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Lecture 9

This

**preview**shows half of the first page. to view the full**2 pages of the document.**CHAPTER 9 – Survey Research

Lecture 9

1) Recap: Probability Sampling, Random Sampling, Random Assignment

Sampling Experiment: experiments which entail selecting a particular set of elements (the sample) from a real,

potential…

Descriptive Statistics

vs.

Inferential Statistics

Probability Sampling

•Representativeness

•Self Selection Bias: must be very mindful of this bias

•Random Sampling: procedure for selecting subjects that ensure each member in population

has equal chance of being selected; sampling that is random is likely to be representative in

some sense, important to note that randomness is how you pick the subjects not the number

of subjects/representative they are

vs.

Nonprobability Sampling: concerns of generalizability of stimulus responses/sampling

Brunswick’s “Representative Design”

Example: Responses to Abstract vs. Representational Art in Introverts and Extraverts

Independent Variable: Sociability measured by the EPI

Dependent Variable: preference/hedonic ratings/heart rate/blood pressure

2) Relative Importance of Sampling Procedures

•Survey Research vs. Other Descriptive Research Methods

•3 Major Survey Techniques

o1) Face-to Face Interviews

o2) Written Surveys

o3) Phone Surveys

3) Nomothetic vs. Idiographic Approaches and the Role of the Participant

- Idiographic: engaged relationship with that subject (particularized)

- Nomothetic: look at subject n context of distribution/sample space (generalized)

* Rich Description vs. The Determination of Functional Relationships

* Case Studies and Small N Studies vs. Group Comparisons

*“The Subject as “Particularized Other” vs. “Generalized Other”

4) Surveys and Sampling Plans

-Sampling Plan: procedure which specify how participants will be selected in a survey study

-Probability Sampling: everyone in population has equal chance of being selected

* Unbiased Samples: value you obtain on any administration of a test that you give should on average (not

every time) coincide with the true population value

* Error of Estimate: distance between the sample and population statistic (you want your error of estimate

in your research to be small)

* Stable Samples: variability, characteristic of probability sampling

* Homogeneous: when variability is small, you don’t need that large of a sample, when samples are

homogeneous, we can employ simple random sampling

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