sampling

1. Sampling

2. Sampling Most survey research involves selecting a sample because of the cost and time involved in surveying the entire population.

3. Types of Samples Probability Sampling Regarded as the best; most scientific Everyone in the population has an equal chance of being selected Non-Probability Sampling Non-scientific Sample may not be (generally isn�t) representative of the general population

4. Probability Sampling Simple Random Sample Each individual in the population has an equal chance of being selected. An example: Put everyone's names in a hat and then draw them out.

5. Probability Sampling Stratified Random Sample Used to ensure that sub-groups within a population are represented proportionally in the sample. Example: If the state is divided into six geographic regions and the Southeast Region contains 15% of the total population under study, then 15% of the sample has to be drawn from this region.

6. Stratified Random Sample

7. Probability Sampling Cluster Sampling Random selection of groups that already exist. Example: To do a study of Horticulture I Ag students, you would randomly select schools, then randomly select Hort I classes from within the schools

8. Cluster or Multi-Stage Sampling

9. Probability Sampling Systematic Random Sample The sample is drawn from a numbered list of people. A person is randomly picked near the top of the list, then every Nth name is selected after that (Nth could be 3rd, 7th, 10th or whatever number is needed to get the correct sample size).

10. Systematic Random Sample (every 3rd person selected) Bob Adams Billy Benham Sue Conners Ward Dunlap Teresa Elgin Bob Franks Cindy Gomez Dan Headley Aaron Jackson Sue Kimmons Todd Larson Barb Morris Helen Newcomb Inez Oppenheimer Tad Porter Linda Rush Robert Sims Tina Thompson

11. Non-Probability Sampling Convenience (also called accidental sample) The researcher selects whomever is convenient Example: A researcher at the mall selects the first five people who walk by to get their opinion of a product.

12. Non-Probability Sampling Purposive (or judgmental sample) Individuals are selected because of their expertise, specialized knowledge, or characteristics. Example: To learn more about emerging trends or issues in the field, you might want to survey the professional organization leaders.

13. Non-Probability Sampling Snowball Sampling (also know as chain or network sampling) A small group is initially identified . After data are collected from them, they are asked to identify others who might have specialized knowledge regarding the topic; those thus identified recommend others.

14. How Big Does the Sample Need to Be? Researchers and statisticians have developed formulas and tables that show how big the sample has to be. Generally, two things are needed in order to used these tools How big is the population? How much chance of error are you willing to accept (confidence level and confidence interval)

15. Common Sample Size Experts Cochran�s Q Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: Wiley Krejcie & Morgan Krejcie, R.V. & Morgan, D.W. (1970). Determining sample size for research activities. Educational & Psychological Measurement, 30, 607-610.

16. Sample size With the Cochran formula, you have to plug in data and manually calculate an answer Most people don�t want to do this Krejcie and Morgan have developed a table (presented on the next page).

17. Krejcie & Morgan

18. Error No matter if you use Krejcie and Morgan or Cochran or one of the other formulas you typically must specify Confidence Level Confidence Interval In other words you determine how accurate your sample has to be

19. Confidence Level The confidence level tells you how sure you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers use the 95% confidence level.

20. Confidence Interval The confidence interval is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer.

21. The Easy Way to Determine Sample Size Go to http://www.surveysystem.com/sscalc.htm and enter your figures.

22. The Mechanics of Selecting a Sample Put everyone�s name on a piece of paper and draw names out of a hat. (not very efficient use of time for large groups) Use a table of random numbers Number all the people in the population, then use a table of random numbers (found in statistics books or on the web) to identify which individuals to select.

23. Selecting a Sample Go to http://www.randomizer.org/form.htm and have numbers automatically generated for you.

24. Other Views According to Gay & Diehl, (1992), generally the number of respondents acceptable for a study depends upon the type of research involved - descriptive, correlational or experimental.

25. Gay and Diehl (1992) For descriptive research the sample should be 10% of population. But if the population is small then 20% may be required. (I do not agree with this, but some folks do)

26. Gay and Diehl (1992) In correlational research at least 30 subjects are required to establish a relationship. For experimental research, 30 subjects per group is often cited as the minimum.

27. Collecting Data The mailed survey is a common method for collecting data. Personal interviews, phone interviews, group administrated instruments and web-based surveys can also be used. There are some obvious and not-so-obvious advantages and disadvantages of these data collection techniques. These are covered in AEE 579. In this class we will focus on the mailed survey.

28. Getting High Response Rates from Mailed Surveys Print on colored paper Mail on a Thursday or Friday Use incentives (pencils, pens, stamps, money, food, coupons, phone cards stickers, drawings, etc.)

29. Getting High Response Rates from Mailed Surveys Two weeks after your initial mailing, contact the non-respondents with a postcard, phone call or second mailing of the instrument (I prefer the later) Consider a 3rd mailing if needed

30. Getting High Response Rates from Mailed Surveys Include an addressed stamped reply envelope Put your name and mailing address on the actual instrument Type �over� on the bottom of each page if there are questions on the back. Make the instrument as short as possible (but still contain the questions you need)

31. Getting High Response Rates from Mailed Surveys Include a cover letter where the importance and significance of the research is clearly described. Have a prominent person sign the cover letter. Have clear specific directions for completing the instrument.

32. Electronic Surveys Consider using an electronic survey Zoomerang Survey Builder (a CALS product) Survey Monkey They are quick, costs less and data can be ready to analyze shortly after it is collected

33. Non-Responders Consider a telephone interview with a random sample of non-responders Compare early and late responders. If they are the same on selected variables, one can conclude non-responders are similar to non-responders.

34. How do I know who the non-responders are? Have a code number on the instrument (address this in the cover letter and tell why it is there; only to contact non-responders) Have a code on the return envelope (a small number written inside the envelope or a line in the return address that has no real meaning other than to identify the respondent�Department 87, 88, 89, etc.) Place a code number under the stamp on the return envelope

35. What is a Good Response Rate? In Agricultural and Extension Education, 70% is the norm Some authors say 50% is OK (Gay), while others (Dillman) believe you should get 80%.