Spss COMPLEX SAMPLES 12.0 User Manual

™

SPSS Compl

ex Samples

12.0

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Preface

SPSS 12.0 is a powerful software package for microcomputer data management and
analysis. The Complex Samples option is an add-on enhancement that provides a set
of proce

dures for selecting and analyzing samples according to complex designs.

The Comp

 Specif
 Prepar
 Freque
 Descri
 Crosst
 Ratio s

Installation

To install Complex Samples, follow the instructions for adding and removing features
in the in
the SPSS Setup icon.)

Compati

SPSS is d
your system for specific information on minimum and recommended requirements.

lex Samples option includes procedures for:

ying and drawing from a complex sample plan.
ing a publicly available complex sample for analysis.

ncy tables for complex samples.
ptive statistics for complex samples.
abulation tables for complex samples.

tatistics for complex samples.

stallation instructions supplied with the SPSS Base. (To start, double-click on

bility

esigned to run on many computer systems. See the materials that came with

iii

Serial Numbers

Your serial number is your identification number with SPSS Inc. You will need this
serial number when you call SPSS Inc. for information regarding support, payment,
or an upgrade

Customer Service

d system. The serial number was provided with your Base system.

If you have any questions concerning your shipment or account, contact your local
office, lis

tedontheSPSSWebsiteathttp://www.spss.com/worldwide. Please have

your serial number ready for identification.

Training Se

SPSS Inc. p

minars

rovides both p ublic 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 SPSS
Web site at

Technical Support

http://www.spss.com/worldwide.

The services of SPSS Technical Support are available to registered customers.
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s may contact Technical Support for assistance in using SPSS or for
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ntheSPSSWebsiteathttp://www.spss.com/worldwide.Bepreparedto

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nal Publications

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iv

The SPSS Statistical Procedures Companion, by Marija Norušis, is being prepared
for publication by Prentice Hall. It contains overviews of the procedures in the SPSS
Base, plus Lo

gistic Regression, General Linear Models, and Linear Mixed Models.

Further information will be available on the SPSS Web site at http://www.spss.com

Store, select your country, and click Books).

(click

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also send e-mail to [e-mail].

About This Manual

This manual documents the graphical user interface for the procedures included in
the Complex Samples module. Illustrations of dialog boxes are taken from SPSS
for Windo

ws. Dialog boxes in other operating systems are similar. The Complex
Samples command syntax is included in the SPSS 12.0 Syntax Reference Guide,
available on the product CD-ROM.

Contacting SPSS

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v

Contents

1 Introductio

n to SPSS Complex Samples

Procedures 1

PropertiesofComplexSamples ................................. .1

UsageofComplexSamplesProcedures........................... .3

FurtherReadings ............................................ .4

2 Sampling from a Complex Design 5

CreatingaNewSamplePlan ................................... .6

SamplingWizard:DesignVariables .............................. .7

SamplingWizard:SamplingMethod.............................. .9

SamplingWizard:SampleSize................................. .11

SamplingWizard:OutputVariables.............................. .13

SamplingWizard:PlanSummary............................... .15

SamplingWizard:DrawSampleSelectionOptions.................. .16

SamplingWizard:DrawSampleOutputFiles....................... .17

SamplingWizard:Finish...................................... .18

ModifyinganExistingSamplePlan.............................. .19

SamplingWizard:PlanSummary............................... .20

RunninganExistingSamplePlan ............................... .21

CSPLAN and CSSELECT Commands Additional Features . . . . . . . . . . . . . . . 21

3 Preparing a Complex Sample for Analysis 23

CreatingaNewAnalysisPlan.................................. .24

AnalysisPreparationWizard:DesignVariables..................... .25

AnalysisPreparationWizard:EstimationMethod................... .27

vii

AnalysisPreparationWizard:Size .............................. .28

AnalysisPreparationWizard:StageSummary ..................... .30

AnalysisPreparationWizard:Finish............................. .31

ModifyinganExistingAnalysisPlan............................. .32

AnalysisPreparationWizard:PlanSummary ...................... .33

4 Complex Samples Plan 35

5 Complex Sam

Complex Samples Freq uencies Data Considerations . . . . . . . . . . . . . . . . . . 37

ObtainingComplexSamplesFrequencies......................... .38

ComplexSamplesFrequenciesStatistics......................... .39

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

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

ples Frequencies 37

6 Complex Samples Descriptives 43

ComplexSamplesDescriptivesDataConsiderations................. .43

ObtainingComplexSamplesDescriptives......................... .43

ComplexSamplesDescriptivesStatistics......................... .45

ComplexSamplesDescriptivesMissingValues..................... .46

ComplexSamplesOptions .................................... .46

7 Complex Samples Crosstabs 49

ComplexSamplesCrosstabsDataConsiderations................... .49

viii

ObtainingComplexSamplesCrosstabs........................... .50

ComplexSamplesCrosstabsStatistics........................... .51

ComplexSamplesMissingValues............................... .53

ComplexSamplesOptions .................................... .53

8 Complex Samples Ratios 55

omplexSamplesRatiosDataConsiderations ..................... .55

C

btainingComplexSamplesRatios.............................. .55

O

omplexSamplesRatiosStatistics.............................. .57

C

omplexSamplesRatiosMissingValues ......................... .58

C

omplexSamplesOptions .................................... .58

C

9C

omplex Samples Sampling Wizard 61

ObtainingaSamplefromaFullSamplingFrame.................... .61

ObtainingaSamplefromaPartialSamplingFrame.................. .75

RelatedProcedures......................................... .93

10 Complex Samples Analysis Preparation Wizard 95

Using the Complex Samples Analysis Preparation Wizard to Rea dy NHIS

PublicData................................................ .95

RelatedProcedures......................................... .98

ix

11 Complex Samples Frequencies 99

requencies Analysis of Nutritional Supplements Usage. . . . . . . . . . . . . . . 99

F

elatedProcedures ........................................ .104

R

12C

omplex Samples Descriptives 105

Using Complex Samples Descriptives to Analyze Activity Levels . . . . . . . . 105

RelatedProcedures........................................ .110

13 Complex Samples Crosstabs 111

Using Crosstabs to Measure the Relative Risk of an Event . . . . . . . . . . . . 111

RelatedProcedures........................................ .118

14 Complex Samples Ratios 119

Using Complex Samples Ratios to Aid Property Value Assessment . . . . . . 119

RelatedProcedures........................................ .125

Bibliography 127

Index 129

x

Chapter

1

Introductio

ntoSPSSComplex

Samples Procedures

An inherent assumption of analytical procedures in traditional software packages
isthattheobservationsinadatafilerepresentasimplerandomsamplefromthe
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 SPSS 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

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.

1

2

Chapter 1

Clustering. Cluster sampling involves the selection of groups o f sampling units, or

clusters. For example, clusters may be schools, hospitals, or geographical areas,
and samplin

g 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. T

hen 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 cou

ld 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 a design.

Nonrando

m sampling.

When selection at random is difficult to obtain, units can be

sampled systematically (at a fixed interval) or sequentially.

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.

Unrestr

icted 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

ng weights in order to properly analyze a complex sample. Note that these

sampli
weights should be used entirely within the Complex Samples option and should not
be used with other analytical procedures via the WeightCases procedure, which treats

sascasereplications.

weight

3

Usage of Comp

Your usage o
primary types of users are those who:

 Plan and carry out surveys according to complex designs, possibly analyzing the

sample late

 Analyze sa

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

Regardlessofwhichtypeofuseryouare,youneedtosupplydesigninformationto
Complex Samples procedures. This information is stored in a plan file for easy reuse.

Plan Files

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

Sampling plan. The specifications given in the Sampling Wizard define a sample

design that is used to draw a complex sample. The sampling plan file contains those
specific
estimation methods suitable for the specified sample design.

ations. The sampling plan file also contains a default analysis plan that uses

Introduction t

o SPSS Complex Samples Procedures

lex Samples Procedures

f Complex Samples procedures depends on your particular needs. The

r. The primary tool for surveyors is the Sampling Wizard.

mple data files previously obtained according to complex designs.

Analysis plan. This plan file contains information needed by Complex Samples

analysis
The plan includes the sample structure, estimation methods for each stage, and
references to required variables, such as sample weights. The Analysis Preparation
Wizard a

There ar

 Asurve

procedures to properly compute variance estimates for a complex sample.

llows you to create and edit analysis plans.

e several advantages to saving your specifications in a plan file, including:

yor 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.

4

Chapter 1



An analyst who doesn't have access to the sampling plan file can specify an

analysis pl
procedure.

 A designer of large-scale public use samples can publish the sampling plan file,

thus simplifying the instructions for analysts and avoiding the need for each
analyst to s

Further Readings

For more information on sampling techniques, see the following texts:

Cochran, W. G. 1977. Sampling Techniques. 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
Publishin

g Society.

an and refer to that plan from each Complex Samples analysis

pecify his or her own analysis plans.

Chapter

2

Sampling fro

Figure 2-1

Sampling Wizard Welcome step

maComplexDesign

The Sampling Wizard guides you through the steps for creating, modifying, or

uting a sampling plan file. Before using the Wizard, you should have a

exec
well-defined target population, a list of sampling units, and an appropriate sample
design in mind.

5

6

Chapter 2

Creating a Ne

E From the me

Analyze

Complex Samples

Select a Sample...

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

E

E Click Next to continue through the Wizard.
E Optionally, in the Define Variables step, you can define strata, clusters, and input

sample weights. After you define these, click Next.

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

If you select
Otherwise, click Next and then:

E In the Sample Size step, specify the number or proportion of units to sample.

You can now click

 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 var

 Choose wh
 Paste your

wSamplePlan

nus choose:

PPS Brewer or PPS Murthy, you can click Finish to draw the sample.

Finish to draw the sample. Optionally, in further steps, you can:

iables.

eretosaveoutputdata.
selections as command syntax.

7

Sampling Wiz

Figure 2-2

Sampling Wizard Design Variables step

ard: Design Variables

Sampling from a

Complex Design

tep allows you to select stratification and clustering variables and to define input

This s
sample weights. You can also specify a label for t he stage.

Stratify By. The cross-classification of stratification variables defines distinct

subpopulations, or strata. Separate samples are obtained for each stratum. To improve

recision of your estimates, units within strata should be as homogeneous as

the p
possible for the characteristics of interest.

Clusters. Cluster variables define groups of observational units, or clusters. Clusters

seful when directly sampling observational units from the population is

are u
expensive or impossible; instead, you can sample clusters from the population and
then sample observational units from the selected clusters. However, the use of

sters can introduce correlations among sampling units, resulting in a loss of

clu

8

Chapter 2

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 necessary in the use of
several different sampling methods. For more information, see “Sampling Wizard:
Sampling Method” on page 9.

Input Sampl

e 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 numeric variable containing these weights in the first stage of the
current de

sign. Sample weights are computed automatically for subsequent stages

of the current design.

Stage Label. You can specify an optional string label for each stage. This is used in

the outpu

t to 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 i

nallsteps.Variablesreturnedtothesource list appear in the list in all steps.

Tree Controls for Navigating the Sampling Wizard

On the left side of each step in the Sampling Wizard is an outline of all the steps. You
can navi
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
individ

gatetheWizardbyclickingonthenameofanenabledstepintheoutline.

ual steps for more information on why a given step may be invalid.

9

Sampling Wiz

Figure 2-3

SamplingWizardMethodstep

ard: Sampling Method

Sampling from a

Complex Design

tep allows you to specify how to select cases from the working data file.

This s

d.

Metho

types allow you to choose whether to sample with replacement (WR) or without
replacement (WOR). See the type descriptions for more information. Note that some
prob
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

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

ability-proportional-to-size (PPS) types are available only when clusters have

selected with or without replacement.

10

Chapter 2



Simple Systematic. Units are selected at a fixed interval throughout the sampling

frame (or st

rata,iftheyhavebeenspecified)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

proportion

al to size. Any units can be selected with replacement; only clusters

can be sampled without replacement.

 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

probabili

 PPS Brewe

ty proportional to cluster size and without replacement.

r.

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 wi

th 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
replacem

ent. It is an extension of Brewer's method. A cluster variable must be

specified to use this method.

 Use W R estimation for 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 wi

th-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 b

oundsontheMOS,overridinganyvaluesfoundintheMOSvariableor

computed from the data. These options are available only in stage 1.

11

Sampling Wiz

Figure 2-4

Sampling Wizard Sample Size step

ard: Sample Size

Sampling from a

Complex Design

tep allows you to specify the number or proportion of units to sample within

This s
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

ne strata.

defi

s.

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

Unit

ue.

 Val

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

ld also be no greater than 1.

shou

12

Chapter 2



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

via the Defi

 Read values

ne Unequal Sizes dialog box.

from variable.

Allows you to select a numeric variable that contains

size values for strata.

is selected, you have the option to set lower and upper bounds on

If

Proporti

ons

the number of units sampled.

Define Uneq

Figure 2-5

Define Unequal Sizes dialog box

The Def

Size Sp

or cluster variables, one stratum/cluster combination per row. Eligible grid variables
include all stratification variables from the current and previous stages and all cluster
varia
to the Exclude list. Enter sizes in the rightmost column. Click
to toggle the display of value labels and data values for stratification and cluster
vari

ual Sizes

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

ecifications grid .

bles from previous stages. Variables can be reordered within the grid or moved

ables in the grid cells. Cells that contain unlabeled values always show values.

The grid displays the cross-classifications of up to five strata

Labels or Values

13

Click Refresh Strata to repopulate the grid with each combination of labeled data
values for variables in the grid.

Exclude. To

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

Sampling Wi

Figure 2-6

Sampling Wizard Output Variables step

Sampling from a

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

Complex Design

zard: Output Variables

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

This s

ation size.

Popul

The root name for the saved variable is PopulationSize_.

Sample proportion. The sampling rate at a given stage. The root name for the saved

able is SamplingRate_.

vari

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

14

Chapter 2

Sample size. The number of units drawn at a given stage. The root name for the

saved variable is SampleSize_.

ht.

Sample weig

This is the inverse of the inclusion probabilities. The root name 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 root name

for the save

Cumulative weight. The cumulative sample weight over stages previous to

dvariableisInclusionProbability_.

and including the current one. The root name for the saved variable is

SampleWei

Index. Identifies units selected multiple times within a given stage. The root name for

ghtCumulative_.

the saved variable is Index_.
Note:Save

d variable root names include an integer suffix that reflects the stage

number—for example, PopulationSize_1_ for the saved population size for stage 1.

15

Sampling Wiz

Figure 2-7

Sampling Wizard Plan Summary step

ard: Plan Summary

Sampling from a

Complex Design

s the last step within each stage, providing a summary of the sample design

This i
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.

16

Chapter 2

Sampling Wiz

Figure 2-8

Sampling Wizard Draw Sample Selection Options step

ard: Draw Sample Selection O ptions

tep allows you to choose whether to draw a sample. You can also control other

This s
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,

e 2 cannot be drawn unless stage 1 is also drawn. When editing or executing a

stag
plan, you cannot resample locked stages.

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

ude user-missing values.

Incl

so, user-missing values are treated as a separate category.

This determines whether user-missing values are valid. If

17

Sampling from a

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

SamplingWizardDrawSampleOutputFilesstep

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

dedtotheworkingdatafileorsavedtoanexternalfile.Ifanexternalfileis

be ad
specified, the sampling output variables and variables in the working data file for
the selected cases are saved to the file.

18

Chapter 2

Joint p robabilities. These options let you determine where joint probabilities are

written. Joint probabilities are produced if the PPS WOR, PPS Brewer, PPS
Sampford, o

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

r PSS Murthy method is selected and WR estimation is not specified.

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

r subsequent stages.

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

selections into a syntax window.

your

When editing a plan, you can save the edited plan to a new file or overwrite the

existing plan file.

19

Modifying an

E From the me

Analyze

Complex Samples

Select a Sample...

Select Edit a sample design and choose a plan file to edit.

E

E Click Next to continue through the Wizard.
E Review the sampling plan in the Plan Summary step, and then click Next.

Subsequent s
steps for more information.

E Navigate to the Finish step, and specify a new name for the edited plan file or choose

to overwrite the existing plan file.

Optionally, you can:

 Specify stages that have already been sampled.
 Remove stages from the plan.

Sampling from a

Complex Design

Existing Sample Plan

nus choose:

teps are largely the same as for a new design. See the Help for individual

20

Chapter 2

Sampling Wiz

Figure 2-11

Sampling Wizard Plan Summary step

ard: Plan Summary

tep allows you to review the sampling plan and indicate stages that have already

This s
been sampled. If editing a plan, you can also remove stages from the plan.

Previously sampled stages. If an extended sampling frame is not available, you will

have to execute a multistage sampling design one stage at a time. Select which stages

already been sampled from the drop-down list. Any stages that have been

have
executed are locked; they are not available in the Draw Sample Selection Options
step, and they cannot be altered when editing a plan.

Remo

ve stages.

You can remove stages 2 and 3 from a multistage design.

21

Complex Design

RunninganEx

E From the me

Analyze

Complex Samples

Select a Sample...

Select Draw a sample and choose a plan file to run.

E

E Click Next to continue through the Wizard.
E Review the sampling plan in the Plan Summary step, and then click Next.
E The individual steps containing stage information are skipped when executing a

Sampling from a

isting Sample Plan

nus choose:

sample plan. You can now go on to the Finish step at any time.

Optionally, you can:

 Specify stages that have already been sampled.

CSPLAN and CSSELECT Commands Additional Features

The SPSS command language also allows you to:

 Specify custom names for output variables.
 Control the output in the Viewer. For example, you can suppress the stagewise

summary of t

he plan that is displayed if a sample is designed or modified,

suppress the summary of the distribution of sampled cases by strata that is shown
if the sample design is executed, and request a case processing summary.

 Choose a subset of variables in the working data file to write to an external

sample fil

e.

See the SPSS Command Syntax Reference for complete syntax information.

Chapter

3

Preparing a C
Analysis

Figure 3-1

Analysis Preparation Wizard Welcome step

omplex Sample for

23

24

Chapter 3

The Analysis Preparation Wizard guides you through the steps for creating or
modifying an analysis plan for use with the various Complex Samples analysis
procedures

. Before using the Wizard, you should have a sample drawn according to a

complex design.

Creating a new plan is most useful when you do not have access to the sampling

plan file us

ed to draw the sample (recall that the sampling plan contains a default
analysis plan). If you do have access to the sampling plan file used to draw the
sample, you can use the default analysis plan contained in the sampling plan file or
override t

he default analysis specifications and save your changes to a new file.

Creating a New Analysis Plan

E From the menus choose:

Analyze

Complex Sa

Prepare fo

mples

r Analysis...

E

Select Cr

eate a plan f ile

, and choose a plan filename to which you will save the

analysis plan.

E Click Next to continue through the Wizard.
E Specify the variable containing sample weights in the Design Variables step,

optionally defining strata and clusters.

You can now click

 Select the method for estimating standard errors in the Estimation Method step.
 Specify the number of units sampled or the inclusion probability per unit in

Finish to save the plan. Optionally, in further steps you can:

the Size step.

 Add a second or third stage to the design.
 Paste your selections as command syntax.

25

Analysis Pre

Figure 3-2

Analysis Preparation Wizard Design Variables step

Preparing a Com

paration Wizard: Design Variables

plex Sample for Analysis

tep allows you to identify the stratification and clustering variables and define

This s
sample weights. You can also provide a label for the stage.

Strata. The cross-classification of stratification variables define distinct

subpopulations, or strata. Your total sample represents the combination of

pendent samples from each stratum.

inde

Clusters. Cluster variables define groups of observational units, or clusters. Samples

drawninmultiplestagesselectclustersinthe earlier stages and then subsample units

the selected clusters. When analyzing a data file obtained by sampling clusters

from
with replacement, you should include the duplication index as a cluster variable.

Sample Weights. You must provide sample weights in the first stage. Sample weights

omputed automatically for subsequent stages of the current design.

are c

26

Chapter 3

Stage Label. You can specify an optional string label for each stage. This is used in

the output to help identify stagewise information.
Note: The so

urce variable list has the same contents 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 the source list show up in all steps.

Tree Controls for Navigating the Analysis Wizard

At the left side of each step of the Analysis Wizard is an outline of all the steps. You
can navigate the Wizard by clicking on the name of an enabled step in the outline.
Steps are e
previous step has been given the minimum required specifications for t hat step. For
more information on why a given step may be invalid, see the Help for individual
steps.

nabled as long as all previous steps are valid–that is, as long as each

27

Analysis Pre

Figure 3-3

Analysis Preparation Wizard Estimation Method step

Preparing a Com

paration Wizard: Estimation Method

plex Sample for Analysis

tep allows you to specify an estimation method for the stage.

This s

mpling with replacement).

WR (sa

sampling from a finite population, since it assumes that the sample was taken from
an infinite population. When the population for the stage is much larger than the

le, this is a reasonable assumption. WR estimation can be specified only in the

samp
final stage of a design; the Wizard will not allow you to add another stage if you
select WR estimation.

l WOR (equal probability sampling with out replacement).

Equa

includes the finite population correction and assumes that units are sampled with
equal probability. Equal WOR can be specified in any stage of a design.

WR estimation does not include a correction for

Equal WOR estimation

28

Chapter 3

Unequal WOR ( unequal probability sampling without replacement). In addition to using

the finite population correction, Unequal WOR accounts for sampling units (usually
clusters) s
only in the first stage.

elected with unequal probability. This estimation method is available

Analysis Pr

Figure 3-4

Analysis Preparation Wizard Size step

eparation Wizard: Size

step is used to specify inclusion probabilities or population sizes for the current

This
stage. Sizes can be fixed or can vary across strata. For the purpose of specifying sizes,
clusters specified in previous stages can be used to define strata.

Units. You can specify exact population sizes or the probabilities with which units

sampled.

were

29



Value. A single value is applied to all strata. If Population Sizes is selected as the

unit metric
selected, you should enter a value between 0 and 1, inclusive.

 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

Define Unequal Sizes

Figure 3-5

Define Unequal Sizes dialog box

Preparing a Com

, you should enter a non-negative integer. If

for strata.

plex Sample for Analysis

Inclusion Probabilities is

The Define Unequal Sizes dialog box allows you to enter sizes on a per-stratum basis.

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

ster variables, one stratum/cluster combination per row. Eligible grid variables

or clu
include all stratification variables from the current and previous stages and all cluster
variables from previous stages. Variables can be reordered within the grid or moved

Exclude list. Enter sizes in the rightmost column. Click

to the

Labels or Values

to toggle the display of value labels and data values for stratification and cluster
variables in the grid cells. Cells that contain unlabeled values always show values.

30

Chapter 3

Click Refresh Strata to repopulate the grid with each combination of labeled data
values for variables in the grid.

Exclude. To

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

Analysis Pr

Figure 3-6

Analysis Preparation Wizard Stage Summary step

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

eparation Wizard: Stage Summary

s the last step within each stage, providing a summary of the analysis design

This i
specifications through the current stage. From here, you can either proceed to the
next stage (creating it if necessary) or save the analysis specifications.

If you cannot add another stage, it is likely because:

 No cluster variable was specified in the Design Variables step.

31



You selected WR estimation in the Estimation Method step.

 This is the third stage of the analysis, and the Wizard supports a maximum of

three stages

.

Analysis Preparation Wizard: Finish

Figure 3-7

Analysis Preparation Wizard Finish step

Preparing a Com

plex Sample for Analysis

This is the final step. You can save the plan file now or paste your selections to

ax window.

asynt

When editing a plan, you can save the edited plan to a new file or overwrite the

existing plan file.

32

Chapter 3

Modifying an

E From the me

Analyze

Complex Samples

Prepare for Analysis...

Select Editaplanfile, and choose a plan filename to which you will save the analysis

E

plan.

E Click Next to continue through the Wizard.
E Review the analysis plan in the Plan Summary step, and then click Next.

Subsequent steps are largely the same as for a new design. See the help for individual
steps for more information.

E Navigate to the Finish step, and specify a new name for the edited plan file or choose

to overwrit

Optionally

 Remove st

Existing Analysis Plan

nus choose:

e the existing plan file.

, you can:

ages from the plan.

33

Analysis Pre

Figure 3-8

Analysis Preparation Wizard Plan Summary step

Preparing a Com

paration Wizard: Plan Summary

plex Sample for Analysis

tep allows you to review the analysis plan and remove stages from the plan.

This s

e Stages.

Remov

must have at least one stage, you can edit but not remove stage 1 from the design.

Youcanremovestages2and3fromamultistagedesign.Sinceaplan

Chapter

4

Complex Samp

Complex Samples analysis procedures require analysis specifications from an
analysis or sample plan file in order to provide valid results.

Figure 4-1

Complex Samples Plan dialog box

les Plan

Plan. Specify the path of an analysis or sample plan file.
Joint Pr o babilities. In order to use Unequal WOR estimation for c lusters drawn

using a PPS WOR method, you need to specify a separate file containing the joint
probabilities. This file is created by the Sampling Wizard during sampling.

35

Chapter

5

Complex Samp

The Complex Samples Frequencies procedure produces frequency tables for selected
variables and displays univariate statistics. Optionally, you can request statistics by
subgroups, defined by one or more categorical variables.

Example. Using the Complex Samples Frequencies procedure, you can obtain

univariate tabular statistics for vitamin usage among U.S. citizens, based on the
results of the National Health Interview Survey (NHIS) and with an appropriate
analysis plan for this public use data.

Statistics. The procedure produces estimates of cell population sizes and table

percentages, plus standard errors, confidence intervals, coefficients of variation,
design effects, square roots of design effects, cumulative values, and unweighted
counts for each estimate. Additionally, chi-square and likelihood ratio statistics are
computed for the test of equal cell proportions.

les Frequencies

Complex Samples Frequencies Data Considerations

Data. Variables for which frequency tables are produced should be categorical.

Subpopulation variables can be string or numeric, but should be categorical.

Assumptions. The cases in the d ata file represent a sample from a complex design

that should be analyzed according to the specifications in the file selected in the
Plan dialog box.

37

38

Chapter 5

Obtaining Co

E From the me

Analyze

Complex Samples

Frequencies...

Select a plan file and optionally select a custom joint probabilities file.

E

E Click Continue.

Figure 5-1

Frequencie

mplex Samples Frequencies

nus choose:

sdialogbox

E Select

at least one frequency variable.

Optionally, you can:

 Specify variables to define subpopulations. Statistics are computed separately for

each su

bpopulation.

39

Complex Samp

Figure 5-2

Complex Samples Frequencies Statistics dialog box

Cells. This group allows you to request estimates of the cell population sizes and

table percentages.

Statistics. This group produces statistics associated with the population size or table

percentage.

 Standard error. The standard error of the estimate.
 Confidence interval. A confidence interval for the estimate, using the specified

level.

 Coeffcient of variation. The ratio of the standard error o f the estimate to the

estimate.

 Unweighted count. The number of units used to compute the estimate.
 Design effect. The ratio of the variance of the estimate to the variance obtained

by assuming that the sample is a simple random sample. This is a measure of
the effect of specifying a complex design, where values further from 1 indicate
greater effects.

 Square root of design effect. This is a measure of the effect of specifying a

complex design, where smaller values indicate greater effects.

 Cumulative values. The cumulative estimate through each value of the variable.

les Frequencies Statistics

Complex Sample

s Frequencies

40

Chapter 5

Test of equal cell proportions. This produces chi-square and likelihood ratio tests of

the hypothesis that the categories of a variable have equal frequencies. Separate
tests are pe

rformed for each variable.

Complex Samples Missing Values

Figure 5-3

Missing Values dialog box

Tables. This group determines which cases are used in the analysis.

 Use all available data. Missing values are determined on a table-by-table

hus the cases used to compute statistics may vary across frequency or

basis, t
crosstabulation tables.

 Ensure consistent case base. Missing values are determined across all variables,

thus the cases used to compute statistics are consistent across tables.

Categorical Design Variables. This group determines whether user-missing values are

rinvalid.

valid o

41

Complex Samp

Figure 5-4

Options dialog box

Display subpop ula tions. You can choose to have subpopulations displayed in the

same table

les Options

or in separate tables.

Complex Sample

s Frequencies

Chapter

6

Complex Samp

The Complex Samples Descriptives procedure displays univariate summary statistics
for several variables. Optionally, you can request statistics by subgroups, defined
by one or more categorical variables.

Example. Using the Complex Samples Descriptives procedure, you can obtain

univariate descriptive statistics for the activity levels of U.S. citizens, based on the
results of the National Health Interview Survey (NHIS) and with an appropriate
analysis plan for this public use data.

Statistics. The procedure produces means and sums, plus t-tests, standard errors,

confidence intervals, coefficients of variation, unweighted counts, population sizes,
design effects, and square roots of design effects for each estimate.

les Descriptives

Complex Samples Descriptives Data Considerations

Data. Measures should be scale variables. Subpopulation variables can be string

or numeric, but should b e categorical.

Assumptions. The cases in the d ata file represent a sample from a complex design

that should be analyzed according to the specifications in the file selected in the
Plan dialog box.

Obtaining Complex Samples Descriptives

E From the menus choose:

Analyze

Complex Samples

Descriptives...

Select a plan file and optionally select a custom joint probabilities file.

E

43

44

Chapter 6

E

Click Continue.

Figure 6-1

Descriptive

E Select

s dialog box

at least one measure variable.

Optionally, you can:

 Specify variables to define subpopulations. Statistics are computed separately for

each su

bpopulation.

45

Complex Samp

Figure 6-2

Descriptives Statistics dialog box

Summaries. This group allows you to request estimates of the means and sums of the

measure variables. Additionally, you can request t-tests of the estimates against
a specif

ied value.

les Descriptives Statistics

Complex Sample

s Descriptives

ics.

Statist

 Standa

 Confid

This group p roduces statistics associated with the mean or sum.

rd error.

ence interval.

The standard error of the estimate.

A confidence interval for the estimate, using the specified

level.

 Coeffcient of variation. The ratio of the standard error o f the estimate to the

estimate.

 Unweighted count. The number of units used to compute the estimate.
 Population size. The estimated number of units in the population.
 Design effect. The ratio of the variance of the estimate to the variance obtained by

ng the sample is a simple random sample. This is a measure of the effect of

assumi
specifying a complex design, where values further from 1 indicate greater effects.

 Square root of design effect. This is a measure of the effect of specifying a

complex design, where smaller values indicate greater effects.

46

Chapter 6

Complex Samp

Figure 6-3

Descriptives Missing Values dialog box

Statistics for Measure Variables. This group determines which cases are used in

the anal

 Use all

 Ensure consistent case base. Missing values are determined across all variables,

ysis.

basis, thus the cases used to compute statistics may vary across measure variables.

thus the cases used to compute statistics are consistent.

les Descriptives Missing Values

available data.

Missing values are determinedonavariable-by-variable

Categorical Design Variables. This group determines whether user-missing values are

valid or invalid.

Complex Samples Options

Figure 6-4

Options dialog box

47

Complex Sample

s Descriptives

Display subpop ula tions. You can choose to have subpopulations displayed in the

same table or in separate tables.

Chapter

7

Complex Samp

The Complex Samples Crosstabs procedure produces crosstabulation tables for pairs
of selected variables and displays two-way statistics. Optionally, you can request
statistics by subgroups, defined by one or more categorical variables.

Example. Using the Complex Samples Crosstabs procedure, you can obtain

crossclassification statistics for smoking frequency by vitamin usage of U.S. citizens,
based on the results of the National Health Interview Survey (NHIS) and with an
appropriate analysis plan for this public use data.

Statistics. The procedure produces estimates of cell population sizes and row, column,

and table percentages, plus standard errors, confidence intervals, coefficients of
variation, expected values, design effects, square roots of design effects, residuals,
adjusted residuals, and unweighted counts for each estimate. The odds ratio, relative
risk, and risk difference are computed for 2-by-2 tables. Additionally, Pearson and
likelihood ratio statistics are computed for the test of independence of the row and
column variables.

les Crosstabs

Complex Samples Crosstabs Data Considerations

Data. Row and column variables should be categorical. Subpopulation variables can

be string or numeric, but should be categorical.

Assumptions. The cases in the d ata file represent a sample from a complex design

that should be analyzed according to the specifications in the file selected in the
Plan dialog box.

49

50

Chapter 7

Obtaining Co

E From the me

Analyze

Complex Samples

Crosstabs...

Select a plan file and optionally select a custom joint probabilities file.

E

E Click Continue.

Figure 7-1

Crosstabs d

mplex Samples Crosstabs

nus choose:

ialog box

E Select

at least one row variable and one column variable.

Optionally, you can:

 Specify variables to define subpopulations. Statistics are computed separately for

each su

bpopulation.

51

Complex Samp

Figure 7-2

Crosstabs Statistics dialog box

les Crosstabs Statistics

Complex Sample

s Crosstabs

Cells. This group allows you to request estimates of the cell population sizes and row,

n, and table percentages.

colum

stics.

Stati

This group produces statistics associated with the population size and row,

column, and table percentages.

 Standard error. The standard error of the estimate.
 Confidence interval. A confidence interval for the estimate, using the specified

.

level

 Coef

ficient of variation.

The ratio of the standard error of the estimate to the

estimate.

 Expected values. The expected value of the estimate, under the hypothesis of

independence of the row and column variable.

 Unweighted count. The number of units used to compute the estimate.

52

Chapter 7



Design effect. The ratio of the variance of the estimate to the variance obtained by

assuming th

e sample is a simple random sample. This is a measure of the effect of

specifying a complex design, where values further from 1 indicate greater effects.

 Square root of design effect. This is a measure of the effect of specifying a

complex design, where smaller values indicate greater effects.

 Residuals. The expected value is the number of cases you would expect in the

cell if ther

e were no relationship between the two variables. A positive residual
indicates that there are more cases in the cell than there would be if the row and
column variables were independent.

 Adjusted residuals. The residual for a cell (observed minus expected value)

divided by

an estimate of its standard error. The resulting standardized residual is

expressed in standard deviation units above or below the mean.

Summaries for 2-by-2 Tables. This group produces statistics for tables in which the row

and column

variable each have two categories. Each is a measure of the strength of

the association between the presence of a factor and the occurrence of an event.

 Odds ratio. The odds ratio can be used as an estimate of relative risk when the

occurren

 Relativ

ce of the factor is rare.

erisk.

The ratio of the risk of an event in the presence of the factor to the

risk of the event in the absence of the factor.

 Risk difference. Thedifferencebetweentheriskofaneventinthepresenceofthe

factor and the risk of the event in the absence of the factor.

Test of independence of rows and columns. This produces chi-square and likelihood

ratio tests of the hypothesis that a row and column variable are independent. Separate

e performed for each pair of variables.

tests ar

53

Complex Samp

Figure 7-3

Missing Values dialog box

Tables. This group determines which cases are used in the analysis.

 Use all available data. Missing values are determined on a table-by-table

basis, thus the cases used to compute statistics may vary across frequency or
crossta

 Ensure

thus the cases used to compute statistics are consistent across tables.

les Missing Values

bulation tables.

consistent case base.

Missing values are determined across all variables,

Complex Sample

s Crosstabs

Comple

Categor

valid or invalid.

ical Design Variables.

x Samples Options

Figure 7-4

Options dialog box

This group determines whether user-missing values are

54

Chapter 7

Display subpop ula tions. You can choose to have subpopulations displayed in the

same table or in separate tables.

Chapter

8

Complex Samp

The Complex Samples Ratios procedure displays univariate summary statistics for
ratios of variables. Optionally, you can request statistics by subgroups, defined
by one or more categorical variables.

Example. Using the Complex Samples Ratios procedure, you can obtain descriptive

statistics for the ratio of current property value to last assessed value, based on the
results o f a statewide survey carried out according to a complex design and with an
appropriate analysis plan for the data.

Statistics. The procedure produces ratio estimates, t-tests, standard errors, confidence

intervals, coefficients of variation, unweighted counts, population sizes, design
effects, and square roots of design effects.

les Ratios

Complex Samples Ratios Data Considerations

Data. Numerators and denominators should be positive-valued scale variables.

Subpopulation variables can be string or numeric but should be categorical.

Assumptions. The cases in the d ata file represent a sample from a complex design

that should be analyzed according to the specifications in the file selected in the
Plan dialog box.

Obtaining Complex Samples Ratios

E From the menus choose:

Analyze

Complex Samples

Ratios...

Select a plan file and, optionally, select a custom joint probabilities file.

E

55

56

Chapter 8

E

Click Continue.

Figure 8-1

Complex Samp

E Select

Optiona

 Specif

les Ratios dialog box

at least one numerator variable and denominator variable.

lly, you can:

y variables to define subgroups for which statistics are produced.

57

Complex Samp

Figure 8-2

Statistics dialog box

Statistics. This group produces statistics associated with the ratio estimate.

 Standard error. The standard error of the estimate.
 Confidence interval. A confidence interval for the estimate, using the specified

level.

 Coeffi

 Unweighted count. The number of units used to compute the estimate.
 Population size. The estimated number of units in the population.
 Design effect. The ratio of the variance of the estimate to the variance obtained by

 Square

cient of variation.

estimate.

assuming the sample is a simple random sample. This is a measure of the effect of
specify

complex design, where smaller values indicate greater effects.

Complex Sample

sRatios

les Ratios Statistics

The ratio of the standard error of the estimate to the

ing a complex design, where values further from 1 indicate greater effects.

root of design effect.

This is a measure of the effect of specifying a

t-test. You can request t-tests of the estimates against a specified value.

58

Chapter 8

Complex Samp

Figure 8-3

Missing Values dialog box

Ratios. This group determines which cases are used in the analysis.

 Use all available data. Missing values are determined on a ratio-by-ratio basis;

thus, the cases used to compute statistics may vary across numerator-denominator
pairs.

 Ensure

thus, the cases used to compute statistics are consistent.

les Ratios Missing Values

consistent case base.

Missing values are determined across all variables;

Categorical Design Variables. This group determines whether user-missing values are

rinvalid.

valid o

Complex Samples Options

Figure 8-4

Options dialog box

59

Complex Sample

sRatios

Display subpop ula tions. You can choose to have subpopulations displayed in the

same table or in separate tables.

Chapter

9

Complex Samp

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.

les Sampling Wizard

Obtaining a Sample from a Full Sampling Frame

A state agency is charged with ensuring fair property taxes from county to county.
Taxes are based on the appraised value of the property, so the agency wants to survey
a sample of properties by county to be sure that each county's records are equally
up-to-date. However, resources for obtaining current appraisals are limited, so it's
important that what is available is used wisely. The agency decides to employ
complex sampling methodology to select a sample of properties.

A listing of properties is collected in property_assess_cs.sav.UsetheComplex

Samples Sampling Wizard to select a sample.

Using the Wizard

E TorunaComplexSamplesSamplingWizard analysis, from the menus choose:

Analyze

Complex Samples

Select a Sample...

61

62

Chapter 9

Figure 9-1

Welcome step

E Select Design a sample and type c:\property_assess.csplan asthenameoftheplan

file.

E Click Next.

63

Figure 9-2

Design Variables step

Complex Sample

s Sampling Wizard

E Select County as a stratification variable.
E Select Township as a cluster variable.
E Click Next,thenclickNext in the Method step.

This design structure means that independent samples are drawn for each county.
In this stage, townships are drawn as the primary sampling unit using the default
method, Simple Random Sampling.

64

Chapter 9

Figure 9-3

Sample Size step

E Type 4 as the value for the number of clusters to select in this stage.
E Click Next,thenclickNext in the Output Variables step.

65

Figure 9-4

Stage Summary step

Complex Sample

s Sampling Wizard

E Select Yes,addStage2now.
E Click Next.

66

Chapter 9

Figure 9-5

Design Variables step, stage 2

E Select Neighborhood as a stratification variable.
E Click Next,thenclickNext in the Method step.

This design structure means that independent samples are drawn for each
neighborhood of the townships drawn in stage 1. In this stage, properties are drawn as
the primary sampling unit using Simple Random Sampling.

67

Figure 9-6

Sample Size step

Complex Sample

s Sampling Wizard

E Select Proportions from the Units drop-down list.
E Type 0.2 as the value of the proportion of units to sample from e ach s trata.
E Click Next,thenclickNext in the Output Variables step.

68

Chapter 9

Figure 9-7

Stage Summary step

E Look over the sampling design, then click Next.

69

Figure 9-8

Draw Sample:Selection Options step

Complex Sample

s Sampling Wizard

E Select Custom value for the type of random seed to use and type 241972 as the value.

Using a custom value allows you to replicate the results of this example exactly.

E Click Next, then click Next in the Draw Sample:Output Files step.

70

Chapter 9

Figure 9-9

Finish step

E Click Finish.

These selections produce the sampling plan file property_assess.csplan and draw a
sample according to that plan.

71

Plan Summary

Figure 9-10

Plan Summary

Complex Sample

s Sampling Wizard

The summary table reviews your sampling plan, and it is useful for making sure the
plan represents your intentions.

72

Chapter 9

Sampling Summary

Figure 9-11

Stage Summar

y

This summary table reviews the first stage of sampling, and it is useful for checking
that the sampling went according to plan. Four townships were sampled from each
county, a

s requested.

73

Figure 9-12

Stage Summar y

Complex Sample

s Sampling Wizard

This summary table (the top part of which is shown here) reviews the second stage of
sampling. It is also useful for checking that the sampling went according to plan.
Approximately 20% of the properties were sampled from each neighborhood from
each township sampled in the first stage, as requested.

74

Chapter 9

Sample Results

Figure 9-13

Data Editor w

ith sample results

You can see the sampling results in the Data Editor. Five new variables were saved to
the working file, representing the inclusion probabilities and cumulative sampling

s for each stage, plus the final sampling weights.

weight

 Cases
 Cases

with values for these variables were selected to the sample.
with system-missing values for the variables were not selected.

The age

ncy will now use its resources to collect current valuations for the properties
selected in the sample. Once those valuations are available, you can process the
sample with Complex Samples analysis procedures, using the sampling plan

rty_assess.csplan to provide the sampling specifications.

prope

75

Complex Sample

Obtaining a S

Acompanyis

ample from a Partial Sampling Frame

interested in compiling and selling a database of high-quality survey
information. The survey sample should be representative, but efficiently carried
out, so complex sampling methods are used. The full sampling design calls for
the followi

Stage Strata Clusters

1 Region Province
2 District City
3 Subdivision

ng structure:

In the third stage, households are the primary sampling unit, and selected households
will be surveyed. However, information is only easily available to the city level, so
the company plans to execute the first two stages of the design now, then collect
information on the numbers of subdivisions and households from the sampled cities.
The available information to the city level is collected in demo_cs_1.sav.Usethe
Complex Samples Sampling Wizard to draw the first two stages.

Using the Wizard to Sample from the First Partial Frame

s Sampling Wizard

E To run the Complex Samples Sampling Wizard, from the menus choose:

Analyze

Complex Samples

Select a Sample...

76

Chapter 9

Figure 9-14

Welcome step

E Select Designasampleand type c:\demo_1.csplan as the name of the plan file.
E Click Next.

77

Figure 9-15

Design Variables step

Complex Sample

s Sampling Wizard

E Select Region as a stratification variable.
E Select Province as a cluster variable.
E Click Next,thenclickNext in the Method step.

This design structure means that independent samples are drawn for each region. In
this stage, provinces are drawn as the primary sampling unit using the default method,
Simple Random Sampling.

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Chapter 9

Figure 9-16

Sample Size step

E Type 3 as the value for the number of clusters to select in this stage.
E Click Next,thenclickNext in the Output Variables step.

79

Figure 9-17

Stage Summary step

Complex Sample

s Sampling Wizard

E Select Yes,addStage2now.
E Click Next.

80

Chapter 9

Figure 9-18

Design Variables step, stage 2

E Select District as a stratification variable.
E Select City as a cluster variable.
E Click Next,thenclickNext in the Method step.

This design structure means that independent samples are drawn for each district. In
this stage, cities are drawn as the primary sampling unit using the default method,
Simple Random Sampling.

81

Figure 9-19

Sample Size step

Complex Sample

s Sampling Wizard

E Select Proportions from the Units drop-down list.
E Type 0.1 as the value of the proportion of units to sample from e ach s trata.
E Click Next,thenclickNext in the Output Variables step.

82

Chapter 9

Figure 9-20

Stage Summary step

E Look over the sampling design, then click Next.

83

Figure 9-21

Draw Sample:Selection Options step

Complex Sample

s Sampling Wizard

E Select Custom value for the type of random seed to use and type 241972 as the value.

Using a custom value allows you to replicate the results of this example exactly.

E Click Next, then click Next in the Draw Sample:Output Files step.

84

Chapter 9

Figure 9-22

Finish step

E Click Finish.

These selections produce the sampling plan file demo_1.csplan and draw a sample
according to that plan.

85

Sample Results

Figure 9-23

Data Editor w

ith sample results

Complex Sample

s Sampling Wizard

You can see the sampling results in the Data Editor. Five new variables were saved to
the working file, representing the inclusion probabilities and cumulative sampling
weights for each stage, plus the “final” sampling weights for the first two stages.

 Cities with values for these variables were selected to the sample.
 Cities with system-missing values for the variables were not selected.

For each city selected, the company acquired subdivision and household unit
information and placed it in demo_cs_2.sav. Use this file and the Sampling Wizard to
sample the third stage of this design.

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Chapter 9

Using the Wizard to Sample from the Second Partial Frame

E To run the Complex Samples Sampling Wizard, from the menus choose:

Analyze

Complex Samples

Select a Sample...

Figure 9-24

Welcome step

E Select Designasampleand type c:\demo_2.csplan as the name of the plan file.
E Click Next.

87

Figure 9-25

Design Variables step

Complex Sample

s Sampling Wizard

E Select Subdivision as a stratification variable.
E Select Cumulative Sampling Weight for Stage 2 as the input sample weight variable.
E Click Next,thenclickNext in the Method step.

This design structure means that independent samples are drawn for each subdivision.
In this stage, household units are drawn as the primary sampling unit using the default
method, Simple Random Sampling.

88

Chapter 9

Figure 9-26

Sample Size step

E Type 0.2 as the value for the proportion of units to select in this stage.
E Click Next,thenclickNext in the Output Variables step.

89

Figure 9-27

Stage Summary step

Complex Sample

s Sampling Wizard

E Look over the sampling design, then click Next.

90

Chapter 9

Figure 9-28

Draw Sample:Selection Options step

E Select Custom value for the type of random seed to use and type 4231946 as the value.
E Click Next, then click Next in the Draw Sample:Output Files step.