Quantitative Data Analysis: A Companion for Accounting and Information Systems Research

170 Pages·2017·2.68 MB·English

by  Willem Mertens

Willem Mertens · Amedeo Pugliese
Jan Recker
Quantitative
Data
Analysis
A Companion for Accounting and
Information Systems Research
Quantitative Data Analysis
Willem Mertens (cid:129) Amedeo Pugliese (cid:129)
Jan Recker
Quantitative Data Analysis
A Companion for Accounting and
Information Systems Research
WillemMertens AmedeoPugliese
QUTBusinessSchool Dept.ofEconomicsandManagement
QueenslandUniversityofTechnology UniversityofPadova
Brisbane,Queensland Padova,Italy
Australia
SchoolofAccountancy
QueenslandUniversityofTechnology
Brisbane,Queensland
Australia
JanRecker
QUTBusinessSchool
QueenslandUniversityofTechnology
Brisbane,Queensland
Australia
ISBN978-3-319-42699-0 ISBN978-3-319-42700-3 (eBook)
DOI10.1007/978-3-319-42700-3
LibraryofCongressControlNumber:2016954196
#SpringerInternationalPublishingSwitzerland2017
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Preface
Quantitative Data Analysis for Accounting and Information Systems Research
guides postgraduate research students and early career researchers in choosing
andexecutingappropriatedataanalysismethodstoanswertheirresearchquestions.
Itsupportsresearcherswhentheyareplanningtocollectdataandwhentheyhave
datathattheywanttoanalyze.Usingavarietyofexamplesandlaylanguage,this
bookprovideshands-onguidelineson(1)whentousewhichdataanalysismethod,
(2) what kind of data and data structure are required for each of the methods,
(3) what each method does and how the methods should be used, and (4) how to
reportresults.
This book is not intended to provide an exhaustive overview of data-analysis
methods,nordoesitexplainthemethodsindepth.Instead,itguidesresearchersin
applyingtherightmethodsintherightway.Moreskilledresearcherscanalsouse
the book to refresh their knowledge or as a checklist to avoid skipping important
steps. It explains the most commonly used methods in an intuitive and hands-on
way, pointing out more advanced resources along the way. As such, it does not
aspiretocompetewithmanualslikethoseofStevens[1],Field[2],orCrawley[3].
Wearenotstatisticiansbutresearcherswhoapplystatistics,1sothebookcoversthe
issues that commonly affect others like us, who are engaging in quantitative
empiricalresearch.
QuantitativeDataAnalysisforAccountingandInformationSystemsResearchis
thebookwewouldhavelikedtohavehadasasupportinourownresearch.Every
chapter provides an unintimidating starting point for building your data-analysis
skills,theinformationrequiredtorunthemostcommonanalysesandreportthem,
andpointerstomoreextensiveresources.Attheriskofsayingthingsthatmaynot
be entirely true in the purest statistical sense, we try to keep the language of this
bookassimpleaspossible.Assuch,thebookisbriefandwritteninalanguagethat
we hope everyone can understand—from students to researchers to people who
wishtostudytheorganizationsinwhichtheywork.Ourgoalistohelpyouconduct
academicresearchofhighqualityanddotherightthingsright—nottomakeyoua
1If youarea statistician orsimplymore observantthanwe are, weinvite you totell usifyou
identifyanerror.
v
vi Preface
statisticsexpert—sothisbookisnotaboutstatisticsbutaboutapplyingstatisticsto
theresearchquestionsthatkeepyouawakeatnight.(Wedoubtthesequestionsare
aboutcollinearity,butiftheyare,thismaynotbethebookyouarelookingfor.)
In brief, this book is a software-independent starting point for answering the
question:WhatmethodsdoIusetoanswermyresearchquestionsandhow?
Wehopeyouhavefun!
Brisbane,QLD,Australia WillemMertens
Padova,Italy AmedeoPugliese
JanRecker
References
1. StevensJP(2009)Appliedmultivariatestatisticsforthesocialsciences.TaylorandFrancis,
LLC,London
2. FieldAP(2013)DiscoveringstatisticsusingIBMSPSSstatistics,andsexanddrugsandrock
‘n’roll,4thedn.Sage,London
3. CrawleyMJ(2013)TheRbook,2ndedn.Wiley,WestSussex
Acknowledgments
Manypeoplehavecontributedtothedevelopmentofthisbookanditscontent.We
are grateful to everyone who helped us discover, explain, and write down our
understandingof what matters inusing statisticsfor dataanalysis. Although there
aremanyofyououtthere,wewouldliketothankafewinparticular.
First, we are grateful for the support of the QUT’s School of Management,
School of Accountancy and Information Systems School, for supporting us in the
development and conduct of the Advanced Data Analysis workshop series; this
book would not have been possible without it. Special thanks go to Professor
Michael Rosemann and Professor Peter Green for their inspiring entrepreneurial
spirit, flexibility, and support. Second, we are grateful that so many of our
colleagues and students attended these workshops and discussed and challenged
ourunderstandingofdataanalysismethodsandthewaywetaughtthem.
Finally, the ones that contributed—or perhaps suffered—most are our lovely
wives,Laura,Claudia,andLaura.Thankyouforyoursupport,yourpatience,and
for sharing some of our headaches. You make our lives 89% more enjoyable
(p<.001,[75–100]).
September2016 WillemMertens
AmedeoPugliese
JanRecker
vii
Contents
1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 IntroductiontotheBasics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 NavigatingtheWorldofStatistics—AndThisBook. . . . . . . . . . 3
1.3 WhatThisBookDoesNotCover. . . . . . . . . . . . . . . . . . . . . . . 5
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 ComparingDifferencesAcrossGroups. . . . . . . . . . . . . . . . . . . . . . 7
2.1 OneorTwoGroups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 MorethanTwoGroups:One-WayANOVA.. . . . .. . . .. . . . .. 10
2.3 MorethanTwoGroupingVariables:FactorialANOVA. . . . . . . 12
2.4 MorethanOneDependentVariable:MultivariateANOVA. . . . 14
2.5 MoreAdvancedModels:CovarianceandRepeatedMeasures. . . 16
2.6 WhentoUseGroupComparisons. . . . . . . . . . . . . . . . . . . . . . . 17
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Assessing(Innocuous)Relationships. . . . . . . . . . . . . . . . . . . . . . . . 21
3.1 WhatAreRegressionModels?. . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 WhenDoWeUseRegressionModels?. . . . . . . . . . . . . . . . . . . 24
3.3 HowDoWeExamineRegressionModels?. . . . . . . . . . . . . . . . 26
3.4 HowDoWeReportRegressionAnalyses?. . . . . . . . . . . . . . . . . 31
3.5 WhatIf.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4 ModelswithLatentConceptsandMultipleRelationships:
StructuralEquationModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1 WhatAreStructuralEquationModels?. . . . . . . . . . . . . . . . . . . 37
4.2 WhenDoWeUseStructuralEquationModels?. . . . . . . . . . . . . 41
4.3 HowDoWeExamineStructuralEquationModels?. . . . . . . . . . 43
4.4 HowDoWeReportStructuralEquationModelAnalyses?. . . . . 49
4.5 WhatIf.... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5 NestedDataandMultilevelModels:HierarchicalLinearModeling... 61
5.1 WhatAreHierarchicalLinearModels?. . . . . . . . . . . . . . . . . . . 61
5.2 WhenDoWeUseHLMs?. . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
ix
x Contents
5.3 HowDoWeInvestigateHLMs?. . . . . . . . . . . . . . . . . . . . . . . . 64
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6 AnalyzingLongitudinalandPanelData. . . . . . . . . . . . . . . . . . . . . 73
6.1 WhatAreLongitudinalandPanelData?. . . . . . . . . . . . . . . . . . 73
6.2 ClusteringasaWaytoDealwithNestedness. . . . . . . . . . . . . . . 77
6.3 WhichModelsCanWeUsetoAnalyzeLongitudinalData?. . . . 81
6.4 EstimatingandReportingFixed-EffectsandRandom-Effects
Models.. . . .. . . .. . . .. . . .. . . . .. . . .. . . .. . . .. . . .. . . .. 84
6.5 WhentoUseOLS,Fixed-Effects,andRandom-EffectsModels. . . 92
6.6 FinalRemarks,SuggestionsandYourBestTool:Thinking!. . . . 96
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7 Causality:EndogeneityBiasesandPossibleRemedies. . . . . . . . . . 99
7.1 YourResearchQuestionIsCausal:WhatDoesthatMean?. . . . . 100
7.2 Self-SelectionandEndogeneity. . . . . . . . . . . . . . . . . . . . . . . . . 105
7.3 SpecifyingOLSModelstoMinimizeEndogeneityConcerns. . . 107
7.4 MoreComplexDesignstoSupportCausalClaims. . . . . . . . . . . 111
7.5 SomeCaveatsandLimitations. . . . . . . . . . . . . . . . . . . . . . . . . 132
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
8 HowtoStartAnalyzing,TestAssumptionsandDeal
withthatPeskyp-Value. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.1 Structuring,Cleaning,andSummarizingData. . . . . . . . . . . . . . 136
8.1.1 StructuringData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8.1.2 CleaningData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
8.1.3 ExploringData:SummaryStatisticsandVisualization. . . 142
8.2 TestingAssumptions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.2.1 IndependenceofObservations. . . . . . . . . . . . . . . . . . . . 143
8.2.2 Normality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
8.2.3 HomogeneityofVarianceandHomoscedasticity. . . . . . . 148
8.2.4 Linearity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.2.5 WhatifAssumptionsAreViolated?. . . . . . . . . . . . . . . . 150
8.3 MindfullyInterpretingStatistics:TheCaseofthep-Value. . . . . 152
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
9 KeepingTrackandStayingSane. . . . . . . . . . . . . . . . . . . . . . . . . . 157
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
1
Introduction
Data, data, data. More data is available to us now than ever before. As work and
privateactivitiesareincreasinglyfacilitatedandenactedbyourdigitaldevices,we
leave traces that can be picked up and analyzed anytime and anywhere. Data
collected through surveys, archives, and experiments also remain relevant, as
digital traces do not necessarily reflect perceptions, attitudes, and intentions.
What also has not changed is that data is meaningless until it is analyzed. That is
what this book is about: analyzing data. More precisely: analyzing quantitative
data.Numbers.
Dataanalysisisaniterativeprocessofmanipulatingandinterpretingnumbersto
extractmeaningfromthem—answerresearchquestions,testhypotheses,orexplore
meaningsthatcanbederivedinductivelyfromthedata.Thisexplorationisthefirst
stepofanydataanalysis:werunafewbasicmanipulationsandteststosummarize
the data in meaningful statistics, such as means and standard deviations; we
visualize the data; and we try to improve our understanding of the information in
thedata.
Of course, before you can start analyzing, you need to obtain data and have a
rough idea of the meaning you want to extract through analysis. Therefore, every
chapterbrieflydiscusseswhenandwhyyoumaywanttousethemethodsdiscussed
inthatchapter,including thetypeofquestionstypicallyansweredandthetypeof
data analyzed using that method. We hope that this approach will help you
understand how theory, research designs, research questions, data, and analysis
dependononeanother.Thecredibilityofdataisderivedfromtheresearchdesign;
andthecredibilityofdataanalysisisderivedfromitsgroundingintheory.
Beforewestartdiscussingdataanalysismethods,wewanttosummarizethekey
conceptsusedinthisbookandgiveyouaroadmapforusingthebookandchoosing
therightanalysismethod.
#SpringerInternationalPublishingSwitzerland2017 1
W.Mertensetal.,QuantitativeDataAnalysis,DOI10.1007/978-3-319-42700-3_1