SOC 411 - Canadian Association for Community Service Learning

Introduction to Quantitative Methods
SOC 411
Instructor: A. Noack
Email:
anoack@ryerson.ca
Telephone: 416-979-5000 x 2249
Ryerson University
Winter 2009
Office: Jorgenson Hall 308
Office hours: Thursday 12:30 pm - 2:30 pm or by appointment
Lecture: Wednesday 3:10 pm - 5:00 pm KHE 125
Labs: Thursday 10:10 am - 11:00 am POD 356
Thursday 11:10 am - 12:00 pm POD 356
Calendar Description:
This course develops an understanding of quantitative data in social research. No familiarity with
statistics is assumed. Students will gain the skills and procedures needed to explore social issues
using statistics, and well as read, understand and critically evaluate published quantitative
research. Students will learn statistical software designed for social science. Emphasis will be on
practical applications of techniques, and on interpretation of results rather than their
mathematical derivations.
This course includes a Service Learning option. Service Learning gives you the opportunity to
enhance your academic learning by participating in a project in the larger community and
reflecting on this experience. See the Appendix at the end of this syllabus for more information.
Course designation: Required for Sociology degree students
Prerequisites: ACS 301 and SOC 105
Course Goals:
This course is designed to introduce you to quantitative research and data analysis in sociology.
The emphasis is on manipulating numerical data in order to learn more about the social world.
The use of statistical procedures is treated as a ‘craft’ skill that requires thoughtful sociological
decision-making and ongoing practice. By the end of this course, students should:
• understand the logic of quantification and quantitative research
• be able to accurately describe a single variable
• be able to accurately describe and test a relationship between two variables
• understand the logic of statistical inference and hypothesis testing
• be able to discuss and interpret quantitative data using sociological ideas
• be able to understand and critically assess published research that relies on
quantitative data

Weekly Topic and Reading Schedule:
This course includes a weekly lecture and a lab session. The lectures will introduce the main
ideas for each week; The lab sessions will give you the opportunity to apply the knowledge from
the lectures and gain practical data analysis experience. Attendance at all lectures and lab
sessions is critical in order for you to do well in this course.
The textbook for this course is available in the Ryerson Bookstore:
Haan, Michael (2009). An Introduction to Statistics for Canadian Social
Scientists. Toronto: Oxford University Press.
A number of additional articles are used to supplement the textbook. These articles are intended
to give you practice reading and understanding quantitative research results. Links to all of the
additional readings can be found on the course Blackboard site. You will need a valid library
barcode and PIN to access some of these readings from home.
Date Topic/Activity Readings
January 7 Introduction to Quantitative Data Analysis • Ch. 1: Why Should I Want to Learn Statistics?
• Ch. 2: How Much Math Do I Need to Learn
8 LAB: Introduction to the Arts Lab/SPSS
Statistics?
14 Univariate Analysis I: Describing Variables and
Distributions
• Ch. 3: Univariate Statistics
• Clark, Delayed Transitions of Young Adults
15 LAB: Variables, Values and Frequencies
21 Introduction to Probability & The Normal Curve • Ch.4: Introduction to Probability
• Ch.5: The Normal Curve
22 LAB: Graphical Display in SPSS
28 Univariate Analysis II: Measures of Central
Tendency and Dispersion
EXERCISE 1 DUE
29 LAB: Means & SD; Medians & IQR
• Ch 6: Measures of Central Tendency &
Dispersion
• Ch 7: Standard Deviations, Standard Scores &
the Normal Distribution
• Frank, Studying and Working
February 4 Samples and Populations • Ch. 8: Samples and Populations
• Ch. 9: Generalizing from Samples to
5 LAB: Standard Errors and Confidence Intervals
Populations
11 MIDTERM TEST None
12 No labs this week
18/19 Reading week - no classes
25 Bivariate Analysis I: Relationships between
Discrete Variables
• Ch. 12: Bivariate Statistics for Nominal Data
26 LAB: Lambda & Gamma
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March 4 Bivariate Analysis I: Relationships between
Discrete Variables (cont’d)
EXERCISE 2 DUE
• Ch. 13: Bivariate Statistics for Ordinal Data
• Schellenberg, Perceptions of Canadians
5 LAB: Crosstabulations and Chi-square
11 Bivariate Analysis II: Relationships between
Discrete and Continuous Variables
12
LAB: Comparing Means & ANOVA
• Ch.10: Using the t-Distribution to Compare the
Means of Population Subgroups to
Population Means
• Ch. 15: One-Way Analysis of Variance
• Clark, Pockets of Belief
18 Bivariate Analysis III: Relationships between
Continuous Variables
EXERCISE 3 DUE
19 LAB: Correlations & Scatterplots
• Ch. 14: Bivariate Statistics for Interval/Ratio
Data
• Gadbois & Bowker, Gender Differences in the
Relationships between Extracurricular
Activities Participation...
25 Regression I: Bivariate Predictions • Ch. 16: Regression 1 - Modelling Continuous
26 LAB: Linear Regression
Outcomes
April 1 Regression II: Multivariate Predictions
EXERCISE 4 DUE
2 LAB: Multivariate Regression & Residuals
8 Looking Back and Looking Ahead
PARTICIPATION SELF-EVALUATION
• Ch. 18: Regression Diagnostics
• Gazso-Windle & McMullin, Doing Domestic
Labour
None
9 LAB: Final Exam Review/Study Session
Student Evaluation:
Data Analysis Exercises (4 x 12% each) 48% 1: January 28, 2009
2: March 4, 2009
3: March 18, 2009
4: April 1, 2009
Midterm Test 20% February 11, 2009
Final Exam 20% Scheduled by the registrar
Lecture & Lab Participation 12% Ongoing
The final day to drop a winter course is March 17. The faculty course survey will be administered
from March 18- April 7.
Students who are registered with the Access Centre should speak with me during the first week of
class in order to arrange appropriate accommodations. Students who find that the schedule in this
course conflicts with their religious observances should speak with me during the first two weeks of
class in order to arrange appropriate accommodations.
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About the Data Analysis Exercises
The best way to learn quantitative data analysis is through ongoing practice and feedback. To
facilitate this learning, this course is designed around a series of small exercises that will build on
each other. The specific requirements of each exercise will be posted on the course blackboard site
one week before each exercise is due.
Exercises must be submitted during class time on the day they are due. Please keep a backup copy of
all work that you submit. Exercises will normally be returned to students a week after they are
submitted.
You will work with your own Statistics Canada dataset for each exercise. These datasets will be
labelled by last name and can be downloaded from the ‘Exercises’ area of the course Blackboard
site. Because each student will have a slightly different dataset, your results will be different from
other students in the class. In order to maintain standards of academic integrity, you must complete
each exercise using your own dataset. It is your responsibility to ensure that all of the work that you
submit in this course is your own, individual work.
About the Midterm Test and Final Exam
The midterm test and final exam in this course will ask you to apply the concepts that you have
learned throughout the term. You will not be required to memorize any mathematical formulae for
the test or exam; all required formulae will be given to you with the test/exam.
You must complete both the midterm test and the final exam in this course. If you do not complete
both the test and the exam, you will automatically be assigned a grade of ‘F’ in the course.
The format of the midterm test will be discussed during lecture on February 4. The format of the
final exam will be discussed during lecture on April 8.
About Class Participation
Students will be evaluated on their ongoing participation in class discussions and lab activities.
‘Participation' includes speaking, listening and engaging in both large and small group contexts.
Being prepared for class by doing the readings in advance will ensure that you are able to
meaningfully participate. Both the quality and the quantity of your participation will be assessed.
Because you cannot participate in class if you do not attend, attendance will often be formally or
informally recorded.
Part of your participation grade will be based on the completion of short activities that you will be
asked to submit at the end of some labs. Another part of your participation grade will be based on a
self-evaluation that you will complete in class on April 8. Students who do not complete a selfevaluation
on that day will not be able to provide input into their participation grade. Participation
grades will be assigned at the end of the term.
In order to provide a classroom space where everybody is able to learn, it is imperative that you
remain respectful of other people. Racist, sexist, classist, ageist, ableist, homophobic and other
discriminatory behaviour, comments or activities are not acceptable.
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Technical Requirements for the Course:
Mathematical Knowledge
Some students may be worried about whether
or not they have enough mathematical
knowledge to do well in this course.
Although this course does not emphasize
mathematical formulae or derivations, you
will need some math skills. At the start of the
course, you should:
• be able to add, subtract,
multiply and divide using
a calculator
• understand how brackets work in a mathematical equation
• understand and be able to use mathematical ‘order of operations’ (BEDMAS:
Brackets, Exponents, Division, Multiplication, Addition, Subtraction)
• understand the idea of percentages, proportions and fractions
• be able to calculate a percentage using a calculator
• understand the idea of positive and negative numbers
• understand and be able to calculate the ‘square’ or ‘square root’ of a positive or
negative number using a calculator
If you are unsure about your skills in any of these areas, please come and see me at the beginning of
the term in order to refresh your skills. Chapter 2 in your textbook also provides a review of some
math fundamentals.
Some new mathematical skills and procedures will also be taught as part of this course. If any of
these ideas become confusing, please come and talk with me as soon as possible.
Participating in the Service Learning option in this course might also help you to become more
comfortable with basic math skills and increase your confidence about learning and doing
mathematics. See the Appendix of the syllabus for more information.
Computing Resources
The labs and exercises in this course use a computer program called the ‘Statistical Package for the
Social Sciences’ (SPSS), version 15 or 16. You can access this software in the Arts Lab in POD 356.
You can also access this software from other computers that are connected to the internet using
Ryerson’s ‘Virtual Applications’ system.
To access SPSS from outside the Arts Lab, go to https://vapps.ryerson.ca. The first time you use the
system, you will need to download and install a small program that lets you connect with Ryerson’s
server. Once you have downloaded the program, you will need to logon with your Matrix ID and
password to use SPSS. For detailed instructions on how to use Ryerson’s Virtual Applications, go to
http://www.ryerson.ca/acs/usersguide/virtualapps.htm
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At the end of your textbook, you will find a companion lab manual to SPSS that includes step-bystep
instructions for the most common procedures. A series of SPSS help tutorials are also available
on the department’s ‘Statistics in Sociology’ website.
I recommend that you use a memory key to store and transport your electronic files for this course.
Many of the data files that we will be using are too big to send as email attachments; a memory key
is the most efficient way to transfer data.
How to Succeed in this Course:
Attend all of the lectures and labs
I expect that students will attend all lectures and labs and arrive on time. Arriving late to class is
disruptive for everyone. Regular attendance is necessary for you to successfully complete this
course; Each week’s material builds on concepts and ideas from the previous weeks. The lectures
will cover material that is not found in your readings and lab activities will give you important
practical experience.
Pay attention in class
I strongly discourage the use of laptops in the classroom. It is especially difficult to take notes about
statistical ideas on a word processor. Laptops also often provide a distraction for you and for other
students around you. Students who do use laptops in the classroom will be asked to sit in a
designated location in order to minimize the disruption to other students.
Participate in the lectures and labs
Lecture and lab participation forms a key part of the learning in this course. Activities in lectures
will give you the opportunity to practice interpreting quantitative data in a supportive environment.
Lab activities will give you the opportunity to practice using statistical analysis software. Ask
questions as soon as you encounter any difficulties.
Although I realize that some students may be hesitant to speak in class, learning to effectively
explain your ideas to other people is an important part of your university education. Students who
have concerns about speaking in class should make an appointment with me at the beginning of the
term so that we can develop some practical strategies for increasing your confidence and comfort in
the classroom.
Do the readings before you come to lecture
You should complete the assigned readings prior to arriving in lecture each week. As you read, be
sure to make notes about anything that is confusing and any questions you have and bring these to
my attention. Students who do not keep up with the readings may find it difficult to understand and
engage with the lecture and lab material.
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Do your own work
Although I encourage you to help one another, the only way to learn this material is to work through
the ideas and exercises yourself. All of the work that you submit in this course must be your own.
Plagiarism will not be tolerated. Where there is a suspicion of plagiarism, students may be required
to submit their work to www.turnitin.com
Take advantage of the help offered by the Peer Support Tutors
As part of a pilot program, the Faculty of Arts has arranged for Peer Support Tutors in this course to
provide you with an additional source support for students learning statistics. Two fourth-year
Ryerson sociology students are responsible for giving you informal, one-on-one help. They will be
available during the lab time to help you with any computing issues, and one additional day a week
to help you with any difficulties you have with manipulating data for your exercises.
Consider doing the Service Learning option
Many people believe that the best way to truly learn something is to teach it to someone else. The
Service Learning option gives you this opportunity. You do not need to be particularly strong in
mathematics to do the Service Learning option - you only need to be willing to learn. Participating in
Service Learning will help you to apply your knowledge and to put the concepts you learn in this
course into a larger context.
Hand in your exercises on-time
Keeping up with the data analysis exercises will help you to quickly identify any conceptual or
technical misunderstandings. Late exercises will be penalized with a reduction of ½ a mark (out of
10) per day. I will not accept exercises that are more than one week late. Keep a copy of all work
that you submit. If you cannot submit your assignment in person, please place them in the sociology
department drop box. DO NOT leave assignments under my office door or email them to me.
Come and see me as soon as you find anything confusing
For questions about the course content or understanding specific statistical ideas, it’s best to talk
with me in person. Come and visit me during my office hours; if you cannot attend during the posted
time, please contact me to arrange an alternate time to meet. I also spend quite a bit of time in my
office, so you may want to try just dropping by to see if I’m available.
For basic administrative questions, the best way to contact me is by email. Please use your
@ryerson.ca email to contact me - there is no guarantee that other emails will be delivered to my
inbox. I check my email regularly, and will endeavour to respond to you within 48 hours. If you
have not heard back from me after 48 hours, please send a second email to confirm that I received
your original message.
ENJOY THE COURSE!
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Appendix A:
Service Learning Option
What is the Service Learning (SL) Option for this Course?
This Service Learning option in this course gives students the opportunity to teach basic data
management concepts to Grade 8 students from Ryerson Community Public School. The emphasis
will be on using mathematics as a tool for learning more about the people and community around
Ryerson Public School.
Ryerson Community Public School is located in the Kensington-Chinatown area of Toronto. Almost
two-thirds of students come from homes where English is not the first language. Approximately one
in every ten Ryerson PS students have been living in Canada for two years or less, and one in five
students have been living in Canada for five years or less. Over the past decade, changes to the
education funding model in Ontario have resulted in fewer resources for ESL programs and
educational assistants in Toronto elementary schools.
Many of the students in the Grade 8 class we are paired with are working below their grade level.
Students from SOC 411 will work with Grade 8 students in a group tutoring program we are calling
the Ryerson 2 Program. The goal for the Ryerson PS students is to build their math confidence and
skills, as well as to introduce them to university students and post-secondary education. The goal for
Ryerson U students is also to become more comfortable with math, and to solidify your knowledge
of data management and statistics by teaching key concepts to others.
Some concepts covered in the early part of this course overlap with data management curriculum for
Grade 8 students. In the first part of the Winter term, Ryerson U students will learn this material,
visit a Ryerson PS Grade 8 math class, and attend a session on effective tutoring. You will then
develop a worksheet that teaches a basic data management concept and also illustrates some aspect
of the social world around Ryerson PS. Following the February break, Ryerson U students will meet
weekly with Ryerson PS students to provide tutoring and build students’ skills and confidence. In
the first two weeks, you’ll be assisting Ryerson PS students with textbook problems. In the last four
weeks, you will use your worksheet (and the worksheets of other people doing SL) when meeting
with the Ryerson PS students.
Why would I want to do Service Learning?
Students who participate in the SL option will further develop their understanding of quantitative
data analysis, practice their teaching skills and contribute to community development by promoting
access to post-secondary education. This option was developed in order to help sociology students
who feel they are not particularly strong in math build their confidence. It will also provide new
challenges for students who are already quite comfortable with their math skills.
The SL option will also help you build career skills and gain practical community experience. This
option might be particularly interesting to students who are considering a career in teaching.
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How do I apply for Service Learning? How will selections be made?
If you are interested in the SL option, please complete the Student Application Form and a Police
Reference Check Authorization Form (both available on the class Blackboard site) and submit them
to Andie by noon on January 15.
The Police Reference Check (PRC) is required by the Toronto District School Board because you’ll
be working directly with youth. If there are offenses on your record, the PRC will come back to the
organization as “not cleared”. The detailed information is not revealed to the Service Learning
team or our community partners but rather is mailed to you. If you have a criminal record, you
may still be able to volunteer if you choose to disclose your offense. The final decision rests with the
community partner.
Students will be informed of selection decisions by January 19 and must confirm their intent to
participate in the SL option no later than January 21. I hope that 10-12 students will participate in
the SL option.
How will Service Learning students be evaluated?
SL students will have a slightly different assignment and evaluation structure than the other students
in the course. The midterm exam, the final exam, and the lecture/lab participation component will be
the same as for other students. The 2 nd and 3 rd data analysis exercises will also be the same as for
other students. The 1 st and 4 th data analysis exercises will be replaced by developing a worksheet,
and completing two short reflection exercises. The deadlines for these items are slightly different for
students doing the SL option in order to accommodate the project schedule.
SL Student Evaluation (items in bold are unique to SL students):
Data Analysis Exercises (2 x 12% each) 24% Exercise 2: March 4, 2009
Exercise 3: March 18, 2009
Reflexive Exercises (2 x 6% each) 12% Reflection 1a: January 27, 2009
1b: February 4, 2009
Reflection 2: April 8, 2009
Worksheet Development 12% February 25, 2009
Midterm Test 20% February 11, 2009
Final Exam 20% Scheduled by the registrar
Lecture & Lab Participation 12% Ongoing
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SL Activities Timeline:
Thurs. January 15, noon
January 19-21
January 23-February 2
Wed. January 28, 9-11 am
Tues. February 3, 5-7 pm
SL Applications due to Andie by noon
SL students selected and confirmed
participate in an orientation to SL and learning through experience
Ryerson University students visit Ryerson PS
Tutor training with Jason and Jack; select worksheet concepts
Ryerson 2 Program Dates:
February 26, March 5, March 12, March 26, April 2, and April 9
(no session on March 19 because of March Break;
bring worksheets starting on March 12)
Lunch session: 12:00-12:30 (arrive at 11:45 and leave at 12:45)
After-school session: 3:45 – 4:15 (arrive at 3:30 and leave 4:30)
* Ryerson U students will attend either the lunch session or the after school session.
Late March/Early April Ryerson Grade 8s visit Ryerson University, hosted by SOC 107
You are welcome to join them in their activities, provided it does not
interfere with your own classes.
SL Reflection #1
About 4 pages
1a: Before the school visit: Go to the website www.torontohealthprofiles.com to find out more about
the neighbourhood that the school is located in (the school is in the Chinatown-Kensington area).
Using the data provided on the site, briefly describe two or three characteristics of the
neighbourhood and explain how it compares to Toronto more generally. Focus on data that capture
social inequalities, and use a sociological perspective to interpret your findings. In a paragraph,
describe your expectations about the Ryerson PS students you will be working with. (About 2
pages). This portion of the reflection is due on January 27 th - but the exercise will not be graded
until after the second part of the reflection is submitted.
1b: During and after the school visit: During the school visit on January 28 th , observe the classroom
and the teacher carefully. Identify the strategies that the teacher uses to introduce and illustrate
concepts to students. Explain which teaching strategies seem to be the most effective and which
teaching strategies seem to be less effective, and speculate about why. Give specific examples from
your observations in your descriptions. Based on your observations, describe the strategies that you
think will be most useful when tutoring these students and explain why you think they will be useful.
(About 2 pages). This portion of the reflection is due on February 4 th . Your reflections in this
component may be shared with the teacher you are working with.
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Worksheet Development
Worksheet: 1-2 pages
Written explanation: 2-3 pages
Choose a basic data management concept to develop a worksheet about. Please coordinate with
other students to make sure that you do not duplicate topics. Using the instructions provided in the
February 3 rd workshop, develop a worksheet that highlights some aspect of people’s health in the
Ryerson PS neighbourhood compared to people’s health in Toronto overall. These data are
available in the SL section of the course Blackboard site. More information about these data and
interactive maps are available at www.torontohealthprofiles.ca.
Possible worksheet concepts include:
• making and interpreting frequency tables
• making and interpreting stem and leaf plots
• making and interpreting bar graphs
• making and interpreting pie graphs
• the difference between a sample and a population
• using percentages to make comparisons between groups
Develop a 1-2 page worksheet that will help a Ryerson PS student learn the concept you have
selected. Your worksheet should begin by setting out a problem, and then guide the students through
the steps needed to solve the problem with a series of simple instructions and/or questions. Be sure
to include some questions that will help the student to use math as a tool for understanding more
about their community and social world.
In 2-3 pages, explain the decisions you made when you developed your worksheet. Be sure to:
• identify the data management concept that you are illustrating in your worksheet
• identify the key elements that a student needs to understand in order to be able to apply
this concept
• explain how the worksheet guides the student through these key elements
• explain how the worksheet helps the student to use the results to understand their social
world
When you hand in this project, please submit a blank worksheet, a worksheet that you have
completed with the correct answers, and your written explanation. You will be evaluated on both the
quality of your worksheet and the written explanation that you provide.
You are encouraged to revise your worksheets after receiving feedback from Andie and working
with the Grade 8 students for the first two weeks. Once you bring your worksheet to the class for the
first time and distribute it to other SL students, please do not make any further changes.
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SL Reflection #2
About 4 pages
Assess the effectiveness of your worksheet as a teaching tool for illustrating your statistical concept.
What aspects of the worksheet were particularly useful for the Ryerson PS students? What aspects of
the worksheet were confusing for the Ryerson PS students? Explain how you know this, using
specific examples from your interactions with the Grade 8 students. Explain how you would change
your worksheet to make it a more effective teaching tool and to better illustrate your data
management concept. Be sure to give a rationale for each of your changes. (About 2 ½ pages).
Based on your experiences while tutoring and in this course more generally, identify some of the
things that hinder people’s learning about mathematics and statistics. Speculate about the best
strategies for overcoming these barriers. Explain why numeracy and statistical literacy are useful
tools for understanding the social world. Go back and review the initial expectations you identified
in your first reflection paper. Then, in a paragraph, briefly reflect on your interactions with the
Ryerson PS students, and describe how your experiences met or challenged the expectations you
originally had. (About 1 ½ pages).
Please also submit a copy of the final worksheet that you used with students along with your written
work.
Additional Service Learning Requirements
Service Learning students will be required to devote more time to this course than other students.
This extra time will include activities such as the training sessions and the time spent at Ryerson PS.
SL students will be graded on their academic assignments not the additional time they have
volunteered. Your timeliness and attendance are important, however, to ensure that the Ryerson PS
students are not disappointed. Remember - you are representing Ryerson University in these
interactions with community members. Students who are not able to conduct themselves in a
professional way will be asked to leave the SL program.
A copy of the worksheet that you produces will be kept by the Grade 8 teacher you are working
with, and may be used by the teacher/school in future years. Andie or the SL office may also use the
worksheets as examples in future years.
During this project, students may become aware of sensitive information about Ryerson PS students
or community members as a result of their weekly interactions at the school. SL students are asked
to keep all information confidential during and after their service. This includes not identifying any
person they meet in SL by name or including identifying information in their reflection assignments.
At the SL orientation, you must also complete the following SL Program paperwork: Student
Agreement Form, Assumption of Risk and Indemnity Agreement, and an Emergency Contact Form.
Travel off of Ryerson’s campus is required for SL students. A limited number of TTC tokens are
available from the SL Office (JOR-344J) for students who find this expense a barrier to
participation. Please contact Lia at ldepauw@arts.ryerson.ca to arrange for tokens.
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