Chapter 8 Producing Data: Sampling HS 67 BPS Chapter 8 1 From Exploration to Inference Exploratory Data Analysis Statistical Inference Purpose: identify and describe patterns in data Purpose: answer specific question Conclusions apply to data and specific circumstance Conclusions apply beyond data and to broad circumstance Conclusions are formal Conclusion are informal HS 67 BPS Chapter 8 2 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) HS 67 BPS Chapter 8 3 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 Source:of JAMA 1995;273(6):461-5 smoking and family history CHD HS 67 BPS Chapter 8 4 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 HS 67 BPS Chapter 8 5 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? HS 67 BPS Chapter 8 6 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) HS 67 BPS Chapter 8 7 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 HS 67 BPS Chapter 8 8 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 HS 67 BPS Chapter 8 9 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 HS 67 BPS Chapter 8 10 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) HS 67 BPS Chapter 8 11 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) HS 67 BPS Chapter 8 12 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 HS 67 BPS Chapter 8 13 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 HS 67 BPS Chapter 8 14