Daniel A. Powers

Desription:

This is an introductory course in statistics for undergraduate majors in sociology.  The basics of descriptive and inferential statistics and quantitative reasoning will be covered.  Descriptive statistics involves organizing and summarizing important characteristics of the data.  Statistical inference involves making informed guesses about the unknown characteristics of a population based on the known characteristics of a sample. Students are expected to know how to carryout elementary mathematical operations.

Required Text:

R. Johnson and P. Kuby (2012) STAT, 2e. Cengage Learning ISBN-10: 0538733500  ISBN-13: 978-0-538-73841-5  (available from http://books.google.com)

Course Requirement:

Exams: There will be 4 in-class examinations graded on a 100 point scale. The lowest exam grade will be dropped.  Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions.  Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

In-Class Assessments: There will be approximately 20 in-class exercises carried out at various points during the course to assess understanding of current topics. These will count 100 points towards the total grade.

Course Description

 This course deals with regression models for discrete and categorical dependent variables. Regression-like models for discrete and categorical outcomes are widely used in applied research. Students in this course should have some prior exposure to linear regression models. This class serves a wide range of students; the material presented here aims to be useful for students at all levels. In keeping with the applied nature of this course, we will provide examples drawn mainly from sociological and demographic research.

Course Requirements

 Grades are based on scores from approximately 5 assignments or problem sets (50%) and a 15-20 page (double spaced) methodologically-focused research paper (50%). Students who are enrolled in the MS in Mathematical Statistics program must complete the extra credit problems in the assignments. Paper proposals must be submitted for approval midway through the course. In lieu of a substantive paper, students may undertake an in-depth exploration of any methodological issues pertaining to the analysis of categorical data (i.e., a methods or applied statistics paper).

Topics

 Topics covered in this course will include:

• an overview of the classical linear regression model
• models for binary data
• models for count data and contingency tables
• models for ordered and unordered categorical data

Extensions to the models above will also be examined, such as hierarchical/multilevel models for categorical responses, as well as treatment-effect and selection models.

Required Text

Powers, Daniel A., and Yu Xie (2008) Statistical Methods for Categorical Data Analysis, 2nd Edition, London: Emerald.

Recommended Text

Long, J. Scott, and Jeremy Freese (2001/2005) Regression Models for Categorical Dependent Variables Using Stata, College Station: Stata Press. 

Description:

This is an introductory course in statistics for undergraduate majors in sociology.  The basics of descriptive and inferential statistics and quantitative reasoning will be covered.  Descriptive statistics involves organizing and summarizing important characteristics of the data.  Statistical inference involves making informed guesses about the unknown characteristics of a population based on the known characteristics of a sample. Students are expected to know how to carryout elementary mathematical operations.

 Required Text:

R. Johnson and P. Kuby (2012) STAT, 2e. Cengage Learning ISBN-10: 0538733500  ISBN-13: 978-0-538-73841-5  (available from http://books.google.com)

Course Rrequirements:

 Exams: There will be 4 in-class examinations graded on a 100 point scale. The lowest exam grade will be dropped.  Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions.  Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

In-Class Assessments: There will be approximately 20 in-class exercises carried out at various points during the course to assess understanding of current topics. These will count 100 points towards the total grade.

DESCRIPTION

This is an introductory course in statistics for undergraduate majors in sociology.  The basics of descriptive and inferential statistics and quantitative reasoning will be covered.  Descriptive statistics involves organizing and summarizing important characteristics of the data.  Statistical inference involves making informed guesses about the unknown characteristics of a population based on the known characteristics of a sample. Students are expected to know how to carryout elementary mathematical operations.

REQUIRED TEXT

 R. Johnson and P. Kuby (2012) STAT, 2e. Cengage Learning ISBN-10: 0538733500  ISBN-13: 978-0-538-73841-5  (available from http://books.google.com)

COURSE REQUIREMENTS

There will be 3 in-class examinations graded on a 100 point scale, and a comprehensive final exam worth 100 points. Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions.  Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

This is a course in statistical methods for longitudinal data analysis. We will cover two main content areas:  multiple regression models for data collected on the same subjects over time (repeated measures/panel data), and methods for modeling event occurrences over time.

 Multilevel/Hierarchical Models for Change

The first half of the course introduces multilevel models for change (i.e., growth curve models), which are appropriate for the analysis of change in a continuous dependent variable over time. Given this general exposure to the topic, students should be able to apply multilevel modeling techniques to any substantive research problem. We will review latent linear growth curve models from the perspective of structural equation modeling (SEM). Growth curve models for categorical outcomes (counts), as well as nonlinear growth curve models, will also be discussed.

 Event History Analysis

The second half of this course deals with event history analysis (i.e., survival analysis, hazard models, etc.), which is a technique for modeling the probability of a transition from one status (or state) to another. We will focus on discrete time and continuous time models that make few assumptions regarding time dependence of the hazard (i.e., semiparametric methods, such as the piecewise constant exponential and the Cox proportional hazard model). We will focus mainly on single transition models.

Students should have taken a previous course in linear regression.

We will use a combination of textbook and handouts posted to Bb.

Textbook

J. D. Singer, and J. B. Willett (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press.

Evaluation

This is an applied course. We will learn by applying the techniques learned in class to specific pedagogical examples in approximately 5 assignments.  No previous programming experience is required. We will use several statistical packages in this course depending on the problem: Stata, SAS, and R. Examples will be provided in each package.

DESCRIPTION

This is an introductory course in statistics for undergraduate majors in sociology.  The basics of descriptive and inferential statistics and quantitative reasoning will be covered.  Descriptive statistics involves organizing and summarizing important characteristics of the data.  Statistical inference involves making informed guesses about the unknown characteristics of a population based on the known characteristics of a sample. Students are expected to know how to carryout elementary mathematical operations.

REQUIRED TEXT

 R. Johnson and P. Kuby (2012) STAT, 2e. Cengage Learning ISBN-10: 0538733500  ISBN-13: 978-0-538-73841-5  (available from http://books.google.com)

 COURSE REQUIREMENTS

There will be 3 in-class examinations graded on a 100 point scale, and a comprehensive final exam worth 100 points. Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions.  Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

Cross listed with SSC 385

Course Description

This course deals with regression models for discrete and categorical dependent variables. Regression-like models for discrete and categorical outcomes are widely used in applied research. Students in this course should have some prior exposure to linear regression models. This class serves a wide range of students; the material presented here aims to be useful for students at all levels. In keeping with the applied nature of this course, we will provide examples drawn mainly from sociological and demographic research.

Course Requirements

 Grades are based on scores from approximately 5 assignments or problem sets (50%) and a 15-20 page (double spaced) methodologically-focused research paper (50%). Students who are enrolled in the MS in Mathematical Statistics program must complete the extra credit problems in the assignments. Paper proposals must be submitted for approval no later than Nov. 15. In lieu of a substantive paper, students may undertake an in-depth exploration of any methodological issues pertaining to the analysis of categorical data (i.e., a methods or applied statistics paper).

Topics

Topics covered in this course will include:

• an overview of the classical linear regression model
• models for binary data
• models for count data and contingency tables
• models for ordered and unordered categorical data

Extensions to the models above will also be examined, such as hierarchical/multilevel models for categorical responses, as well as treatment-effect and selection models.

Recommended Texts

Powers, Daniel A., and Yu Xie (2008) Statistical Methods for Categorical Data Analysis, 2nd Edition, London: Emerald.

Long, J. Scott, and Jeremy Freese (2001/2005) Regression Models for Categorical Dependent Variables Using Stata, College Station: Stata Press. 

DESCRIPTION

This is an introductory course in statistics for undergraduate majors in sociology.  The basics of descriptive and inferential statistics and quantitative reasoning will be covered.  Descriptive statistics involves organizing and summarizing important characteristics of the data.  Statistical inference involves making informed guesses about the unknown characteristics of a population based on the known characteristics of a sample. Students are expected to know how to carryout elementary mathematical operations.

REQUIRED TEXT

R. Johnson and P. Kuby (2012) STAT, 2e. Cengage Learning ISBN-10: 0538733500  ISBN-13: 978-0-538-73841-5  (available from http://books.google.com)

COURSE REQUIREMENTS

There will be 3 in-class examinations graded on a 100 point scale, and a comprehensive final exam worth 100 points. Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions.  Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

This is a course in statistical methods for longitudinal data analysis. We will cover two main content areas:  multiple regression models for data collected on the same subjects over time (repeated measures/panel data), and methods for modeling event occurrences over time.

 Multilevel/Hierarchical Models for Change

The first half of the course introduces multilevel models for change (i.e., growth curve models), which are appropriate for the analysis of change in a continuous dependent variable over time. Given this general exposure to the topic, students should be able to apply multilevel modeling techniques to any substantive research problem. We will review latent linear growth curve models from the perspective of structural equation modeling (SEM). Growth curve models for categorical outcomes (counts), as well as nonlinear growth curve models, will also be discussed.

 Event History Analysis

The second half of this course deals with event history analysis (i.e., survival analysis, hazard models, etc.), which is a technique for modeling the probability of a transition from one status (or state) to another. We will focus on discrete time and continuous time models that make few assumptions regarding time dependence of the hazard (i.e., semiparametric methods, such as the piecewise constant exponential and the Cox proportional hazard model). We will focus mainly on single transition models.

Students should have taken a previous course in linear regression.

We will use a combination of textbook and handouts posted to Bb.

 Textbook

J. D. Singer, and J. B. Willett (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press.

Evaluation

This is an applied course. We will learn by applying the techniques learned in class to specific pedagogical examples in approximately 5 assignments.  No previous programming experience is required. We will use several statistical packages in this course depending on the problem: Stata, SAS, and R. Examples will be provided in each package.

Professor:  Dr. Dan Powers

This course covers basic statistical methods and concepts in the social sciences. It is intended to give graduate students a foundation in quantitative sociological methods in preparation for more advanced quantitative methods courses in sociology and other fields. Topics include: frequency and probability distributions, probability theory, random variables, sampling distributions, estimation, and inference. The first half of the course deals primarily with methods for descriptive statistics and the theoretical foundations of inference. The second half of the course focuses on statistical techniques and various applications including the use of t-tests for comparing means and proportions, Analysis of Variance (ANOVA) for understanding the relationship between categorical factors and a continuous dependent variable, contingency tables and measures of association for categorical and ordinal data, and simple and multiple regression techniques for the analysis of the relationship between continuous independent variables on a continuous dependent variable.

 Prerequisite

 A previous statistics course is required.

 Required Texts

 Agresti, Alan and Barbara Finlay (2009) Statistical Methods for the Social Sciences, 4th Edition, Pearson ISBN-10: 0130272957 ISBN-13:  9780130272959.

Lawrence C. Hamilton (2009) Statistics with STATA, Cengage ISBN-10: 0-495-55786-2 ISBN-13: 978-0-495-55786-9

 Reserve Readings

William Hays (1963-1994), William Hays (1st-5th Editions) (on  reserve at PCL).

Course Requirements and Grading

Exams: There will be two in-class exams and a take-home final exam. Each in-class exam covers only the material since the previous exam.  The final exam is a comprehensive exam, covering material drawn mainly from the second half of the course and focusing on statistical applications.

Assignments:  Students will be required to learn how to manipulate statistical formulas and to work with Stata. Homework will be assigned on a bi-weekly basis. Assignments, exams, and the final are weighted as follows: Homework assignments 15%, First Exam 25%, Midterm Exam 25% and Final Exam 35%.

Course Description

 This course deals with regression models for discrete and categorical dependent variables. Regression-like models for discrete and categorical outcomes are widely used in applied research. Students in this course should have some prior exposure to linear regression models. This class serves a wide range of students; the material presented here aims to be useful for students at all levels. In keeping with the applied nature of this course, we will provide examples drawn mainly from sociological and demographic research.

 Course Requirements

Grades are based on scores from approximately 5 assignments or problem sets (50%) and a 15-20 page (double spaced) methodologically-focused research paper (50%). Students who are enrolled in the MS in Mathematical Statistics program must complete the extra credit problems in the assignments. Paper proposals must be submitted for approval no later than Nov. 15. In lieu of a substantive paper, students may undertake an in-depth exploration of any methodological issues pertaining to the analysis of categorical data (i.e., a methods or applied statistics paper).

 Topics

 Topics covered in this course will include:

• an overview of the classical linear regression model
• models for binary data
• models for count data and contingency tables
• models for ordered and unordered categorical data

Extensions to the models above will also be examined, such as hierarchical/multilevel models for categorical responses, as well as treatment-effect and selection models.

 Recommended Texts

Powers, Daniel A., and Yu Xie (2008) Statistical Methods for Categorical Data Analysis, 2nd Edition, London: Emerald.

 Long, J. Scott, and Jeremy Freese (2001/2005) Regression Models for Categorical Dependent Variables Using Stata, College Station: Stata Press. 

Description:

This is an introductory course in statistics for undergraduate majors in sociology. The basics of descriptive and inferential statistics and general quantitative reasoning will be covered. Descriptive statistics involves organizing and summarizing important characteristics of the data. Statistical inference involves making informed guesses about the unknown characteristics of some population based on the known characteristics of a sample. Students are expected to have the ability to carry out elementary mathematical operations.

 Required Text:

R. Johnson and P. Kuby, Just the Essentials: of Elementary Statistics, 10th Edition (2007)

 Grading Policy:

Exams: There will be 3 in-class examinations graded on a 100 point scale, and a comprehensive final exam worth 100 points. Roughly 75% to 90% of the points on the examinations are accounted for by problems requiring the student to work toward a solution, with the remainder accounted for by true and false or multiple choice questions. Examinations will be based entirely on topics covered in lectures. In-class examinations are non-cumulative; they cover only the material since the previous exam. Students must take all exams to pass the course. Make up exams will be given only in the case of documented emergencies or illness.

Problems: There will be 5 problem sets worth a total of 200 points. Problem sets include material from the book as well as handout problems. Problem sets must be received in class no later than the dates indicated. No credit will be given for assignments turned in late.

Cross listed with SSC 385

PREREQUISITES:  SOC384L or the equivalent (i.e., a prior graduate statistics course.)

OBJECTIVES:  This course is oriented towards second semester graduate students in sociology. It provides an introduction to the use of multiple regression and related models.  We will seek a balance between theory and practice.  We will consider the basic statistical concepts necessary to apply these models, but we will not emphasize mathematical derivations and statistical theory per se.  After completing this course, students should have enough knowledge to understand the main ideas and issues involved in most quantitative research articles in the major sociological journals. They should also be much better prepared to complete a major quantitative research project of their own or enroll in more advanced statistics courses. 

COMPUTER:  Several homework assignments will require the use of a computer and statistical software.  Students may use any computer and statistical software to solve the homework problems but instruction will be provided in Stata.

TEXTBOOK: Applied Regression Analysis and Generalized Linear Models by John Fox

REQUIREMENTS AND GRADING:  Homework will be due approximately every 2 weeks. There will be 7 homework assignments during the semester, but the lowest score will be automatically dropped (only the best 6 scores will be counted).  There will also be a midterm exam and a final exam.  In determining course grades, homework will count 120 points; the midterm exam will count 100 points; and the final exam will count 180 points.  Course grades will be assigned as follows (out of a total of 400 points for the course):  A 400-380; A- 379-360; B+ 359-340; B 339-320; B- 319-300; C+ 299-280; C 279-260; C- 259-240; D 239-220; F 219-0.  I will give up to 5 bonus (i.e., additional or extra) points at the end of the semester for students with consistent and positive class participation. The final exam will be held during the regularly scheduled final examination time as determined for this class by the University.  The exams are open-book but closed-computer.

TOPICS: introduction, simple regression, least squares estimation, mathematical assumptions of regression model, statistical inference, properties of estimators, MLE, multiple regression, dummy variables, interactions, analysis of covariance, analysis of variance, regression diagnostics, multicollinearity, heteroscedasticity, autocorrelation, generalized least squares, transformations, nonlinearities, generalized linear models.

Cross listed with SSC 385

This is a course in statistical methods for longitudinal data analysis. We will cover two main content areas:  multiple regression models for data collected on the same subjects over time (repeated measures/panel data), and methods for modeling event occurrences over time.

Multilevel/Hierarchical Models for Change

The first half of the course introduces multilevel models for change (i.e., growth curve models), which are appropriate for the analysis of change in a continuous dependent variable over time. Given this general exposure to the topic, students should be able to apply multilevel modeling techniques to any substantive research problem. We will review latent linear growth curve models from the perspective of structural equation modeling (SEM). Growth curve models for categorical outcomes (counts), as well as nonlinear growth curve models, will also be discussed.

Event History Analysis

The second half of this course deals with event history analysis (i.e., survival analysis, hazard models, etc.), which is a technique for modeling the probability of a transition from one status (or state) to another. We will focus on discrete time and continuous time models that make few assumptions regarding time dependence of the hazard (i.e., semiparametric methods, such as the piecewise constant exponential and the Cox proportional hazard model). We will focus mainly on single transition models.

Students should have taken a previous course in linear regression.

We will use a combination of textbook and handouts posted to Bb.

Textbook

J. D. Singer, and J. B. Willett (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford University Press.

Evaluation

This is an applied course. We will learn by applying the techniques learned in class to specific pedagogical examples in approximately 5 assignments.  No previous programming experience is required. We will use several statistical packages in this course depending on the problem: Stata, SAS, and R. Examples will be provided in each package.

Course Description

This course provides an overview of the major sociological approaches to the study of social stratification and inequality. We begin with an examination of the concepts of social stratification social inequality, with an emphasis on the major dimensions of stratification in the U.S. Next we examine the major theoretical traditions that form the basis for contemporary class analysis. We then focus on the distribution of income and wealth in the U.S. over the past 60 years and look at recent changes in this distribution and various explanations for change. We discuss the major class divisions in the U.S. and examine effects of transformation of the U.S. economy on particular classes. Next we examine forms and processes of stratification, with a focus on patterns of social mobility and differences in these processes and outcomes by race/ethnicity and gender, and over time. The ability to process a significant amount of quantitative information is desirable. A previous course in statistics is highly recommended but not required.

Course Objectives

Social inequality has increased over the past half century to levels unseen since the gilded age of the 1920s when inequality in the U.S. reached a peak. This class aims to inform students about the nature of social inequality and the social class hierarchies that exist in the contemporary U.S., with an emphasis on the contributions of sociologists to the theoretical understanding and major explanations of social inequality. Students will gain a theoretically and empirically grounded sense of the major dimensions of social inequality and stratification in the U.S. and the processes that have shaped trends in social inequality over the past 60 years. Lectures will provide opportunities for discussions centered on specific questions, scenarios, and current newsworthy items relating to social inequality and social stratification in the U.S. Students should gain a perspective about the relevance of social stratification in a period of increasingly unequal rewards and high levels of socioeconomic uncertainty.

Required Texts

Marger, Martin N. (2010) Social Stratification: Patterns and Processes (5th Edition).  Boston, MA.: McGraw Hill.

Gilbert, Dennis (2010) The American Class Structure: In an Age of Growing Inequality (8th Edition). Los Angeles, CA: Pine Forge Press.

Supplemental Readings

Additional reading materials and links to current-event topics in inequality will be posted to Blackboard.

Meets with SSC 385

Course Description
This course deals with regression models for discrete and categorical dependent variables. Regression-like models for discrete and categorical outcomes are widely used in applied research. Students in this course should have some prior exposure to linear regression models. This class serves a wide range of students; the material presented here aims to be useful for students at all levels. Most of the material in the exercises and handouts is based on applied, as opposed to theoretical (i.e., mathematical statistical) problems. In keeping with the applied nature of this course, we will provide examples drawn mainly from sociological and demographic research.

Course Requirements
Grades are based on scores from approximately 5 assignments or problem sets (50%) and a 15-20 page (double spaced) methodologically-focused research paper (50%). Students who are enrolled in the MS in Mathematical Statistics program must complete the extra credit problems in the assignments. Paper proposals must be submitted for approval no later than Nov. 15. In lieu of a substantive paper, students may undertake an in-depth exploration of any methodological issues pertaining to the analysis of categorical data (i.e., a methods or applied statistics paper).

Topics
Topics covered in this course will include:
•    an overview of the classical linear regression model
•    models for binary data
•    models for count data and contingency tables
•    models for ordered and unordered categorical data
Extensions to the models above will also be examined, such as hierarchical/multilevel models for categorical responses, as well as treatment-effect and selection models.

Recommended Texts
Powers, Daniel A., and Yu Xie (2008) Statistical Methods for Categorical Data Analysis, 2nd Edition, London: Emerald.
Long, J. Scott, and Jeremy Freese (2001/2005) Regression Models for Categorical

Please see attached file

Instructor: Dan Powers (e-mail: dpowers@mail.la.utexas.edu)

Oce Hours: BUR 502 MWF 2:30{3:30PM, and by appointment (232-6335)

Course Description

This course overviews the major sociological approaches to the study of social stratication and

inequality. We begin with an examination of the concepts of social stratication social inequality,

with an emphasis on the major dimensions of stratication in the U.S. Next we examine the major

theoretical traditions that form the basis for contemporary class analysis. We then focus on the

distribution of income and wealth in the U.S. over the past 60 years and look at recent changes in

this distribution along with various explanations for change. We discuss the major class divisions

in the U.S. and examine some of the major eects of the transformation of the U.S. economy on

particular classes. We will examine forms and processes of stratication, with a focus on patterns

of social mobility and dierences in processes and outcomes by race and gender and over time.

Students should be prepared to process a considerable amount of quantitative information in the

form of tables and graphs. A previous course in statistics is highly recommended but not required.

Required Texts

Marger, Martin N. (2008) Social Stratication: Patterns and Processes (4th Edition). Boston,

MA: McGraw Hill.

Gilbert, Dennis (2008) The American Class Structure: In an Age of Growing Inequality (7th

Edition). Los Angeles, CA: Pine Forge Press.

Grusky, David B., and Szonja Szelenyi (2006) Inequality. Boulder: Westview Press.

Course Requirements and Policies

Lectures: Class meets three time per week. Students are expected to attend lectures and

participate in class. Between 75% and 85% of the exam material will be drawn directly from the

lectures. The instructor will post slides of the lecture material to Bb on a weekly basis after the

material has been presented in class.

Readings: Social stratication is a core area of sociology with a long tradition of scholarship

resulting in a considerable amount of published work. Readings for this course will average 50

pages or more per week. Students are expected to keep up to date on the readings and should be

prepared to participate in class discussions on current topics covered in the readings.

Examinations and Comments: There will be 4 in-class examinations worth 100 points each.

All exams will consist of roughly 35 multiple choice questions and 10 true false questions. Make

up exams will be given only in the case of documented emergencies. In addition to the exams,

students will be asked to participate by writing several short essays (250{500 words). These

essays are designed to assess understanding of the current topics covered in the readings and will

be graded on a 0{5 point scale. A good essay (i.e., in the 3{5 point range) would consist of

carefully crafted responses to specic questions or topics. Essays will be weighted to account for

SOC329{Social Stratication and Inequality 2

approximately one fth (20%) of the course grade, or 100 out of the 500 points possible. Cases of

scholastic dishonesty will be dealt with according to standard UT protocol.

Grading: Grades will be determined as follows:

Approximate Percentage Total Points Letter Grade

90% 450 A

80{89% 400{449 B

65{79% 325{399 C

50{64% 250{324 D

<50% < 250 F

Syllabus

A tentative schedule of the topics is provided below. The numbers in parentheses indicate

chapters. Corresponding page numbers are also provided.

Overview of Major Concepts

Week 1 Introduction to Social Stratication and Inequality

Reading Marger (1) Grusky & Szelenyi (1{2)

Pages 1{25 1{20

Weeks 2{3 Major Theories of Inequality

Reading Marger (2) Gilbert (1{2, 11) Grusky & Szelenyi (3{6)

Pages 26{52 1{37, 228{240 21{70

Weeks 4{5 Wealth, Income, and Forms and Patterns of Inequality in Contemporary U.S.

Reading Marger (3) Gilbert (3{4)

Pages 53{78 39{92

Exam 1 Sept. 25th

Social Classes in the U.S.

Week 6 The Upper Classes

Readings Marger (4, 13) Gilbert (8) Grusky & Szelenyi (7)

Pages 79{105, 355{388 148{178 71{86

Week 7 The Middle and Working Classes

Readings Marger (5) Gilbert (5, 9)

Pages 106{140 93{121, 179{202

Week 8 Poverty, the Poor and Public Policy

Reading Marger (6, 9) Gilbert (10) Grusky & Szelenyi (8{9)

Pages 141{174, 240{267 203{227 87{118

Exam 2 Oct. 16th

SOC329{Social Stratication and Inequality 3

Forms and Processes of Stratication

Weeks 9{10 Social Mobility and Processes of Stratication

Readings Marger (7) Gilbert (6{7) Grusky & Szelenyi (15{17)

Pages 175{209 122{147 207{256

Week 11 Ideology, Legitimation, and Socialization

Reading Marger (8) Gilbert (5, 9) Grusky & Szelenyi (18)

Pages 210{239 93{121, 179{202 257{272

Exam 3 Nov. 6th

Race/Ethnic and Gender Stratication

Week 12{13 Race

Reading Marger (10{11) Grusky & Szelenyi (10{12)

Pages 268{322 119{178

Week 14{15 Gender

Reading Marger (12) Grusky & Szelenyi (13{14)

Pages 323{354 179{206

Exam 4 Dec. 4th