This document defines key concepts related to population and sampling in research methods. It discusses the differences between populations and samples, and the importance of carefully defining both. It also covers probability and non-probability sampling techniques like simple random sampling, stratified sampling, cluster sampling, and quota sampling. The advantages and disadvantages of different sampling methods are presented.
4. Definition of Population
Population should be:
Carefully and Fully Defined
Relevant to Research Question
5. Definition of Sample
Importance of Sample Data
Quality of Sample Based on:
Overall Sample Size
How members chosen to be part of sample
6. Advantages of Sample
Time
Money
Disadvantages of Sample
Less accurate
Subject to error
Decision made on practical grounds
7. Population Parameter
Characteristics of population quantified as a number
▪ Examples: Proportion, Mean/Average, etc.
Estimator
Numerically estimates the value of population
characteristic, or population parameter
8. Sample Statistic
An estimator of a population parameter derived from a
population sample
Element
Also called a unit of analysis
A single occurrence, realization, or instance of the
objects or entities being studied
▪ Examples: Individuals, States, Cities, Countries,
Speeches, Wars
9. Stratum
Population subdivided into groups of similar
elements before a sample is drawn
Subgroup of a population sharing characteristics
Examples:
JU students stratified by class, major, or GPA
Latin Graduation Honors
10.
11.
12. Sampling Frame
Particular population from which sample is drawn
Closer sampling frame is to population of interest or
theoretical population, the better off you are
Example: The Literary Digest Poll?
13. If sampling frame is incomplete or inappropriate, then
sample bias will occur
Sample will be unrepresentative of the population,
and inaccurate conclusions may result
Sample bias caused by a biased selection of elements,
even if frame is complete and accurate
Sampling Unit
Entity listed in a sampling frame
14. Probability Sample
Sample for which each element in the total
population has a known probability of being
included in sample
Nonprobability Sample
Sample in which each element in the total
population has an unknown probability of being
selected
15.
16. Each element and combination of elements has an
equal chance of being selected
What has to happen for this to occur?
Short Class Activity on Random Numbers
17. Attempt to make draft process fairer
How did the lottery process work?
Selective Service (SS) estimated that anyone
with number higher than 200 would not be
called
Found negative correlation between day of
birth and lottery number
19. Elements selected from list at predetermined
intervals (e.g. every Kth element)
K = Sampling Interval or “skip” between elements
Computed as Population Size (N) / Sample Size (n)
Useful when dealing with long list of population
elements (e.g. all SC justices)
Often used in product testing
20. Probability sample where elements sharing
one or more characteristics are grouped
Two MainTypes:
Proportionate Sample
Disproportionate Sample
21. Stratified sample were each stratum
represented in proportion to its size in
population
Example: Congress of the Future
Sampling Fraction
22. Stratified sample where each stratum is NOT
represented in proportion to size in population
Issue ofWeighting
23. Probability sample in which sampling frame
initially consists of clusters of elements
Groups / clusters of elements are identified and
listed as sampling units
Within each sampling unit, certain elements are
identified and sampled
Example: Public Opinion Polling
24. Advantages:
Allows researchers to get around problem of
acquiring list of elements in target population
Can reduce fieldwork costs
Disadvantage:
Greater level of imprecision
25.
26. Goal: To study a diverse and usually limited
number of observations
Researcher exercises considerable discretion
Example: Fenno and Home Style
27. Elements are included because they are
convenient or easy for a researcher to study
Used for exploratory research or when target
population is impossible to define / locate
28. Sample in which elements are sampled in
proportion to their representation in
population
Similar to proportionate stratified sampling,
but elements are quota sample are NOT
chosen in reasoned or probabilistic manner
29.
30. Initial respondents used to identify others
who might qualify for inclusion in sample
Useful when trying to study members in
elusive population:
Draft Dodgers
Political Protestors
Drug Users