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Chapter 1: Measurement

Chapter 1: Measurement. In Chapter 1:. 1.1 What is Biostatistics? 1.2 Organization of Data 1.3 Types of Measurements 1.4 Data Quality. Biostatistics . Statistics is not merely a compilation of computational techniques It is a way of learning from data

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Chapter 1: Measurement

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  1. Chapter 1: Measurement

  2. In Chapter 1: 1.1 What is Biostatistics? 1.2 Organization of Data1.3 Types of Measurements 1.4 Data Quality

  3. Biostatistics • Statistics is not merely a compilation of computational techniques • It is a way of learning from data • Biostatistics is concerned with learning from biological, public health, and other health data

  4. Biostatisticians are: Data detectives who uncover patterns and clues through data description and exploration Data judges who confirm and ad adjudicate decision using inferential methods

  5. Measurement • Measurement ≡ the assigning of numbers and codes according to prior-set rules (Stevens, 1946). • Three main types of measurements: • Categorical (nominal) • Ordinal • Quantitative (scale)

  6. Categorical Measurements Classify observations into named categories Examples • HIV status (positive or negative) • SEX (male or female) • BLOOD PRESSURE classified as hypo-tensive, normo-tensive, borderline hypertensive, or hypertensive

  7. Ordinal Measurements Categories that can be put in rank order Examples: • STAGE OF CANCER classified as stage I, stage II, stage III, stage IV • OPINIONclassified as strongly agree (5), agree (4), neutral (3), disagree (2), strongly disagree (1); so-called Liekert scale

  8. Quantitative Measurements Numerical values with equal spacing between numerical values (like number line) Examples: • AGE (years) • SERUM CHOLESTEROL (mg/dL) • T4 cell count (per dL)

  9. Example: Weight Change and Heart Disease • Investigate effect of weight gain on coronary heart disease (CHD) risk • 115,818 women 30- to 55-years of age, all free of CHD • Follow over 14 years to determine CHD occurrence • Measure the following variables: Source: Willett et al., 1995

  10. Smoker (current, former, no) CHD onset (yes or no) Family history of CHD (yes or no) Non-smoker, light-smoker, moderate smoker, heavy smoker BMI (kgs/m3) Age (years) Weight presently Weight at age 18 Measurement Scales Examples (cont.) Categorical vars Ordinal var Quantitative vars

  11. Variable, Value, Observation • Observation unit upon which measurements are made, e.g., person, place, or thing • Variable the [generic] thing being measured, e.g., AGE, HIV status • Value a realized measurement, e.g., an age of “27”, a “positive” HIV test

  12. Data Collection Form Each questionnaire contains an observation Data Collection Form Var1 (ID) 1 Var2 (AGE) 27 Var3 (SEX) F Var4 (HIV) Y Var5 (KAPOSISARC) Y Var6 (REPORTDATE)4/25/89 Var7 (OPPORTUNIS) N Each question corresponds to a variable

  13. Example: U.S. Census Form

  14. Data Table • Each row corresponds to an observation • Each column contains information on a variable • Each cell in the table contains a value

  15. Data Table Example 2: Cigarette Use and Lung Cancer Variables cig1930 = per capita cigarette use in 1930 mortality = lung cancer mortality per 100,000 in 1950 Unit of observation is region, not individual

  16. Data Quality • An analysis is only as good as its data • GIGO ≡ garbage in, garbage out • Validity = freedom from systematic error • Objectivity =seeing things as they are without making it conform to a worldview • Consider how the wording of a question can influence validity and objectivity

  17. Choose Your Ethos Blackburn, S. (2005). Oxford Univ. Press Frankfurt, H. G. (2005). Princeton University Press BS is manipulative and has a preferred outcome. Science bends over backwards to consider alternatives.

  18. “I cannot give any scientist of any age any better advice than this: The intensity of the conviction that a hypothesis is true has no bearing on whether it is true or not.” Peter Medawar Scientific Ethos

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