Survey Documentation and Analysis (SDA)

1. Survey Documentation and Analysis (SDA)

2. Workshop Agenda • Overview • What is online analysis? • Available SDA data sets • Statistical procedures (Frequencies, Crosstabs, Regression) • Recoding, subsetting, downloading • Teaching resources for SDA and developing instructional materials

3. SSRIC Social Science Research & Instructional Councilhttp://www.ssric.org

4. The Council • Oldest CSU discipline council • Founded in 1972 • Representatives from CSU campuses meet three times per year • Negotiates with data providers for access to data • Promotes use of data analysis in research and teaching

5. The Council • Annual student research conference • at CSU Long Beach in 2008 • at CSU Sacramento in 2009 • Sponsors travel to ICPSR summer workshops in Ann Arbor, Michigan • http://www.ssric.org/participate/icpsr_summer • Works with Field Research • Question credits to California Field Poll • Selects faculty fellow

6. What is Online Analysis? • “Online data analysis" refers to the ability to perform statistical analysis using special Web-based software as an alternative to downloading data into a standalone statistical package on your computer. • The software we’re using is called Survey Documentation and Analysis (SDA), which was developed at the University of California, Berkeley.

7. Alternative Statistical Packages • You can get a complete list of available online statistical packages at http://statpages.org/ • Some of these include: • OpenStat • ViSta • Statext • SISA

8. Advantages • Many like SDA are free – don’t require a site license • Only require a computer with an internet connection • Some like SDA are easy to learn • Can show students how to use some of them in 30 minutes or less

9. Disadvantages • Some online statistical packages (certainly not all) are limited in what they can do statistically • Documentation is not very good for some • Some (like SDA) can only be used with data sets that have already been created in a format that can be read by that package

10. Available SDA Data Sets

11. SDA Data Sets • While SDA is an extremely easy statistical package to learn to use, it’s difficult to create SDA data sets. • You have to purchase a SDA site license to create a data set and then learn how to use it. • So we typically use SDA data sets that have been created for us.

12. Sources for SDA Data Sets • SDA Archive located at UC Berkeley (http://sda.berkeley.edu/archive.htm) • ICPSR Topical Archives (http://www.icpsr.org/cocoon/ICPSR/all/archives.xml) • Field data located at UC Berkeley (http://ucdata.berkeley.edu/data_record.php?recid=3#analyze) • List of SDA data sets at CSU Long Beach (http://www.csulb.edu/library/eref/datasets.html) • University of Denver’s IDEA project (http://www.du.edu/idea/data.htm

13. SDA Archive at UC Berkeley (http://sda.berkeley.edu/archive.htm) • GSS Cumulative Datafile (1972-2008; 2008 is a preliminary version). • ANES Cumulative Datafile (1948-2000) and ANES datafiles for 1996, 2000, and 2004. • Census microdata including 2000-2003 American Community Surveys and 1990 and 2000 U.S. 1% PUMS with separate files for 2000 and 1990 California PUMS.

14. ICPSR • National Archive of Computerized Data on Aging (http://www.icpsr.umich.edu/NACDA/) • National Archive of Criminal Justice Data (http://www.icpsr.umich.edu/NACJD/) • Substance Abuse and Mental Health Data Archive (http://www.icpsr.umich.edu/SAMHDA/) • International Archive of Education Data (http://www.icpsr.umich.edu/IAED/)

15. Field Datahttp://ucdata.berkeley.edu/data_record.php?recid=3#analyze • Field Polls from 1956 through 2006 are available as publicly-accessible SDA data sets • More recent Field Polls are available as SPSS data sets (through FTP) for CSU faculty, staff, and students.

16. Other Sources of SDA Data Sets at ICPSR • Voting Behavior: The 2004 Election by Charles Prysby and Carmine Scavo (http://www.icpsr.umich.edu/SETUPS/) • Investigating Community and Social Capital by Lori Weber (http://www.icpsr.umich.edu/ICSC/index.htm)

17. Statistical Procedures

18. Available Statistical Procedures • Frequencies and crosstabulation (discussed in this workshop) • Comparison of means • Correlation matrix • Comparison of correlations • Multiple regression (discussed in this workshop) • Logit/Probit regression

19. Using SDA • Select the data set • Look at the codebook • Decide what statistical procedure to use • Fill in what you want to do • Run it

20. Data Set • We’re going to use the GSS 1972-2008 Cumulative Data File (2008 is preliminary data) • http://sda.berkeley.edu/archive.htm • We’re going to use three variables • SEX • RELITEN • PORNLAW

21. Frequencies • List the variables you want to use • ROW: SEX,RELITEN,PORNLAW • Click on “Run the Table”

24. Crosstabs • Now let’s use RELITEN as our independent variable and PORNLAW as our dependent variable to create two bivariate crosstabulations. • List the variables • ROW: PORNLAW • COLUMN: RELITEN

25. Crosstabulation Continued • Options • Percentaging: column • Statistics • Question text • Color coding • Run the Table

28. Your Turn • Let’s run two more bivariate crosstabs • Independent variable: SEX • Dependent variables: RELITEN and PORNLAW • Go ahead and run these crosstabs

29. What Did we Discover? • RELITEN is strongly related to PORNLAW. • SEX is also related to both RELITEN and PORNLAW. • Could the relationship between RELITEN and PORNLAW be spurious? SEX is related to both RELITEN and PORNLAW and could be creating the relationship between RELITEN and PORNLAW. • How do we test this possibility? Let’s run a three-variable crosstabulation with RELITEN as our independent variable, PORNLAW as our dependent variable, and SEX as our control variable.

30. Multivariate Crosstabulation • List the variables • ROW: PORNLAW • COLUMN: RELITEN • CONTROL: SEX • Options • Percentaging: column • Statistics • Question text • Color coding

34. Spuriousness • Was the relationship between RELITEN and PORNLAW spurious due to SEX? • How do you know? • Does that mean that the relationship can never be spurious?

35. Regression • Crosstabulation is used when all the variables are categorical. • What do we do when our variables are continuous (i.e., interval and/or ratio)? • Regression is the answer.

36. Bivariate Regression • Let’s look at the relationship between the respondent’s socioeconomic status (SEI) and the amount of television one watches (TVHOURS). • List the variables • Dependent: TVHOURS • Independent: SEI • Options • T-Tests • Correlation matrix • Color coding • Question Text

39. Multivariate Regression • Now let’s add in another variable: SEX • But sex is not a continuous variable. How do we enter a variable like SEX into the regression analysis? Answer: create a dummy variable. • Dummy variables take on the values of 1 and 0.

40. Creating a Dummy Variable • SEX (d:1) • SEX is the name of the variable to want to make into a dummy variable • d indicates that you want to create a dummy variable • 1 indicates that the value 1 will be assigned the value 1. All other values will be assigned the value 0. • Run the table

43. Recoding, Subsetting, Downloading

44. ATTEND0 Never1 Less than once a year2 Once a year 3 Several times a year 4 Once a month 5 2 to 3 times a month 6 Nearly Every Wk 7 Every week 8 More than once a week 9 DK/NA (Missing) ATTENDR 1 Seldom (0 to 3) 2 Sometimes (4 to 5) 3 Often (6 to 8) 9 Missing (9) Recoding Existing VariablesExample (from GSS Cumulative File): ATTEND (How often Respondent attends religious services)

49. Your Turn Recode AGE into the following categories: 1 = 18-29 2 = 30-64 3 = 65 and older Obtain FREQUENCIES for the result

50. For More Information, See: http://sda.berkeley.edu/HELPDOCS/helpnewv.htm#recode