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