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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

In Chapter 1:

1.1 What is Biostatistics?

1.2 Organization of Data1.3 Types of Measurements

1.4 Data Quality

biostatistics
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
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

measurement
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
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
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
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
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

measurement scales examples cont
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
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
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
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
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
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
choose your ethos
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.

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

Scientific Ethos