Chapter 1: Measurement

1. Chapter 1: Measurement

2. In Chapter 1: 1.1 What is Biostatistics? 1.2 Organization of Data?1.3 Types of Measurements 1.4 Data Quality

3. Biostatistics • Statistics is not merely a compilation of computational techniques • Statistics • is a way of learning from data • is concerned with all elements of study design, data collection and analysis of numerical data • does requirejudgment • Biostatistics is statistics applied to biological and health problems

4. Biostatisticians are: • Data detectives • who uncover patterns and clues • This involves exploratory data analysis (EDA) and descriptive statistics • Data judges • who judge and confirm clues • This involves statistical inference

5. Measurement • Measurement (defined): the assigning of numbers and codes according to prior-set rules (Stevens, 1946). • There are three broad types of measurements: • Categorical • Ordinal • Quantitative

6. Measurement Scales • Categorical - classify observations into named categories, • e.g., HIV status classified as “positive” or “negative” • Ordinal - categories that can be put in rank order • e.g., Stage of cancer classified as stage I, stage II, stage III, stage IV • Quantitative – true numerical values that can be put on a number line • e.g., age (years) • e.g., Serum cholesterol (mg/dL)

7. Illustrative Example: Weight Change and Heart Disease • This study sought to to determine the effect of weight change on coronary heart disease risk. It studied 115,818 women 30- to 55-years of age, free of CHD over 14 years. Measurements included • Body mass index (BMI) at study entry • BMI at age 18 • CHD case onset (yes or no) Source: Willett et al., 1995

8. 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 Illustrative Example (cont.) Examples of Variables Categorical Ordinal Quantitative

9. Variable, Value, Observation • Observation the unit upon which measurements are made, can be an individual or aggregate • Variable the generic thing we measure • e.g., AGE of a person • e.g., HIV status of a person • Value a realized measurement • e.g.,“27” • e.g.,“positive”

10. Data Collection Form On this 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

11. U.S. Census Form

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

13. Illustrative Example: Cigarette Consumption and Lung Cancer cig1930 = per capita cigarette use in 1930 mortality = lung cancer mortality per 100,000 in 1950 Unit of observation in these data are individual regions, not individual people.

14. Data Quality • An analysis is only as good as its data • GIGO ≡ garbage in, garbage out • Does a variable measure what it purports to? • 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

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

16. “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