I. ASCRC General Education Form (revised 3/19/14)
Use to propose new general education courses (except writing courses), to change or
renew existing gen ed courses and to remove designations for existing gen ed courses.
Note: One-time-only general education designation may be requested for experimental courses
(X91-previously X95), granted only for the semester taught. A NEW request must be
submitted for the course to receive subsequent general education status.
Group
II. Mathematics
VII: Social Sciences
(submit
III. Language
VIII: Ethics & Human Values
separate forms X III Exception: Symbolic Systems * IX: American & European
if requesting
more than one
IV: Expressive Arts
X: Indigenous & Global
general
V: Literary & Artistic Studies
XI: Natural Sciences
education
w/ lab  w/out lab 
VI: Historical & Cultural Studies
group
*
Require
a
Symbolic
Systems
Request
Form.
designation)
Dept/Program Sociology
Course #
202
Course Title
Prerequisite
Social Statistics
Math 117
Credits
II. Endorsement/Approvals
Complete the form and obtain signatures before submitting to Faculty Senate Office
Please type / print name Signature
Instructor
Dusten Hollist
Kathy Kuipers
Jackson Bunch
Phone / Email 243-2843; 243-4381; 243-5863
Program Chair Kathy J. Kuipers
Dean
Chris Comer
III. Type of request
New
One-time Only
Renew X
Reason for Gen Ed inclusion, change or deletion
3
Date
January
14,
2015
Change
Remove
The course has been approved by
several majors to satisfy their
symbolic systems requirement.
Description of change
IV. Description and purpose of the general education course: General Education courses
must be introductory and foundational within the offering department or within the General
Education Group. They must emphasize breadth, context, and connectedness; and relate course
content to students’ future lives: See Preamble.
U 202 Social Statistics 3 cr. Offered every term. The goal of this course is to introduce basic
statistical concepts and techniques. The information gained provides a foundation to
understand the statistics often visible in our daily lives, in the newspaper and other popular
media (i.e. television and radio). It also provides the tools needed to enroll in more advanced
statistics courses. There are a variety of topics covered in this course. These will range from
basic organization of data, graphic presentation of data, probability, sampling distributions and
statistical inference. Required of all majors.
V. Criteria: Briefly explain how this course meets the criteria for the group.
1. Symbolic systems courses: rigorously present This course presents a mapping between
a mapping between a real-world system and a
social interaction (behavioral, emotional, and
human abstraction system;
cognitive) and the language and symbols of
statistics (numbers, charts, and graphs.)
2. apply analysis, reasoning and creative
It applies statistical analysis, probability
thinking in the understanding and manipulation theory, and inferential logic in understanding
of symbolic codes;
and manipulating statistical symbols and
numbers.
3. utilizes alternative methods of
It uses the results of statistical calculations
communication, perception, and expression in
and manipulations to describe, clarify and
order to encourage rigorous thinking.
interpret social interaction, encouraging
more rigorous and challenging thinking.
VI. Student Learning Goals: Briefly explain how this course will meet the applicable learning
goals.
1. Students taking a symbolic systems course
Upon completion of this course, students
will
will be able to demonstrate an
be able to: demonstrate an understanding of the
understanding of the symbols used in
symbols and the transformations of the system;
statistical research and how they transform
numbers to give them meaning.
2. relay and interpret information in terms of the Students will be able to relay and interpret
given symbolic system;
social information in terms of statistical
symbols, operations, and reasoning
3. Apply creative thinking using the symbolic
They will be able to apply creative thinking
system in order to solve problems and
skills using the language and logic of
communicate ideas.
statistical analysis in order to address a
variety of applied and theoretical social
problems and communicate them to both a
professional and general audience
VII. Assessment: How are the learning goals above measured? Please list at least one
assignment, activity or test question for each goal.
1.The mid-term and final exams include a variety of questions that allow students to
demonstrate their understanding of statistical symbols. Students also calculate problems on the
computational portions of these exams, demonstrating the ability to make transformations
within the system.
2. In 9+ problem sets students relay information about the statistical features of sociological
and social scientific phenomena using the language of the symbolic system.
3. In 9+ problem sets, students use statistical software to solve problems and communicate
ideas about sociological and social scientific phenomena.
VIII. Justification: Normally, general education courses will not carry pre-requisites, will
carry at least 3 credits, and will be numbered at the 100-200 level. If the course has more than
one pre-requisite, carries fewer than three credits, or is upper division (numbered above the 200
level), provide rationale for exception(s).
IX. Syllabus: Paste syllabus below or attach and send digital copy with form.  The syllabus
should clearly describe learning outcomes related to the above criteria and learning goals.
University of Montana
Department of Sociology
Spring 2014
Sociology 202
Social Statistics
Professor: Kathy Kuipers
Office:
Social Science, room 311
Hours:
Monday (2:10-3:00) and Wednesday (3-5:00) and by appointment
Phone:
243-4381 (office); 327-9777 (home—only in emergencies)
Email:
kathy.kuipers@umontana.edu
TA:
Preceptors:
Course Objectives:
Social Statistics is a course in basic statistical concepts and techniques. The purpose of the course is to provide a
basic understanding of statistics and statistical methodology with an emphasis on social science applications. This
course will focus on learning statistics through working with real data. Consequently, emphases will be placed on
the applied understanding of statistical methods, the use of computer applications, and critical interpretation of
results. In taking this approach, our goal will be to give you a practical feel for statistics and to motivate your
interest in the research process. Upon completion of the course, you should be able to demonstrate an
understanding of the symbols used in statistical research and how they transform numbers to give them meaning.
This will include relaying, interpreting, and effectively communicating social information in terms of statistical
symbols, operations, and reasoning; and applying creative thinking skills using the language and logic of statistical
analysis in order to address a variety of applied and theoretical social problems.
LEARNING GOALS
Upon completion of the course, you will be able to demonstrate an understanding of the symbols
used in statistical research and how they transform numbers to give them meaning. This will
include relaying, interpreting, and effectively communicating social information in terms of
statistical symbols, operations, and reasoning; and applying creative thinking skills using the
language and logic of statistical analysis in order to address a variety of applied and theoretical
social problems.
Course Structure:
The complete course includes a lecture once a week, on Mondays. You are also required to register for and attend
a lab that meets twice a week, on Wednesdays and Fridays. On Mondays I will introduce material for the week
through lecture. Wednesdays, will be devoted to preparing students for the problem set assignments, lecture and
discussion about the problems, and hands-on analyses or demonstrations using the statistical program SPSS. On
Fridays, we will continue with in-class work based on Monday’s lecture and Wednesday’s instruction, link the
material presented to the problem set, and answer additional questions. This syllabus contains the schedule of
topics and assignments for BOTH the lecture and the lab, even though you have registered for them separately.
Getting Help:
My office hours are listed above. We also have three TAs for the class: a graduate student in sociology, Mike
King, and two undergraduate preceptors, sociology majors who took the class and got top grades in it, Dustin and
Ryan. We are available to meet with you during our listed office hours and also by appointment and the best way
to reach us is by email or our office phones. If you send an email question to any of us, it’s best to send the
question to more than one of us—that way you will get a quicker answer and we all will be informed about where
students need extra help.
Prerequisites:
The formal prerequisites for this course are the successful completion of Math 117. This will assure that you have
some basic understanding of the concepts and principles of sociology and are familiar with sociological questions
and research answers. Additionally, you will have some facility with quantification as a preparation for the
calculations you will be required to perform. Also, it helps to have taken or be taking Soc. 318, the research
methods class, for a better understanding of how data and research fit together.
Course Requirements:
What can we say? You want to pass this class? DON’T MISS CLASS! NEVER! Attendance is required because
you will miss material that is essential for you to do well in the class. That material is not available in the book or
online. I take attendance periodically and there will be in-class activities that contribute to your grade.
Additionally, this is one class where it is important NOT to fall behind, and missing the lecture on Monday or the
lab activities on Wednesday or Friday will put you seriously behind. There also will be a number of opportunities
for extra credit points that are available only through class participation or due the following class meeting.
Missing class will make those points unavailable to you. (Be forewarned—we do NOT accept late extra credit
assignments.)
Readings:
Most readings will come from the primary, required text, available at the campus bookstore, Elementary Statistics
in Social Research, eleventh edition, by Levin, Fox, and Forde. It is important that you complete the required
readings before they are discussed in class. While you may find readings somewhat confusing initially, you
should plan on familiarizing yourself with concepts, terms, and formulae before they are discussed in class and
then reviewing the readings after the lecture to clarify what was unclear the first time. You should bring the text to
class on Mondays to consult during lecture, on Wednesdays for reference in the explanation of problem set
assignments, and on Fridays for in-class work and consulting statistical tables in the Appendices.
You will need to use the software program, SPSS, for your problem set assignments. SPSS stands for
“Statistical Program for the Social Sciences” and is the software that you will use when you analyze data. You can
access SPSS on any of the computers in the SSRL (Social Sciences Building) and in many of the other computer
labs around campus. You may want to have your own copy of SPSS (a base version available for rent at a reduced
price for students) so that you can conduct data analysis at home, on your own computer. See the link and details
for obtaining the software on our class Moodle site. WE WILL USE SPSS FOR ALL OF THE PROBLEM SETS.
Our goal is to help you learn about statistics in the social sciences and do well in the course. To that end,
in addition to lectures, readings, and help from your professor and TAs, we have made three online resources
available to you: Moodle, itunesU, and Mysockit.
You already may be familiar with the course supplement, Moodle. In order to be prepared for class, you
will need to check Moodle regularly—at the very least, before each class meeting for announcements, readings,
and extra information. Direct your browser to http://umonline.umt.edu/ We will use Moodle as a supplement—for
communication, problem sets, and exams. (You must access Moodle for the midterm exam and the final.)
Moodle also contains an online “grade book” where your scores will be posted. Other ways to access Moodle are
from the UM Homepage (the menu on the right) or from “ONE Stop.” As they become available, the syllabus,
additional data, handouts, assignments, grades, and other information will be posted on the site. Bookmark this
site and visit it regularly.
The second online resource that we will use is itunesU. This source also may be accessed from
“my.umt.edu.” Click on the itunesU logo on the left in ONE Stop. It will open itunes on your computer (or you
will need to download itunes for free) and then you will see a screen that shows the courses at the University of
Montana that have material that you may access. (You will only be able to access our course material if you are
registered for the course.) As the semester progresses, recorded lectures with Power Point material will be
available for you to download and view using itunes. You should NOT think of these as substitutions for
attending class—NOT all lectures will be posted and, of those posted, NOT all will be given in class. These are
separate, supplemental lectures with some course-lecture overlap. By viewing the lectures and material on itunes
U before their assigned date, you will be prepared to do the hands-on exercises in class.
If you purchase your textbook from the bookstore, you will have an access card for Mysockit, an online
resource published by Allyn & Bacon for use with the textbook problems. You will need to access data sets and
other resources at this cite so you MUST have the card. It is free, but only included with your textbook. You
should also be able to gain access with used textbooks—we’ll give you more details about that.
Assignments and Evaluation:
1) Problem Sets and In-class Projects (40%): Problems and small projects will be assigned throughout the
semester. Most will be homework (ten assignments), although you will occasionally have time during class to
work on portions of them. In addition to readings, you should count on having at least one or two assignments
each week that will require following instructions in the book, using the computer, and working with data.
2)
3)
4)
5)
You always will be required to turn in the computer output in addition to the written answers based on the
output.
Class Attendance (5%): Attendance will be taken on an intermittent basis. Additional in-class exercises also
will be collected and used for the calculation of an attendance score.
Mid-Term Exam (25%): There will be one midterm exam (in two parts) on March 27 and March 29. You will
take the in-class, computational part of the exam on the first date. The other part (multiple choice, matching,
fill in the blank) will be taken on Moodle in class on the second date. NO MAKE-UPS WILL BE GIVEN.
Final Exam (30%): The final exam will be in the same format as the midterm exam (two parts). You will take
the computational part during the first hour of the time scheduled for our final. The second part will be taken
in-class on Moodle after you have completed the computational part, in the second hour of our examination
period.
Late assignments will be penalized (points deducted) and, after a certain period, will no longer be accepted.
A few words about plagiarism and academic dishonesty:
“Plagiarism is the representing of another’s work as one’s own. It is a particularly intolerable offense in the
academic community and is strictly forbidden. Students who plagiarize may fail the course and may be remanded
to Academic Court for possible suspension or expulsion.” (Taken from The University of Montana Student
Conduct Code, available online: http://life.umt.edu/vpsa/student_conduct.php) Plagiarism includes:
 Copying from another’s examination or allowing another to copy from one’s own exam
 Unpermitted collaboration—especially on exams
 Unpermitted sharing of lab assignments and data—your problem sets should be your own—output may
not be photocopied.
 Giving or receiving unpermitted aid on an examination.
Make sure that your work is your own. Don’t get confused by what is acceptable and what is not. In this class,
discussion of ideas and statistical methods is permitted, and even encouraged among classmates. Writing
collaboration, however, is not permitted and students should be careful not to work directly from a classmate’s
notes, not to copy another’s paper or exam, and not to let others view their exam. If this is unclear, please ask. Be
careful!
Preliminary Course Schedule:
The material in the course will be presented in a series of lectures, organized around three basic topics: Descriptive
Statistics, Probability Theory, and Inferential Statistics. Each lecture will be followed by a lab task (usually using
SPSS) and a homework assignment due the following week. The lab task will be introduced on Wednesdays with
the homework assignment usually due the following Wednesday (see dates below.) This schedule is
TENTATIVE, however. While due dates are highly unlikely to change, material content and exercises may
change as we see that we need to spend more or less time on a particular topic. It is YOUR RESPONSIBILITY to
keep up with the schedule by attending class regularly and checking the Moodle announcements frequently and, of
course, doing the readings and homework assignments. All readings below are from our textbook unless noted.
Tentative Class Outline
Week 1 (January 28-February 1)
Monday
Figuring things out—where, what, and when—collecting and coding data
Wednesday and Friday
Getting acquainted with data—levels of measurement, error, bias, rounding
Read:
Chpt. 1, pp. 1-10; Appendix A, pp. 481-96
View:
itunes U lecture: “Using SPSS”
Handout: Assignment #1, jump drive/memory stick with data sets, due February 13
Introduction to Moodle, the lab and SPSS
Week 2 (February 4-8)
Monday
DESCRIPTIVE STATISTICS
A First Look at Data Organization (types of variables, frequency distributions, sample and population, statistics
and the social sciences)
Read:
Chpt. 1, pp. 11-24
Wednesday and Friday
Read:
Appendix A, pp. 496-506
Familiarize yourself with Moodle, check for announcements and tips, and work on your first assignment. Check
out the resources available to you on Mysockit. Don’t put off figuring out how Moodle works.
Responses to
Moodle Survey Bring jump drive/memory stick to class.
Week 3 (February 11-15)
Monday
More on Distributions and Data: Terminology
Read:
Chpt. 2
Wednesday and Friday
More about SPSS and What the Data Mean
Read:
Appendix 2 (if you need it); check with the TAs to review what you don’t know
View:
itunes U lecture: “Percentages and Graphical Devices”
Handout: Problem Set #2, selected exercises due February 20
Assignment #1, jump
drive/memory stick with data sets
Week 4 (February 18-22)
Monday
NO CLASS, PRESIDENT’S DAY HOLIDAY
Central Tendency and Variability (mean, median, mode, standard deviation)
Wednesday and Friday
Read:
Chpt. 3
View:
itunes U lecture: “Central Tendency”
Read:
pp. 132-134
Handout: Problem Set #3, selected exercises due February 27
Problem Set #2, selected exercises
Week 5 (February 25-March 1)
Monday
Lecture: Standard Deviation
Read: Chpt. 4
Wednesday and Friday
View:
itunes U lecture: “Variability”; Handout: Problem Set #4, selected exercises due March 6
Problem Set #3, selected exercises
Week 6 (March 4-8)
Monday
PROBABILITY THEORY
Introduction to Probability (basic concepts and sampling)
Read:
Chpts. 5 and skim 6
Wednesday and Friday
Theoretical Distributions (normal, t, F)
View:
itunes U lecture: “The Normal Curve”
Handout: Problem Set #5, selected exercises due March 13
due
Problem Set #4, selected exercises
Week 7 (March 11-15)
Monday
More Probability and Sampling
Read:
Chpts. 5 (reread) and 6
Wednesday and Friday
INFERENTIAL STATISTICS
Hypothesis Testing (one-sample hypothesis tests, confidence intervals)
Read:
pp. 208-209;
Handout: Problem Set #6, selected exercises due March 20
View:
itunes U lecture: “Sampling”
Problem Set #5, selected exercises
Week 8 (March 18-22)
Monday
Hypothesis Testing (differences between means)
Read:
Chpt. 6 (reread, make sure you understand this chapter!) and 7
Wednesday and Friday
Read:
Chpt. 7
Handout: Problem Set #7 selected exercises due April 10
View:
itunes U lecture: “t-tests”
Do Extra Credit Practice Exam on Moodle
Week 9 (March 25-29)
Monday
Hypothesis Testing for Relationships Between 2 Variables: ANOVA
Read:
Chpt. 8
Wednesday
View:
itunes U lecture: “ANOVA”
Midterm: on Moodle IN CLASS
Problem Set #6, selected exercises
Friday
Midterm:
in-class computational exam
Midterm: in-class computational exam
Midterm: on Moodle IN CLASS
Week 10 Spring Break
Week 11 (April 8-12)
Monday
Reread:
Chpt. 8
Wednesday and Friday
View:
itunes U lecture: “Two-Way Analysis of Variance”
Handout: Problem Set #8, selected exercises due April 17
Problem Set #7, selected exercises
Week 12 (April 15-19)
Monday
Hypothesis Testing for Relationships Between 2 Variables: Cross tabulation and Chi-square tests
Read:
Chpt. 9, Levin and Fox
Wednesday and Friday
Read:
pp. 341-342
Handout: Problem Set #9, selected exercises due April 24
View:
itunes U lecture: “Chi-Square”
Problem Set #8, selected exercises
Week 13 (April 22-26)
Monday
Correlation
Read:
Chpt. 10
Wednesday and Friday
View:
itunes U lecture: “Correlations”; complete in-class exercises
Handout: Problem Set #10, selected exercises due May 1
Week 14 (April 29-May 3)
Monday
Regression Analysis
Read:
Chpt. 11
Wednesday and Friday
View:
itunes U lecture: “Regression”; complete in-class exercises
NO LATE PROBLEM SETS ACCEPTED AFTER THIS DATE
Problem Set #9, selected exercises
Problem Set #10, selected exercises
ACCEPTED AFTER THIS DATE
NO LATE PROBLEM SETS
Week 15 (May 6-10)
Monday
Choosing Statistical Procedures to Research Problems
Read:
Chpt. 13
Wednesday and Friday
In-class exercises and review
Course Assessment
Finals Week
Final Exam: IN CLASS on Moodle; in-class computational
Final Exams
Section 01: Thursday, May 16 at 3:20-5:20
Section 02: Wednesday, May 15 at 3:20-5:20
Please note: Approved general education changes will take effect next fall.
General education instructors will be expected to provide sample assessment items and
corresponding responses to the Assessment Advisory Committee.