Chapter 7 Survey research

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

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”

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

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.

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?

Improving a poor survey item: Rewritten questions .

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

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

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

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

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

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

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