2 Learning Objectives Define Population And Sample Distinguish Between Target And Accessible PopulationDiscuss Probability And Nonprobability Sampling ProceduresCompare Four Methods Of Probability Sampling
3 Learning Objectives Compare Three Methods Of Nonprobability Sampling Determine Which Sampling Technique To Use In Various Types Of Research StudiesCompare Longitudinal And Cross-Sectional StudiesEnumerate Factors To Be Considered In Deciding The Size Of The Sample3
4 Learning Objectives Discuss Sampling Error And Sampling Bias Critique The Sampling Procedure Described In Research Reports And Articles4
5 Learning Objective One Define Population And Sample
6 Population Complete set of persons or objects Common characteristic Of interest to the researcher
7 SampleSubset of a populationSample represents the population.
8 Sample Selection Representation of the population Method for getting the sampleSample size for the study
9 Sample Terms Element Sampling frame Single member of a population Listing of all elementsStudy sample, if from this frame
10 Learning Objective Two Distinguish Between Target And Accessible Population 10
11 Population TermsTarget populationAccessible population
12 Target Population Definition Entire group of people or objects People or objects meet designated set of criteria.Generalization of the findings
13 Accessible Population DefinitionGroup of people or objectsResearcher has access to them.
14 Population Importance Conclusions based on dataData from accessible populationDecisions made from study results
15 Learning Objective Three Discuss Probability And Nonprobability Sampling Procedures 15
16 Types of Sampling Methods ProbabilityNonprobability
17 Probability Sampling Uses random sampling procedures Selects sample from elements or members of populationTypesSimpleStratifiedClusterSystematic
18 Nonprobability Sampling Uses nonrandom sampling proceduresSelects sample from elements or members of populationTypesConvenienceQuotaPurposive
19 Learning Objective Four Compare Four Methods Of Probability Sampling 19
20 Probability SamplingSimple randomStratifiedClusterSystematic
21 Random Selection Key word in sample selection Every subject has an equal chance.
22 Probability Sampling Allows researcher to estimate the chance Helps with inferential statistics with greater confidenceGives the ability to generalize the findings
23 Simple Random Sampling Type of probability samplingImportance of this samplingEqual chance of selectionIndependent chance of selection
24 Advantages of Simple Random Sampling Little knowledge of population is needed.Most unbiased of probability methodEasy to analyze data and compute errors
25 Disadvantages of Simple Random Sampling Complete listing of population is necessary.It is time consuming to use.
26 Steps for Simple Random Sampling Identify the accessible population or list of elementsChoose the method for getting the sampleNote how easy it is through this exampleNames of elements on slips of paperPapers are placed into a hat.Individual draws a slip of paper.Individual continues until sample number is met.
27 Stratified Random Sampling Type of probability samplingPopulation is divided into subgroups or strata.Simple random sample taken from each strata
28 Approaches for Stratified Random Sampling Proportional stratified samplingDetermine sampling fraction for each stratumEnsure that this stratum is equalProportion in total populationDisproportional stratified samplingDetermine stratum is representedUsed when strata are very unequalNote the key word disproportional
29 Advantages of Stratified Random Sampling (cont’d) Increases probability of being representativeEnsures adequate number of cases for strata
30 Disadvantages of Stratified Random Sampling Requires accurate knowledge of populationMay be costly to prepare stratified listsStatistics are more complicated.
31 Cluster Random Stratified Sampling Large groups or clusters, not people, are selected from population.Simple, stratified or systematic random sampling may be used during each phase of sampling.
32 Advantages of Cluster Random Sampling Saves time and moneyArrangements made with small number sampling unitsCharacteristics of clusters or population can be estimated.
33 Disadvantages of Cluster Random Sampling Causes a larger sampling errorRequires each member assignment of population to clusterUses a more complicated statistic analysis
34 Systematic Random Sampling Type of probability samplingEvery kth element is selected.ProcessObtain a listing of populationDetermine the sample sizeDetermine the sampling interval (k = N/n)Select random starting pointSelect every kth element
35 Advantages of Systematic Random Sampling Easy to draw sampleEconomicalTime-saving technique
36 Disadvantages of Systematic Random Sampling Samples may be biased.After first sample is chosen, no longer equal chance
37 Learning Objective Five Compare Three Methods Of Nonprobability Sampling 37
38 Nonprobability Sampling Sample elements are chosen nonrandomly.Produces biased sampleEach element of the population may not be included in the sample.Restricts generalizations made about study findings
40 Convenience SamplingChooses the most readily available subject or objectDoes not guarantee that the subject or object is typical of the population
41 Snowball Sampling Type of convenience sampling method Study subjects recruit other potential subjects.May be called network samplingMay find people reluctant to volunteer
42 Quota Sampling Type of nonprobability sampling Researcher selects sample to reflect characteristics.Examples of stratum
43 Quota Sampling Age Gender Educational background Number of elements in each stratumNumber is in proportion to size of total population.
44 Purposive Sampling Type of nonprobability sampling Researcher uses personal judgment in subject selection.Each subject chosen is considered representative of population.Many qualitative studies use this technique.
45 Nonprobability Sampling Procedures AdvantagesTimeMoneyDisadvantagesNonrandomNot able to generalize findings
46 Learning Objective Six Determine Which Sampling Technique To Use In Various Types Of Research Studies46
47 Research Studies Use voluntary subjects Follow the ethics of research Subjects must voluntarily agree.Subjects may refuse to participate.
48 Research Data Based on voluntary responses from subjects Biased sample occurs if subjects do not participate.
49 Volunteers As Subjects Sample selection varies.Subjects volunteer for a study.Researcher approaches subjects.
50 Random Sampling or Random Assignment Each subject has equal probability of being included.Random assignmentProcedure to ensure that each subject has equal chance
51 Threefold Randomization Process Used for experimental studiesHelps represent the ideal study procedureSteps to ensure the processSubjects randomly selected from populationSubjects randomly assigned to groupsExperimental treatments randomly assigned to groups
52 Learning Objective Seven Compare Longitudinal And Cross-Sectional Studies 52
53 Classification of Research Studies LongitudinalCross-sectional
54 Longitudinal Research Study Subjects are followed over time.A cohort study is one example.Subjects are studied based onSimilar age groupSimilar background
55 Longitudinal Research Study (cont’d) Data are gathered.Same subjectsSeveral timesTells influence of time
56 Cross-Sectional Study Subjects checked at one point in timeData collected from groups of peopleData may represent differences inAgesTime periodsDevelopmental statesImportant considerations
57 Longitudinal Versus Cross-Sectional Studies Longitudinal studiesAccurate means of studying changes over timeStudies take a long time to perform.Cross-sectional studiesLess expensiveTake less timeEasier to conduct
58 Learning Objective Eight Enumerate Factors To Be Considered In Deciding The Size Of The Sample 58
59 Sample Size No simple rules Qualitative studies use much smaller samples than quantitative studies.Factors to consider for sample sizes in quantitative studiesHomogeneity of populationDegree of precision desired by the researcherType of sampling procedure that is used
60 Sample Size (cont’d) Central limit theorem Sampling distribution of the mean
61 Larger Sample Sizes Many uncontrolled variables are present. Small differences are expected in members.Population must be divided into subgroups.Dropout rate among subjects is expected to be high.Statistical tests are used that require a minimum sample size.
62 Power Analysis Helps to determine sample size May prevent type II errorHelps to detect statistical significance
63 Power Analysis (cont’d) Low power; type II error highExternal funding sources require it.Helps determine the optimum sample size
64 Nursing Research Studies Usually limited to small convenience samplesGeneralizations to total population difficultSmall sample sizes warrant replication studies.Similar results from replication help with generalization.
65 Learning Objective Nine Discuss Sampling Error And Sampling Bias 65
66 Sampling Error Random fluctuations in data Not under the control of the researcherChance variations occur when sample is chosen.
67 Sampling Bias Bias when samples are not carefully selected All nonprobability sampling methods have it.May occur in probability sampling methodsSubjects decide not to participate when chosen.Final sample is now not representative of population.
68 Learning Objective Ten Critique The Sampling Procedure Described In Research Reports And Articles 68
69 Critiquing the Population and Samples Is the target population identified?Is the accessible population identified?Was a probability or nonprobability sampling method used?Is the specific sampling method named?Is the sampling method described?Is the sampling method appropriate for the study?
70 Critiquing the Population and Samples (cont’d) Are the demographic characteristics of the sample presented?Is the sample size adequate?Was power analysis used to determine the sample size?Is the sample representative of the population?
71 Critiquing the Population and Samples (cont’d) Are potential sampling biases identified?Is subject dropout discussed?