Chapter 1: How do we get “good” data?
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Transcript of Chapter 1: How do we get “good” data?
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Chapter 1: How do we get “good” data?
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What does the word “statistics”mean to you?
• Definition
• Applications
• Where you’ve seen statistics before
• Your feelings about statistics …
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Course Layout
• More conceptual than computational– I will give reading assignments very often.
• More frequent smaller quizzes• Book breakdown:– I. Producing Data
– II. Organizing Data
– III. Chance
– IV. Inference
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Questionnaire
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Opening Day Questionnaire
• In groups of 3, compile the data (for #2, #7) and prepare a short report of a couple of things.
– Done on whiteboards.
– Graphs, tables, statistics, etc.
– Color!
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Questionnaire
• What are the individuals? Variables?
– Definitions, p. 5
• Type of study?
– Observational? (p. 9)
– Experiment? (p. 16)
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Homework
• Reading, pp. 3-17
• Prepare a dotplot for questionnaire item #9.
– See Activity 1.1, p. 4.
• Exercises 1.1 and 1.4, pp. 7-8
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Comparing Observational Studiesand Experiments
• Definitions, p. 9 and p. 16
• Give two examples of each.
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Populations and Samples (p. 10)
• Population: The whole thing
• Sample: A subset of the whole thing
– Statistics is usually concerned with taking a sample to infer something about the population.
• Census (p. 13): Entire population is included in the sample (or at least there is an attempt to do so).
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Exercises
• 1.8, p. 13
• 1.12, p. 17
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Homework
• Read: Statistics in Summary, p. 20• 1.15, p. 18• 1.20 and 1.23, p. 21• Read: pp. 22-35• Section 1.1 quiz on Thursday• Extra credit opportunity:– Application 1.1, p. 19
– Due on or before 1.18.09 (Monday)
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Section 1.2: Measuring
• We must have an operational definition of the construct we want to measure.
– For example, it’s one thing to say we want to measure intelligence (the construct), but it is quite another to actually measure it (operational definitions).
• Valid measure: p. 28
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Valid Measurements for …
• Physical fitness
• Happiness
• “Well-educated”
• Student “readiness” for college
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USDA Statement on Laura Lynn 2% Milk (which does not contain rBGH growth hormone)
• “Milk from a cow supplemented with rbGH is not different from that of a non-supplemented cow.”
• See sidebar, p. 33
– “The Great One”
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Predictive Validity (p. 31)
• Application 1.2A, p. 32
• Excel file: Predictive validity for SAT at Rice University
• Employment law:– http://www.employment-testing.com/validity.htm– Sonia Sotomayor article in New York (hiring practices
for fire fighters): http://www.newyorker.com/reporting/2010/01/11/100111fa_fact_collins?currentPage=all
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Measurement Definitions
• p. 24:
– measure, instrument, units, variable
• Exercise 1.24, p. 27
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Homework
• Look over examples 1.14 and 1.15, p. 30
• Exercises:
– 1.31 and 1.32, p. 33
– 1.34, p. 34
• Reading: pp. 34-42
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Measurement Validity
• We’ve spoken about the need for a measurement to be valid.– Definition, p. 28
• Ways we establish evidence of validity:– Predictive validity (e.g., SAT vs. college GPA)– Face validity: Have a panel of experts (SME) study our
instrument for measuring.• There are statistics for measuring this (dissertation, p. 41)
– Statistical methods• Correlations with other similar measurements• Use as independent variable in designed experiments
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Measurement Reliability (p. 35)
• In addition to using valid measurements, our measurements must be reliable.
– Reliable=repeatable results
• Ways to establish evidence of reliability:
– Test-retest
– Parallel tests
– Statistical methods, including internal consistency evaluations.
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Bias (p. 35)
• Systematically overstates or understates the true value of a property.
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Bias and Reliability
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Scales Example
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Practice
• See Example 1.17, p. 35
• Exercises:
– 1.35, p. 39
– 1.42, p. 42
– 1.44, p. 43
– 1.48, p. 44
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More practice, section 1.2
• Exercises, pp. 39-40:
– 1.37,1.38,1.39,1.41
• Section 1.2 quiz tomorrow (Tuesday)
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Section 1.3: Do the numbers make sense?
• What they did not tell us … numbers have a context
– p. 46
• Are the numbers plausible?
– p. 49
• Are the numbers too good to be true?
– p. 50
– Fake data? Too precise?
• Is the arithmetic right?
– p. 51
• Is there a hidden agenda?
– p. 53
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Section 1.3 problems
• pp. 55-58:
– 1.55, 1.59, 1.62, 1.64
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Chapter 1 Review Exercises
• pp. 59-62:
– 1.71, 1.73, 1.75, 1.79