Today’s Lecture Session
1- Finish Measurement (scales & indices on separate powerpoint)2- Sampling3- Practice Questions for Quiz 1
SamplingSampling
Neuman & Robson: Chapter 7
Why Sample? Some Issues:
Time, cost, accuracy Accuracy/ representativityinteresting general introduction of
sampling for public in readings folder
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The Logic of Sampling
What is a sample? Key Ideas & Basic Terminology
• Link to good introduction to concepts & issues• Population, target population– the universe of phenomena we want to study– Can be people, things, practices
• Sampling Frame (conceptual & operational issues)– how can we locate the population we wish to study?
Examples:• Residents of a city? Telephone book, voters lists• News broadcasts? Broadcast corporation archives? …• Telecommunications technologies?.... • Homeless teenagers?• “ethnic” media providers in BC (print, broadcast…)
Diagram of key ideas & terms
Target Population
• Conceptual definition: the entire group – about which the researcher wishes to draw conclusions.
• Example Suppose we take a group of homeless men aged 35-40 who live in the downtown east side and are HIV positive. 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.
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.
Examples of Populations
More Examples of Populations
More Basic Terminology
• Sampling element (recall: unit of analysis)e.g., person, group, city block, news
broadcast, advertisement, etc…
Recall: Importance of Choosing Appropriate Unit of Analysis for Research• 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
Sampling ratio
• a proportion of a population
• e.g., 3 out of 100 people• e.g., 3% of the universe
Factors Influencing Choice of Sampling Technique
• Speed • Cost• Accuracy• Knowledge of target population• Access to sampling frame
Types of NonprobabilitySamples
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Non-probability SamplingHaphazard, accidental, convenience
(ex. “Person on the street” interview)
Babbie (1995: 192)
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Quota Sampling
Why have quotas?
• 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)
Purposive or Judgemental
• Range of different types
• Hard-to-find groups
• Representatives of different types in a typology
• Deviant Case (a type of purposive sampling) – cases with unusual characteristics
• Success stories• Exceptional cases
Snowball Snowball (network, chain, referral, reputational)(network, chain, referral, reputational)New technologies (New technologies (Data mining & the “blogosphere”)
Jim
Anne
PatPeter
Paul
Jorge TimLarry
DennisEdith
Susan
SallyJoyce
Kim
Chris
Bob
Maria
Bill
Donna
Neuman (2000: 199)
Sociogram of Friendship Relations
Sequential Sampling
• theoretical sampling• Notion of saturation (when you stop finding
new information)
Other forms of non-probability Sampling
• Example: New Example: New technologies & technologies & techniques for techniques for “sampling” (illustration “sampling” (illustration from from Data mining & the “blogosphere”)
• NB: High technology NB: High technology techniques not techniques not necessarily necessarily “probabilistic”“probabilistic”
Issues in Non-probability sampling
• Bias?Bias?• Is the sample Is the sample representativerepresentative? ? • Types of sampling problems:Types of sampling problems:– AlphaAlpha: find a trend in the sample that does not : find a trend in the sample that does not
exist in the populationexist in the population– BetaBeta: do not find a trend in the sample that exists : do not find a trend in the sample that exists
in the populationin the population
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Probability Sampling
• Populations, Elements, and Sampling Frames– Sampling element– Target population– Sampling ratio– Sampling frame– Parameter
Principles of Probability Sampling
• eacheach member of the population an member of the population an equal equal chance of chance of being chosen within specified parameters being chosen within specified parameters
• AdvantagesAdvantages– ideal for statistical purposes ideal for statistical purposes
• DisadvantagesDisadvantages– hard to achieve in practice hard to achieve in practice – requires an accurate list (sampling frame or operational requires an accurate list (sampling frame or operational
definition) of the whole population definition) of the whole population – expensiveexpensive
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Types of Probability Sampleslink to useful webpage: http://www.socialresearchmethods.net/kb/sampprob.php
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Another Type of Probability Sample
• Probability Proportionate to Size– probability proportionate to size (PPS)– Random-Digit Dialing
Types of Simple Random Samples
• With replacement– Leave selected sampling elements in the sampling
frame– Only if your research design allows for same
element to be chosen more than once
• Without replacement– Remove selected sampling elements already chosen– When you do not want the same elements chosen
more than once
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How to Draw Simple Random and Systematic Samples
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How to Draw Simple Random and Systematic Samples
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How to Draw Simple Random and Systematic Samples
2. Systematic Sample (every “n”th person) With Random Start
Babbie (1995: 211)
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Problems with Systematic Sampling of Cyclical Data
Biases or “regularities” in
some types of sampling
frames (ex. Property
owners’ names of
heterosexual couples listed
with man’s name first,
etc…)
Stratified
Stratified Sampling
• Used when information is needed about
subgroups
• Divide population into subgroups before using
random sampling technique
Stratified Sampling:Sampling Disproportionately and Weightingng
Babbie (1995: 222)
Stratified Sampling Example
• Box 7.7
Cluster Sampling• When you
lack good sampling frame or cost too high
Singleton, et al (1993: 156)
Other Sampling Techniques
• Probability Proportionate to Size (PPS)
• Random Digit Dialing
Sample Size?
• Statistical methods to estimate confidence intervals—(overhead)
• Past experience (rule of thumb)• Smaller populations, larger sampling ratios• Factors:
goals of study (number of variables and type of analysis)
features of populations
Evaluating Sampling
• Is the sample representative of the population under
study?
• Assessing Equal chance of being chosen
• Examine Sampling distribution of parameters of
population
• Use Central Limit Theorem to calculate Confidence
Intervals and estimate Margin of Error
Sampling Distribution
• Box 7.4
Graph of Sampling Distribution• Box 7.4
Normal Distribution
Inferences
• Use samples drawn using probabilistic techniques to make inferences about the target population
• Important for many types of research & statistical analysis techniques (inferential statistics)
Neuman (2000: 226)
Another Selection Process: Random Assignment (experimental research)
Neuman (2000: 226)
Comparison with Random Sampling
Sample Questions for Quiz 1 (powerpoint)
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