SamplingSampling

Why Sample? Why Sample?

Time, costTime, costAccuracy & representativenessAccuracy & representativeness

time-sensitive issuestime-sensitive issues

What is a sample? Key Ideas & Basic TerminologyWhat is a sample? Key Ideas & Basic Terminology Sampling Guide Sampling Guide (general introduction) in Reading Folder(general introduction) in Reading Folder PopulationPopulation, target population, target population

the universe of phenomena we want to studythe universe of phenomena we want to study Can be people, things, practicesCan be people, things, practices

Sampling FrameSampling Frame (conceptual & operational issues) (conceptual & operational issues) how can we locate the population we wish to study? Examples:how can we locate the population we wish to study? Examples:

Residents of a city? Telephone book, voters listsResidents of a city? Telephone book, voters lists Newsbroadcasts? Broadcast corporation archives? …Newsbroadcasts? Broadcast corporation archives? … Telecommunications technologies?.... Telecommunications technologies?.... Homeless teenagers?Homeless teenagers? ““ethnic” media providers in BC (print, broadcast…)ethnic” media providers in BC (print, broadcast…)

Diagram of key ideas & termsDiagram of key ideas & terms

Target Population Target Population Target Population--Conceptual definition:Target Population--Conceptual definition:

the entire group about which the researcher wishes to draw conclusions.the entire group about which the researcher wishes to draw conclusions. ExampleExample

Suppose we want to study homeless men aged 35-40 who live in Suppose we want to study homeless men aged 35-40 who live in the downtown east side and are HIV positive. the downtown east side and are HIV positive. The purpose of this study could be to compare the effectiveness of two The purpose of this study could be to compare the effectiveness of two

AIDs prevention campaigns, one that encourages the men to seek access AIDs prevention campaigns, one that encourages the men to seek access to care at drop-in clinics and the other that involves distribution of to care at drop-in clinics and the other that involves distribution of information and supplies by community health workers at shelters and on information and supplies by community health workers at shelters and on the street. the street.

The target population here would be all men meeting the same general The target population here would be all men meeting the same general conditions as those actually included in the sample drawn for the study.conditions as those actually included in the sample drawn for the study.

What sampling frames could we use to draw our samples? What sampling frames could we use to draw our samples?

Bad sampling frame Bad sampling frame

= parameters do not accurately represent = parameters do not accurately represent target populationtarget population e.g., a list of people in the phone directory e.g., a list of people in the phone directory

does not reflect all the people in a town does not reflect all the people in a town because not everyone has a phone or is listed because not everyone has a phone or is listed in the directory.in the directory.

Recall: Videoclip from Ask a Silly Question (play videoclip)Recall: Videoclip from Ask a Silly Question (play videoclip)

Ice Storm, electricity disruption, telephone surveyIce Storm, electricity disruption, telephone survey Target Population: Hydro company usersTarget Population: Hydro company users Sampling frame: unclear, probably phonebook or Sampling frame: unclear, probably phonebook or

phone numbers of subscribersphone numbers of subscribers Problem: people with no electricity not at home but Problem: people with no electricity not at home but

in sheltersin shelters Famous examples from the past: Polls of voters Famous examples from the past: Polls of voters

before election (people with phones or car owners not before election (people with phones or car owners not representative of total voters, or opinions not yet representative of total voters, or opinions not yet formed)formed)

More Basic TerminologyMore Basic Terminology

Sampling element (recall: unit of analysis)Sampling element (recall: unit of analysis) e.g., person, group, city block, news e.g., person, group, city block, news

broadcast, advertisement, etc…broadcast, advertisement, etc…

Recall: Units of Analysis (Individuals)Recall: Units of Analysis (Individuals)

Recall: Units of Analysis (Families)Recall: Units of Analysis (Families)

( Households)( Households)

Recall: Importance of Choosing Appropriate Unit of Analysis for ResearchRecall: Importance of Choosing Appropriate Unit of Analysis for Research Recall example: Ecological Fallacy (cheating) Recall example: Ecological Fallacy (cheating) Unit of analysis here is a “class” of students. Classes Unit of analysis here is a “class” of students. Classes

with more males had more cheatingwith more males had more cheating

What happens if we compare number and gender of cheaters? (unit of analysis “students”)

What happens if we compare number and gender of cheaters? (unit of analysis “students”)

Do males cheat more than females?Do males cheat more than females? Same absolute number of male and female Same absolute number of male and female

cheaters in each classcheaters in each class

Comparison of % and # of cheaters by genderComparison of % and # of cheaters by gender

Recall: Ecological Fallacy & ReductionismRecall: 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 TerminologyMore Basic Terminology

Sampling ratioSampling ratio a proportion of a populationa proportion of a population

e.g., 3 out of 100 peoplee.g., 3 out of 100 people e.g., 3% of the universee.g., 3% of the universe

Factors Influencing Choice of Sampling TechniqueFactors Influencing Choice of Sampling Technique Speed Speed CostCost AccuracyAccuracy Assumptions about distribution of characteristics of Assumptions about distribution of characteristics of

populationpopulation link to stats Can site link to stats Can site

http://www.statcan.ca/english/edu/power/ch13/non_prhttp://www.statcan.ca/english/edu/power/ch13/non_probability/non_probability.htmobability/non_probability.htm

Availability of means of access (sampling frame)Availability of means of access (sampling frame) Nature of research question(s) & objectivesNature of research question(s) & objectives

Some types of Non-probability SamplingSome types of Non-probability Sampling

1. Haphazard, accidental, convenience1. Haphazard, accidental, convenience(ex. “Person on the street” interview)(ex. “Person on the street” interview)

2. Quota (predetermined groups)2. Quota (predetermined groups)

3. Purposive or Judgemental 3. Purposive or Judgemental Deviant case (type of purposive sampling) Deviant case (type of purposive sampling)

4. Snowball (network, chain, referral, reputation) & volunteer4. Snowball (network, chain, referral, reputation) & volunteer

Also--multi-stage sampling designsAlso--multi-stage sampling designs

Non-probability Sampling1. Haphazard, accidental, convenience

(ex. “Person on the street” interview)

Non-probability Sampling1. Haphazard, accidental, convenience

(ex. “Person on the street” interview)

Babbie (1995: 192)

Non-probability Sampling 2. Quota (predetermined groups)Non-probability Sampling 2. Quota (predetermined groups)

Neuman (2000: 197)

Why have quotas?Why have quotas?

Ex. populations with unequal representation Ex. populations with unequal representation of groups under studyof groups under study Comparative studies of minority groups with Comparative studies of minority groups with

majority or groups that are not equally majority or groups that are not equally represented in populationrepresented in population Study of different experiences of hospital staff with Study of different experiences of hospital staff with

technological change (nurses, nurses aids, doctors, technological change (nurses, nurses aids, doctors, pharmacists…different sizes of staff, different pharmacists…different sizes of staff, different numbers)numbers)

Non-probability Sampling 3. Purposive or Judgemental Non-probability Sampling 3. Purposive or Judgemental

Unique/singular/particular casesUnique/singular/particular cases

Hard-to-find groups Hard-to-find groups Leaders (“success stories”)Leaders (“success stories”)

Range of different typesRange of different types

Non-probability Sampling 4. Snowball . Snowball (network, chain, referral, reputational)(network, chain, referral, reputational)

Non-probability Sampling 4. Snowball . Snowball (network, chain, referral, reputational)(network, chain, referral, reputational)

Jim

Anne

PatPeter

Paul

Jorge TimLarry

DennisEdith

Susan

SallyJoyce

Kim

Chris

Bob

Maria

Bill

Donna

Neuman (2000: 199)

Sociogram of Friendship Relations

Issues in Non-probability samplingIssues in Non-probability sampling

Bias?Bias? Is the sample representative? Is the sample representative? Types of sampling problems:Types of sampling problems:

Alpha: find a trend in the sample that does not Alpha: find a trend in the sample that does not exist in the populationexist in the population

Beta: do not find a trend in the sample that Beta: do not find a trend in the sample that exists in the populationexists in the population

Types of Probability SamplingTypes of Probability Sampling

1. Simple Random Sample1. Simple Random Sample

2. Systematic Sample2. Systematic Sample

3. 3. Stratified SamplingStratified Sampling

4. Cluster Sampling4. Cluster Sampling

See: Statistics Canada siteSee: Statistics Canada sitehttp://www.statcan.ca/english/edu/power/ch13/probability/probability.htmhttp://www.statcan.ca/english/edu/power/ch13/probability/probability.htm

Simple Random SampleSimple Random Sample With/without replacement?With/without replacement? Must take into account Must take into account

characteristics of population characteristics of population & sampling frame& sampling frame

Develop a sampling frame & Develop a sampling frame & Number sampling frame unitsNumber sampling frame units

Select elements using Select elements using mathematically random mathematically random procedure procedure Table of random numbersTable of random numbers random number generatorrandom number generator Other statistical softwareOther statistical software

Link: How to use a table of Link: How to use a table of random numbersrandom numbers

Principles of Probability SamplingPrinciples of Probability Sampling

eacheach member of the population an member of the population an equal equal chance of being chosen chance of being chosen within specified parameters 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 definition) of the requires an accurate list (sampling frame or operational definition) of the

whole population whole population expensiveexpensive

How to Do a Simple Random SampleHow to Do a Simple Random Sample Develop sampling frameDevelop sampling frame Locate and identify selected elementLocate and identify selected element Link to helpful websiteLink to helpful website

2. Systematic Sample (every “n”th person) With Random Start2. Systematic Sample (every “n”th person) With Random Start

Babbie (1995: 211)

Problems with Systematic SamplingProblems with Systematic Sampling Biases or “regularities” in some types of Biases or “regularities” in some types of

sampling frames (ex. Property owners’ sampling frames (ex. Property owners’

names of heterosexual couples listed with names of heterosexual couples listed with

man’s name first, etc…)man’s name first, etc…)

UUrban studies example)rban studies example)

Other TypesOther Types StratifiedStratified

Neuman

(2000: 209)

Stratified Sampling:Sampling Disproportionately and Weightingng

Stratified Sampling:Sampling Disproportionately and Weightingng

Babbie (1995: 222)

Stratified SamplingStratified Sampling

Used when information is needed about Used when information is needed about

subgroupssubgroups

Divide population into subgroups before Divide population into subgroups before

using random sampling techniqueusing random sampling technique

Other TypesOther Types

ClusterCluster When is it When is it

used?used? lack good lack good

sampling sampling frame or cost frame or cost too hightoo high

Singleton, et al (1993: 156)

Other Sampling Techniques (cont”d)Other Sampling Techniques (cont”d) Probability Proportionate to Size (PPS) Probability Proportionate to Size (PPS)

Random Digit Dialing Random Digit Dialing

New Technologies: Data Mining & the BlogosphereNew Technologies: Data Mining & the Blogosphere

Jan. 3, 2007 Jan. 3, 2007 image with image with Boingboing Boingboing as largest as largest node node (source: (source:

http://datamining.typepad.com/data_mihttp://datamining.typepad.com/data_mi

ning/2007/01/the_blogospherening/2007/01/the_blogosphere.html).html)

Sample Size?Sample Size?

Statistical methods to estimate confidence intervalsStatistical methods to estimate confidence intervals Past experience (rule of thumb)Past experience (rule of thumb) Smaller populations, larger sampling ratiosSmaller populations, larger sampling ratios Other factors:Other factors:

goals of study goals of study number of variables and type of analysisnumber of variables and type of analysis features of populations of populations In qualitative methods: notion of In qualitative methods: notion of Saturation Saturation

(Bertaux)(Bertaux)

Examples of sampling issues & techniquesExamples of sampling issues & techniques Survey about football (soccer) marketSurvey about football (soccer) market Rural poverty project and sampling issuesRural poverty project and sampling issues

Issues/notions in Probability SamplingIssues/notions in Probability Sampling

Assessing Equal chance of being chosenAssessing Equal chance of being chosen

Standard deviationStandard deviation

Sampling errorSampling error

Sampling distributionSampling distribution

Central limit theoremCentral limit theorem

Confidence intervals (margin of error)Confidence intervals (margin of error)

Techniques for Assessing Probability SamplingTechniques for Assessing Probability Sampling

Standard deviationStandard deviation Sampling errorSampling error Sampling distributionSampling distribution Central limit theoremCentral limit theorem Confidence intervals (margin of error)Confidence intervals (margin of error)

Inferences (Logic of Sampling)Inferences (Logic of Sampling)

Use data collected about probabilistic Use data collected about probabilistic samples to make statistical inferences about samples to make statistical inferences about target populationtarget population

Note: inferences made about the Note: inferences made about the probability (likelihood) that the probability (likelihood) that the observations were or were not due to chanceobservations were or were not due to chance