Data in the Classroom
CSU Fresno
November 1, 2010
3/10/2016
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Presenters
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John Korey
 Cal Poly Pomona, Political Science
 jlkorey@csupomona.edu
 Ed Nelson
 CSU Fresno, Sociology
 ednelson@csufresno.edu
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Workshop Agenda
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Introductions (Ed Nelson)
SSRIC (Ed)
Data for this workshop (John Korey)
Issues and examples
 Experimental design (John)
 Sampling and Statistical Inference (Ed)
 Causality and contingency tables (Ed and
John)
 Fun with graphics (John)
 Change over time (John)
Where can we get the data? (John)
What are we doing this year at Fresno State? (Ed)
Evaluations
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SSRIC
Social Science Research & Instructional
Council
http://www.ssric.org
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The Council
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Oldest CSU affinity group -- founded in 1972
Each campus has a representative
Works to provide access to data
Promotes use of data analysis in research
and teaching
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The Council
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Annual student research conference on
April 29 at San Jose State University
 Sponsors attendance at the ICPSR summer
workshops in Ann Arbor, Michigan
 http://www.ssric.org/participate/icpsr_summ
er
 Works with the Field Institute -- selects faculty
fellow (12 questions) – proposal due April 15
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Datasets for This Workshop
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Based on SPSS for Windows 16.0: A Basic Tutorial
(http://www.ssric.org/trd/spss16)
 General Social Survey (GSS) 2006 Subset
Based on Introduction to Research Methods
(http://www.csupomona.edu/~jlkorey/POWERMUTT/i
ndex.html)
 American National Election Study (ANES) 2004
Subset
 GSS Cumulative File Subset
 ANES 2000-2002-2004 Panel Study Subset
 U.S. Senate
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Issues and Examples
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Experimental design
Sampling and statistical inference
Causality and contingency tables
Fun with graphics
Change over time
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Experimental Design
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Design Requirements
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Experiments
 Random assignment to groups
 Manipulation by experimenter of
independent (predictor) variable
 Quasi-experiments
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Types of Experiments
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Laboratory
 Field
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Laboratory Experiment:
Prisoner’s Dilemma
HOMICIDE DIVISION
INTERROGATION ROOM A
HOMICIDE DIVISION
INTERROGATION ROOM B
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Laboratory Experiment:
Prisoner’s Dilemma
INTERROGATION IN PROGRESS
DO NOT ENTER
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Laboratory Experiment:
Prisoner’s Dilemma
JACK’S BAIL BONDS
“I’ll get you out if it takes 20 years.”
909/869-4619
24/7
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Laboratory Experiment:
Prisoner’s Dilemma
Outcomes
KEY:
A'S OUTCOME
B'S OUTCOME
B TALKS
B DOESN’T TALK
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A TALKS
A DOESN'T TALK
10 YEARS
10 YEARS
DEATH
1 YEAR
1 YEAR
DEATH
WALK
WALK
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Field Experiments
Gosnell (1927)
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Gerber and Green (2000)
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Resources
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The Center for Experimental Social Science
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Experimental Design in Survey
Research
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Telephone vs. face to face (2000 ANES)
 Question wording:
 Do you favor or oppose doing away with
the DEATH tax?
 Do you favor or oppose doing away with
the ESTATE tax?
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House
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Estate
http://en.wikipedia.org/wiki/File:Ashford_castle.jpg
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Results
(2002 ANES)
Favor abolishing “death tax”: 74.3%
 Favor abolishing “estate tax”: 71.5%
p = n.s.
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Sampling and Statistical
Inference
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What do we want to make sure
our students understand?
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Populations and samples
Parameters and statistics
Sampling variability
Margin of error
Confidence intervals and confidence levels
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Basic principle
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Samples vary
 What factors influence sampling variability?
 Size of sample
 Population variability
 How sample was selected
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Using Simulations to Teach
Statistical Inference
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Draw repeated random samples
 Compute sample statistic
 Construct chart showing the distribution of
these sample statistics
 Demonstration – see
http://constats.atech.tufts.edu
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Estimators and Estimates
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An estimator is the method and an estimate
is the numerical result
 Demonstration – see
http://inspire.stat.ucla.edu/unit_09/teaching_t
ips.php
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Resources -- Exercises
Rolling dice and flipping coins – see
http://www.causeweb.org/repository/StarLibr
ary/activities/andrews_2003/
 M&M’s – see
http://www.ropercenter.uconn.edu/education/
assignments/polling_basics.pdf
 Drawing cards (Aces to Kings) – Xuanning
Fu (CSU Fresno)
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Resources – Web Sites
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Roper Center -- Fundamentals of polling:
http://www.ropercenter.uconn.edu/education/
polling_fundamentals.html
 American Association for Public Opinion
Research – more on polling -http://www.aapor.org/Poll_andamp_Survey_F
AQs.htm
 Sample size calculator -http://www.surveysystem.com/sscalc.htm
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Causality and
Contingency Tables
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What do we need to do to
establish cause and effect?
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Statistical relationship
 Causal ordering
 Eliminate alternative explanations
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Example
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Religiosity and how to regulate the
distribution of pornography – data set –
gss06_subset_for_classes_modified2.sav
 RELITEN – how religious the respondent is
 PORNLAW – how the respondent feels
about regulating the distribution of
pornography
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Spuriousness
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Are there any alternative explanations (other
than the causal one) for the relationship?
Can we think of any alternative explanations for
RELITEN and PORNLAW?
Gender might account for this relationship.
Women are more religious than men and also
more likely to want to restrict the distribution of
pornography
In other words, the relationship between X and Y
might be spurious. So what we need to do is to
test for spuriousness
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Testing for Spuriousness
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Independent variable (X) is RELITEN
 Dependent variable (Y) is PORNLAW
 Control variable (C) is SEX
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Conclusions
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We found out that the relationship of
RELITEN and PORNLAW was not spurious
when we controlled for SEX
 But does that mean that we can conclude
that the relationship is never spurious?
 What does this say about proving causality?
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Applying this to the
Classroom
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Start with examples that make sense to
students
 Move to examples with real data that
students can run
 Generalize to issues of testing causality
 Can show that a relationship is not causal
(i.e., it’s spurious)
 Can never prove that a relationship is
causal.
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Example: Specification
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Open General Social Survey Subset
 Does level of education influence the
relationship between political views and
party identification?
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Specification (continued)
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From Menu bar, go to:
Analyze  Descriptive Statistics 
Crosstabs
Dependent variable (first box): partyid
Independent variable (second box): polviews
Control variable: (third box): degree
Statistics: Kendall’s taub
Cells: Column percentages
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Specification (continued)
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Look at pattern of Kendall’s taub statistics
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Example: Reactivity
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We know that the race of the interviewer in
face-to-face interviews affects what people
tell us about race
 We know that the perceived race of the
interviewer in telephone interviews also
influences what people tell us
 What about the gender of the interviewer in
face-to-face interviews?
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ANES Example
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Open anes04s
 We’ll going to use three variables
 GENDER – gender of respondent
 INTGENPO – gender of interviewer
 WORKMOM – do you agree or disagree [that a]
working mother can establish just as warm and
secure a relationship with her children as a
mother who does not work?
 Let’s start by using the gender of the interviewer
(INTGENPO) as our independent variable and
WORKMOM as our dependent variable
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ANES Example Continued
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What did we discover? Respondents interviewed by
women are more likely to agree that working
mothers can have a warm relationship with their
children
Now let’s see if this is true for both male and female
respondents. Let’s control for GENDER – gender of
the respondent
We discover that it is true for both men and women.
It appears that the gender of the interviewer does
influence what people tell us about working mothers
and their children
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ANES Example Implications
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Since about 75% of the interviewers in this
survey were women, this has some serious
implications.
 This suggests that we will overestimate the
percent of people that feel that working
mothers can have a warm relationship with
their children
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Fun with Graphics
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Box and Whiskers Plots
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Open senate file (senate_mod.sav)
 Compare acu and dwnom scores
1.
Graphs  Legacy Dialogs  Boxplots 
Clustered  Summarize by Separate
Variables  Define
2.
1st box: acu, dwnom; 2nd box: party; 3rd
box: name; OK
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Box and Whiskers Plots
(continued)
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Convert acu and dwnom to Z scores
1.
Analyze  Descriptive Statistics 
Descriptives
2.
Move acu and dwnom to right window
3.
Check Save standardized values as
variables
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Box and Whiskers Plots
(continued)
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Compare Zacu and Zdwnom scores
1.
Graphs  Legacy Dialogs  Boxplots 
Clustered  Summarize by Separate
Variables  Define
2.
1st box: Zacu, Zdwnom; 2nd and 3rd boxes
remain the same; OK
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Sample Size and the “Margin
of (Sampling) Error”
http://www.surveysystem.com/sscalc.htm
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Just the Facts
http://pollingreport.com/guns.htm
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Poll Aggregators
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http://www.pollster.com/polls/
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Do It Yourself
Prognostication
http://uselectionatlas.org/PRED/
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Resources
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Examples of Assignments (Roper Center)
Polling 101: Fundamentals of Polling (Roper
Center)
Polling 201: Analyzing Surveys (Roper
Center)
Polling for Dummies
Sample size calculator (Creative Research
Systems)
Sampling Distributions (Tufts)
Polling and Survey FAQs (AAPOR)
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Change Over Time
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Objectives
To explain:
Trend and cohort analysis
(gsscums.sav)
Panel studies (anespanl.sav)
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Age Cohorts
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GI Generation (born 1927 or earlier)
Silent Generation (1928-1945)
Baby Boomers (1946-1964)
Generation X (1965-1981)
Generation Y (1982 or later)
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Procedure
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SPSS line charts
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Dependent Variables
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Values recoded into two
categories (0 and 100) as nearly
equal in size as possible.
Example:
Confidence in press is
recoded as 100 (a lot or only some)
and 0 (hardly any or none).
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resulting line graph can be
interpreted as the percent of
respondents coded as 100, that is,
having at least some confidence in
the press.
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Trend Analysis: Daily
Newspaper Readership
(Commands)
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Open gsscums.sav
 Click on Graphs -> Legacy Dialogs ->
Interactive -> Line
 Move NEWS to first window on right, and
YEAR to second window. Click on OK
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Trend Analysis: Daily
Newspaper Readership
(Results)
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Cohort Analysis
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To illustrate:
 Generational replacement
 Life cycle patterns
 Across the board change
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Cohort Analysis: Daily
Newspaper Readership
(Commands)
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Open gsscums.sav
 Click on Graphs -> Legacy Dialogs ->
Interactive -> Line
 Move NEWS to first window on right, YEAR
to second window, and COHORT to third
window. Click on OK
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Cohort Analysis: Daily
Newspaper Readership
(Results)
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More Cohort Analysis
Repeat above commands (first without, then
with, COHORT), but instead of NEWS, use
TVHOURS (over 2 hours per day watching
TV), then CONPRESS (at least some
confidence in the press)
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Even More Cohort Analysis
Repeat above, but try the following:
 GRASS (favor legalization of marijuana)
 RACMAR (oppose interracial marriage)
 TRUST (think most people can be trusted)
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Panel Studies
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Open anespanl.sav
 Did respondents in 2004 recall their 2000
vote differently than they had in 2000?
 Click on Analyze -> Descriptive Statistics ->
Frequencies
 Obtain frequency distributions for P200004
and P200000.
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Panel Studies
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Did the relationship between party
identification and feelings about Ralph Nader
change between 2000 (pre-election) and
2004?
 Click on Analyze -> Compare Means ->
Means.
 Move NADR00PR and NADR04 to first
window on right, and PTYID300 to second
window. Click on OK.
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Where Can We Get Data?
Data resources on or linked from the SSRIC
website:
http://www.ssric.org/data
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Social Science Databases
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The California State University subscribes to three
data bases to support teaching and research
Data bases
 Inter-university Consortium for Political and Social
Research (ICPSR) at the University of Michigan
 Field Poll in San Francisco
 Roper Center for Public Opinion Research at the
University of Connecticut
 General Social Survey and American National
Election Studies are available through these
databases
These are available to campuses by annual
subscription
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Proxy Servers
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On-campus access to data bases is IP
authenticated
Off-campus access to ICPSR and Roper
through your campus’ proxy server
For ICPSR, account only needs to be
authenticated from on campus or via proxy
server every six months; otherwise, can be
accessed from anywhere.
Off-campus access not available for Field
data
Another alternative: set up a VPN on your
home computer
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Where Do We Get the Data?
•SSRIC: http://www.ssric.org/data
•Pew: http://people-press.org/dataarchive/
•PPIC:
http://www.ppic.org/main/datadepot.asp
•Berkeley’s SDA archive:
http://sda.berkeley.edu/archive.htm
•ICPSR: http://www.icpsr.org
•Roper: http://www.ropercenter.uconn.edu
•Field
Public : ftp://128.32.165.222:2121/
(download spss files)
CSU and UC only ( analyze online):
http://ucdata.berkeley.edu/data_record.p
hp?recid=3#analyze
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What are we doing this year at
Fresno State?
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Workshops for faculty and staff

Teaching with Data (September 23)
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Data in the classroom (November 1 with special guest
presenter John Korey, Political Science, CSU Pomona)
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Online statistical packages (SDA) (early spring)
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SPSS (introductory and intermediate) (late spring)
Encourage students to present their research at student
research conferences (SRC)
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SSRIC’s SRC in San Jose on April 29
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Santa Clara University’s Anthropology and Sociology SRC
in April
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CSU’s Student Research Competition in Fresno on May 6-7
Presentations at the department level
One-on-one consultations with faculty
Surveys to get faculty’s input and feelings
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Evaluations
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