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)