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Lecture 9

# PSYC 2030 Lecture Notes - Lecture 9: Nomothetic

Department
Psychology
Course Code
PSYC 2030
Professor
Krista A Phillips
Lecture
9

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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