Textbook Notes (362,734)
Psychology (9,545)
PSYB01H3 (585)
Chapter 5

# Chapter 5

3 Pages
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School
University of Toronto Scarborough
Department
Psychology
Course
PSYB01H3
Professor
David Nussbaum
Semester
Fall

Description
Chapter 5: Sampling and Survey Research Substantive Theme: Happiness Selecting Research Participants Sampling: selection of individuals or other entities to represent a larger population of interest Census: study entire population; expensive and time consuming Sample Planning Define the Population  Varies depending on population ex. Students disable elderly and adults report similar as do men and women but there are cultural differences  Cross-population generalizability: need to compare results obtained from samples of different populations Define Sample Components  Elements: units of a population  Sampling frame: the list from which the elements of a population are selected  Population: entire set of individuals or other entities to which study findings are to be generalized  Representative sample: sample that looks like the population from which it was selected in all respects relevant to the study  Sampling error: the differences between the characteristics of a sample and the characteristics of a population from which it was selected  Estimating sampling error o Inferential statistics: mathematical tool for estimating how likely it is that a statistical result based on data from a random sample is representative of the population from which the sample was selected o Sampling distribution: graph of mean values for all samples; is normal  Mean is equal to population parameter o Random sampling error: variation owing to pure chance not systematic sampling error; causes bell shape; may or may not result in unrepresentative sample o Sample statistic: the value of a statistic ex mean computed from sample data o Population parameter: value of a statistic computed using data for the entire population o Confidence intervals and limits: more random samples more confidence smaller interval Sampling Methods Probability sampling methods: sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample Nonprobability sampling methods: sampling methods that do not let us know the likelihood of selecting each element Probability of selection: the likelihood that an element will be selected from the population into the sample; sample size/population size Random sampling: cases are selected only on the basis of chance Probability Sampling Methods  No systematic bias; nothing but chance determines elements included  Number of cases is more important than proportion of population that sample is  Simple Random Sampling: probability for each item is the same; sample size/ population size; ex random number generator, random digit dialling  Systematic random sampling: first element is random then every nth element is selected o Watch out for periodicity ex. Houses on a block in sampling interval  Stratified Random Sampling: separate into groups/strata based on relevant characteristic so same in strata them randomly sample from each strata; ensures appropriate representation of elements o Proportionate: sample is selected so distribution of characteristic in sample matches distribution in population o Disproportionate:  Cluster Sampling: randomly choose from naturally occurring clusters and then random sampling within those clusters Nonprobability Sampling Methods Availability sampling: dont know if representative; take whoever is available or easy to find; haphazard, accidental or convenience  Quota Sampling: set quotas to ensure that the sample represents certain characteristics in proportion to how they would be in the population; no random sampling  Writing Survey Questions  Avoid confusing phrasing o shorter words and sentences (<20 words) o Avo
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