Intro to Research Chapter 7 Week 5
Intro to Research Chapter 7 Week 5
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This 4 page Class Notes was uploaded by Kim Notetaker on Sunday October 2, 2016. The Class Notes belongs to at Armstrong State University taught by in Fall 2016. Since its upload, it has received 28 views.
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Date Created: 10/02/16
Chapter 7 The basics of sampling The important terminology: o Population: entire set of people you are interested in. o Sample: the subset of people you actually study. o Census: a study that involves the entire population. IMPORTANT QUESTION: Do your findings generalize from your sample to your population? Representative Sample Representative sample: all members of the population have an equal chance of being included in the sample. o Good external validity, generalizability. o Difficult to accomplish. Bias (unrepresentative) sample: some people in the population have a much better chance being included in the sample than others. Biased Sample 2 types of biased samples: o Convenience sample: sampling from those who are readily available to participate. Most of psychology researchers use this sample type. This is what our own study was. o Self-selection: sampling from those who volunteer. Big problem for internet polls. Online reviews are bimodal. Getting a representative sample Random sampling: the process of creating a representative sample, such that each population member has an equal chance of selection. o Also called probability sampling. NOTE: Unrelated to random assignment, which is done in an experimental setting. Types of random sampling. Simple random sampling: sample chosen completely at random from the population. o Requires a sampling frame: a full list of people in the population. Cluster sampling: 3 Steps. o Step 1: break population into clusters. o Step 2: randomly sample the clusters. o Step 3: use everyone from the selected cluster. Multistage sampling: similar but Step 3 changes. o Step 3: randomly sample people from each randomly selected cluster. Can use more than 2 stages. NOTE: remember it easier by saying that you “cluster the cluster.” Stratified sampling: similar to cluster sampling, but uses demographic groups instead clusters. o 3 Steps. Step 1: identify the demographic groups for which you want to ensure appropriate representations. Step 2: determine the percent representation of each group within the population. Step 3: randomly sample the right number of people from that group to ensure correct percent representation within the sample. Oversampling: this is stratified random sampling but changing step 3. 2 o Step 3: randomly sample MORE THAN the right number of people. Systematic sampling: pick a number and you sample that number person that goes by. o An example: you want to survey every 5 person that walks through the library doors. The EAR method of data uses this strategy for sampling bits of ambient sound. Challenges with random sampling 2 primary challenges. o Contacting participants: over-represents people who are “reachable,” and under-represents those who aren’t. No phone. No mailing address. No internet. o Response rate: even people who are reachable may not want to participate. o Possible solutions: Follow up. Give incentives. Check how people who respond differ than those who don’t. Type of biased sampling techniques Convenience sampling Purposive sampling: non-randomly recruiting a particular type of participants. o An example question: how many cancer patients see a therapist at least once peer week? 3 Snowball sampling: recruitment via participant’s social networks. Quota sampling: pick a target number of participants in a particular category, recruit until you get that number. o Similar to stratified but minus the randomness. Think of it like weeding out the people until you get who and what you want. Who volunteers for research? Compared to non-volunteers, typical research volunteers tend to be… o Higher social economic status. o Higher IQ scores. o Higher in need of social approval. o More conscientious. o More extraverted. 4
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