PUBLIC HEALTH 1 PG APA 3 references on SCIENTIFIC METHOD

Chapter 1 Measurement October 14 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 • Statistics –is a way of learning from data – is concerned with all elements of study design, data collection and analysis of numerical data – does require judgment •Biostatistics is statistics applied to biological and health problems 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 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 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) Illustrative Example: Weight Change and Heart Disease •This study sought 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 Illustrative Example (cont.) Examples of Variables • 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/m 3) • Age (years) • Weight presently • Weight at age 18 Quantitative Categorical Ordinal 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 • Va l u e ≡ a realized measurement – e.g., “27” – e.g., “positive” Figure 1.1 Four observations with five variables each Data Table AGE SEX HIV ONSET INFECT 24 M Y 12-OCT -07 Y 14 M N 30-M AY -05 Y 32 F N 11-NOV -06 N • Each row corresponds to an observation • Each column contains information on a variable • Each cell in the table contains a value Unit of observation in these data are individual regions, not individual people. 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 Choose Your Ethos •BS is manipulative and has a predetermined outcome. • Science “bends over backwards” to consider alternatives. Scientific Ethos “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