Dr. Michael R. Hyman, NMSU

Sample Design

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Photographic Example of How Sampling Works

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Sampling Terminology

• Population or universe

• Population element

• Census

• Sample

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Population/Universe

• Any complete group

– People

– Sales territories

– Stores

• Total group from which information is needed

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Census

Investigation of all individual elements that make up a population

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Sample

Subset of a larger population of interest

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Define the target population

Select a sampling frame

Conduct fieldwork

Determine if probability or non-probability sampling method will be chosen

Plan procedure for selecting sampling units

Determine sample size

Select actual sampling units

Stages in Selectinga Sample

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Define Target Population

• Look at research objectives

• Relevant population

• Operationally define

• Consider alternatives and convenience

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Select Sampling Frame

• List of elements from which sample may be drawn

• Mailing and commercial lists can be problematic (more on this later)

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Sampling Units

• Group selected for the sample

• Can be persons, households, businesses, et cetera

• Primary sampling units

• Secondary sampling units

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Choose Probability or Non-probability Sample

• Probability sample

• Known, nonzero probability for every element

• Non-probability sample

• Probability of selecting any particular member is unknown

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Conditions Favoring Non-probability vs. Probability Samples

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Different Sampling Techniques

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Non-probability Samples

• Convenience

• Judgment

• Quota

• Snowball

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Convenience Sample

• Also called haphazard or accidental sampling

• Sampling procedure for obtaining people or units that are convenient to researchers

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Discrepancy between Implied and Ideal Populations in Convenience Sampling

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Judgment Sample

• Also called purposive sampling

• Experienced person selects sample based on his or her judgment about some appropriate characteristics required of sample members

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Discrepancy between Implied and Ideal Populations in Judgment Sampling

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Quota Sample

• Various population subgroups are represented on pertinent sample characteristics to the extent desired by researchers

• Do not confuse with stratified sampling (discussed later)

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Representative Quota Sample Requirements

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Snowball Sample

• Initial respondents selected by probability methods

• Additional respondents obtained from information provided by initial respondents

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Probability Samples

• Simple random sample

• Systematic sample

• Stratified sample

• Cluster sample

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Simple Random Sample

Ensures each element in the population has an equal chance of selection

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Systematic Sample

• A simple process

• Every nth name from list will be drawn

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Stratified Sample

• Probability sample

• Sub-samples drawn within different strata

• Each stratum more or less equal on some characteristic

• Do not confuse with quota sample

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Drawing a Stratified Sample: Example

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Disproportionate Stratified Random Sampling Used by A.C. Nielsen

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Cluster Sample

• Purpose: to sample economically while retaining characteristics of a probability sample

• Primary sampling unit is not individual element in population

• Instead, it is larger cluster of elements located in proximity to one another

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Examples of Populations and Clusters

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More Examples of Clusters

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Strengths and Weakness of Sampling Techniques

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Bases for Choosing a Sample Design

• Degree of accuracy

• Resources

• Time

• Advanced knowledge of population

• National versus local

• Need for statistical analysis

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After Sample Design is Selected

• Determine sample size

• Select actual sample units

• Conduct fieldwork

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Sampling Error

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Types of Sampling Errors

• Sampling frame error

• Random sampling error

• Non-response error

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Errors Associated with Sampling

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Random Sampling Error

• Difference between sample results and result of a census conducted using identical procedures

• Statistical fluctuation due to chance variations

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Key Aspects of Sample Frame Error

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Systematic Errors

• Non-sampling errors

• Unrepresentative sample results caused by flawed study design or imperfections in execution rather than chance

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Example Mailing Lists

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More Mailing List Examples

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• Representativeness

• Omissions and duplications

• Recency

Problems with Lists

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• Directories not current

• Demographics and socioeconomics of voluntary non-list members differ from list members

• Solution

– Random digit dialing

– Add ‘1’ to listed number

Directories and Telephone Interviewing

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Weighting Samples

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Weighting a Sample

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Internet Samples

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Internet Sampling is Unique

• Internet surveys allow researchers to rapidly reach a large sample

• Survey should be kept open long enough so all sample units can participate

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Advantages and Disadvantages

• Internet samples may be representative of target populations

– e.g., visitors to a Web site

• Hard to reach subjects may participate

• Major disadvantage

– Lack of PC ownership & Internet access among certain population segments

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Web Site Visitors

• Unrestricted samples are clearly convenience samples

• Randomly selecting visitors• Questionnaire request randomly

"pops up" • Over-representing more frequent

visitors

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Panel Samples

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Panel Samples• Typically yield high response rates

– Members may be compensated for time with sweepstake or small cash incentive

• Database on members

– Demographic and other information from previous questionnaires

• Select quota samples based on product ownership, demographics, lifestyle, or other characteristics

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Recap

• Basic sampling terminology

• Stages in selecting a sample

– From target population definition to drawing the sample

• Non-probability vs. probability samples

– Types and appropriate usage

• Sampling error

• Internet and panel samples