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Lecture 9

Lecture 9.docx

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Western University
Psychology 2800E
Doug Hazlewood

Part 1: Definitions A. Population: Entire group that we want to learn about  Can consist of:  Individual people  Groups (e.g., all "clubs" on campus; companies on TSE; countries in UN)  Archived material (e.g., all letters to Gazette editor; all course evaluations at Western)  “abstract” things (e.g, outcome of an infinite number of coin tosses) B. Sampling Frame: Reflects procedures used to identify members of population  An “operational definition” of the population  Examples:  Population: All eligible voters; all residents of London  Who/what we want to learn about  Sampling Frame: Voter registration lists; phone book listings  Who/what we actually study  Ideally the sampling frame will be the same as the population (so everyone in population is also in sampling frame)  Can only generalize findings to the sampling frame C. Sample: Subset of members drawn from sampling frame (e.g., 100 from voter lists) D. Element: Each individual in a sample E. Sampling Methods:  Fall into two categories: 1. Probability sampling: each member of population [sampling frame] has a known probability of being included in the sample  Produces representative samples (similar to population [sampling frame])  Can generalization sample findings to entire population [SF] 2. Non-probability sampling: Population members have unknown probability of being included in the sample  Sample is not representative of the population and cannot be generalized to the entire population [SF] Part 2: Non-Probability Sampling Methods A. Convenience Samples (select most convenient, easily accessible sample) E.g.,  Studying introductory psychology “subject pools”;  Volunteers (“people on the street”; magazine, TV, radio, internet survey) B. Purposive Samples: Purposely select people with some characteristic (e.g., “business leaders”)  Not randomly selecting; it’s a non-probability sample C. Quota (“haphazard”) samples: haphazardly select sample that “looks like” population. E.g.,  If population consists of 50% female and 10% Asian, we would get the same “quotas” in our sample  Not in a strictly random way  No guarantee that participants are representative of a larger group; cannot generalize Part 3: Probability Sampling Methods A. Simple Random Samples: Each member of population (or SF) has an equal and independent probability of being selected. E.g.,  Get a list of students in class (sampling frame); e.g., registrar’s list/everyone write name down on list  Determine sample size (e.g., 20); n = sample size, N = population size  Randomly select 20 names from list. E.g.,  Select names from hat, or (better)  Assign unique number to each student (1 to N)  Use a table of random numbers (p. 232-234 of W&M)  Best way to select representative sample  Can generalize from this sample to a larger population BUT. Simple random sample is not particle if the list is very long (e.g., phone book listings in London)  Table of random numbers would have to be very long to include all phone numbers  How do you know whether a selected number is in the phone book?  Could number each listing in the phone book from (000001 to 350,000) a
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