1: Measurement and Sampling

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1 Measurement and Sampling

• What is biostatistics?
• What is measurement?How do we sample populations?

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HS 167 Logistics

• Syllabus materials (text, lab workbook,
  calculator)
• Calendar and assignments are on
  www.sjsu.edu/biostat ? click HS167 (become
  familiar with Web site)
• Exam1 10/9, Exam2 11/13, Final Thur 12/13
  245
• Lab 0 and Lab 1 (Tu and We lab may have
  additional time to complete Lab 1)
• Text (reading) pp. 1 10, 15 19 (note
  vocabulary on p. 11)
• Exercises 1.1 1.6, 1.8, 1.9, 2.1 2.3, 2.11
  2.13 due at beginning of next lecture
• Yahoo group send email to hs167-F07-subscribe_at_yah
  oogroups.com 
• Academic integrity (do your own work)
• Odd-numbered exercises and lab work ? OK to get
  help from friends
• Even numbered exercises exams ? do NOT get help
  from friends
• How to get a good grade
• Attend all classes and labs (attendance required)
• Stay on task
• Read text (listed to Nancy)
• Do Lab HWs diligently
• Do not cut corners

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Biostatistics

• is not merely a compilation of computational
  techniques
• is a way of learning from data
• is concerned with all many elements of study
  design and analysis (not just computations)
• requires more judgment than math (pay attention
  to vocabulary)
• is statistics applied to biological and health
  problems

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Biostatistics involves

• A data detective element
• Uncovering patterns and clues
• This is a combination of exploratory data
  analysis (EDA) and descriptive statistics
• A data judge element
• Confirmation of clues
• This often requires inferential methods

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Measurement

• Measurement assigning of numbers and codes
  according to prior-set rules
• Three types of statistical measurements
• Categorical classify observations into named
  (nominal) categories
• e.g., HIV classified as positive or negative
• Ordinal ranked categories
• e.g., OPINION ranked 5 strongly agree, 4
  agree, 3 neutral, and so on
• Quantitative numbers with equal spacing
• e.g., AGE in years
• e.g., BLOOD_PRESSURE in mm Hg

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Illustrative Example Weight Change and Heart
Disease
Source Willett et al., 1995

• Goal to determine the effect of weight change on
  coronary heart disease risk
• 115,818 women 30- to 55-years of age, free of CHD
• Body mass index (BMI, kg/m2) determined at entry
  to study
• Body weight determined as of age 18
• Subjects followed for 14 years
• Number of CHD onsets (fatal and nonfatal) counted
  (1292 cases)

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Illustrative Example (cont.)
Variables

• Smoker or nonsmoker
• Family history of heart disease (yes or no)
• Non-smoker, light-smoker, moderate smoker, heavy
  smoker
• BMI (kgs/m3)
• Age (years)
• Weight presently
• Weight at age 18

Categorical
Ordinal
Quantitative
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Variable, Value, Observation

• Observation ? the unit upon which measurements
  are made
• Can be an individual (e.g., a person)
• Can be an aggregate of individuals (e.g., a
  region)
• 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

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Data Structure (Forms)
Observation 1
Data Collection Form Var1 (ID) 1 Var2 (AGE)
27 Var3 (SEX) F Var4 (HIV) Y Var5 (KAPOSISAR
C) Y Var6 (REPORTDATE) 4/25/89 Var7
(OPPORTUNIS) N
Observation 2
Observation 3
Observation 4
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U.S. Census Form
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Data Structure (Table)
Observations ? rowsVariables ? columnsValues ?
cells
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Illustrative Example Cigarette Consumption and
Lung Cancer
Variables country name of country/region cig193
0 per capita cigarette consumption,
1930 mortalit lung cancer deaths per 100,000 in
1950
Note Unit of observation in this data set are
regions (not people)
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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 your worldview
• Discussion on avoiding bias when questioning
  e.g., consider the word jam

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Ethos Which do you choose?
Blackburn, S. (2005). Truth. Oxford Univ. Press
Frankfurt, H. G. (2005). On Bullshit. Princeton
University Press
The difference is intention and method BS has a
predetermined outcome. Truth is earnest in its
intent and does not bend the facts to a
predetermined outcome.
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Truth Versus Perception
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
1915-1987
Platos Allegory of the Cave We observe shadows
on the wall. The truth lies outside.
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Two Types of Statistical Studies

• Surveys quantify population characteristics
• e.g., of population that is overweight
• e.g., expected life span
• Comparative Studies determine relationships
  between variables
• e.g., relationship between weight gain and heart
  disease risk
• e.g., relationship between alcohol consumption
  and esophageal cancer risk
• We start by considering survey sampling

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Sampling for a Survey

• We seldom (if never) study an entire population
• Take a subset (sample) of the population
• Use characteristics of the sample to infer
  population characteristics
• Select a probability sample
• chance determines which individuals are selected
• Avoid non-probability samples
• Discuss volunteer bias as an example

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Simple Random Sample (SRS)

• SRS (definition) every possible sample from the
  population has the same probability
• this is the most basic type of probability sample
• SRSs have sampling independence
• selection of one individual does not influence
  selection of any other
• SRSs can be done with replacement or without
  replacement (both methods are usually valid)
• Sampling fraction n N probability of
  selection where
• n ? sample size
• N ? population size

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SRS Method

• Compile census listing (sampling frame)
• individuals numbered 1, 2, . . ., N
• Generate n random numbers between 1 and N
• Can be done with random number generator (lab) or
  with table of random digits
• Select individuals based on random number list

You will take a SRS in lab this week