1. Sampling Sampling Big Questions – Main Ideas 1)Should you include everyone or just a sample? 2)Probability versus Non-probability 3)How large? Response rate?

2. Sampling POPULATION: all people who possess the characteristic of interest. The relevant characteristic of a population is the parameter (Who is in and who is out?) UNIVERSE: all non-people (texts) that share the characteristics of interest. CENSUS: when researchers collect data from all members of a population or a universe SAMPLE: subgroup of a population or a universe -- a measurement of a sample with respect to a variable is called a “statistic.” Why do people watch Reality TV? Newspaper readers are more likely to vote for Democrats than are non-readers What types of sexual stereotypes against women does the sitcom Roseanne perpetuate? What persuasive tactics did Bill Clinton use in his public rhetoric as President? Gov’t counts every member of nat’l population Survey every member of a small organization How students at Christian colleges use the internet...

3. Process populationSTEP 1: identify the population you want to describe sampling frameSTEP 2: get a sampling frame (if possible) choose a methodSTEP 3: choose a method for selecting respondents (random/non-random)

4. SAMPLING Population Sample a STATISTIC: the summary description of a given variable in a survey sample. a PARAMETER: the relevant Characteristic of a population generalizable BASIC PRINCIPLE OF PROBABILITY SAMPLING: a sample will IF be representative of the population from which it is selected, IF all members of the population have an equal chance of being selected in the sample on each draw.

5. SAMPLING Population Sample Inherent ERROR: Sampling: the degree to which measurements of the units/subjects selected differ from those of the population as a whole (“margin of error”) Measurement: inconsistencies produced by the instrument used; the way questions are asked, etc. ERROR + or - 2

6. SAMPLES PROBABILITY NON PROBABILITY SIMPLE RANDOM SAMPLE equal chance for selection must have complete list random number table SYSTEMATIC SAMPLE every nth subject sampling rate, 1/4 STRATIFIED SAMPLE selected from homogeneous subsets of the population CLUSTER SAMPLE selects the sample in groups or categories, e.g., classes MULTISTAGE SAMPLE counties, districts, blocks, households CONVENIENCE SAMPLE available, Journals VOLUNTEER SAMPLE advertised, rewards PURPOSIVE SAMPLE selected on basis of specified characteristics, e.g., twins QUOTA SAMPLE selected to meet a pre- determined percentage, e.g., 70% women at SAU HAPHAZARD SAMPLE every nth person SNOWBALL SAMPLE referrals, network

7. SAMPLES PROBABILITY NON PROBABILITY Selected by mathematical guidelines Allows the calculation of sampling error Systematic selection procedures Does not follow mathematical guidelines Does not allow calculation of sampling error Frequently used in media research Less expensive, faster Requires complete population list, cf., talk shows, happily married? For exploratory studies Generalizable Limited generalizability

8. How Large Should Your Sample Be? How much sampling error are you willing to tolerate? (5%, 1%) -- (“standard error”) Larger samples tend to reduce sampling error (true population mean) IF randomly drawn (table)table Depends on your research question and research method Do you want to compare groups? (see p. 433, Reinard) 30 subjects in each group

9. The end

10. Simple Random Sample 357 students from a college of 5000 – Calvin College –Q. Relationship between religious commitment and students’ sense of community on a Christian college campus? STEPSSTEPS 1. Population parameter: All 5000 students who attend Calvin 2. Complete list of population from registrar’s office 3. Assign each person a number 0001 to 5000 4. Random number table - select 357 subjects or EXCEL!5. or EXCEL!

11. Stratified Sample Question: To what degree do students of differing academic ability at SAU differ in communication competence and communication adaptability?Question: To what degree do students of differing academic ability at SAU differ in communication competence and communication adaptability? FIRST –divide students into groups – high, average, low SECOND –randomly sample within each group

12. Multistage Cluster Sampling QUESTION: Americans’ attitudes toward political advertisements in 2004 Presidential campaignQUESTION: Americans’ attitudes toward political advertisements in 2004 Presidential campaign FIRST--select a few states at random SECOND--select a few counties THIRD--a few cities FOURTH--a few streets FIFTH--a few households, few members of each Imagine

13. States Counties Cities Streets Households Individuals Multi-stage Cluster Sample U.S.A.

14. SAMPLING Population Sample ERROR? As Size of Sample Increases Sampling Error Decreases

15. Confidence Interval Survey of SAU students, 300 randomly selected respondents 70% answer “Yes” (community is strong here on campus) +/-9.2Sampling error is +/-9.2 (only because of random sample) add and Subtract 9.2 to 70%This means that you add and Subtract 9.2 to 70% 70%You get a “confidence interval” (60.8 – 70% + 79.2%) 95% confidentWhich means you are 95% confident that the true proportion of the total population falls within this range Thus, if you were to sample from the same population 95 more times, you could be sure that 70% would answer “Yes”, within 9.2 + or –

16. Sample Size Table See Reinard, see p. 436