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Chapter 7: Basic Data Ethics (p. 110)

Institutional review board : Reviews all planned studies in advance in order to protect subjects from possible harm. All organizations that conduct studies must have such a board.

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Chapter 7: Basic Data Ethics (p. 110)

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  1. Institutional review board: Reviews all planned studies in advance in order to protect subjects from possible harm. All organizations that conduct studies must have such a board. Informed consent: This means that the subjects understand the possible risks and benefits involved with the experiment and that they understand that the treatment will be randomly selected (i.e., they might not get the new drug). Confidentiality: All individual data must be kept confidential. Only statistical summaries of subjects may be made public. Chapter 7: Basic Data Ethics (p. 110)

  2. Experiments that study the effectiveness of medical treatments on actual patients. Only way to see true effects of new treatments. Pose some risk to the subjects since the treatment effects are unknown. Must balance future benefits against present risks Clinical Trails (p. 113)

  3. Behavior and Social Science Experiments “Ethical Principles” of the American Psychological Association • Informed consent unless merely observing behavior in public • In some situations, subjects are given vague information before and full information after experiment

  4. Chapter 8: Measuring • How would you measure height? • How would you measure IQ? • How would you measure leisure time?

  5. Definitions (p. 127) • To measure a property means to assign numbers to individuals as a way of representing the property. • An instrument is often used to make measurements. • Examples: Ruler, scale, exam, survey, etc. • A variable is a result of a measurement that takes on different values for individuals.

  6. What would we use to measure: Height? • Tape measure or ruler, inches or cm Weight? • Scale, pounds or grams College readiness? • SAT, grades in high school Highway safety? • Deaths from accidents each year Knowledge of Statistics? • HW, Exams

  7. Things to ask about variables in a statistical study: (p. 128) • Exactly how is the variable defined? • Is the variable a valid way to describe the property it claims to measure? • How accurate are the measurements?

  8. Valid Measurements A variable is a valid measure of a property if it is relevant and appropriate as a measure of that property. (p. 130) • Does the process measure what we want it to measure? • Which is a valid for measuring on Dean’s List? • Measure your weight and record you as on the Dean’s List if you weigh over 150 pounds. • Measure your GPA and record you as on the Dean’s List if it is 3.5 or above.

  9. Rates vs. Counts • Often, a rate (fraction, proportion, or percent) at which something occurs is a more valid measure than a count of occurrences. • Measuring unemployment:Should we simply measure the number of people who are unemployed and compare how this changed from month to month or year to year? • Better to compute a rate: Makes comparisons between time periods valid.Unemployment rate = # unemployed/# in workforceHave to define what it means to be in the work force and to be unemployed.

  10. Example 3 (pp. 128-129): Change in how government measures unemployment occurred in 1994

  11. Predictive Validity • SAT as a measure of “college readiness.” What is “college readiness?” Can a test measure this? • A measure of a property has predictive validity if it can be used to predict success on tasks that are related to the property to be measured. • SAT has predictive validity; students who do well on the SAT tend to have high college GPAs. • Statistical Controversies: SAT Exams in College Admissions on p. 133.

  12. Accuracy of Measurements: • A measurement process is biased if it systematically overstates or understates the true value of the variable. • Unbiased if it consistently states the true value of the variable. • A measurement process is reliable if repeated measurements on the same unit give the same (or approximately the same) results. • Unreliable if this is not the case.

  13. Speedometer Examples Speedometer always reads 5 m.p.h. fast • Suppose a speedometer always reads 5 m.p.h. fast (i.e., if true speed is 25 m.p.h., the speedometer reads 30 m.p.h.) • The measurement of the speed from this speedometer is a biased result. Speedometer bounces around • Suppose another speedometer bounces around (i.e., jiggles from about 20 m.p.h. to about 90 m.p.h. when going at a constant speed). • The measurement of his speed from this speedometer is unreliable or not precise.

  14. Improving Measurements To improve reliability: • Measure multiple times and use the average of the measurements as the final value. To reduce bias: • Need to get an accurate measuring instrument.

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