1. Correlational Research

2. Goal: Description and Prediction
Look at relationships amongst naturally occurring variables. There are no “IVs”.
Not a “true” experiment. No basis for cause/effect type conclusions.

3. Examples Survey/questionnaire research (Chapter 5)
Observational research (Chapter 4)
Quasi-experimental research (Chapter 10)
Single-subject/small-n research (Chapter 9)
Mostly non-statistical, some use of correlation

4. Today’s Lecture: Survey/Questionnaire Research
Some terminology used in Survey Research:
Survey- to examine or look at closely
Census- a census is a survey of a population where EVERY member of the population is included in the survey data
Questionnaire- a questionnaire is an instrument used to measure in a survey. It is a set of predetermined questions asked of all respondents

5. In order to conduct a survey, you would select a sample from your population
measure that sample
and then draw inferences about the population based on the sample
This is true in ALL research actually
How do you select an appropriate sample that will give you a good chance of answering your research questions?

6. Sampling: a three step process
Define your population of interest (A population is the set of all cases of interest)
2) Develop a sampling frame-a physical list of all members of the population
3) Choose a technique for selecting a subset of members from the frame that will be “representative” of your population. You want an “unbiased” sample .

7. Population, Sampling Frame, Sample, Element

8. Example: Attitudes toward domestic violence
Target population: Enrolled UMASS students.
Sampling frame: a list of all currently enrolled UMASS students. Where would you get this list? Would the list be complete?
Sample: select a subset of students from the list.

9. What would your “sampling frame” be if your target population were all college students in the New England?
What would it be if your target population were all college students in the US?

10. There are many different methods for selecting members from a population to use as a survey sample
Some are better than others
Some serve a particular purpose
Two general categories of sampling techniques: Non-probability and Probability
We will talk about Non-probability samples first

11. Non-Probability Samples
A sampling procedure in which there is no way to estimate the probability of any specific element’s inclusion in the sample.
We will cover two non-probability samples: Convenience sample and Snowball sample

12. The Convenience Sample
The most common form of non-probability sample is the convenience sample
convenience sample: elements are selected based on availability and willingness to participate
Example: Shere Hite (1987), Women and Love: A Cultural Revolution in Progress

13. Mailed 100,000 surveys to various women’s organizations in 43 states
Part of survey asked about women’s sexual fantasies.
People objected to the idea that the information in the book was representative of typical American woman’s sexual fantasy
Shere Hite maintained that the results were based on a large number (4500) surveys

14. 4500 is a lot of surveys but actual response rate was only 4.5%
Could those women who did respond be very different from those who did not respond???
In general, a 50% response rate is considered “adequate”, 60%= good, 70%= very good.
Typical response rate= 30%

15. Snowball Sample
a non-probability sampling technique that is especially useful when the target population is not one where you can easily get a sampling frame
Find one or two possible participants and then use these to locate others

16. Example
A survey of the safe-sex practices of prostitutes in Springfield
How (where) would you find a complete list of Springfield prostitutes?
Unlikely to find such a list
Locate one or two prostitutes in Springfield willing to participate in the survey
Ask them for contact information on others who might participate

17. Probability Sampling
The probability of any particular element being included in the sample can be determined (calculated)
This does NOT mean each element has an EQUAL chance of inclusion
We will cover four Probability Samples: Simple Random, Systematic, Stratified Random, and Stratified Proportionate

18. Simple Random Sample Not so simple
A probability sample where every element of the population has an equal chance of inclusion
Must use a formal random method for selecting elements from the sampling frame
random # table, coin, dice, random # generator

19. Example #1
Go to library at 11 AM on Thursday and hand a survey to every person passing by
Is this a truly random sample of UMASS students?
Why/why not?

20. Example #2
Use a list of all students currently enrolled at UMASS from the registrar
Randomly select 50 students, contact via phone
Ask if they would be willing to respond to a short, 5-min phone survey
Is this a simple random sample?
Why/Why not?

21. What would you call these samples?

22. In Psychology, if you work with people, you will always have a convenience sample because all people must be asked for their participation and have the right to refuse…thus they must be willing
We only use willing participants

23. Systematic Sample
A probability sample that is similar to a simple random sample
rather than using a formal randomization process to select elements, you use a systematic process such a “choose every nth element” from the list.
This is much faster and generates a sample that, while not truly random, is unlikely to be biased

24. You do need to make sure “n” is set so that you will span the entire list
Suppose you want a sample of 100 students and your list is 1,000 students long
If you used n=3, then you would only cover the first 300 people on the list

25. If the list is ordered systematically in anyway (by year of graduation, for example) you could end up with a very biased sample.
If you used n=10, you would cover the entire list and have lower risk of a biased sample

26. Stratified Samples
A probability sample where you break the sampling frame into “strata” (layers or categories) and then sample from each strata
Two types: Stratified Random and Stratified Proportionate

27. Stratified Random Sample
Break frame into strata and randomly sample an equal number of participants from each strata
Useful when you do inferential statistical tests between (amongst) some particular types of participants where some categories are underrepresented relative to others
Creates approximately equal group sizes so that you can use inferential statistics as a tool to analyze results

28. Example: Attitudes toward Euthanasia at a college campus
Interested in the relationship of religion and attitudes toward euthanasia
want a sample of 200 students
want to contrast attitudes of those of Christian religions (90% of population) and Atheists (10% of population)
In order to use inferential stats, you need fairly equal group sizes

29. If you use random sampling you would likely end up with about 90% (180) Christian and 10% (20) Atheist
Split sampling frame into a list of Christian and a list of Atheists
Sample an equal number (50%) of participants from each list (100 Christian and 100 Atheist)

30. Stratified Proportionate Sample
Break frame into strata and randomly sample from each strata such that the proportions in the population are the same as the proportions in the sample.
This sampling technique would be best if you want a truly representative sample in order to describe the “typical” attitude

31. Example: Attitudes toward night-time safety on campus
Want a sample of 200 students
Know that this attitude differs quite a bit for men versus women
Want to describe “typical” UMASS students’ attitude, you are NOT interested in contrasting male and female attitudes.

32. Population is 80 % women and 20% men
Select a sample such that it reflects these proportions, 80% women (160 women) and 20% men (40 men)

33. Three Survey Designs Cross-sectional design
Used for descriptive purposes, describe characteristics of the population or differences between two or more populations
One sample drawn from the population at one point in time
Example: the surveys done in Psych 241 labs

34. Successive Independent Samples Design
Same population but different samples surveyed multiple times over some time period
Can look for changes in the population
Example:
do a survey on attitudes toward drug use in 1970, 1980, 1990, 2010 using same survey and same population (UMASS students) but different sample (people) each time

35. Second example: Sax et al (2006)
Survey done every year since 1966
350,000 each year from 700 colleges and universities
Largest, longest empirical study of higher education in US
Over 10 million students so far asked about 40 questions

36. Data from Sax et al

37. Longitudinal Design:
Same population, same survey, and same sample (same people) surveyed repeatedly over time
Very labor intensive, costly and difficult but can provide some information about why populations change.

38. Example Longitudinal study of eating habits and disorders
Mostly studied in adolescents
What happens as people become young adults? Middle-aged?

39. Followed 625 women and 276 men beginning in college
Followed 625 women and 276 men beginning in college. Surveyed in 1982, 1992, 2002
1992: women’s eating-disorder symptoms, chronic dieting, and body dissatisfaction decreased. Men, who rarely reported problems in college, saw an increase in weight gain, dieting, and symptoms of disordered eating 10 years after college.
2002: Across the time period, women demonstrated the most overall body dissatisfaction, most dieting, most disordered eating. Body weight increased for both men and women across the 20 years. Men’s dieting and weight dissatisfaction was greatest in the 1992 survey, women’s greatest dissatisfaction was in the 1982 survey and decreased across the time period to be at its lowest in 2002 (less dieting, less disordered eating, less dissatisfaction).