Sampling
Why Sample?
Time,
cost
Accuracy & representativeness
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time-sensitive issues
What is a sample? Key Ideas &
Basic Terminology
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Sampling Guide (general introduction) in Reading Folder
Population, target population
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the universe of phenomena we want to study
Can be people, things, practices
Sampling Frame (conceptual & operational issues)
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how can we locate the population we wish to study? Examples:
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Residents of a city? Telephone book, voters lists
Newsbroadcasts? Broadcast corporation archives? …
Telecommunications technologies?....
Homeless teenagers?
“ethnic” media providers in BC (print, broadcast…)
Diagram of key ideas & terms
Target Population
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Target Population--Conceptual definition:
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Example
Suppose we want to study homeless men aged 35-40 who live in
the downtown east side and are HIV positive.
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the entire group about which the researcher wishes to draw conclusions.
The purpose of this study could be to compare the effectiveness of two
AIDs prevention campaigns, one that encourages the men to seek access to
care at drop-in clinics and the other that involves distribution of
information and supplies by community health workers at shelters and on
the street.
The target population here would be all men meeting the same general
conditions as those actually included in the sample drawn for the study.
What sampling frames could we use to draw our samples?
Bad sampling frame
= parameters do not accurately represent
target population
e.g.,
a list of people in the phone directory
does not reflect all the people in a town
because not everyone has a phone or is listed
in the directory.
Recall: Videoclip from Ask a Silly
Question (play videoclip)
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Ice Storm, electricity disruption, telephone survey
Target Population: Hydro company users
Sampling frame: unclear, probably phonebook or
phone numbers of subscribers
Problem: people with no electricity not at home but
in shelters
Famous examples from the past: Polls of voters
before election (people with phones or car owners not
representative of total voters, or opinions not yet
formed)
More Basic Terminology
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Sampling element (recall: unit of analysis)
 e.g., person, group, city block, news
broadcast, advertisement, etc…
Recall: Units of Analysis
(Individuals)
Recall: Units of Analysis
(Families)
( Households)
Recall: Importance of Choosing
Appropriate Unit of Analysis for Research
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Recall example: Ecological Fallacy (cheating)
Unit of analysis here is a “class” of students. Classes
with more males had more cheating
What happens if we compare number
and gender of cheaters? (unit of
analysis “students”)
Do males cheat more than females?
 Same absolute number of male and female
cheaters in each class
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Comparison of % and # of
cheaters by gender
Recall: Ecological Fallacy & Reductionism
ecological fallacy--wrong unit of analysis
(too high)
reductionism--wrong unit of analysis (too low)
reductionism--wrong unit of analysis (too low)
More Basic Terminology
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Sampling ratio
a
proportion of a population
e.g., 3 out of 100 people
 e.g., 3% of the universe
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Factors Influencing Choice of
Sampling Technique
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Speed
Cost
Accuracy
Assumptions about distribution of characteristics
of population
link to stats Can site
http://www.statcan.ca/english/edu/power/ch13/non
_probability/non_probability.htm
Availability of means of access (sampling frame)
Nature of research question(s) & objectives
Some types of Non-probability
Sampling
1. Haphazard, accidental, convenience
(ex. “Person on the street” interview)
2. Quota (predetermined groups)
3. Purposive or Judgemental
Deviant case (type of purposive sampling)
4. Snowball (network, chain, referral, reputation) & volunteer
Also--multi-stage sampling designs
Non-probability Sampling
1. Haphazard, accidental, convenience
(ex. “Person on the street” interview)
Babbie (1995: 192)
Non-probability Sampling
2. Quota (predetermined groups)
Neuman (2000: 197)
Why have quotas?
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Ex. populations with unequal representation
of groups under study
 Comparative
studies of minority groups with
majority or groups that are not equally
represented in population
 Study
of different experiences of hospital staff with
technological change (nurses, nurses aids, doctors,
pharmacists…different sizes of staff, different
numbers)
Non-probability Sampling
3. Purposive or Judgemental
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Unique/singular/particular cases
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Hard-to-find groups
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Leaders (“success stories”)
Range of different types
Non-probability Sampling
4. Snowball (network, chain, referral, reputational)
Sociogram of Friendship Relations
Jim
Maria
Chris
Anne
Bill
Peter
Kim
Bob
Pat
Joyce
Sally
Paul
Jorge
Tim
Larry
Susan
Edith
Dennis
Donna
Neuman (2000: 199)
Issues in Non-probability sampling
Bias?
 Is the sample representative?
 Types of sampling problems:
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 Alpha:
find a trend in the sample that does not
exist in the population
 Beta: do not find a trend in the sample that
exists in the population
Types of Probability Sampling
1. Simple Random Sample
2. Systematic Sample
3. Stratified Sampling
4. Cluster Sampling
See: Statistics Canada site
http://www.statcan.ca/english/edu/power/ch13/probability/probability.htm
Simple Random Sample
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With/without replacement?
Must take into account
characteristics of population
& sampling frame
Develop a sampling frame
& Number sampling frame
units
Select elements using
mathematically random
procedure
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Table of random numbers
random number generator
Other statistical software
Link: How to use a table of
random numbers
Principles of Probability Sampling
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each member of the population an equal chance of being chosen
within specified parameters
Advantages
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ideal for statistical purposes
Disadvantages
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hard to achieve in practice
requires an accurate list (sampling frame or operational definition) of the
whole population
expensive
How to Do a Simple Random
Sample
Develop sampling frame
 Locate and identify selected element
 Link to helpful website
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2. Systematic Sample (every “n”th person)
With Random Start
Babbie (1995: 211)
Problems with Systematic
Sampling
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Biases or “regularities” in some types of
sampling frames (ex. Property owners’
names of heterosexual couples listed with
man’s name first, etc…)
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Urban studies example)
Other Types
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Neuman
(2000: 209)
Stratified
Stratified Sampling:
Sampling Disproportionately and
Weighting
Babbie (1995: 222)
Stratified Sampling
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Used when information is needed about
subgroups
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Divide population into subgroups before
using random sampling technique
Other Types
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Cluster
When is it
used?
 lack
good
sampling
frame or cost
too high
Singleton, et al (1993: 156)
Other Sampling Techniques
(cont”d)
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Probability Proportionate to Size (PPS)
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Random Digit Dialing
New Technologies: Data Mining
& the Blogosphere
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Jan. 3, 2007
image with
Boingboing as
largest node
(source:
http://datamining.typepad.com/data_mi
ning/2007/01/the_blogosphere.html)
Sample Size?
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Statistical methods to estimate confidence
intervals
Past experience (rule of thumb)
Smaller populations, larger sampling ratios
Other factors:
 goals of study
 number of variables and type of analysis
 features of populations
 In qualitative methods: notion of Saturation
(Bertaux)
Examples of sampling issues &
techniques
Survey about football (soccer) market
 Rural poverty project and sampling issues
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Issues/notions in Probability
Sampling
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Assessing Equal chance of being chosen
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Standard deviation
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Sampling error
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Sampling distribution
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Central limit theorem
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Confidence intervals (margin of error)
Techniques for Assessing
Probability Sampling
Standard deviation
 Sampling error
 Sampling distribution
 Central limit theorem
 Confidence intervals (margin of error)
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Inferences (Logic of Sampling)
Use data collected about probabilistic
samples to make statistical inferences about
target population
 Note: inferences made about the probability
(likelihood) that the observations were or
were not due to chance
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