SOC 221A: GRAD STATISTICS I (69720)
Sociology 221A: Graduate Statistics I (69070)
Tuesdays 9:00am-11:50am in SSPB 4206
Instructor: Evan Schofer
Contact: schofer@uci.edu
Office Hours: Tuesday 12:00-1:00pm in SSPB 4271
Lab Instructor / TA: Martin Jacinto
contact: mjacint1@uci.edu
Office Hours: Tuesday 1:00pm-2:00pm in SST 619
Overview
This course covers the basic principles of statistics necessary to conduct social science research. These include basic descriptive and inferential statistics, with emphasis on commonly used methods such as crosstabs, and OLS regression. In addition, the course laboratory provides instruction in the use of a statistical software package Stata.
Readings
The course uses on a common statistics textbook:
Agresti, Alan. 2017. Statistical Methods for the Social Sciences, Fifth edition. Pearson.
You may also use the 4th edition (written with Barbara Finlay), which is less expensive.
Also, there is a book to help you learn statistical analysis using Stata:
Acock, Alan. 2018. A Gentle Introduction to Stata, Sixth Edition. Stata Press.
I haven't looked at older editions of this book, but I expect that they would be fine.
Course Information
Assignments and evaluation:
Weekly Assignments. Your final course grade is based on weekly assignments, each weighted equally as 10% of your final course grade. These assignments may take different forms, including math problem sets, online quizzes, in-class quizzes or tests, lab assignments, or short written papers.
Final grades. Your final grade will be computed by averaging your grades (weighted as described above). I may apply a curve to the final combined score to adjust grades (mainly if scores are low). This typically increases people's grades (compared to a standard percentage scale where 90%+ are As, 80+ are B's, etc). In the event of a borderline grade, I may use my discretion in adjusting grades based on course participation and effort. Incompletes will not be given, except in unusual circumstance.
Schedule & reading assignments
NOTE: I may occasionally make minor changes to the reading assignments. Any changes will be small and made well in advance of their due date.
Fall 2019: Sept 26-Dec 6, Holidays on Nov 11, Nov 28, Nov 29.
Class meetings: Oct 1, 8, 15, 22, 29, Nov 5, 12, 19, 26, Dec 3.
Week 1 (Oct 1): Review: Data and Descriptive Statistics
Review as needed:
Section 1.2: Descriptive and Inferential Statistics
Section 2.1: Variables and Their Measurement
Section 3.1: Describing Data with Tables and Graphs
Lab reading:
Briefly skim: Intro to Stata, Chapter 1
Week 2 (Oct 8): Descriptive statistics, Samples & populations;
Review as needed:
Section 3.2: Describing the Center of the Data
Section 3.3: Describing Variability of the Data
Section 3.4: Measures of Position
Section 1.2: Descriptive and Inferential Statistics
Section 3.6: Sample Statistics and Population Parameters
Lab reading:
Intro to Stata, Chapter 2
Week 3 (Oct 15): Probability; Sampling distribution of the mean
Section 4.1: Introduction to Probability
Section 4.2: Probability Distributions for Discrete and Continuous Variables
Section 4.3: The Normal Probability Distribution
Section 4.4: Sampling Distributions Describe How Statistics Vary
Section 4.5: Sampling Distribution of the Sample Means
Lab Reading:
Intro to Stata, Chapter 4, Section 4.1-4.3 only.
Week 4: (Oct 22): Confidence Intervals, Statistical Inference, Differences in means
Section 5.3: Confidence Interval for a Mean
Chapter 6 intro
Section 6.1: Five Parts of a Significance Test
Section 6.2: Significance Test for a Mean
Section 6.4: Decisions and Types of Errors in Tests
Section 6.5: Limitations of Significance Tests
Lab reading:
Intro to Stata, Chapter 3, Section 3.3-3.5 only.
Week 5 (Oct 29): Hypothesis tests for differences in Means
Section 7.3: Quantitative Data: Comparing Two Means
Section 12.3: Comparing Several Means: Analysis of Variation
Lab Reading:
Intro to Stata, Chapter 7, Section 7.1-7.8 only.
Week 6: (Nov 5): Categorical variables: Comparing proportions, contingency tables
Section 7.2: Categorical Data: Comparing Two Proportions
Section 8.1: Contingency Tables
Section 8.2: Chi-Squared Test of Independence
Lab Reading:
Intro to Stata, Chapter 6, Section 6.1-6.4 only.
Week 7 (Nov 12): Associations between categorical variables, odds ratios
Section 8.3: Residuals: Detecting the Pattern of Association
Section 8.4: Measuring Association in Contingency Tables
Section 8.5: Association Between Ordinal Variables
Lab Reading:
Intro to Stata, Chapter 6, Section 6.5-6.6 only.
Intro to Stata, Chapter 9, Section 9.1-9.3, 9.5-9.6 only.
Week 8 (Nov 19): Correlation and regression
Section 9.1: Linear Relationships
Section 9.2: Least Squares Prediction Equation
Section 9.3: The Linear Regression Model
Section 9.4: Measuring Linear Association: The Correlation
Intro to Stata, Chapter 8, Section 8.1-8.5 only.
Week 9 (Nov 26): Correlation and Regression
Section 9.5: Inferences for the Slope and Correlation
Section 9.6: Model Assumptions and Violations
Intro to Stata, Chapter 8, Section 8.6 only.
Intro to Stata, Chapter 10, Section 10.1-10.2 only.
Week 10 (Dec 3): Multiple Regression
Section 10.1: Association and Causality
Section 10.2: Controlling for other Variables
Section: 11.1: The Multiple Regression Model
Section: 11.2: Multiple Correlation and R-square
Intro to Stata, Chapter 10, Section 10.3-10.7 only.
Week 11: Finals Week
This course has no final exam.
Policies
Assignment Policies:
Section Attendance/Assignments/Quizzes. Unless otherwise indicated by your section instructor, missed in-class activities, in-class or online quizzes, or in-class assignments cannot be done later.
Missed Exams. Typically, students who miss exams receive a zero. DO NOT MAKE TRAVEL PLANS ON THE DATE OF THE MIDTERM OR FINAL EXAM. You are welcome to inquire about alternative exam arrangements, but we may not be able to accommodate you, given the size of the class and the number of requests we typically receive.
Grade Corrections/Changes. If you believe that you received an incorrect grade on an exam or assignment, make an appointment with your TA. If you have spoken with your TA and you feel the issue is still not resolved, let me know.
Cheating, Plagiarism, etc. Academic violations such as cheating and plagiarism will be dealt with very severely, based on the specifics of the case. I may use software to detect text plagiarized from the web or other sources. If requested, students in this course must provide computer copies of their written work for examination by plagiarism detection software.
University Policies
Students in this course must abide by all relevant university policies, ranging from issues of general behavior to academic issues such as plagiarism. It is your responsibility to be aware of university policies.
My Policies
Respect. All participants in this course (including myself) should strive to treat others – and their ideas – with respect. The course material and class discussion will cover sensitive topics ranging from immigration and welfare to racial and ethnic identity. Disagreements may arise. Try to be aware of and show respect for other people’s views and perspectives, even if you do not agree. However, if you find yourself upset by class discussion or feel that students are behaving inappropriately, please raise the issue with your TA or with me – either in class, afterward, or anonymously – so the situation can be addressed. Often, conflicts are rooted in simple misunderstandings, but sometimes they reflect more serious issues that can only be resolved if things are brought to my attention.
Support for inclusion and diversity. UCI is an incredibly diverse institution with regard to race, ethnicity, immigration status, gender identity, language, religion, etc. I seek to provide a supportive and welcoming environment for all, and ask that you do your best to do the same.
Commitment to Learning. There is no point in taking a course if you are not committed to learning. That means doing the readings, showing up, concentrating, and participating in class discussion and group activities. I strive to make the class interesting, but make no mistake: this is not entertainment. We will struggle through ideas and readings that are difficult and may seem boring if you do not yet understand them. It is your responsibility to be committed.
Professionalism. I expect you to conduct yourself professionally. If you show up and do your work, you will most likely do well. If you don’t show up and fail to do the work, expect a bad grade. Take responsibility for your actions. (I really dislike it when students come to me and say “I haven’t been coming to class and missed the exam. But, I really need this course to graduate/keep my financial aid/get into law school/make my parents happy. Can’t you give me a better grade?” If success in this class is really so important to you, just plan ahead and do the work. The course material isn't that hard!)
Course Summary:
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