SamplingSampling

Why Sample? Some Issues: Why Sample? Some Issues:

Time, cost, accuracyTime, cost, accuracyaccuracy/ representativenessaccuracy/ representativenessLink to interesting general Link to interesting general

introduction of sampling for introduction of sampling for publicpublic

Link to website advertising services of market Link to website advertising services of market research firmresearch firm

What is a sample? Key Ideas & Basic TerminologyWhat is a sample? Key Ideas & Basic Terminology LinkLink to good introduction to concepts & issues to good introduction to concepts & issues 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? how can we locate the population we wish to study?

Examples: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 & terms in samplingDiagram of key ideas & terms in sampling

Target Population Target Population

Conceptual definition: the entire group Conceptual definition: the entire group about which the researcher wishes to draw conclusions.about which the researcher wishes to draw conclusions.

ExampleExample Suppose we take a group of homeless men aged 35-40 who live 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 in the downtown east side and are HIV positive. The purpose of this study could be to compare the effectiveness of two AIDs this study could be to compare the effectiveness of two AIDs prevention campaigns, one that encourages the men to seek prevention campaigns, one that encourages the men to seek access to care at drop-in clinics and the other that involves access to care at drop-in clinics and the other that involves distribution of information and supplies by community health distribution of information and supplies by community health workers at shelters and on the street. The target population here workers at shelters and on the street. The target population here would be all men meeting the same general conditions as those would be all men meeting the same general conditions as those actually included in the sample drawn for the study.actually included in the sample drawn for the study.

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

broadcast, advertisement, type of media broadcast, advertisement, type of media coverage, etc…coverage, etc…

Sampling RatioSampling Ratio

a proportion of a populationa proportion of a population e.g., 3 out of 7 peoplee.g., 3 out of 7 people e.g., 3% of the universee.g., 3% of the universe

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 Study of different experiences of hospital staff with technological change (nurses, nurses aids, with technological change (nurses, nurses aids, doctors, pharmacists…different sizes of staff, doctors, pharmacists…different sizes of staff, different numbers)different numbers)

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

Unique/singular/particular casesUnique/singular/particular cases

Range of different typesRange of different types

Hard-to-find groups Hard-to-find groups Leaders (“success stories”)Leaders (“success stories”) Link to example of Link to example of Ipsos Ipsos Reid study Reid study on on

conducting business abroadconducting business abroad

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)

Often uses SociogramsOften uses Sociograms Link to instructions for doing sociogramsLink to instructions for doing sociograms

Non-probability Samples5. Deviant case (type of purposive sampling) Non-probability Samples5. Deviant case (type of purposive sampling)

x

x x x x

x x x x

x x x x

x

New technologies New technologies & mapp mapping interactions New technologies New technologies & mapp mapping interactions

Data mining & the “blogosphere”)Data mining & the “blogosphere”) On-line observation of social networksOn-line observation of social networks

Visualizations & samplingVisualizations & sampling

Conversation ClockConversation Clock Karrie G. Karahalios and Tony Bergstrom. Visualizing audio in group table conversation. IEEE Karrie G. Karahalios and Tony Bergstrom. Visualizing audio in group table conversation. IEEE

TableTop2006TableTop2006.. Social spaces group (Illinois)Social spaces group (Illinois)

“Virtual” Communication“Virtual” Communication

Visual Who project at MIT: visuals, moreVisual Who project at MIT: visuals, more Patterns of presence & associationPatterns of presence & association

Issues in Non-probability samplingIssues in Non-probability sampling

Sampling BiasSampling Bias

Is the sample representative? Of what? Of Is the sample representative? Of what? Of whom? whom?

Types of Probability SamplingTypes of Probability Sampling

1. Simple Random Sample: 1. Simple Random Sample: linklink

How to Do a Simple Random SampleHow to Do a Simple Random Sample Develop sampling frameDevelop sampling frame Select elements using mathematically Select elements using mathematically

random procedure random procedure e.g. Table of random numberse.g. Table of random numbers

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…)

Urban studies example)Urban 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 Types(cont’d)Other Types(cont’d) 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 TechniquesOther Sampling Techniques

Probability Proportionate to Size (PPS) Probability Proportionate to Size (PPS)

Random Digit Dialing Random Digit Dialing

Special IssuesSpecial Issues

Hidden populationsHidden populations Sample sizeSample size

statistical measures (degree of confidence, statistical measures (degree of confidence, variation)variation)

““rule of thumb”rule of thumb” smaller sampling size, larger ratiosmaller sampling size, larger ratio # of variables & attributes# of variables & attributes

Sample Size?Sample Size?

Statistical methods to estimate confidence Statistical methods to estimate confidence intervals—(overhead)intervals—(overhead)

Past experience (rule of thumb)Past experience (rule of thumb) Smaller populations, larger sampling ratiosSmaller populations, larger sampling ratios Factors:Factors:

goals of study (number of variables and goals of study (number of variables and type of analysis)type of analysis)

features of populations of populations

Survey about football (soccer) marketSurvey about football (soccer) market http://www.http://www.sportfivesportfive.com/index..com/index.phpphp

?id=318&L=1%20%282#1379?id=318&L=1%20%282#1379

Sampling Advice for Development ProjectSampling Advice for Development Project Rural poverty Rural poverty projectproject and and samplingsampling issues issues

More Issues/notions in Probability SamplingMore Issues/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)

If time: Introduction to Standard Deviation If time: Introduction to Standard Deviation

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Neuman (2000: 321)Neuman (2000: 321)

Calculation of Standard DeviationCalculation of Standard Deviation

Neuman (2000: 321)Neuman (2000: 321)

Standard Deviation FormulaStandard Deviation Formula

Neuman (2000: 321)Neuman (2000: 321)

Calculation of Standard DeviationCalculation of Standard Deviation

Neuman (2000: 321)Neuman (2000: 321)

Interpreting Standard DeviationInterpreting Standard Deviation

amount of variation from meanamount of variation from mean social meaning depends on exact casesocial meaning depends on exact case

Logic of SamplingLogic of Sampling

Use samples to make Use samples to make inferences inferences about about target populationtarget population

Note: Note: Distinction between descriptive and inferential Distinction between descriptive and inferential

statisticsstatistics probabilistic sampling techniques needed for probabilistic sampling techniques needed for

inferential statisticsinferential statistics