9/3/20151 Data in the Classroom CSU Fresno November 1, 2010

9/3/20152 Presenters  John Korey Cal Poly Pomona, Political Science Cal Poly Pomona, Political Science  Ed Nelson CSU Fresno, Sociology CSU Fresno, Sociology

9/3/20153 Workshop Agenda  Introductions (Ed Nelson)  SSRIC (Ed)  Data for this workshop (John Korey)  Issues and examples Experimental design (John) Experimental design (John) Sampling and Statistical Inference (Ed) Sampling and Statistical Inference (Ed) Causality and contingency tables (Ed and John) Causality and contingency tables (Ed and John) Fun with graphics (John) Fun with graphics (John) Change over time (John) Change over time (John)  Where can we get the data? (John)  What are we doing this year at Fresno State? (Ed)  Evaluations

9/3/20154 SSRIC Social Science Research & Instructional Council

9/3/20155 The Council  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

9/3/20156 The Council  Annual student research conference on April 29 at San Jose State University  Sponsors attendance at the ICPSR summer workshops in Ann Arbor, Michigan er er er er  Works with the Field Institute -- selects faculty fellow (12 questions) – proposal due April 15

Datasets for This Workshop  Based on SPSS for Windows 16.0: A Basic Tutorial ( General Social Survey (GSS) 2006 Subset General Social Survey (GSS) 2006 Subset  Based on Introduction to Research Methods ( ndex.html) ndex.htmlhttp:// ndex.html American National Election Study (ANES) 2004 Subset American National Election Study (ANES) 2004 Subset GSS Cumulative File Subset GSS Cumulative File Subset ANES Panel Study Subset ANES Panel Study Subset U.S. Senate U.S. Senate 9/3/20157

8 Issues and Examples Experimental design Experimental design Sampling and statistical inference Sampling and statistical inference Causality and contingency tables Causality and contingency tables Fun with graphics Fun with graphics Change over time Change over time

9/3/20159 Experimental Design

9/3/ Design Requirements  Experiments Random assignment to groups Random assignment to groups Manipulation by experimenter of independent (predictor) variable Manipulation by experimenter of independent (predictor) variable  Quasi-experiments

9/3/ Types of Experiments  Laboratory  Field

9/3/ Laboratory Experiment: Prisoner’s Dilemma HOMICIDE DIVISION INTERROGATION ROOM A HOMICIDE DIVISION INTERROGATION ROOM B

9/3/ Laboratory Experiment: Prisoner’s Dilemma INTERROGATION IN PROGRESS DO NOT ENTER

9/3/ Laboratory Experiment: Prisoner’s Dilemma JACK’S BAIL BONDS “I’ll get you out if it takes 20 years.” 909/ /7

9/3/ Laboratory Experiment: Prisoner’s Dilemma Outcomes KEY: A'S OUTCOME B'S OUTCOME A TALKSA DOESN'T TALK B TALKS10 YEARS DEATH 1 YEAR B DOESN’T TALK 1 YEAR DEATHWALK

9/3/ Field Experiments Gosnell (1927) Gerber and Green (2000)

9/3/ Resources  The Center for Experimental Social Science The Center for Experimental Social Science The Center for Experimental Social Science

9/3/ Experimental Design in Survey Research  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 DEATH tax? Do you favor or oppose doing away with the ESTATE tax? Do you favor or oppose doing away with the ESTATE tax?

9/3/ House

9/3/ Estate

9/3/ Results (2002 ANES)  Favor abolishing “death tax”: 74.3%  Favor abolishing “estate tax”: 71.5% p = n.s.

9/3/ Sampling and Statistical Inference

9/3/ What do we want to make sure our students understand?   Populations and samples   Parameters and statistics   Sampling variability   Margin of error   Confidence intervals and confidence levels

9/3/ Basic principle   Samples vary   What factors influence sampling variability? Size of sample Population variability How sample was selected

9/3/ Using Simulations to Teach Statistical Inference   Draw repeated random samples   Compute sample statistic   Construct chart showing the distribution of these sample statistics   Demonstration – see

9/3/ Estimators and Estimates   An estimator is the method and an estimate is the numerical result   Demonstration – see ips.php ips.php

9/3/ Resources -- Exercises   Rolling dice and flipping coins – see ary/activities/andrews_2003/ ary/activities/andrews_2003/   M&M’s – see assignments/polling_basics.pdf assignments/polling_basics.pdf   Drawing cards (Aces to Kings) – Xuanning Fu (CSU Fresno)

9/3/ Resources – Web Sites   Roper Center -- Fundamentals of polling: polling_fundamentals.html polling_fundamentals.html   American Association for Public Opinion Research – more on polling -- AQs.htm AQs.htm   Sample size calculator

9/3/ Causality and Contingency Tables

9/3/ What do we need to do to establish cause and effect?  Statistical relationship  Causal ordering  Eliminate alternative explanations

9/3/ Example  Religiosity and how to regulate the distribution of pornography – data set – gss06_subset_for_classes_modified2.sav RELITEN – how religious the respondent is RELITEN – how religious the respondent is PORNLAW – how the respondent feels about regulating the distribution of pornography PORNLAW – how the respondent feels about regulating the distribution of pornography

9/3/ Spuriousness  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

9/3/ Testing for Spuriousness  Independent variable (X) is RELITEN  Dependent variable (Y) is PORNLAW  Control variable (C) is SEX

9/3/ Conclusions  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?

9/3/ Applying this to the Classroom  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 show that a relationship is not causal (i.e., it’s spurious) Can never prove that a relationship is causal. Can never prove that a relationship is causal.

9/3/ Example: Specification  Open General Social Survey Subset  Does level of education influence the relationship between political views and party identification?

9/3/ Specification (continued)  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 tau b Cells: Column percentages

9/3/ Specification (continued)  Look at pattern of Kendall’s tau b statistics

9/3/ Example: Reactivity  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?

9/3/ ANES Example  Open anes04s  We’ll going to use three variables GENDER – gender of respondent GENDER – gender of respondent INTGENPO – gender of interviewer 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? 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  Let’s start by using the gender of the interviewer (INTGENPO) as our independent variable and WORKMOM as our dependent variable

9/3/ ANES Example Continued  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

9/3/ ANES Example Implications  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

9/3/ Fun with Graphics

9/3/ Box and Whiskers Plots  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; 2 nd box: party; 3 rd box: name; OK

9/3/ Box and Whiskers Plots (continued)  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

9/3/ Box and Whiskers Plots (continued)  Compare Zacu and Zdwnom scores 1. Graphs  Legacy Dialogs  Boxplots  Clustered  Summarize by Separate Variables  Define 2. 1st box: Zacu, Zdwnom; 2 nd and 3 rd boxes remain the same; OK

9/3/ Sample Size and the “Margin of (Sampling) Error”

9/3/ Just the Facts

9/3/ Poll Aggregators

Do It Yourself Prognostication 9/3/201550

9/3/ Resources  Examples of Assignments (Roper Center) Examples of Assignments Examples of Assignments  Polling 101: Fundamentals of Polling (Roper Center) Polling 101: Fundamentals of Polling 101: Fundamentals of Polling  Polling 201: Analyzing Surveys (Roper Center) Polling 201: Analyzing Surveys Polling 201: Analyzing Surveys  Polling for Dummies Polling for Dummies Polling for Dummies  Sample size calculator (Creative Research Systems) Sample size calculator Sample size calculator  Sampling Distributions (Tufts) Sampling Distributions Sampling Distributions  Polling and Survey FAQs (AAPOR) Polling and Survey FAQs Polling and Survey FAQs

9/3/ Change Over Time

9/3/ Objectives To explain:  Trend and cohort analysis (gsscums.sav)  Panel studies (anespanl.sav)

9/3/ Age Cohorts  GI Generation (born 1927 or earlier)  Silent Generation ( )  Baby Boomers ( )  Generation X ( )  Generation Y (1982 or later)

9/3/ Procedure  SPSS line charts

9/3/ Dependent Variables  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).  The resulting line graph can be interpreted as the percent of respondents coded as 100, that is, having at least some confidence in the press.

9/3/ Trend Analysis: Daily Newspaper Readership (Commands)  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

9/3/ Trend Analysis: Daily Newspaper Readership (Results)

9/3/ Cohort Analysis  To illustrate: Generational replacement Generational replacement Life cycle patterns Life cycle patterns Across the board change Across the board change

9/3/ Cohort Analysis: Daily Newspaper Readership (Commands)  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

9/3/ Cohort Analysis: Daily Newspaper Readership (Results)

9/3/ 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)

9/3/ 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)

9/3/ Panel Studies  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 P and P

9/3/ Panel Studies  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.

9/3/ Where Can We Get Data? Data resources on or linked from the SSRIC website:

9/3/ Social Science Databases  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 Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan Field Poll in San Francisco Field Poll in San Francisco Roper Center for Public Opinion Research at the University of Connecticut Roper Center for Public Opinion Research at the University of Connecticut General Social Survey and American National Election Studies are available through these databases General Social Survey and American National Election Studies are available through these databases These are available to campuses by annual subscription These are available to campuses by annual subscription

9/3/ Proxy Servers  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

9/3/ Where Do We Get the Data? SSRIC: SSRIC: Pew: Pew: PPIC: PPIC: Berkeley’s SDA archive: Berkeley’s SDA archive: ICPSR: ICPSR: Roper: Roper: Field Field Public : ftp:// :2121/ (download spss files) ftp:// :2121/ CSU and UC only ( analyze online): hp?recid=3#analyze hp?recid=3#analyze hp?recid=3#analyze

9/3/ What are we doing this year at Fresno State?   Workshops for faculty and staff Teaching with Data (September 23) Data in the classroom (November 1 with special guest presenter John Korey, Political Science, CSU Pomona) Online statistical packages (SDA) (early spring) SPSS (introductory and intermediate) (late spring)   Encourage students to present their research at student research conferences (SRC) SSRIC’s SRC in San Jose on April 29 Santa Clara University’s Anthropology and Sociology SRC in April 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

9/3/ Evaluations