1. SP 225 Lecture 4 Introduction to Data and Levels of Measurement

2. GENERAL SOCIAL SURVEY AND SPSS Manipulating Data Sheets

3. How Do We Collect Data to Test Hypothesis?  Natural Sciences Physical Measurements Observations  Social Sciences Survey Data Community Observations Interviews

4. Preparing Data For Analysis  Enter data from survey to spreadsheet  Adopt a common meaning for spreadsheet entries Called a ‘code’ Used to standardize entries Protect against data entry errors

5. Survey Data  Standardizing and interpreting responses  Managing missing or erroneous responses

6. Designing Better Surveys  Remove opportunities for question misinterpretation  Change survey administration method

7. General Social Survey  Began in 1972  Began as an annual study  Currently conducted on even years  Administered by the University of Chicago National Opinion Research Center  Funded by the National Institute of Science and Foundations

8. Usage  Over 12,000 research uses  250,000 students use the data annually

9. Study Design  Block Design  Cluster Design  Methodology has been varied to understand any impact methodology has on study results  We can analyze it as a random sample

10. Study Questions  Some questions asked every year  Other mini-studies administered each year

11. Topics by Year  Extending Scales on Race, Abortion, and Feminism. (1977)  Attitudes on the Military and Military Recruitment. (1982)  Social Networks. (1985)  Sociopolitical Participation. (1987)  Religion. (1988)  Intergroup Relations. (1990)  Work Organizations. (1991)  Culture. (1993)  Family Mobility and Multiculturalism. (1994)  Mental Health, Emotions, Gender, Markets and Giving and Volunteering (1996)  Job Experiences, Mental Health, Medical Care, Medical Ethics, Culture, Religion/Religion and Health. (1998)

12. Guide to GSS Contents  http://webapp.icpsr.umich.edu/GSS/inde x.html

13. Manipulating Data in SPSS  Opening data sheets  Viewing and reading data  Creating an SPSS data sheet

14. LEVELS OF MEASUREMENT

15. Levels of Measurement All data can be classified into one of four categories  Nominal  Ordinal  Interval  Ratio

16. Implication of the Level of Measurement  Analysis  Mathematical Calculations of the Data Each level of measurement represents an increasing degree of complexity in the data

17. Levels of Measurement  Nominal: data consisting of names and labels (i.e.. Color)  Ordinal: data can be arranged in order but differences between data values are meaningless (i.e.. Rating systems)  Interval: data that can be arranged in order with meaningful differences between data points but no natural zero. (i.e.. Years)  Ratio: data that has the properties of interval data with a natural zero (i.e.. Weight)

18. Levels of Measurement

19. Name the Level of Measurement  Consumer reports magazine ratings of “best buy”, “recommended” and “not recommended”  Number of yes responses when 500 students are asked if they have ever engaged in binge drinking  Temperature in New York

20. M&M Example  What characteristics of a bag of M&M’s are nominal, ordinal or interval/ratio in measurement?

21. Levels of Measurement in Data Collection  Identifying levels of measurement from a survey question  Changing the level of measurement  Identifying levels of measurement from a described research project