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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FIGURE 11.2Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
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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11KCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
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11LCopyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
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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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FIGURE 11.3Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
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FIGURE 11.4Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
Precision vs. ConfidencePrecision vs. Confidence
More Precision
Less Confidence
More Confidence
Less Precision
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TABLE 11.3Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
Pg. 294
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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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Sampling exercisesSampling exercises“What kinds of sampling designs for….”“What kinds of sampling designs for….”
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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Sampling exercisesSampling exercises“What kinds of sampling designs for….”“What kinds of sampling designs for….”
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.