Sample Designs and Sampling Procedures


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Business Research Methods William G. Zikmund :

Business Research Methods William G. Zikmund Chapter 16: Sample Designs and Sampling Procedures

Sampling Terminology:

Sampling Terminology Sample Population or universe Population element Census


Sample Subset of a larger population


Population Any complete group People Sales territories Stores


Census Investigation of all individual elements that make up a population

PowerPoint Presentation:

Define the target population Select a sampling frame Conduct fieldwork Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Stages in the Selection of a Sample

Target Population:

Target Population Relevant population Operationally define Comic book reader?

Sampling Frame:

Sampling Frame A list of elements from which the sample may be drawn Working population Mailing lists - data base marketers Sampling frame error

Sampling Units:

Sampling Units Group selected for the sample Primary Sampling Units (PSU) Secondary Sampling Units Tertiary Sampling Units

Random Sampling Error:

Random Sampling Error The difference between the sample results and the result of a census conducted using identical procedures Statistical fluctuation due to chance variations

Systematic Errors:

Systematic Errors Nonsampling errors Unrepresentative sample results Not due to chance Due to study design or imperfections in execution

Errors Associated with Sampling:

Errors Associated with Sampling Sampling frame error Random sampling error Nonresponse error

Two Major Categories of Sampling:

Two Major Categories of Sampling Probability sampling Known, nonzero probability for every element Nonprobability sampling Probability of selecting any particular member is unknown

Nonprobability Sampling:

Nonprobability Sampling Convenience Judgment Quota Snowball

Probability Sampling:

Probability Sampling Simple random sample Systematic sample Stratified sample Cluster sample Multistage area sample

Convenience Sampling :

Convenience Sampling Also called haphazard or accidental sampling The sampling procedure of obtaining the people or units that are most conveniently available

Judgment Sampling :

Judgment Sampling Also called purposive sampling An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member

Quota Sampling :

Quota Sampling Ensures that the various subgroups in a population are represented on pertinent sample characteristics To the exact extent that the investigators desire It should not be confused with stratified sampling.

Snowball Sampling :

Snowball Sampling A variety of procedures Initial respondents are selected by probability methods Additional respondents are obtained from information provided by the initial respondents

Simple Random Sampling :

Simple Random Sampling A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample

Systematic Sampling :

Systematic Sampling A simple process Every nth name from the list will be drawn

Stratified Sampling:

Stratified Sampling Probability sample Subsamples are drawn within different strata Each stratum is more or less equal on some characteristic Do not confuse with quota sample

Cluster Sampling:

Cluster Sampling The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. The primary sampling unit is no longer the individual element in the population The primary sampling unit is a larger cluster of elements located in proximity to one another

Examples of Clusters:

Population Element Possible Clusters in the United States U.S. adult population States Counties Metropolitan Statistical Area Census tracts Blocks Households Examples of Clusters

Examples of Clusters:

Population Element Possible Clusters in the United States College seniors Colleges Manufacturing firms Counties Metropolitan Statistical Areas Localities Plants Examples of Clusters

Examples of Clusters:

Population Element Possible Clusters in the United States Airline travelers Airports Planes Sports fans Football stadiums Basketball arenas Baseball parks Examples of Clusters

What is the Appropriate Sample Design?:

What is the Appropriate Sample Design? Degree of accuracy Resources Time Advanced knowledge of the population National versus local Need for statistical analysis

Internet Sampling is Unique:

Internet Sampling is Unique Internet surveys allow researchers to rapidly reach a large sample. Speed is both an advantage and a disadvantage. Sample size requirements can be met overnight or almost instantaneously. Survey should be kept open long enough so all sample units can participate.

Internet Sampling:

Internet Sampling Major disadvantage lack of computer ownership and Internet access among certain segments of the population Yet Internet samples may be representative of a target populations. target population - visitors to a particular Web site. Hard to reach subjects may participate

Web Site Visitors:

Web Site Visitors Unrestricted samples are clearly convenience samples Randomly selecting visitors Questionnaire request randomly "pops up" Over- representing the more frequent visitors

Panel Samples:

Panel Samples Typically yield a high response rate Members may be compensated for their time with a sweepstake or a small, cash incentive. Database on members Demographic and other information from previous questionnaires Select quota samples based on product ownership, lifestyle, or other characteristics. Probability Samples from Large Panels

Internet Samples:

Internet Samples Recruited Ad Hoc Samples Opt-in Lists

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