statistical Computing Assignments

Readings include textbook and lecture notes.

","","","10 November 2022"
"52848","SOC","Sociology","8811","001","Advanced Social Statistics","David Knoke","Lecture","","","No Special Consent Required","In Person Term Based","","Graduate Student","","January 18, 2022 through May 2, 2022 Tuesday, Thursday 02:30PM - 03:45PM TCWESTBANK BlegH 210","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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","Click this link for more detailed course information: http://classinfo.umn.edu/?knoke001+SOC8811+Spring2022
","Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, and multilevel modeling or structural equation models.","","","3 data analysis papers on the three topics, each 33.3% of the course grade.","No exams","60% Lecture
10% Discussion
30% Laboratory","12 Pages Reading Per Week
40 Pages Writing Per Term
3 Data Analysis Paper(s)","","http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf ","17 September 2018"
"48772","SOC","Sociology","8811","001","Advanced Social Statistics","Tom VanHeuvelen","Lecture","","","No Special Consent Required","Partially Online","","Graduate Student","","January 19, 2021 through May 3, 2021 Tuesday, Thursday 02:30PM - 03:45PM TCWESTBANK BlegH 115
January 19, 2021 through May 3, 2021 Tuesday, Thursday 02:30PM - 03:45PM TCWESTBANK . ONLINE-HYB","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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","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
","1. Produce, interpret, and report results from complex statistical models

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

Delivery Medium

","","","January 20, 2015 through May 8, 2015 Monday, Wednesday 01:00PM - 02:15PM TCWESTBANK SocSci 1114","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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","","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.","","","Other Grading Information: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.","","70% Lecture
30% Laboratory","5-15 Pages Reading Per Week
Other Workload: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.","","","18 April 2013"
"52128","SOC","Sociology","8811","001","Advanced Social Statistics","Zack Almquist","Lecture","","","No Special Consent Required","In Person Term Based","Delivery Medium

","","","January 21, 2014 through May 9, 2014 Tuesday, Thursday 11:15AM - 12:30PM TCWESTBANK SocSci 1114","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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.","","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.","","","Other Grading Information: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.","","70% Lecture
30% Laboratory","5-15 Pages Reading Per Week
Other Workload: weekly/bi-weekly assignments, 1 take-home exam, 1 research paper.","","","18 April 2013"
"47131","SOC","Sociology","8811","001","Advanced Social Statistics","David Knoke","Lecture","","","No Special Consent Required","In Person Term Based","Delivery Medium

","","","January 22, 2013 through May 10, 2013 Tuesday, Thursday 11:15AM - 12:30PM TCWESTBANK SocSci 1114","Spring 2025, Spring 2024, Spring 2023, Spring 2022","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.","","Statistical methods for analyzing social data. Topics for Spring 2012: logistic regression, event history analysis, structural equation models.","","","100% Reports/Papers","","60% Lecture
10% Discussion
30% Laboratory","12 Pages Reading Per Week
40 Pages Writing Per Term
3 Paper(s)","","http://classinfo.umn.edu/syllabi/knoke001_SOC8811_Spring2016.pdf ","7 November 2011"