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Chapter 8

Chapter 8. Producing Data: Sampling . From Exploration to Inference. Types of Studies. Observational studies → individuals are studied without an experimental intervention (e.g., most surveys)

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Chapter 8

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  1. Chapter 8 Producing Data: Sampling BPS Chapter 8

  2. From Exploration to Inference BPS Chapter 8

  3. Types of Studies • Observational studies→individuals are studied without an experimental intervention (e.g., most surveys) • Experimental studies→individuals receive an experimental intervention to determine its effect (e.g., a study of a drug effectiveness) BPS Chapter 8

  4. Example of an Observational Study (Weight Gain & CHD) • Purpose: understand relationship between weight gain and coronary heart disease (CHD) • 115,818 women, 30 to 55 years of age, recruited in 1976 • Measure weight and height at age 18 and at recruitment, record weight gain • Followed individuals for 14 years • Record fatal and nonfatal CHD outcomes (1292 cases) • Adjusted results for lurking variables such as smoking and family history of CHD Source: JAMA 1995;273(6):461-5 BPS Chapter 8

  5. Illustrative Example: Results Compared to subjects who gained less than 11 pounds: • Subjects who gains 11 to 17 lbs: 25% more likely to develop CHD • 17 to 24 lbs gained: 64% more likely • 24 to 44 lbs gained: 92% more likely • 44+ lbs gained: 165% more likely BPS Chapter 8

  6. Illustrative Example (Questions) • What is the population in this study? • What is the sample? • What makes this study observational? • Can we say that weight gain caused CHD? • Can we say weight gain is associated with CHD? BPS Chapter 8

  7. Sample Quality • Poor quality samples favor a certain outcome  misleading results  sampling bias • Examples • Voluntary response sampling: Allows individuals to choose to be in the study, e.g., call-in polls (pp. 178–9 in text) • Convenience sampling: individuals that are easiest to reach are selected, e.g., Interviewing at the mall (p. 179) BPS Chapter 8

  8. Voluntary Response Bias • To prepare for her book Women and Love, Shere Hite sent questionnaires to 100,000 women asking about love and sexual relationships • Only 4.5% responded • Respondents “were fed up with men and eager to fight them…” • Selection bias: “angry women [were] more likely” to respond  sampling bias BPS Chapter 8

  9. Convenience Sample • A lab study was conducted to see if a drug affected physical activity in lab animals • The lab assistant reached into the cage to select the mice for study • The less active mice were chosen  made it seem like the drug decreased physical activity  sampling bias BPS Chapter 8

  10. Simple Random Sample (SRS) • To avoid sampling biases, use chance (random) mechanisms to select subjects • The most basic random sampling mechanism  Simple Random Sample (SRS) • SRSs  every conceivable subset has the same chance to be studied BPS Chapter 8

  11. Selecting a SRS • Methods: we can “pick them from a hat”, use a random number generator, or use a table of random digits (Table B) to derive our sample • We will use Table B • Each digit 0 to 9 is equally likely • Entries are independent (knowledge of one entry gives no information about any other entries) BPS Chapter 8

  12. Choosing a Simple Random Sample (SRS) STEP 1: Label each individual in the population with a identification number STEP 2: Use Table B to select numbers at random (enter table at a different location each time it is used) BPS Chapter 8

  13. Selecting a SRS (Illustration) • Population of N = 30 individuals • Labeled the individuals 01 – 30 • Select a row in table at random • Enter table at different random location each time (e.g., to illustrate, enter at row 106) • Row 106 with lines to indicate pairs 68|41|7 3|50|13| 15|52|9 • First two individuals relevant entries are 13 and 15 BPS Chapter 8

  14. Remainder of Chapter • Not responsible for the sampling designs discussed on pp. 200–201 • Are responsible for the cautions (pp. 201–202) • Undercoverage: some population groups left out of sampling process  sampling bias • Nonresponse bias: some individuals do not respond or refuse to participate  sampling bias • Even good quality samples may not be a perfect reflection of the population due to random sampling error  unavoidable & dealt with in future chapters BPS Chapter 8

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