Chapter 1: Measurement

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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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### In Chapter 1:

1.1 What is Biostatistics?

1.2 Organization of Data1.3 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
• Biostatistics is concerned with learning from biological, public health, and other health data
Biostatisticians are:

Data detectives who uncover patterns and clues through data description and exploration

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)
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
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
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)
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

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

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
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

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

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

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.

The intensity of the conviction that a hypothesis is true has no bearing on whether it is true or not.”

Peter Medawar

Scientific Ethos