Analyzing Complex Survey Data (Quantitative Applications in the Social Sciences)

105 Pages·2005·0.93 MB·English

by  Dr. Eun Sul Lee

Series/Number07–071
ANALYZING COMPLEX
SURVEY DATA
Second Edition
EunSulLee
DivisionofBiostatistics,SchoolofPublicHealth,
UniversityofTexasHealthScienceCenter—Houston
RonaldN.Forthofer
SAGEPUBLICATIONS
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ThousandOaks London NewDelhi
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Lee,EunSul.
Analyzingcomplexsurveydata/EunSulLee,RonaldN.Forthofer.—2nded.
p.cm.—(Quantitativeapplicationsinthesocialsciences;vol.71)
Includesbibliographicalreferencesandindex.
ISBN0-7619-3038-8(pbk.:alk.paper)
1.Mathematicalstatistics.2.Socialsurveys—Statisticalmethods.I.Forthofer,RonN.,
1944-II.Title.III.Series:Sageuniversitypapersseries.Quantitativeapplicationsinthe
socialsciences;no.07–71.
QA276.L33942006
(cid:1)
001.422—dc22 2005009612
Thisbookisprintedonacid-freepaper.
05 06 07 08 09 10 9 8 7 6 5 4 3 2 1
AcquisitionsEditor: LisaCuevasShaw
EditorialAssistant: KarenGiaWong
ProductionEditor: MelanieBirdsall
CopyEditor: A.J.Sobczak
Typesetter: C&MDigitals(P)Ltd
CONTENTS
SeriesEditor’sIntroduction v
Acknowledgments vii
1. Introduction 1
2. SampleDesignandSurveyData 3
TypesofSampling 4
TheNatureofSurveyData 7
ADifferentViewofSurveyData* 9
3. ComplexityofAnalyzingSurveyData 11
AdjustingforDifferentialRepresentation:TheWeight 11
DevelopingtheWeightbyPoststratification 14
AdjustingtheWeightinaFollow-UpSurvey 17
AssessingtheLossorGaininPrecision:TheDesignEffect 18
TheUseofSampleWeightsforSurveyDataAnalysis* 20
4. StrategiesforVarianceEstimation 22
ReplicatedSampling:AGeneralApproach 23
BalancedRepeatedReplication 26
JackknifeRepeatedReplication 29
TheBootstrapMethod 35
TheTaylorSeriesMethod(Linearization) 36
5. PreparingforSurveyDataAnalysis 39
DataRequirementsforSurveyAnalysis 39
ImportanceofPreliminaryAnalysis 41
ChoicesofMethodforVarianceEstimation 43
AvailableComputingResources 44
CreatingReplicateWeights 47
SearchingforAppropriateModelsforSurveyDataAnalysis* 49
6. ConductingSurveyDataAnalysis 49
AStrategyforConductingPreliminaryAnalysis 50
ConductingDescriptiveAnalysis 52
ConductingLinearRegressionAnalysis 57
ConductingContingencyTableAnalysis 61
ConductingLogisticRegressionAnalysis 65
OtherLogisticRegressionModels 69
Design-BasedandModel-BasedAnalyses* 74
7. ConcludingRemarks 78
Notes 80
References 83
Index 88
AbouttheAuthors 91
SERIESEDITOR’SINTRODUCTION
When George Gallup correctly predicted Franklin D. Roosevelt as the
1936presidentialelectionwinner,publicopinionsurveysenteredtheage
of scientific sampling, and the method used then was quota sampling, a
type of nonprobability sampling representative of the target population.
Thesamemethod,however,incorrectly predictedThomasDeweyasthe
1948 winner though Harry S. Truman actually won. The method failed
because quota sampling is nonprobabilistic and because Gallup’s quota
frames were based on the 1940 census, overlooking the urban migration
duringWorldWarII.
Today’s survey sampling has advanced much since the early days, now
relyingonsophisticatedprobabilisticsamplingdesigns.Akeyfeatureisstra-
tification:Thetargetpopulationisdividedintosubpopulationsofstrata,and
thesamplesizesinthestrataarecontrolledbythesamplerandareoftenpro-
portionaltothestratapopulationsizes.Anotherfeatureisclusterandmulti-
stage sampling: Groups are sampled as clusters in a hierarchy of clusters
selectedatvariousstagesuntilthefinalstage,whenindividualelementsare
sampledwithinthefinal-stageclusters.TheGeneralSocialSurvey,forexam-
ple,usesastratifiedmultistageclustersamplingdesign.(Kalton[1983]gives
aniceintroductiontosurveysampling.)
Whenthesurveydesignisofthiscomplexnature,statisticalanalysisofthe
dataisnolongerasimplematterofrunningaregression(oranyothermodel-
ing) analysis. Surveys today all come with sampling weights to assist with
correct statistical inference. Most texts on statistical analysis, by assuming
simplerandomsampling,donotincludetreatmentofsamplingweights,an
omissionthatmayhaveimportantimplicationsformakinginferences.Dur-
ing the last two tothree decades,statisticalmethods for dataanalysishave
also made huge strides. These must have been the reason my predecessor,
MichaelLewis-Beck,whosawthroughtheearlystagesoftheeditorialwork
inthisvolume,chosetohaveasecondeditionofAnalyzingComplexSurvey
Data.
Lee and Forthofer’s second edition of the book brings us up to date in
uniting survey sampling designs and survey data analysis. The authors
beginbyreviewingcommontypesofsurveysampledesignsanddemystify-
ingsamplingweightsbyexplainingwhattheyare,andhowtheyaredevel-
oped and adjusted.They then carefullydiscussthe majorissues ofvariance
estimation and of preliminary as well as multivariate analysis of complex
cross-sectional survey data when sampling weights are taken into account.
Theyfocusonthedesign-basedapproachthatdirectlyengagessampledesigns
intheanalysis(althoughtheyalsodiscussthemodel-basedperspective,which
v
vi
canaugmentadesign-basedapproachinsomeanalyses),andtheyillustrate
theapproachwithpopularsoftwareexamples.Studentsofsurveyanalysiswill
find the text of great use in their efforts in making sample-based statistical
inferences.
—TimFutingLiao
SeriesEditor
vii
ACKNOWLEDGMENTS
We sadly acknowledge that Dr. Ronald J. Lorimor, who had collaborated
on the writing of the first edition of this manuscript, died in 1999. His
insights into survey data analysis remain in this second edition. We are
gratefultoTomW.Smithforansweringquestionsaboutthesampledesign
oftheGeneralSocialSurveyandtoBarryL.Graubard,LuAnnAday,and
MichaelS.Lewis-Beckfortheirthoughtfulsuggestionsforthefirstedition.
Thanksalsoareduetoanonymousreviewersfortheirhelpfulcommentsfor
botheditionsandtoTimF.Liaoforhisthoughtfuladviceforthecontents
ofthesecondedition.Specialthanksgotomanystudentsinourclassesat
theUniversityofTexasSchoolofPublicHealthwhoparticipatedindiscus-
sionsofmanytopicscontainedinthisbook.

ANALYZING COMPLEX
SURVEY DATA, SECOND EDITION
EunSulLee
DivisionofBiostatistics,SchoolofPublicHealth,
UniversityofTexasHealthScienceCenter—Houston
RonaldN.Forthofer
1.INTRODUCTION
Survey analysis often is conducted as if all sample observations were
independently selected with equal probabilities.This analysis iscorrect if
simplerandomsampling(SRS)isusedindatacollection;however,inprac-
ticethesampleselectionismorecomplexthanSRS.Somesampleobserva-
tions may be selected with higher probabilities than others, and some are
included in the sample by virtue of their membership in a certain group
(e.g.,household)ratherthanbeingselectedindependently.Canwesimply
ignorethesedeparturesfromSRSintheanalysisofsurveydata?Isitappro-
priate to use the standard techniques in statistics books for survey data
analysis? Or are there special methods and computer programs available
for a more appropriate analysis of complex survey data? These questions
areaddressedinthefollowingchapters.
Thetypicalsocialsurveytodayreflectsacombinationofstatisticaltheory
and knowledgeabout socialphenomena, and its evolution has been shaped
byexperiencegainedfromtheconductofmanydifferentsurveysduringthe
last70years.Socialsurveyswereconductedtomeettheneedforinformation
to address social, political, and public health issues. Survey agencies were
established within and outside the government in response to this need for
information.Intheearlyattemptstoprovidetherequiredinformation,how-
ever,thesurveygroupsweremostlyconcernedwiththepracticalissuesinthe
fieldwork—such as sampling frame construction, staff training/supervision,
andcostreduction—andtheoreticalsamplingissuesreceivedonlysecondary
emphasis(Stephan,1948).Asthesepracticalmatterswereresolved,modern
samplingpracticehaddevelopedfarbeyondSRS.Complexsampledesigns
hadcometothefore,andwiththem,anumberofanalyticproblems.
Because the early surveys generally needed only descriptive statistics,
there was little interest in analytic problems. More recently, demands for
1