Class Notes (783,221)
SOAN 2120 (387)
Scott Schau (113)
Lecture

# Feb.25 2013 Notes.docx

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School
University of Guelph
Department
Sociology and Anthropology
Course
SOAN 2120
Professor
Scott Schau
Semester
Winter

Description
SOAN*2120 – Week 7 LEC 1 Asia Barclay Monday, February 25, 2013 MIDTERM: - Wednesday Feb 27; 50 mins (may be 45 mins) - 1.5 mins/question, multiple choice - 29 questions, 5 pages - Answer on the paper as well as on the scantron (hand in both, OTHERWISE YOU GET ZERO!) - NO calculators (but for the final exam we will) - Chapters 1-7, everything up to the lecture that occurred just before the break - Equal focus between lecture and textbook - Big concepts that you would have gotten from reading the chapter overall (no little details) - We will begin Chapter 3 on Friday (therefore this is obviously not on the midterm) - Usually gets a class average of 71% for the midterm A. SIMPLE RANDOM SAMPLE (SRS) - Every member of a population has an equal chance of being included in the sample - Only possible to give every member of a population an equal chance of being selected if we can identify every member of the population Failure to do so is a major source of bias - Two populations: theoretical (defined for selection) and sample (actual population that it represents) - This is because our sampling frame is not always the same size as our population - Our sample population then becomes different from our theoretical population, which can be a problem TABLE OF RANDOM NUMBERS - I.e. If you wanted to select 30 individuals out of a class of 690 students for a survey, you take the class list and select 3 numbers because the number “690” has “3” digits in it - Table must be used consistently - Discard numbers greater than 690, and discard repeated numbers - Never re-use numbers for surveys, because otherwise it’s not random anymore - See appendix in textbook for table of random numbers B. SYSTEMATIC SAMPLING - Every nth person is selected n = population/sample size - Potential catch (source of error): Population may be listed in such a way that might affect the sample C. STRATIFIED RANDOM SAMPLING - One way to reduce sampling errors - Purpose of stratification: ensure that different subgroups are represented in the correct proportions Goal in stratification is not to make comparisons across subgroups - Divide population into strata E.g. Divide population into men and women Still doing random sample, but within each of the strata D. SAMPLES OF CONVENIENCE (accidental samples) - Biased - E.g. If a professor is interested in a principle of learning theory as it applies to all college graduate students, but only
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