1 Mgt 540 Research Methods Sampling Issues. 2 Basic Research Progress Explorative - Descriptive...

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Mgt 540Mgt 540Research MethodsResearch Methods

Sampling IssuesSampling Issues

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Basic Research ProgressBasic Research Progress

Explorative - DescriptiveExplorative - Descriptive(Qualitative)(Qualitative)

1.1. Framework / Domain Framework / Domain extant knowledge for extant knowledge for

referencereference2.2. Research DesignResearch Design3.3. Data collection / Data collection /

presentationpresentation4.4. Data analysisData analysis

Emergent themesEmergent themes5.5. Relationship to extant Relationship to extant

knowledge?knowledge? Tie to lit review, other Tie to lit review, other

researchresearch6.6. Findings? Findings?

Possible hypothesis?Possible hypothesis?

Hypothesis TestingHypothesis Testing(Quantative)(Quantative)

1.1. Framework / Framework / Domain Domain Foundation Foundation

2.2. Conceptual Conceptual frameworkframework

3.3. Hypothesis Hypothesis presentationpresentation

4.4. Research DesignResearch Design5.5. Data collection / Data collection /

presentationpresentation6.6. Data analysisData analysis

Confirm/disconfirmConfirm/disconfirm7.7. Findings? Findings?

Possible additional Possible additional hypotheses?hypotheses?

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FROM CHAPTER 11Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Research Design FlowchartResearch Design Flowchart

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SamplingSampling

Why sample?Why sample?Budget restrictionsBudget restrictionsTime constraintsTime constraintsInaccessibility of some population membersInaccessibility of some population membersSufficient accuracy, reliability with good Sufficient accuracy, reliability with good

samplesample Larger sample required for more Larger sample required for more

heterogeneous populationheterogeneous population Randomly chosen sample is Randomly chosen sample is fair fair in the in the

sense that every member of the sense that every member of the population has an equal chance of population has an equal chance of being chosenbeing chosen

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Sampling issues (terms)Sampling issues (terms)

SamplingSamplingSelection of sufficient number of items or Selection of sufficient number of items or

elements so that the properties of the sample elements so that the properties of the sample (statistic) could be generalized to the (statistic) could be generalized to the population (parameter)population (parameter)

Population FramePopulation FrameListing of population elementsListing of population elements

PopulationPopulationEntire group of interest to researcher Entire group of interest to researcher

(people, things, events)(people, things, events) SampleSample

Subgroup of the populationSubgroup of the population SubjectSubject

Single member of a sampleSingle member of a sample ElementElement

Single member of the populationSingle member of the population

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Sampling precisionSampling precision

Precision Precision Degree of sampling errorDegree of sampling errorMeasured by the standard error of Measured by the standard error of the estimatethe estimate

See page 286 andSee page 286 andStatistical tables, beginning on page Statistical tables, beginning on page

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FIGURE 11.1Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

SamplingSampling

= Sample Mean

S = Std. Deviation

μ= Population Mean

σ = Std. Deviation

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Questions for determining Questions for determining samplesample

Relevant target population?Relevant target population? Exact parameters of interest?Exact parameters of interest? Kind of sampling frame Kind of sampling frame

available?available? Sample size needed (for desired Sample size needed (for desired

level of confidence)?level of confidence)? Cost relating to sampling Cost relating to sampling

design?design? Time available to collect data Time available to collect data

from sample?from sample?

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Sampling FrameSampling Frame

The empirical representation of The empirical representation of the theoretical universe of the theoretical universe of interestinterest

In theory may be the entire In theory may be the entire populationpopulation

But, for exampleBut, for exampleNot all own telephones (for a telephone Not all own telephones (for a telephone

survey)survey)Some may be homeless (for a mail Some may be homeless (for a mail

survey) survey)

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Sampling UnitSampling Unit

Compare to the desired unit of Compare to the desired unit of analysisanalysisIndividualsIndividualsDyadsDyadsWork groups, teamsWork groups, teamsCompaniesCompaniesIndustriesIndustriesMarketsMarkets

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11BCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Sampling IssuesSampling Issues

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11CCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Probability & Non Probability Probability & Non Probability SamplingSampling

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Simple Random SamplingSimple Random Sampling

Most representative for most Most representative for most purposespurposesDisadvantagesDisadvantages

Cumbersome and tediousCumbersome and tediousEntire listing of all elements in the Entire listing of all elements in the desired population are usually not desired population are usually not availableavailable

Very expensiveVery expensiveNot the most efficient designNot the most efficient design

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Complex probability Complex probability sampling(s)sampling(s)

1.1. SystematicSystematic2.2. Stratified random samplingStratified random sampling3.3. Cluster samplingCluster sampling4.4. Area samplingArea sampling5.5. Double samplingDouble sampling

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11FCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Systematic samplingSystematic sampling

Every nth element is sampled, Every nth element is sampled, starting from a randomly chosen starting from a randomly chosen elementelement

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11GCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Stratified random samplingStratified random sampling

Number of mutually exclusive Number of mutually exclusive sub-populations or stratasub-populations or stratae.g. university students divided into e.g. university students divided into juniors, seniors, etc.juniors, seniors, etc.

Homogeneity within stratum and Homogeneity within stratum and heterogeneity between strataheterogeneity between strata

Statistical efficiency greater in Statistical efficiency greater in stratified samplesstratified samples

Sub-groups can be analyzedSub-groups can be analyzedDifferent methods of analysis can Different methods of analysis can be used for different sub-groupsbe used for different sub-groups

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11HCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Stratified random sampleStratified random sample

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TABLE 11.1Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Stratified random sampleStratified random sample

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11JCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

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Non-Probability SamplingNon-Probability Sampling

Convenience samples Convenience samples the researcher’s convenience – the researcher’s convenience –

unrestrictedunrestricted Purposive samplesPurposive samples

Judgment sampling – expert selection of Judgment sampling – expert selection of respondentsrespondents

Quota sampling – ensuring representation Quota sampling – ensuring representation of certain groups, individualsof certain groups, individuals

Snowball sampling – initially selected Snowball sampling – initially selected respondents (by probability or not) respondents (by probability or not) refer later ones refer later ones

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11MCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

PrecisionPrecision

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11NCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Sampling considerationsSampling considerations

What is the relevant population?What is the relevant population? What type of sample should be What type of sample should be

drawn?drawn? What sampling frame should be What sampling frame should be

used?used? What are the parameters of What are the parameters of

interest?interest? How much accuracy and How much accuracy and

precision are desired?precision are desired? What is the sample size needed?What is the sample size needed? What are the sampling costs?What are the sampling costs?

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11OCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Sample size considerationsSample size considerations

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11PCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E

Sampling EfficiencySampling Efficiency

Using n = sample size, S = standard error

Efficiency is achieved when:

Keeping n constant, you achieve a smaller S

Reduce nkeep the same level of S

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Precision vs. ConfidencePrecision vs. Confidence

More Precision

Less Confidence

More Confidence

Less Precision

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Relevance of sample size Relevance of sample size Refer back to diagram on page 175Refer back to diagram on page 175

Purpose of Research?Purpose of Research?ExploratoryExploratoryDiscoveryDiscoveryHypothesis testing?Hypothesis testing?

Types of investigation?Types of investigation?Differences?Differences?Correlations?Correlations?Causality?Causality?

Unit of analysis?Unit of analysis? Data Collection method?Data Collection method?

Qualitative?Qualitative?Quantitative?Quantitative?

Measurement / Measures?Measurement / Measures?

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Sampling exercisesSampling exercises“What kinds of sampling designs for….”“What kinds of sampling designs for….”

A study to get a quick idea of the A study to get a quick idea of the medical acceptability of a new medical acceptability of a new aspirin substitute which cannot aspirin substitute which cannot be dispensed over the counter be dispensed over the counter without prescription.without prescription.Purposive judgment samplingPurposive judgment sampling

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A study involving a sample of 325 A study involving a sample of 325 students in a university where students in a university where 2,000 students are enrolled.2,000 students are enrolled.A systematic sampling design (using A systematic sampling design (using a university listing of studentsa university listing of students

An investigation of the career An investigation of the career salience of professionals in the salience of professionals in the fields of medicine, engineering, fields of medicine, engineering, business, and law.business, and law.A stratified random sampling with A stratified random sampling with stratification along profession, gender, stratification along profession, gender, age, etc.age, etc.

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The generalizability of the The generalizability of the attitudes of blue collar workers attitudes of blue collar workers from a sample of 184, to the total from a sample of 184, to the total population of 350 blue collar population of 350 blue collar workers in the entire factory of a workers in the entire factory of a particular company.particular company.Simple random sampling (because Simple random sampling (because of the high importance attached to of the high importance attached to generalizabilitygeneralizability

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Sampling exercise Sampling exercise (problem)(problem)You want to estimate the production days that

would be lost during the next three months by sampling the vacation intentions of a few employees. You randomly select 36 employees in the organization and find that the average number of days they intend taking off is 16 during the coming three Summer months, with a standard deviation of seven (7) days. Based on these sample statistics, you want to estimate at a 99 percent confidence level, the days that will be lost due to the entire population of workers taking vacation time during the next three months, so that the plant manager knows how much temporary help he should plan on hiring during the summer months in order for work to proceed smoothly.

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Exercise calculationExercise calculation

Solution:

μ = ± z S

S = S/√n = 7/6 = 1.167

μ = 16 ± (2.576 x 1.167)

= 16 ± 3.01

= 12.99 to 19.01 = Sample Meanμ = Population MeanS = Std. DeviationS = Standard Error

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If there are 100 employees in the If there are 100 employees in the organization expected to take vacation organization expected to take vacation during Summer, then, the most during Summer, then, the most optimistic estimation of the days lost optimistic estimation of the days lost through vacation time during the through vacation time during the summer would be (13 x 100 =) 1m300 summer would be (13 x 100 =) 1m300 days and the most pessimistic would be days and the most pessimistic would be (19 x 100 =) 1900 days. This would (19 x 100 =) 1900 days. This would mean that temporary help would be mean that temporary help would be needed anywhere between 1,300 and needed anywhere between 1,300 and 1,900 days worth of labor for production 1,900 days worth of labor for production to proceed smoothly.to proceed smoothly.

To narrow the gap – (increase To narrow the gap – (increase precision) requires sacrificing precision) requires sacrificing confidence – choose your risk.confidence – choose your risk.

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