1. Chapter 7
Survey research

2. Why conduct surveys
They provide a method for asking people personal information
They give a “snapshot” of public opinion
Complement experimental research findings
Multiple methods are necessary to understanding behavior
To get accurate answers for research questions
Disadvantage: personal bias
Response set

3. What to ask Defining objectives Question wording Attitudes and beliefs
Facts and demographics
Behaviors
Question wording
Simplicity
Things to avoid
Avoid questions that ask two things at once
Avoid loaded questions
Avoid negative wording
“Yea-saying” and “nay-saying”

4. Responses to questions
Closed vs. open ended questions
Number of response alternatives
Rating scales
Likert scale – an interval scale commonly used
A news reporter should have 5 yrs of experience as a journalist
SD D U A SA
Graphic rating scale – requires a mark along a 100mm line; a ruler is then placed on the line to obtain the a numerical score

5. Finalizing the questionnaire
Format
Survey should be attractive and professional
NO spelling errors
Leave enough space between items
Keep things consistent
Refine questions by giving the survey a ‘trial run’ and ask participants to think aloud while answering.

6. Improving a poor survey item
Identify what is wrong
How should it be written
Have you had an upset stomach lately
Highly prejudiced people are usually hostile and not very smart
Do you agree with most people that violations of seat belt laws should result in harsh penalties?

7. Improving a poor survey item: Rewritten questions.

8. Administering surveys
Questionnaires
Personal administration
Mail
Internet
Interviews
Face to face
Telephone
Focus group

9. Sampling from a population
Confidence interval
An interval of values within which there is a given level of confidence (e.g. 95%) where the population value lies
Gives information about the likely amount of error
This type of error is a.k.a. sampling error or margin or error
Calculation is based on the size of the sample

10. Sampling from a population
Sample size
Large sample sizes reduces the size of the confidence interval
Large samples yield data that accurately reflect the true population
Mathematical formula

11. Sampling techniques
Probability sampling– each member of the population has a probability of being chosen; important when trying to make a precise statement about the population
Simple random sampling
Every member has an equal probability of being selected from the sample
Stratified random sampling
Division into subgroups and then random sampling from those subgroups
This type of sampling includes an accurate reflection of the numerical composition of the various subgroups

12. Sampling techniques Probability sampling
Cluster sampling
Identify ‘clusters’ of individuals and sample from the clusters
All individuals in the cluster are sampled
Clusters are based on naturally occurring groupings, like geographical areas or institutions
Overall drawbacks: time consuming, expensive & difficult

13. Sampling techniques
Non probability sampling – the probability of choosing any particular member of the population is unknown; these techniques are arbitrary in comparison to probability sampling techniques
Haphazard sampling
a.k.a convenience sampling
Sampling individuals on a ‘first-come’ basis
Drawback: your results may not generalize to your intended population
Quota sampling
Choosing a sample that reflects the numerical composition of various subgroups in the population
Data is collected using haphazard techniques

14. Evaluating samples Sampling frame Response rate
The actual population of individuals (or clusters) from which a random sample will be drawn
Response rate
The %age of people in the sample who completed the survey
Indicates amount of bias there might be in the final respondents
Incentives can be used to increase response rates