Correlational Research

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.

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

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

• 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?

Sampling: a three step process

1) 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 .

Population, Sampling Frame, Sample, Element

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.

• 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?

• 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

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

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

• 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

• 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%

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

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

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

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

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?

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?

• What would you call these samples?

• 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

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

• 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

• 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

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

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

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

• 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)

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

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.

• 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)

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

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

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

Data from Sax et al

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.

Example

• Longitudinal study of eating habits and disorders

• Mostly studied in adolescents

• What happens as people become young adults? Middle-aged?

• 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).