lecture 9 sampling design and procedure. population and sample population –the entire group that...
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Population and SamplePopulation and Sample
PopulationPopulation–The entire group that the The entire group that the
researcher wishes to investigateresearcher wishes to investigateElementElement–A single member of the A single member of the
populationpopulation
Population (Sampling) FramePopulation (Sampling) Frame
– A listing of all the elements in the A listing of all the elements in the population from which the sample is drawnpopulation from which the sample is drawn
SampleSample
– A subset of the populationA subset of the population
SubjectSubject
– A single member of the sampleA single member of the sample
Population and SamplePopulation and Sample
CENSUSCENSUS
INVESTIGATION OF ALL INDIVIDUAL INVESTIGATION OF ALL INDIVIDUAL ELEMENTS THAT MAKE UP A ELEMENTS THAT MAKE UP A POPULATIONPOPULATION
TARGET POPULATIONTARGET POPULATION
RELEVANT POPULATIONRELEVANT POPULATION
OPERATIONALLY DEFINEOPERATIONALLY DEFINE
COMIC BOOK READER?COMIC BOOK READER?
Why Sample?Why Sample?
Greater accuracy
Availability of elementsAvailability of elements
Greater speed
Greater speed
Sampling provides
Sampling provides
Lower costLower cost
SAMPLING FRAMESAMPLING FRAME
A LIST OF ELEMENTS FROM WHICH A LIST OF ELEMENTS FROM WHICH THE SAMPLE MAY BE DRAWNTHE SAMPLE MAY BE DRAWN
WORKING POPULATIONWORKING POPULATION
MAILING LISTS - DATA BASE MAILING LISTS - DATA BASE MARKETERSMARKETERS
SAMPLING FRAME ERRORSAMPLING FRAME ERROR
SamplingSampling
Process of selecting a sufficient Process of selecting a sufficient number of elements from the number of elements from the populationpopulation
Reasons for Sampling: practicality Reasons for Sampling: practicality (time and resources), destructive (time and resources), destructive samplingsampling
Need for a representative sample Need for a representative sample
SAMPLING UNITSSAMPLING UNITS
GROUP SELECTED FOR THE SAMPLEGROUP SELECTED FOR THE SAMPLE
PRIMARY SAMPLING UNITS (PSU)PRIMARY SAMPLING UNITS (PSU)
SECONDARY SAMPLING UNITSSECONDARY SAMPLING UNITS
TERTIARY SAMPLING UNITSTERTIARY SAMPLING UNITS
TWO MAJOR CATEGORIES OF TWO MAJOR CATEGORIES OF SAMPLINGSAMPLING
PROBABILITY SAMPLINGPROBABILITY SAMPLINGKNOWN, NONZERO PROBABLITY FOR KNOWN, NONZERO PROBABLITY FOR EVERY ELEMENTEVERY ELEMENT
NONPROBABLITY SAMPLINGNONPROBABLITY SAMPLINGPROBABLITY OF SELECTING ANY PROBABLITY OF SELECTING ANY PARTICULAR MEMBER IS UNKNOWNPARTICULAR MEMBER IS UNKNOWN
Probability and NonprobabilityProbability and NonprobabilitySamplingSampling
Probability SamplingProbability Sampling– Elements in the population have known Elements in the population have known
chance of being chosenchance of being chosen– Used when the representativeness of the Used when the representativeness of the
sample is of importancesample is of importance
Nonprobability SamplingNonprobability Sampling– The elements do not have a known or The elements do not have a known or
predetermined chance of being selected as predetermined chance of being selected as subjectssubjects
Probability SamplingProbability Sampling
Unrestricted/Simple Random SamplingUnrestricted/Simple Random Sampling– Every element in the population has a known and equal Every element in the population has a known and equal
chance of being selected as a subjectchance of being selected as a subject– Has the least bias and offers the most generalizabilityHas the least bias and offers the most generalizability
Restricted/Complex Probability SamplingRestricted/Complex Probability Sampling • Systematic SamplingSystematic Sampling• Stratified Random SamplingStratified Random Sampling• Cluster Sampling (USM, UM, etc)Cluster Sampling (USM, UM, etc)• Area SamplingArea Sampling• Double Sampling (USM and then grad students)Double Sampling (USM and then grad students)
PROBABLITY SAMPLINGPROBABLITY SAMPLING
SIMPLE RANDOM SAMPLESIMPLE RANDOM SAMPLE
SYSTEMATIC SAMPLESYSTEMATIC SAMPLE
STRATIFIED SAMPLESTRATIFIED SAMPLE
CLUSTER SAMPLECLUSTER SAMPLE
MULTISTAGE AREA SAMPLEMULTISTAGE AREA SAMPLE
SIMPLE RANDOM SIMPLE RANDOM SAMPLING SAMPLING
a sampling procedure that ensures that a sampling procedure that ensures that each element in the population will have each element in the population will have an equal chance of being included in an equal chance of being included in the samplethe sample
Simple RandomSimple Random
AdvantagesAdvantages Easy to implement Easy to implement
with random with random dialingdialing
DisadvantagesDisadvantages Requires list of Requires list of
population population elementselements
Time consumingTime consuming Uses larger sample Uses larger sample
sizessizes Produces larger Produces larger
errorserrors High costHigh cost
SYSTEMATIC SAMPLING SYSTEMATIC SAMPLING
A simple processA simple process
every every nnth name from the list will be drawnth name from the list will be drawn
SystematicSystematic
AdvantagesAdvantages Simple to designSimple to design Easier than simple Easier than simple
randomrandom Easy to determine Easy to determine
sampling sampling distribution of distribution of mean or proportionmean or proportion
DisadvantagesDisadvantages Periodicity within Periodicity within
population may population may skew sample and skew sample and resultsresults
Trends in list may Trends in list may bias resultsbias results
Moderate costModerate cost
STRATIFIED SAMPLINGSTRATIFIED SAMPLING
Probability sampleProbability sample
Subsamples are drawn within different Subsamples are drawn within different stratastrata
Each stratum is more or less equal on Each stratum is more or less equal on some characteristicsome characteristic
Do not confuse with quota sampleDo not confuse with quota sample
StratifiedStratified
AdvantagesAdvantages Control of sample size Control of sample size
in stratain strata Increased statistical Increased statistical
efficiencyefficiency Provides data to Provides data to
represent and analyze represent and analyze subgroupssubgroups
Enables use of Enables use of different methods in different methods in stratastrata
DisadvantagesDisadvantages Increased error will Increased error will
result if subgroups are result if subgroups are selected at different selected at different ratesrates
Especially expensive if Especially expensive if strata on population strata on population must be created must be created
High costHigh cost
CLUSTERCLUSTER SAMPLINGSAMPLINGThe purpose of cluster sampling is to The purpose of cluster sampling is to sample economically while retaining sample economically while retaining the characteristics of a probability the characteristics of a probability sample.sample.The primary sampling unit is no longer The primary sampling unit is no longer the individual element in the the individual element in the population. population. The primary sampling unit is a larger The primary sampling unit is a larger cluster of elements located in proximity cluster of elements located in proximity to one another.to one another.
Population Element Possible Clusters in Malaysia
Malaysian adult population StatesDistrictsMetropolitan Statistical AreaCensus tractsBlocksHouseholds
EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
College seniors CollegesManufacturing firms Districts
Metropolitan Statistical AreasLocalitiesPlants
EXAMPLES OF CLUSTERS
Population Element Possible Clusters in Malaysia
Airline travelers AirportsPlanes
Sports fans Football stadiaBasketball arenasBaseball parks
EXAMPLES OF CLUSTERS
Cluster Cluster
AdvantagesAdvantages Provides an unbiased Provides an unbiased
estimate of population estimate of population parameters if properly parameters if properly donedone
Economically more Economically more efficient than simple efficient than simple randomrandom
Lowest cost per Lowest cost per samplesample
Easy to do without listEasy to do without list
DisadvantagesDisadvantages Often lower statistical Often lower statistical
efficiency due to efficiency due to subgroups being subgroups being homogeneous rather homogeneous rather than heterogeneousthan heterogeneous
Moderate costModerate cost
Stratified and Cluster SamplingStratified and Cluster Sampling
StratifiedStratified Population divided Population divided
into few subgroupsinto few subgroups Homogeneity Homogeneity
within subgroupswithin subgroups Heterogeneity Heterogeneity
between subgroupsbetween subgroups Choice of elements Choice of elements
from within each from within each subgroupsubgroup
ClusterCluster Population divided Population divided
into many into many subgroupssubgroups
Heterogeneity Heterogeneity within subgroupswithin subgroups
Homogeneity Homogeneity between subgroupsbetween subgroups
Random choice of Random choice of subgroups subgroups
DoubleDouble
AdvantagesAdvantages May reduce costs if May reduce costs if
first stage results first stage results in enough data to in enough data to stratify or cluster stratify or cluster the populationthe population
DisadvantagesDisadvantages Increased costs if Increased costs if
discriminately useddiscriminately used
Nonprobability SamplesNonprobability Samples
Cost
FeasibilityFeasibility
TimeTime
IssuesIssues
No need to generalize
Limited objectivesLimited
objectives
Nonprobability Nonprobability Sampling MethodsSampling Methods
ConvenienceConvenience
JudgmentJudgment
QuotaQuota
SnowballSnowball
NONPROBABLITY SAMPLINGNONPROBABLITY SAMPLING
CONVENIENCECONVENIENCE
JUDGMENTJUDGMENT
QUOTAQUOTA
SNOWBALLSNOWBALL
Nonprobability SamplingNonprobability Sampling
Convenience SamplingConvenience Sampling– Based on availability, e.g. students in a Based on availability, e.g. students in a
classroomclassroom
Purposive SamplingPurposive Sampling– Specific targets, because they posses the Specific targets, because they posses the
desired infodesired infoJudgement samplingJudgement samplingQuota samplingQuota sampling
CONVENIENCE SAMPLINGCONVENIENCE SAMPLING
also called haphazard or accidental also called haphazard or accidental samplingsampling
the sampling procedure of obtaining the the sampling procedure of obtaining the people or units that are most people or units that are most conveniently availableconveniently available
QUOTA SAMPLING QUOTA SAMPLING
ensures that the various subgroups in a ensures that the various subgroups in a population are represented on pertinent population are represented on pertinent sample characteristicssample characteristics
to the exact extent that the investigators to the exact extent that the investigators desiredesire
it should not be confused with stratified it should not be confused with stratified samplingsampling
JUDGMENT SAMPLING JUDGMENT SAMPLING
also called purposive sampling also called purposive sampling
an experienced individual selects the an experienced individual selects the sample based on his or her judgment sample based on his or her judgment about some appropriate characteristics about some appropriate characteristics required of the sample memberrequired of the sample member
SNOWBALL SAMPLING SNOWBALL SAMPLING
a variety of procedures a variety of procedures
initial respondents are selected by initial respondents are selected by probability methods probability methods
additional respondents are obtained from additional respondents are obtained from information provided by the initial information provided by the initial respondentsrespondents
Sample SizeSample Size
Factors Determining Sample SizeFactors Determining Sample Size
– Homogeneity of populationHomogeneity of population
– Level of confidenceLevel of confidence
– PrecisionPrecision
– Cost, Time and Resources Cost, Time and Resources
Larger Sample SizesLarger Sample Sizes
Small error range
Number of subgroupsNumber of subgroups
Confidence level
Confidence level
WhenWhen
Population variance
Desired precisionDesired
precision
Roscoe’s Rule of ThumbRoscoe’s Rule of Thumb
>30 and <500 appropriate for most >30 and <500 appropriate for most researchresearch
Not less than 30 for each sub-sampleNot less than 30 for each sub-sample
In multivariate analysis, 10 times or more In multivariate analysis, 10 times or more the number of variablesthe number of variables
Simple experiment with tight controls, 10-Simple experiment with tight controls, 10-20 quite sufficient20 quite sufficient
WHAT IS THE APPROPRIATE WHAT IS THE APPROPRIATE SAMPLE DESIGNSAMPLE DESIGN
DEGREE OF ACCURACYDEGREE OF ACCURACY
RESOURCESRESOURCES
TIMETIME
ADVANCED KNOWLEDGE OF THE ADVANCED KNOWLEDGE OF THE POPULATIONPOPULATION
NATIONAL VERSUS LOCALNATIONAL VERSUS LOCAL
NEED FOR STATISTICAL ANALYSISNEED FOR STATISTICAL ANALYSIS
AFTER THE SAMPLE DESIGN AFTER THE SAMPLE DESIGN IS SELECTEDIS SELECTED
DETERMINE SAMPLE SIZEDETERMINE SAMPLE SIZE
SELECT ACTUAL SAMPLE UNITSSELECT ACTUAL SAMPLE UNITS
CONDUCT FIELDWORKCONDUCT FIELDWORK
SYSTEMATIC ERRORSSYSTEMATIC ERRORS
NONSAMPLING ERRORSNONSAMPLING ERRORS
UNREPRESENTATIVE SAMPLE UNREPRESENTATIVE SAMPLE RESULTSRESULTS
NOT DUE TO CHANCENOT DUE TO CHANCE
DUE TO STUDY DESIGN OR DUE TO STUDY DESIGN OR IMPERFECTIONS IN EXECUTIONIMPERFECTIONS IN EXECUTION
ERRORS ASSOCIATED ERRORS ASSOCIATED WITH SAMPLINGWITH SAMPLING
SAMPLING FRAME ERRORSAMPLING FRAME ERROR
RANDOM SAMPLING ERROR RANDOM SAMPLING ERROR
NONRESPONSE ERRORNONRESPONSE ERROR
RANDOM SAMPLING ERRORRANDOM SAMPLING ERROR
THE DIFFERENCE BETWEEN THE THE DIFFERENCE BETWEEN THE SAMPLE RESULTS AND THE RESULT SAMPLE RESULTS AND THE RESULT OF A CENSUS CONDUCTED USING OF A CENSUS CONDUCTED USING IDENTICAL PROCEDURESIDENTICAL PROCEDURES
STATISTICAL FLUCTUATION DUE TO STATISTICAL FLUCTUATION DUE TO CHANCE VARIATIONSCHANCE VARIATIONS
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 Selectionof a Sample