IBM Water System SPSS COMPLEX SAMPLES 19 User manual

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IBM SPSS Complex Samples 19
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© Copyright SPSS Inc. 1989, 2010.
Preface
IBM® SPSS® S tatistics is a comprehensive syste m for analyzing data. The Complex Samples
optional add-on module provides the additional analytic techniques described in this manual. The
Complex Samples add-on module must be used with the SPSS Statistics Core system and is
completely integrated into that system.
About SPSS Inc., an IBM Company
SPSS Inc., an IBM Company, is a leading global provider of predictive analytic software
and solutions. The company’s complete portfolio of products data collection, statistics,
modeling and deployment captures peoples attitudes and opinions, predicts outcomes of
future customer interactions, and then acts on these insights by embedding analytics into business
processes. SPSS Inc. solutions address interconnected business objectives across an entire
organization by focusing on the convergence of analytics, IT architecture, and business processes.
Commercial, government, and academic customers worldwide rely on SPSS Inc. technology as
a competitive advantage in attracting, retaining, and growing customers, while reducing fraud
and m itigating risk. SPSS Inc. was acquired by IBM in October 2009. For more information,
visit http://www.spss.com.
Technical support
Technical support is available to maintenance cus
tomers. Customers may contact
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© Copyright SPSS Inc. 1989, 2010
iii
Additional Publications
The SPSS Statistics: Guide to Data Analysis, SPSS Statistics: Statistical Procedures Companion,
and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija Norušis and
published by Prentice Hall, are available as suggested supplemental material. These publications
cover statistical procedures in the SPSS Statistics Base module, Advanced Statistics module
and Regression module. Whether you are just getting starting in data analysis or are ready for
advanced applications, these books will help you make best use of the capabilities found within
the IBM® SPSS® Statistics offering. For additional information including publication contents
and sample chapters, please see the authors website: http://www.norusis.com
iv
Contents
Part I: User’s Guide
1 Introduction to Complex Samples Procedures 1
PropertiesofComplexSamples .................................................. 1
UsageofComplexSamplesProcedures............................................ 2
PlanFiles................................................................ 2
FurtherReadings ............................................................. 3
2 Sampling from a Complex Design 4
Creating a Ne
wSamplePlan .................................................... 4
Sampling Wiz
ard:DesignVariables ............................................... 6
Tree Co ntrol
sforNavigatingtheSamplingWizard................................. 7
SamplingWizard:SamplingMethod............................................... 8
SamplingWizard:SampleSize...................................................10
DefineUnequalSizes.......................................................11
SamplingWizard:OutputVariables................................................12
SamplingWizard:PlanSummary .................................................13
SamplingWizard:DrawSampleSelectionOptions....................................14
SamplingWizard:DrawSampleOutputFiles.........................................15
SamplingWizard:Finish........................................................16
ModifyinganExistingSamplePlan................................................16
SamplingWizard:PlanSummary .................................................17
RunninganExistingSamplePlan .................................................18
CSPLANandCSSELECTCommandsAdditionalFeatures................................18
3 Preparing a Complex Sample for A nalysis 19
CreatingaNewAnalysisPlan....................................................20
AnalysisPreparationWizard:DesignVariables.......................................20
TreeControlsforNavigatingtheAnalysisWizard..................................21
v
AnalysisPreparationWizard:EstimationMethod.....................................22
AnalysisPreparationWizard:Size ................................................23
DefineUnequalSizes.......................................................24
AnalysisPreparationWizard:PlanSummary ........................................25
AnalysisPreparationWizard:Finish...............................................26
ModifyinganExistingAnalysisPlan...............................................26
AnalysisPreparationWizard:PlanSummary ........................................27
4 Complex Samples Plan 28
5 Complex Samples Frequencies 29
Complex Sampl
esFrequenciesStatistics...........................................30
Complex Sample
sMissingValues.................................................31
Complex Sample
sOptions ......................................................32
6 Complex Sampl
es Descriptives 33
ComplexSamplesDescriptivesStatistics...........................................34
ComplexSamplesDescriptivesMissingValues.......................................35
ComplexSamplesOptions ......................................................36
7 Complex Samples Crosstabs 37
ComplexSamplesCrosstabsStatistics.............................................39
ComplexSamplesMissingValues.................................................40
ComplexSamplesOptions ......................................................41
8 Complex Samples Ratios 42
ComplexSamplesRatiosStatistics................................................43
ComplexSamplesRatiosMissingValues ...........................................44
ComplexSamplesOptions ......................................................44
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9 Complex Samples General Linear Model 45
ComplexSamplesGeneralLinearModelStatistics....................................48
ComplexSamplesHypothesisTests ...............................................49
ComplexSamplesGeneralLinearModelEstimatedMeans..............................50
ComplexSamplesGeneralLinearModelSave .......................................51
ComplexSamplesGeneralLinearModelOptions .....................................52
CSGLMCommandAdditionalFeatures.............................................53
10 Complex Samples Logistic Regression 54
ComplexSamplesLogisticRegressionReferenceCategory .............................55
ComplexSamplesLogisticRegressionModel........................................56
ComplexSamplesLogisticRegressionStatistics......................................57
ComplexSamplesHypothesisTests ...............................................59
ComplexSamplesLogisticRegressionOddsRatios....................................60
ComplexSamplesLogisticRegressionSave.........................................61
ComplexSamplesLogisticRegressionOptions.......................................62
CSLOGISTICCommandAdditionalFeatures .........................................63
11 Complex Samples Ordinal Regression 64
Complex Samples Ordinal Regression Re spon se Prob abilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
ComplexSamplesOrdinalRegressionModel ........................................66
ComplexSamplesOrdinalRegressionStatistics......................................68
ComplexSamplesHypothesisTests ...............................................69
ComplexSamplesOrdinalRegressionOddsRatios....................................70
ComplexSamplesOrdinalRegressionSave .........................................71
ComplexSamplesOrdinalRegressionOptions .......................................72
CSORDINALCommandAdditionalFeatures..........................................73
12 Complex Samples Cox Regression 74
DefineEvent ................................................................77
vii
Predictors ..................................................................78
DefineTime-DependentPredictor.............................................79
Subgroups..................................................................80
Model .....................................................................81
Statistics ...................................................................82
Plots ......................................................................84
HypothesisTests .............................................................85
Save ......................................................................86
Export .....................................................................88
Options ....................................................................90
CSCOXREGCommandAdditionalFeatures..........................................91
Part II: Examples
13 Complex Samples Sampling Wizard 93
ObtainingaSamplefromaFullSamplingFrame......................................93
UsingtheWizard .........................................................93
PlanSummary........................................................... 103
SamplingSummary....................................................... 103
SampleResults.......................................................... 104
ObtainingaSamplefromaPartialSamplingFrame................................... 105
UsingtheWizardtoSamplefromtheFirstPartialFrame ........................... 105
SampleResults.......................................................... 118
UsingtheWizardtoSamplefromtheSecondPartialFrame......................... 118
SampleResults.......................................................... 123
Sampling with Probability Proportiona l to Size (PPS). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
UsingtheWizard ........................................................ 123
PlanSummary........................................................... 135
SamplingSummary....................................................... 135
SampleResults.......................................................... 137
RelatedProcedures.......................................................... 139
14 Complex Samples Analysis Preparation Wizard 140
Using the Complex Samples Analysis Preparation Wiza rd to Rea dy NHIS P ublic Data. . . . . . . . . 140
UsingtheWizard......................................................... 140
Summary............................................................... 143
viii
PreparingforAnalysisWhenSamplingWeightsAreNotintheDataFile................... 143
Computing Inclusion Proba bilitie s and Sa mpling Weights . . . . . . . . . . . . . . . . . . . . . . . . . . 143
UsingtheWizard......................................................... 146
Summary............................................................... 154
RelatedProcedures.......................................................... 154
15 Complex Samples Frequencies 155
Using Complex Samples Frequencie s to Analyze Nutritional Supple ment U sag e . . . . . . . . . . . . . 155
RunningtheAnalysis...................................................... 155
FrequencyTable ......................................................... 158
FrequencybySubpopulation................................................ 158
Summary............................................................... 159
RelatedProcedures.......................................................... 159
16 Complex Samples Descriptives 160
UsingComplexSamplesDescriptivestoAnalyzeActivityLevels......................... 160
RunningtheAnalysis...................................................... 160
UnivariateStatistics....................................................... 163
Univariate Sta
tisticsbySubpopulation......................................... 163
Summary............................................................... 164
Related Proced
ures.......................................................... 164
17 Complex Sample
s Crosstabs 165
Using Complex Samples Crosstabs to Measure the Relative Risk of an Event . . . . . . . . . . . . . . . 165
RunningtheAnalysis...................................................... 165
Crosstabulation.......................................................... 168
RiskEstimate ........................................................... 169
RiskEstimatebySubpopulation.............................................. 170
Summary............................................................... 170
RelatedProcedures.......................................................... 170
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18 Complex Samples Ratios 171
UsingComplexSamplesRatiostoAidPropertyValueAssessment....................... 171
RunningtheAnalysis...................................................... 171
Ratios................................................................. 174
PivotedRatiosTable ...................................................... 174
Summary............................................................... 175
RelatedProcedures.......................................................... 175
19 Complex Samples General Linear M odel 176
Using Complex S
amples General Linear Model to Fit a Two-Factor ANOVA . . . . . . . . . . . . . . . . . 176
Running the Ana
lysis...................................................... 176
ModelSummary ......................................................... 181
TestsofModelEffects..................................................... 181
Parameter Esti
mates...................................................... 182
EstimatedMarginalMeans ................................................. 183
Summary .............................................................. 185
RelatedProcedures.......................................................... 185
20 Complex Samples Logistic Regression 186
UsingComplexSamplesLogisticRegressiontoAssessCreditRisk....................... 186
RunningtheAnalysis...................................................... 186
PseudoR-Squares........................................................ 190
Classification............................................................ 191
TestsofModelEffects..................................................... 191
ParameterEstimates...................................................... 192
OddsRatios............................................................. 193
Summary............................................................... 194
RelatedProcedures.......................................................... 194
21 Complex Samples Ordinal Regression 195
UsingComplexSamplesOrdinalRegressiontoAnalyzeSurveyResults.................... 195
RunningtheAnalysis...................................................... 195
PseudoR-Squares........................................................ 200
TestsofModelEffects..................................................... 200
x
ParameterEstimates...................................................... 201
Classification............................................................ 202
OddsRatios............................................................. 203
GeneralizedCumulativeModel .............................................. 204
DroppingNon-SignificantPredictors.......................................... 205
Warnings............................................................... 207
ComparingModels ....................................................... 208
Summary............................................................... 209
RelatedProcedures.......................................................... 209
22 Complex Samples Cox Regression 210
Using a Time-Dependent Predic tor in Complex Samples Cox Regression. . . . . . . . . . . . . . . . . . . 210
PreparingtheData ....................................................... 210
RunningtheAnalysis...................................................... 216
SampleDesignInformation................................................. 221
TestsofModelEffects..................................................... 222
TestofProportionalHazards................................................ 222
AddingaTime-DependentPredictor .......................................... 222
MultipleCasesperSubjectinComplexSamplesCoxRegression ........................ 226
PreparingtheDataforAnalysis.............................................. 227
CreatingaSimpleRandomSamplingAnalysisPlan ............................... 242
RunningtheAnalysis...................................................... 246
SampleDesignInformation................................................. 254
TestsofModelEffects..................................................... 255
ParameterEstimates...................................................... 255
PatternValues........................................................... 256
Log-Minus-LogPlot....................................................... 257
Summary............................................................... 257
xi
Appendices
A Sample Files 258
B Notices 267
Bibliography 269
Index 271
xii
Part I:
User’s Guide
Chapter
1
Introduction to Complex Samples
Procedures
An inherent assumption of analytical procedures in traditional software packages is that the
observations in a data le represent a simple random sample from the population of interest. This
assumption is untenable for an increasing number of companies and researchers who nd it both
cost-effective and convenient to obtain samples in a more structured way.
The Complex Samples option allows you to select a sample according to a complex design and
incorporate the design specications into the data analysis, thus ensuring that your results are valid.
Properties of Complex Samples
A complex sample can differ from a simple random sample in many ways. In a simple random
sample, individual sampling units are selected at random with equal probability and without
replacement (WOR) directly from the entire population. By contrast, a given complex sample
can have some or all o f the following features:
Stratification. Stratied sampling involves selecting samples independently within
non-overlapping subgroups of the population, or strata. For example, strata may be socioeconomic
groups, job categories, age groups, or ethnic groups. With stratication, you can ensure adequate
sample sizes for subgroups of interest, improve the precision of overall estimates, and use different
sampling methods from stratum to stratum.
Clustering. Cluster sampling involves the selection of groups of sampling units, or clusters. For
example, clusters may be schools, hospitals, or geographical areas, and sampling units may be
students, patients, or citizens. Clustering is common in multistage designs and area (geographic)
samples.
Multiple stages. In multistage sampling, you select a rst-stage sample b ased on clusters. Then
you create a second-stage sample by drawing subsamples from the selected clusters. If the
second-stage sample is based on subclusters, you can then add a third stage to the sample. For
example, in the rst stage of a survey, a sample of cities could be drawn. Then, from the selected
cities, households could be sampled. Finally, from the selected households, individuals could be
polled. The Sampling and Analysis Preparation wizards allow you to specify three stages in
adesign.
Nonrandom sampling. When selection at random is d ifcult to obtain, u nits can be sampled
systematically (at a xed interval) or sequentially.
© Copyright SPSS Inc. 1989, 2010
1
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Chapter 1
Unequal selection probabilities. When sampling clusters that contain unequal numbers of units,
you can use probability-proportional-to-size (PPS) sampling to make a clusters selection
probability equal to the proportion of units it contains. PPS sampling can also use more general
weighting schemes to select units.
Unrestricted sampling. Unrestricted sampling selects units with replacement (WR). Thus, an
individual unit can be selected for the sample more than once.
Sampling w eights. Sampling weights are automatically computed while drawing a complex
sample and ideally correspond to the “frequency that each sampling unit represents in the target
population. Therefore, the sum of t he weights over the sample should estimate the population
size. Complex Samples analysis procedures require sampling weights in order to properly analyze
a complex sample. Note that these weights shouldbeusedentirelywithintheComplexSamples
option and should not be used with other analytical procedures via the Weight Cases procedure,
which treats weights as case replications.
Usage of Complex Samples Procedures
Your usage of Complex Samples procedures depends on your particular needs. The primary
types of users are those who:
Plan and carry out surveys according to complex designs, possibly analyzing the sample later.
The primary tool for surveyors is the Sampling Wizard.
Analyze sample data les previously obtained according to complex designs. Before using the
Complex Samples analysis procedures, you may need to use the Analysis Preparation Wizard.
Regardless of which type of user you are, you need to supply design information to Complex
Samples procedures. This information is stored in a plan le for easy reuse.
Plan Files
Aplanle contains complex sample specications. There are two types of plan les:
Sampling plan. The specications given in the Sampling Wizard deneasampledesignthat
is used to draw a complex sample. The sampling plan le contains those specications. The
sampling plan le also contains a default analysis plan that uses estimation methods suitable for
the specied sample design.
Analysis plan. This plan le contains information needed by Complex Samples analysis procedures
to properly compute variance estimates for a complex sample. The plan includes the sample
structure, estimation methods for each stage, and references to required variables, such as sample
weights. The Analysis Preparation Wizard allows you to create and edit analysis plans.
There are several advantages to saving your specications in a plan le, including:
A surveyor can specify the rst stage of a multistage sampling plan and draw rst-stage
units now, collect information o n sampling units for the second stage, and then modify t he
sampling plan to include the second stage.
3
Introduction to Complex Samples Procedures
An analyst who doesn’t have access to the sampling plan le can specify an analysis plan and
refer to that plan from each Complex Samples analysis procedure.
A designer of large-scale public use samples can publish the sampling plan le, which
simplies the instructions for analysts and avoids the need for each analyst to specify his
or her own analysis plans.
Further Readings
For more information on sampling techniques, see the following texts:
Cochran, W. G. 1977. Sampling Techniques, 3rd ed. New York: John Wiley and Sons.
Kish,L.1965. Survey Sampling. New York: John Wiley and Sons.
Kish, L. 1987. Statistical Design for Research. New York: John Wiley and Sons.
Murthy, M. N. 1967. Sampling Theory and Methods. Calcutta, India: Statistical Publishing
Society.
Särndal, C., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling.NewYork:
Springer-Verlag.
Chapter
2
Sampling from a Complex Design
Figure 2-1
Sampling Wizard, Welcome step
The Sampling Wizard guides you through the steps for creating, modifying, or executing a
sampling plan le. Before using the Wizard, you should have a well-dened target population, a
list of sampling units, and an appropriate sample design in mind.
Creating a New Sample Plan
E From the menus choose:
Analyze > Complex Samp les > Select a Sample...
E
Select Design a sample and choose a plan lename to save the sample plan.
© Copyright SPSS Inc. 1989, 2010
4
5
Sampling from a Complex Design
E
Click Next to continue through the Wizard.
E Optionally, in the Design Variables step, you can dene strata, clusters, and input sample weights.
After you dene these, click
Next.
E Optionally, in the Sampling Method step, you can choose a method for selecting items.
If you select
PPS Brewer or PPS Mur thy, you can click Finish to draw the sample. Otherwise,
click Next and then:
E In the Sample Size step, specify the number or proportion of units to sample.
E You can now click Finish to draw the sample.
Optionally, in further steps you can:
Choose output variables to save.
Add a second or third stage to the design.
Set various selection options, including which stages to draw samples from, the random
number seed, and whether to treat user-missing values as valid values of design variables.
Choose where to save output data.
Paste your selections as command syntax.
6
Chapter 2
Sampling Wizard: Design Variables
Figure 2-2
Sampling Wizard, Design Variables step
This step allows you to select stratication and clustering variables and to dene input sample
weights. You can also specify a label for the stage.
Stratify By. The cross-classication of stratication variables denes distinct subpopulations, or
strata. Separate samples are obtained for each stratum. To improve the precision of your estimates,
units within strata should be as homogeneous as possible for the characteristics of interest.
Clusters. Cluster variables dene groups of observational units, or clusters. Clusters are useful
when directly sampling observational units from the population is expensive or impossible;
instead, you can sample clusters from the population and then sample observational units from
the selected clusters. However, the use of clusters can introduce correlations among sampling
units, resulting in a loss of precision. To minimize this effect, units within clusters should be as
heterogeneous as possible for the characteristics of interest. You must dene at least one cluster
variable in order to plan a multistage design. Clusters are also necessary in the use of several
different sampling methods. For more information, see the topic Sampling Wizard: Sampling
Method on p. 8.
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IBM Water System SPSS COMPLEX SAMPLES 19 User manual

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