IBM SPSS COMPLEX SAMPLES 19 User Manual

IBM SPSS Complex Samples 19

Note: Before using this information and the product it supports, read the general information under Notices on p. 267.

This document contains proprietary information of S PSS Inc, an IBM Company. It is provided under a license agreement and is protected by copyright law. The information contained in this publication does not include any product warranties, and any statements provided in this manual should not be interpreted as such.

When you send information to IBM or SPSS, you grant IBM and SPSS a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligationtoyou.

IBM® SPSS ® Statistics is a comprehensive system 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 people’s 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 mitigating risk. SPSS Inc. was acquired by IBM in October 2009. For more information, visit http://www.spss.com.

Preface

Technical support

Technical support is available to maintenance cus Technical Support for assistance in using SPSS Inc. products or for installation help for one of the supported hardware environments. To reach Technical Support, see the SPSS Inc. web site at http://support.spss.com or ﬁ

http://support.spss.com/default.asp?refpage=contactus.asp. Be prepared to identify yourself, your

organization, and your support agreement when requesting assistance.

Customer Service

If you have any questions concerning your shipment or account, contact your local ofﬁce, listed on the Web site at http://www.spss.com/worldwide. Please have your serial number ready for identiﬁcation.

Training Seminars

SPSS Inc. provides both public and onsite training seminars. All seminars feature hands-on workshops. Seminars will be offered in major cities on a regular basis. For more information on these seminars, contact your local ofﬁce, listed on the Web site at http://www.spss.com/worldwide.

tomers. Customers may contact

nd your local ofﬁce via the web site at

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 author’s website: http://www.norusis.com

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

Sampling Wiz

Tree Control

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

wSamplePlan .................................................... 4

ard:DesignVariables ............................................... 6

sforNavigatingtheSamplingWizard................................. 7

3 Preparing a Complex Sample for Analysis 19

CreatingaNewAnalysisPlan....................................................20

AnalysisPreparationWizard:DesignVariables.......................................20

TreeControlsforNavigatingtheAnalysisWizard..................................21

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

Complex Sample

6 Complex Sampl

ComplexSamplesDescriptivesStatistics...........................................34

ComplexSamplesDescriptivesMissingValues.......................................35

ComplexSamplesOptions ......................................................36

esFrequenciesStatistics ...........................................30

sMissingValues.................................................31

sOptions ......................................................32

es Descriptives 33

7 Complex Samples Crosstabs 37

ComplexSamplesCrosstabsStatistics............................................. 39

ComplexSamplesMissingValues.................................................40

ComplexSamplesOptions ......................................................41

8 Complex Samples Ratios 42

ComplexSamplesRatiosStatistics................................................ 43

ComplexSamplesRatiosMissingValues ...........................................44

ComplexSamplesOptions ......................................................44

9 Complex Samples General Linear M odel 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 Response Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Proportional 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 Wizard to Ready NHIS Public Data . . . . . . . . . 140

UsingtheWizard......................................................... 140

Summary............................................................... 143

viii

PreparingforAnalysisWhenSamplingWeightsAreNotintheDataFile................... 143

Computing Inclusion Probabilities and Sampling Weights . . . . . . . . . . . . . . . . . . . . . . . . . . 143

UsingtheWizard......................................................... 146

Summary............................................................... 154

RelatedProcedures.......................................................... 154

15 Complex Samples Frequencies 155

Using Complex Samples Frequencies to Analyze Nutritional Supplement Usage . . . . . . . . . . . . . 155

RunningtheAnalysis...................................................... 155

FrequencyTable ......................................................... 158

FrequencybySubpopulation................................................ 158

Summary............................................................... 159

RelatedProcedures.......................................................... 159

16 Complex Samples Descriptives 160

UsingComplexSamplesDescriptivestoAnalyzeActivityLevels......................... 160

RunningtheAnalysis...................................................... 160

UnivariateStatistics....................................................... 163

Univariate Sta

Summary............................................................... 164

Related Proced

tisticsbySubpopulation......................................... 163

ures .......................................................... 164

17 Complex Sample

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

s C rosstabs 165

18 Complex Samples Ratios 171

UsingComplexSamplesRatiostoAidPropertyValueAssessment....................... 171

RunningtheAnalysis...................................................... 171

Ratios................................................................. 174

PivotedRatiosTable ...................................................... 174

Summary............................................................... 175

RelatedProcedures.......................................................... 175

19 Complex Samples General Linear Model 176

Using Complex S

Running the Ana

ModelSummary ......................................................... 181

TestsofModelEffects..................................................... 181

Parameter Esti

EstimatedMarginalMeans ................................................. 183

Summary .............................................................. 185

RelatedProcedures.......................................................... 185

amples General Linear Model to Fit a Two-Factor ANOVA . . . . . . . . . . . . . . . . . 176

lysis...................................................... 176

mates...................................................... 182

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

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 Predictor in Complex Samples Cox R egressio n. . . . . . . . . . . . . . . . . . . 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

Appendices

A Sample Files 258

B Notices 267

Bibliography 269

Index 271

xii

Part I: User’s Guide

Introduction to Complex Samples Procedures

An inherent assumption of analytical procedures in traditional software packages is that the observations in a data ﬁle represent a simple random sample from the population of interest. This assumption is untenable for an increasing number of companies and researchers who ﬁnd 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 speciﬁcations into the data analysis, thus ensuring that your results are valid.

Properties of Complex Samples

Chapter

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 of the following features:

Stratification. Stratiﬁed 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 stratiﬁcation, 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, a nd 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 ﬁrst-stage sample based 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 ﬁrst 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 a t random is difﬁcult to obtain, units can be sampled

systematically (at a ﬁxed interval) or sequentially.

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 cluster’s 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 weights. 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 the 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 p rimary tool for surveyors is the Sampling Wizard.

 Analyze sample data ﬁles previously obtained according to complex designs. Before using the

Complex Samples analysis procedures, you may need to use the Analysis Preparation Wizard.

Plan Files

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 ﬁle for easy reuse.

Aplanﬁle contains complex sample speciﬁcations. There are two types of plan ﬁles:

Sampling plan. The speciﬁcations given in the Sampling Wizard deﬁneasampledesignthat

is used to draw a complex sample. The sampling plan ﬁle contains those speciﬁcations. The sampling plan ﬁle also contains a default analysis plan that uses estimation methods suitable for the speciﬁed sample design.

Analysis plan. This plan ﬁle 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 speciﬁcations in a plan ﬁle, including:

 A surveyor can specify the ﬁrst stage of a multistage sampling plan and draw ﬁrst-stage

units now, collect information on sampling units for the second stage, and then modify the sampling plan to include the second stage.



An analyst who doesn’t have access to the sampling plan ﬁle 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 ﬁle, which

simpliﬁes the instructions for analysts and avoids the need for each analyst to specify his or her own analysis plans.

Sampling from a Complex Design

Figure 2-1

Sampling Wizard, Welcome step

Chapter

The Sampling Wizard guides you through the steps for creating, modifying, or executing a sampling plan ﬁle. Before using the Wizard, you should have a well-deﬁned 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 Samples > Select a Sample...

Select Design a sample and choose a plan ﬁlename to save the sample plan.

Sampling from a Complex Design

Click Next to continue through the Wizard.

E Optionally, in the Design Variables step, you can deﬁne strata, clusters, and input sample weights.

After you deﬁne these, click

E Optionally, in the Sampling Method step, you can choose a method for selecting items.

Next.

If you select

PPS Brewer or PPS Murthy, 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.

Chapter 2

Sampling Wizard: Design Variables

Figure 2-2

Sampling Wizard, Design Variables step

This step allows you to select stratiﬁcation and clustering variables and to deﬁne input sample weights. You can also specify a label for the stage.

Stratify By. The cross-classiﬁcation of stratiﬁcation variables deﬁnes 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 deﬁne 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 deﬁne at least one cluster variable in order to plan a multistage design. Clusters are also n ecessary in the use of several different sampling methods. For more information, see the topic Sampling Wizard: Sampling

Method on p. 8.

Input Sample Weight. If the current sample design is part of a larger sample design, you may have

sample weights from a previous stage of the larger design. You can specify a n umeric variable containing these weights in the ﬁrst stage of the current design. Sample weights are computed automatically for subsequent stages of the current design.

Stage Label. Youcanspecifyanoptionalstringlabelforeachstage. Thisisusedintheoutputto

help identify stagewise information.

Note: The source variable list has the same content across steps of the Wizard. In other words, variables removed from the source list in a particular step are removed from the list in all steps. Variables returned to t he source list appear in the list in all steps.

Tree Controls for Navigating the Sam pling Wizard

On the left side of each step in the Sampling Wizard is an outline of all the steps. You can navigate theWizardbyclickingonthenameofanenabled step in the outline. Steps are enabled as long as all previous steps are valid—that is, if each previous step has been given the minimum required speciﬁcations for that step. See the Help for individual steps for more information on whyagivenstepmaybeinvalid.

Sampling from a Complex Design

Chapter 2

Sampling Wizard: Sampling Method

Figure 2-3

Sampling Wizard, Sampling Method step

This step allows you to specify how to select cases from the active dataset.

Method. Controls in this group are used to choose a selection method. Some sampling types allow

you to choose whether to sample with replacement (WR) or w ithout replacement (WOR). See the type descriptions for more information. Note that some probability-proportional-to-size (PPS) types are available only when clusters have been deﬁned and that all PPS types are available only in the ﬁrst stage of a design. Moreover, WR methods are available only in the last stage of a design.

 Simple Random Sampling. Units are selected with equal probability. They can be selected

with or without replacement.

 Simple Systematic. Units are selected at a ﬁxed interval throughout the sampling frame (or

strata, if they have been speciﬁed) and extracted without replacement. A randomly selected unit within the ﬁrst interval is chosen as the starting point.

 Simple Sequential. Units are selected sequentially with equal probability and without

replacement.

 PPS. This is a ﬁrst-stage method that selects units at random with probability proportional

to size. Any units can be selected with replacement; only clusters can be sampled without replacement.

Sampling from a Complex Design



PPS Systematic. This is a ﬁrst-stage method that systematically selects units with probability

proportional to size. They are selected without replacement.

 PPS Sequential. This is a ﬁrst-stage method that sequentially selects units with probability

proportional to cluster size and without replacement.

 PPS Brewer. This is a ﬁrst-stage method that selects two clusters from each stratum with

probability proportional to cluster size and without replacement. A cluster variable must be speciﬁed to use this method.

 PPS Murthy. This is a ﬁrst-stage method that selects two clusters from each stratum with

probability proportional to cluster size and without replacement. A cluster variable must be speciﬁed to use this method.

 PPS Sampford. This is a ﬁrst-stage method that selects more than two clusters from each

stratum with probability proportional to cluster size and without replacement. It is an extension of Brewer’s method. A cluster variable must be speciﬁed to use this method.

 Use WR estimation f or analysis. By default, an estimation method is speciﬁed in the plan ﬁle

that is consistent with the selected sampling method. This allows you to use with-replacement estimation even if the sampling method implies WOR estimation. This option is available only in stage 1.

Measure of Size (MOS). If a PPS method is selected, you must specify a measure of size that deﬁnes

the size of each unit. These sizes can be explicitly deﬁned in a variable or they can be computed from the data. Optionally, you can set lower and upper bounds on the MOS, overriding any values found in the MOS variable or computed from the data. These options are available only in stage 1.

Chapter 2

Sampling Wizard: Sample Size

Figure 2-4

Sampling Wizard, Sample Size step

This step allows you to specify the number or proportion of units to sample within the current stage. The sample size can be ﬁxed or it can vary across strata. For the purpose of specifying sample size, clusters chosen in previous stages can be used to deﬁne strata.

Units. You can specify an exact sample size or a proportion of units to sample.

 Value. A single value is applied to all strata. If Counts is selected as the unit metric, you should

enter a positive integer. If

Proportions is selected, you should enter a non-negative value.

Unless sampling with replacement, proportion values should also be no greater than 1.

 Unequal values for strata. Allows you to enter size values on a per-stratum basis via the Deﬁne

Unequal Sizes dialog box.

 Read values from variable. Allows you to select a numeric variable that contains size values

for strata.

If Proportions is selected, you have the option to set lower and upper bounds on the number of units sampled.

Define Unequal Sizes

Figure 2-5

Define Unequal Sizes dialog box

Sampling from a Complex Design

The Deﬁne U

Size Specifications grid. The grid displays the cross-classiﬁcations of up to ﬁve strata or

nequal Sizes dialog box allows you to enter sizes on a per-stratum basis.

cluster variables—one stratum/cluster combination per row. Eligible grid variables include all stratiﬁca

tion variables from the current and previous stages and all cluster variables from previous stages. Variables can be reordered within the grid or moved to the Exclude list. Enter sizes in the rightmost column. Click stratiﬁc

ation and cluster variables in the grid cells. Cells that contain unlabeled values always

show values. Click

Labels or Va lues to toggle the display of value labels and data values for

Refresh Strata to repopulate the grid with each combination of labeled data

values for variables in the grid.

Exclude.

To specify sizes for a subset of stratum/cluster combinations, move one or more variables

to the Exclude list. These variables are not used to deﬁne sample sizes.

Chapter 2

Sampling Wizard: Output Variables

Figure 2-6

Sampling Wizard, Output Variables step

This step allows you to choose variables to save when the sample is drawn.

Population size. The estimated number of units in the population for a given stage. The rootname

for the saved variable is PopulationSize_.

Sample proportion. The sampling rate at a given stage. The rootname for the saved variable is

SamplingRate_.

Sample size. The number of units drawn at a given stage. The rootname for the saved variable

is SampleSize_.

Sample weight. The inverse of the inclusion probabilities. The rootname for the saved variable is

SampleWeight_.

Some stagewise variables are generated automatically. These include:

Inclusion probabilities. The proportion of units drawn at a given stage. The rootname for the saved

variable is InclusionProbability_.

Cumulative weight. The cumulative sample weight over stages previous to and including the

current one. The rootname for the saved variable is SampleWeightCumulative_.

Index. Identiﬁes units selected multiple times within a given stage. The rootname for the saved

variable is Index_.

Note: Saved variable rootnames include an integer sufﬁxthatreﬂects the stage number—for example, PopulationSize_1_ for the saved population size for stage 1.

Sampling Wizard: Plan Summary

Figure 2-7

Sampling Wizard, Plan Summary step

Sampling from a Complex Design

This is the last step within each stage, providing a summary of the sample design speciﬁcations through the current stage. From here, you can either proceed to the next stage (creating it, if necessary) or set options for drawing the sample.

Chapter 2

Sampling Wizard: Draw Sample Selection Options

Figure 2-8

Sampling Wizard, Draw Sample Selection Options step

This step allows you to choose whether to draw a sample. You can also control other sampling options, such as the random seed and missing-value handling.

Draw sample. In addition to choosing whether to draw a sample, you can also choose to execute

part of the sampling design. Stages must be drawn in order—that is, stage 2 cannot be drawn unless stage 1 is also drawn. When editing or executing a plan, you cannot resample locked stages.

Seed. This allows you to choose a seed value for random number generation.

Include user-missing values. This determines whether user-missing values are valid. If so,

user-missing values are treated as a separate category.

Data already sorted. If your sample frame is presorted by the values of the stratiﬁcation variables,

this option allows you to speed the selection process.

Sampling Wizard: Draw Sample Output Files

Figure 2-9

Sampling Wizard, Draw Sample Output Files step

Sampling from a Complex Design

This step allows you to choose where to direct sampled cases, weight variables, joint probabilities, and case selection rules.

Sample data. These options let you determine where sample output is written. It can be added to

the active dataset, written to a new dataset, or saved to an external IBM® SPSS® Statistics data ﬁle. Datasets are available during the current session but are not available in subsequent sessions unless you explicitly save them as data ﬁles. Dataset names must adhere to variable naming rules. If an external ﬁle or new dataset is speciﬁed, the sampling output variables and variables in the active dataset for the selected cases are written.

Joint probabilities. These options let you determine where joint probabilities are written. They are

saved to an external SPSS Statistics data ﬁle. Joint probabilities are produced if the PPS WOR, PPS Brewer, PPS Sampford, or PPS Murthy method is selected and WR estimation is not speciﬁed.

Case selection rules. If you are constructing your sample one stage at a time, you may want to

save the case selection rules to a text ﬁle. They are useful for constructing the subframe for subsequent stages.

Chapter 2

Sampling Wizard: Finish

Figure 2-10

Sampling Wizard, Finish step

This is the ﬁnal step. You can save the plan ﬁle and draw the sample now or paste your selections into a syntax window.

When making changes to stages in the existing plan ﬁle, you can save the edited plan to a new ﬁle or overwrite the existing ﬁle. When adding stages without making changes to existing stages, the Wizard automatically overwrites the existing plan ﬁle. If you want to save the plan to a new ﬁle, select

Paste the syntax generated by the Wizard into a syntax window and change the

ﬁlename in the syntax commands.

Modifying an Existing Sample Plan

E From the menus choose:

Analyze > Complex Samples > Select a Sample...

Select Editasampledesignand choose a plan ﬁle to edit.

E Click Next to continue through the Wizard.

+ 258 hidden pages

IBM SPSS COMPLEX SAMPLES 19 User Manual

Specifications and Main Features

Frequently Asked Questions

User Manual