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Christina Caramanis


M.A., Columbia University

PRC Graduate Research Trainee; NICHD Pre-Doctoral Trainee 2016-2017
Christina Caramanis

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Biography


Christina Nefeli Caramanis is a doctoral student at the LBJ School of Public Affairs and a Graduate Research Trainee at the Population Research Center (PRC) at UT Austin. She holds a B.A. in Sociology and Philosophy from Boston University and an M.A. in Developmental Psychology from Teachers College, Columbia University. Christina’s research interests focus on the dynamic interplay between poverty--both at the community and family levels--and the social, emotional, behavioral, cognitive and academic development of children and youth.  In an effort to inform policy in various community and family settings, she is broadly interested in: (1) looking at the pathways through which antipoverty programs and policies interact with system-wide patterns of behavior and overall child and family well-being, and (2) using higher level statistical modeling and analysis to quantitatively address individual- and system-level change in key family- and child-level processes. Christina is currently an NICHD pre-doctoral trainee in the Population Research Center at UT Austin.

Research Interests: Social welfare policy, family policy and demography, poverty reduction, child development, labor force economics, data science

Courses


SOC F317L • Intro To Social Statistics

82355 • Summer 2020
Meets TTH 10:00AM-11:30AM
Hybrid/Blended
QR MA

Course Description

This course presents a general overview of the statistical methods used in the social sciences. The course is designed to give you a conceptual and rational understanding of today’s most commonly used (and useful) statistical methods.

 

The news is full of statistical claims: In October of 2016, I read in various news sources that Hillary Clinton had an 89% chance of winning the November 2016 election. In the month of March 2020, I have read that the mortality rate for Covid-19 is anywhere from .01% to 10%.

 

Where do the numbers come from? Are they right? What can they tell us about the world? These are the sorts of questions we will ask in Introduction to Social Statistics. Answering them is going to involve doing some math. And while understanding the math behind the statistical concepts we will study is very important, it is even more important that you leave the course with a conceptual understanding of the most commonly used statistical methods. 

 

Why is important to learn statistics? Here are a few reasons:

  • The increasing availability of all kinds of data gives us an unprecedented ability to understand how humans behave. Using numbers to describe the world can help us figure out what is true and important.
  • Statistics are often used to make false or misleading claims: it is important to be able to identify these and explain what the numbers can tell us, and what they cannot.
  • Statistical and analytic skills are marketable: in the government, non-profit, and private sectors, a solid foundation of quantitative and computing skills are often important assets.

 

Quantitative Reasoning Flag

This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.

 

Course requirements & classroom policies

Required texts

  1. Moore & McCabe.  Introduction to the Practice of Statistics, 5th Edition (buy it used – you don’t need a CD with it).  This is the main textbook; we will cover (approximately) Chapters 1, 2, 3, 4, 5, 6, 7, 8.  Unless otherwise noted, references to a book in the course grid refer to Moore & McCabe.
  2. Leon-Guerrero & Frankfort-Nachmias. Essentials of Social Statistics for a Diverse Society, 1st edition (2011).  This is supplementary textbook.  Most readings and problems from this text will be optional.  However, if a reading or problem does not say “optional” on the course grid then it is required.  The chapters that are most relevant to our course material are 2, 3, 4, 5, 6, 7, and 9.  In the course grid, I refer to this book as “LF.”
  3. Students will use Microsoft Excel to prepare projects and assignments.  See the “Guide to videos on Excel” under the “Pages” tab of Canvas for instructions to access videos that will help you learn Excel. 
  4. I will use Canvas to post data and additional readings.

 

Course Outline

A day-by-day course plan grid is posted on Canvas. You should study the grid carefully, as it contains a lesson outline for each class and lab, and mentions when assignments are due. It is, of course, subject to small changes of schedule. The following concepts will be covered in the course: 

 

thinking about data, univariate statistics (chpt 1 in McCabe & Moore)

bivariate statistics (chpt 2 McCabe & Moore)

introducing probability theory (chpt 4 in McCabe & Moore)

statistical inference (parts of chpts 3, 5, 6, 7 in McCabe & Moore)

 

Online Lectures, Required Class Meetings, and Lab

Online Lectures: You will be required to watch 3 asynchronous class lectures per week. These will typically be around 1.5 hours each. The course grid will indicate the day on which the lecture should be watched in order to keep up with the class.

Required Class Meetings: We will meet for eleven 1.5-hour synchronous sessions on Tuesdays and Thursdays to do hands-on exercises, review the homework and tests, and discuss the material presented in the online lectures.

Lab Meetings: Lab meetings will be held on Wednesdays for 75 minutes. This will be a chance for you to review materials and assignments with the lab instructor, with occasional in-class exercises to help solidify certain concepts.

 

Prerequisites

There are no formal prerequisites for this course. However, if you are not proficient in algebra you will struggle in this course. If the math reviewed on the first day of class is unfamiliar, I suggest that you wait to take this course until after you have taken algebra or an equivalent college level math class. I would be happy to put you in touch with an advisor who can help find the right preparatory course for you.

 

Grading philosophy & policy

Your final grade will be comprised of:

  1. Attendance: attendance will be required at every Synchronous class lecture on Tuesdays and Thursdays, and worth 2 points each for a total of 22 points. There are 2 extra points built into the course total, so it is possible to miss one class session without an impact on your grade, however, each class is important, so I would not recommend missing any if you can avoid it!
  2. Reading Review Assignments: In order to help you review each assigned reading before the lecture, I have posted questions on Canvas for you to answer. Each set of 17 sets of reading review questions is worth ½ point, for a total of 8.5 points.
  3. Homework Assignments: Assignments will count for a total of 36.5 points.
  4. Tests: There will be 3 tests worth a total of 35 points.

 

Statistics is hard! Many people (myself included) need to take statistics several times before they really understand the concepts in this course. Yet, the lessons of this course are very important, both for people who want to be informed citizens and for those who additionally want to go on to further study in the social sciences. So it is important to make a strong effort!

 

I will not curve the attendance and assignment grades. In the unlikely event where test grades are very low among essentially all students, I will curve the portion of the grade that comes from tests. 

 

Without denying the potential negative consequences of a poor grade, I would ask that you try not to worry too much about your grade. See this class an opportunity to learn some very useful material that you may not have the opportunity to learn again. I think it is one of the most practical courses you will take in college.

 

There are 102 points in the class, but I will grade out of 100. I will use +/- grading and the following grading scale:

 

103- 94: A

93-90: A-

89-87: B+

86-84: B

83-80: B-

79-77: C+

76-74: C

73-70: C-

69-67: D+

66-64: D

63-60: D-

59 and below

SOC 317L • Intro To Social Statistics

43730 • Spring 2020
Meets TTH 3:30PM-5:00PM GAR 3.116
QR MA

Description:

This course presents a general overview of the statistical methods used in the social sciences. While it’s important that you gain an understanding of the mathematical concepts behind the statistical analyses, it is of even greater importance that you leave this course with a conceptual and rational understanding of today’s most commonly used (and useful) statistical methods.

Truth claims made with statistics are abundant and often have the quality of facts in U.S. social and political life. Unfortunately, because many people do not understand the statistics undergirding these claims, they receive less scrutiny than they deserve. It is my primary goal to ensure that students learn the basic statistical literacy they need to be smart consumers of information. Our increasing reliance on statistics to understand the social world means that statistical and analytic skills are marketable skills. In fact statistics is one of very few classes that sociology majors take that provides them with concretely marketable skills. I believe that giving undergraduates a solid understanding of statistics is a way of democratizing knowledge and its production. In teaching statistics my goals are:

  •   To demystify statistics so that every student can be a smart consumer of quantitative information.

  •  To teach students to think sociologically with and about quantitative information.

  • To provide students with a solid foundation of quantitative and computing skills that could serve

    as assets in subsequent employment and academic settings.

  •   To demonstrate to students that learning statistics has practical applications outside of the       classroom in everyday life.

Texts:

Salkind, Neil J.. 2012. Statistics for People Who (Think They) Hate Statistics: Excel 2010 Edition. 3rd Edition. SAGE Publications. 

Grading and Reqirement:

I will use a non-competitive grading scale. In other words, the grade you receive will not depend on how well others have performed in class. You can earn a maximum of 115 points in this class. Your grade will be based on your mastery of each of the required tasks in the class. The grading scale for the final course grade is as follows: 115-94=A; 90-93=A-; 87-89=B+; 83-86=B; 80-82-B-; 77-79=C+; 73-76=C; 70-72=C-; 67- 69=D+; 63-66=D; 60-62=D-; 59 & below=F.

I do not give incomplete and will not change the final grade for whatever reason. You have plenty of opportunities to do well in this class. Use them.

If you receive a final grade of B+ or higher, I will write a personal recommendation for you in the future, stating that you have significant quantitative and computing skills.CLASS & LAB ATTENDANCE 10 PTS

As will be addressed later in detail, you have two free absences you can choose. However, I’d recommend you to use them only for emergencies. More than two absences will affect your class attendance grades negatively.

 

Curriculum Vitae


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