The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2: Statistical Analysis
Published online:
01 October 2013
Published in print:
21 March 2013
Online ISBN:
9780199971305
Print ISBN:
9780199934898
Contents
End Matter
Index
-
Published:March 2013
Annotate
Cite
'Index', in Todd D. Little (ed.), The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2: Statistical Analysis, Oxford Library of Psychology (2013; online edn, Oxford Academic, 1 Oct. 2013), https://doi.org/, accessed 25 Apr. 2025.
Subject
Psychology
Series
Oxford Library of Psychology
Collection:
Oxford Handbooks Online
Index
- Advanced mixture modeling603–606
- Aiken, Leona S.26–51
- Alternating least squares scaling (ALSCAL)238
- Alternative models for binary outcomes35–36
- Anderson, Rawni A.718–758
- Anselin, Luc154–174
- Approximate discrete model427–428
- Assumptions, violations of28–29
- Baraldi, Amanda N.635–664
- Bayesian configural frequency analysis86–87
- Bayesian models for fMRI189–191
- Beauchaine, Theodore P.612–634
- Binary classification tree682
- Binary logistic regression33–37
- Binary variables58–59
- Binomial test108–109
- Blokland, Gabriëlla A. M.198–218
- Bootstrap methods126–131
- Brose, Annette441–457
- Brown, Timothy A.257–280
- Buskirk, Trent D.106–141
- Card, Noel A.701–717
- Case diagnostics47–49
- Casper, Deborah M.701–717
- Categorical methods52–73
- categorical variables52–61
- measuring strength of association between58–61
- testing for significant association between52–58
- conclusions and future directions71–72
- effect sizes64–66
- key terms55
- symbols used53–55
- Categorical variables
- measuring strength of association between58–61
- testing for significant association between52–58
- Cell frequencies, and configural frequency analysis87
- Chi-Square test119–122
- Classical statistical approaches, overview of7–25
- Classification and regression trees683–690
- cut-point and variable selection bias686–687
- instability of trees690
- interpretation688–690
- prediction and interpretation688
- recursive partitioning683–684
- split selection criteria684–686
- stopping and pruning687
- Classification techniques
- Cluster analysis and MDS239–240
- Clustering and classification techniques517–550
- chapter notation522
- concluding remarks543
- finite mixture and latent class models530–543
- absolute fit assessment542
- class-specific item response probabilities533
- instrument calibration vs. respondent scaling533
- investigating relative performance540
- item-fit assessment542–543
- item response probabilities for five assessment items539
- latent classes as attribute profiles535
- local/conditional independence assumption533
- model-data fit at different levels540–541
- for multiple quantitative response variables532–533
- parameter constraints via the Q-matrix535–536
- parameter values for five assessment items538
- person-fit assessment543
- relative fit assessment541
- for single quantitative response variables531–532
- software packages for540
- statistical structure of unrestricted latent class model534–535
- unconstrained latent class models533–535
- foundational terminology519–522
- exploratory vs. confirmatory techniques520–521
- nonparametric vs. parametric model-based techniques520
- observations vs. variables519
- variable types vs. measurement scales519–520
- glossary of key terms544–546
- introduction to517–518
- nonparametric techniques522–530
- additional example530
- agglomerative vs. divisive approaches522
- basic concepts522
- distance measures for multivariate space525–526
- graphical representation523
- K-means clustering527–529
- measures of intercluster distance526–527
- numerical representation522–523
- partitioning cluster methods527–529
- pre-processing choices for hierarchical techniques523–527
- software for529–530
- range of applications518
- standardization formulas for cluster analysis524
- Cochran’sQ115–117
- The column problem145–146
- Configural frequency analysis (CFA)74–105
- appropriate questions for75–76
- base models78–80
- future directions102
- null hypothesis80–81
- sample models and applications89–102
- longitudinal CFA93–97
- mediation configural frequency analysis97–102
- prediction configural frequency analysis89–91
- two-group CFA91–93
- six steps of86–89
- symbols and definitions103
- technical elements of76–81
- Contextual variable fallacies720–725
- avoiding hierarchically nested data structures724–725
- confusing moderation with additive effects724
- direct effect and evidence of mediation721–722
- mistaking mediation for moderation720–721
- testing mediation with constituent paths721
- using cross-sectional models to test mediation722–724
- Continuous time, models of416–426
- conclusions and future directions426–427
- first-order differential equation model420–423
- second-order differential equation model423–426
- Control variables, associations with61–64
- Coombs’ contribution to MDS238
- Correspondence analysis142–153
- application to other data types151
- canonical correspondence analysis151–152
- correspondence analysis displays146–147
- introductory example143
- measure of fit150–151
- multiple correspondence analysis147–150
- principal component analysis and multidimensional scaling143–147
- statistical inference152
- Coxe, Stefany26–51
- Curve estimation methods131–137
- Data mining678–700
- binary classification tree682
- conclusion698
- other techniques, literature, and software697–698
- Deboeck, Pascal R.411–431
- Dellinger, Anne4
- Density estimation, nonparametric131–135
- Deviation, concepts of87–88
- Diagnostics, model and case47–49
- Differential item functioning65–66
- Ding, Cody S.235–256
- Discrete-time survival factor mixture model, diagram605
- Distance measures48–49
- Donnellan, M. Brent665–677
- Dowsett, Chantelle4
- Dynamic causal models192
- Dynamic factor analysis441–457
- background442–444
- five steps for conducting
- person-specific models448–449
- research questions446
- study design and data collection446–447
- variable selection and data preprocessing447–448
- glossary455
- synopsis454–455
- technical background444–445
- Dynamical systems411–431
- approximate discrete model427–428
- attractors and self-regulation414–416
- concept of412–413
- conclusions and future directions426–427
- language of413–414
- latent differential equation modeling428–430
- Edgeworth, Francis Y.8
- Edwards, Michael4
- Effect sizes
- and categorical methods64–66
- introduction of concept9
- recommendations for best practice23–24
- Eigenvalues21–22
- Electroencephalography, and statistical parametric mapping177
- Enders, Craig K.635–664
- Ensemble methods, of data mining690–697
- bagging690–691
- predictions from ensembles693–695
- random forests691–693
- randomness696–697
- variable importance695–696
- Error covariance matrix184–185
- Estimation theory12–13
- Event history data analysis486–516
- conclusion514
- continuous state space511–514
- continuous time formulation493–499
- basic concepts493–494
- examples497–498
- rate and probability499
- specifications and estimation496–497
- discrete state space509–511
- discrete time formulation492–493
- hazard-rate framework492–493
- motivation488, 490–492
- censoring and time-varying covariates488
- initial statement of the solution491–492
- observability of the dependent variable506–507
- problems created for standard techniques489
- repeated events507–509
- Excess zeros, concept of43–44
- Extensions to space-time160–162
- Factor analysis
- fallacies739–743
- default use of orthogonal rotation741
- misuse of principal components739–740
- number of factors retained in EFA740–741
- other issues in factor analysis742
- summary742–743
- using CFA analysis to confirm EFA analysis741–742
- and MDS239
- Finite mixture modeling551–611
- advanced mixture modeling603–606
- conclusion606–607
- future directions607
- history of mixture modeling554–557
- finite mixture modeling554–555
- latent class analysis555–556
- the more recent past556–557
- as latent variable models552
- list of abbreviations607–608
- as a person-centered approach552–554
- Fisher, Ronald A.8
- Fisherian school of statistics8
- Fisher’s exact test119–122
- Frequentist configural frequency analysis86–87
- Friedman’s test115–117
- Functional magnetic resonance imaging
- and analytic models and designs183–184
- and statistical parametric mapping177
- Functional magnetic resonance imaging (fMRI)
- Bayesian models for189–191
- Gaussian processes460
- Generalized linear models (GLiM)26–51
- diagnostics47–49
- introduction to26–27
- maximum likelihood estimation30–33
- multiple regression27–29
- pseudo-R-squared measures of fit46–47
- summary and conclusions49–50
- three components of a GLiM29–30
- Genes, quantitative analysis of219–234
- association analysis227–233
- case-control association tests227–229
- family-based association tests230–232
- genome-wide association studies232–233
- population stratification229
- quality control and prior data cleaning227
- significance of linkage226–227
- summary233
- Genetics, twin studies198–218
- classical twin model202–215
- assumptions of the model205–208
- extensions to the model208–211
- multivariate modeling211–215
- structural equation modeling203–205
- introduction and overview198–202
- twin studies and beyond215
- Global autocorrelation156–158
- Global configural frequency analysis79
- Gossett, William S.8
- Gottschall, Amanda C.338–360
- Greenacre, Michael J.142–153
- Growth mixture model, diagram605
- Harshman, R.A.8
- Hau, Kit-Tai361–386
- Heteroscedasticity28
- Hierarchical linear model185–186
- History of traditional statistics8–9
- Hox, Joop J.281–294
- Hurdle regression models44–45
- Imaging data, analysis of
- Bayesian methods of analysis186–187
- Bayesian models for fMRI189–191
- classic frequentist probability187–189
- conclusion and future directions195
- dynamic causal models192
- early approaches based on general linear method176–177
- history of imaging methods and analyses176
- multivariate autoregressive models192–193
- parameter estimation182
- spatial normalization and topological influence177–182
- statistical parametric mapping179–180
- steps from image acquisition to analysis180
- statistical parametric mapping177
- structural equation modeling193–195
- Imaging data, analysis of175–197
- Individual differences MDS models238
- Influence measures49
- Intensive longitudinal data
- Interaction
- Interpretation, recommendations for best practice23–24
- Introduction1–6
- Johnson, David4
- K-means clustering527–529
- Kadlec, Kelly M.295–337
- Kendall’s t117–119
- Kisbu-Sakarya, Yasemin338–360
- Kruskal-Wallis test113–115
- Kruskal’s contribution to MDS237–238
- Land use planning models170–171
- Latent class analysis557–584
- a brief history of555–556
- mediation model605
- missing data573–584
- model building565–573
- model estimation561–565
- model formulation557–558
- model interpretation558–561
- Latent differential equation modeling428–430
- Latent mixture modeling, diagram605
- Latent profile analysis584–606
- example of592–600
- example of latent class regression602–603
- latent class regression600–601
- model estimation590
- model interpretation587–590
- post hoc class comparisons603
- Latent transition model, diagram605
- Latent variable interpretation35
- Latent variable measurement models257–280
- conclusion276–277
- confirmatory factor analysis260–266
- exploratory factor analysis258–260
- extensions of confirmatory factor analysis269–273
- future directions277–279
- higher-order models273–276
- hybrid latent variable measurement models266–269
- selected output for confirmatory factor analysis263
- selected output for exploratory structural equation modeling268–269
- Lee, Jason and Steve4
- Leverage measures48
- Limited dependent variables28–29
- Linear regression model163–165
- Linearity28–29
- Local autocorrelation158–159
- Location models169–170
- Longitudinal configural frequency analysis93–97
- Longitudinal data, intensive432–440
- challenges and opportunities438–439
- idiographic-nomothetic continuum434–437
- reactivity436–437
- statistical models437–438
- Longitudinal data analysis387–410
- advances in modeling406–407
- conclusion and discussion406–407
- importance of387–389
- multilevel modeling approach389–397
- curvilinear growth curve model392–393
- error structures396
- linear growth curve model389–392
- nonlinear growth curve model393–394
- spline curve models395
- time-constant covariates396–397
- time-varying covariates397
- structural equation modeling approaches397–406
- autoregressive cross-lagged models401–402
- curvilinear latent curve model398–399
- general assumptions405–406
- latent difference score models403
- linear latent curve model397–398
- nonlinear latent curve model399–401
- parallel process latent curve model403–404
- second-order latent curve model404–405
- Longtitudinal mediation351–353
- Lucas, Richard E.665–677
- MacKinnon, David P.338–360
- Magnetic resonance imaging
- and analytic models and designs183–184
- Bayesian models for189–191
- and statistical parametric mapping177
- Mair, Patrick74–105
- Marsh, Herbert W.361–386
- Masyn, Katherine E.551–611
- McArdle, John J.295–337
- McNemar’s test110–113
- Mean, estimation of460
- Measure of fit150–151
- Measurement error fallacies725–730
- ignorance of latent mixture and multilevel structure728–729
- individual items and composite scores726–728
- the myth about numbers725–726
- reliability and test length728
- unreliability and attenuated effects729–730
- Mediation analysis338–360
- causal inference in348–351
- experimental designs350–351
- principal stratification350
- sequential ignorability assumption348–350
- estimating the mediated effect340–342
- assumptions341
- coefficients approach340–341
- covariates341
- multiple mediators341–342
- point estimation340–342
- standard error342
- history339
- longtitudinal mediation351–353
- autoregressive models352
- latent change score models352–353
- latent growth curve models352
- person-centered approaches353
- three (or more)-wave models351–353
- two-wave models351
- modern appeal339–340
- significance testing and confidence interval estimation342–345
- Bayesian methods343
- categorical and count outcomes343–344
- effect size measures343
- non-normality344
- small samples344–345
- summary and future directions353–354
- Mediation configural frequency analysis97–102
- decisions concerning type98–102
- four base models for97–98
- Medical imaging
- Bayesian models for fMRI189–191
- connectivity of brain regions191–192
- issues in neuroimaging181
- and statistical parametric mapping177
- Meta-analysis701–717
- advanced topics715–716
- alternative effect sizes715
- artifact corrections715–716
- multivariate meta-analysis716
- analysis of mean effect sizes710–713
- fixed-effects means711
- heterogeneity711–712
- random-effects means712–713
- coding effect sizes707–710
- computing effect sizes709–710
- correlation coefficient708
- odds ratios709
- standardized mean differences708–709
- coding study characteristics707
- introduction to701–702
- moderator analyses713–715
- categorical moderators714–715
- limitations to715
- single categorical moderator713–714
- single continuous moderator714
- Metric MDS model237
- MIMIC data323–334
- Missing data fallacies730–732
- attempting to prepare for missing data732
- missing-data treatments and notion of “cheating,”730–732
- Missing data methods635–664
- artificial data example636–637
- atheoretical missing data handling methods639–641
- averaging available items639–640
- last observation carried forward imputation640
- mean imputation639
- similar response pattern imputation640–641
- conclusion661–662
- data analysis examples653–657
- complete data653–654
- not missing at random-based approaches revisited656–657
- not missing at random data655–656
- improving missing at random-based analyses650–653
- dealing with non-normal data652–653
- role of auxiliary variables650–651
- missing at random (MAR)642–648
- maximum likelihood estimation645–648
- multiple imputation642–645
- stochastic regression imputation642
- missing data mechanisms637–639
- not missing at random648–650
- Model diagnostics47
- Modeling
- Models
- Moderation361–386
- analysis of variance364–365
- classic definition of362–363
- confounding nonlinear and interaction effects379–380
- distribution-analytic approaches377–378
- further research379
- graphs of interaction effects363–364
- interactions with more than two continuous variables381–382
- moderated multiple regression approaches365–373
- disordinal interactions371–372
- interactions with continuous observed variables369–371
- multicollinearity involved with product terms372–373
- power in detecting interactions372
- standardized solutions for models with interactions terms368
- tests of statistical significance of interaction effects368–369
- multilevel designs and clustered samples383
- multiple group SEM approach to interaction375
- separate group multiple regression365
- summary378–379
- tests of measurement invariance382–383
- vs. causal ordering380–381
- Molenaar, Peter C. M.441–457
- Morin, Alexandre J. S.361–386
- Mosing, Miriam A.198–218
- Mulaik, S. A.8
- Multidimensional scaling (MDS)235–256
- basics and applications of MDS models240–254
- computer programs for MDS analysis251–252
- individual differences models246–250
- metric model242–243
- new applications252–254
- nonmetric model243–246
- using maximum likelihood estimation250–251
- variety of data240–242
- a brief description of MDS(X) programs253
- and cluster analysis239–240
- conclusion254
- future directions254–255
- and principal component analysis143–147
- terminology and symbols236
- Multilevel regression modeling
- conclusion291
- introduction281–282
- key terms and symbols292
- Multilevel structural equation modeling
- conclusion291
- introduction281–282
- key terms and symbols292
- Multivariate statistics20–23
- Mun, Eun-Young74–105
- Murray, Alan T.154–174
- Nagengast, Benjamin361–386
- Neyman, Jerzy8
- Neyman-Pearson school of statistics8
- Nonmetric MDS model237–238
- Nonparametric statistical techniques106–141
- classical nonparametric methods108–122
- comparing more than two samples113–117
- comparing two dependent samples110–113
- comparing two independent samples109–110
- methods based on a single sample108–109
- nonparametric analysis of nominal data119–122
- nonparametric correlation coefficients117–119
- curve estimation methods131–137
- density estimation131–135
- extensions to multiple regression137
- simple nonparametric regression135–137
- future directions138–139
- glossary of terms139–140
- modern resampling-based methods122–131
- applying permutation tests to one sample122–
- bootstrap confidence interval methods129–130
- bootstrap methods126–129
- bootstrap methods and permutation tests131
- general permutation tests122
- other applications of bootstrap methods130–131
- statistical software for conducting137
- vs. parametric methods137–138
- Null hypothesis
- in configural frequency analysis (CFA)80–81
- Optimization modeling168–171
- Ordinal variables59–61
- Organization of Handbook of Quantitative Methods2–4
- Orthogonal rotation741
- Overdispersed Poisson regression42–43
- P calculated values
- introduction of8
- Parameter estimates and fit statistics450
- Pearson, Karl and Egon S.8
- Pearson’s computations10–11
- Petersen, Trond486–516
- Population stratification229
- Positron emission tomography
- and analytic models and designs182–183
- and statistical parametric mapping177
- Preacher, Kris4
- Prediction configural frequency analysis89–91
- Preference MDS models247
- Price, Larry R.175–197
- Principal component analysis143–147
- Proportional odds model69–71
- Pseudo-R-squared measures of fit46–47
- Quantitative research methodology, common fallacies in718–758
- contextual variable fallacies720–725
- avoiding hierarchically nested data structures724–725
- confusing moderation with additive effects724
- direct effect and evidence of mediation721–722
- mistaking mediation for moderation720–721
- testing mediation with constituent paths721
- using cross-sectional models to test mediation722–724
- factor analysis fallacies739–743
- default use of orthogonal rotation741
- misuse of principal components739–740
- number of factors retained in EFA740–741
- other issues in factor analysis742
- summary742–743
- using CFA analysis to confirm EFA analysis741–742
- introduction to718–720
- measurement error fallacies725–730
- ignorance of latent mixture and multilevel structure728–729
- individual items and composite scores726–728
- the myth about numbers725–726
- reliability and test length728
- unreliability and attenuated effects729–730
- missing data fallacies730–732
- attempting to prepare for missing data732
- missing-data treatments and notion of “cheating,”730–732
- statistical power fallacies736–739
- lack of retrospective power and null hypothesis737
- nonsignificance and null hypothesis736–737
- statistical power as a single, unified concept736
- summary and recommendations737–739
- statistical significance fallacies732–735
- alternative paradigms735
- alternatives and solutions734–735
- p-values and strength of effect733
- p-values reflect replicabililty734
- relationship between significant findings and study success734
- significance of p-value and research hypothesis733
- statistical significance and practical importance733–734
- summary checklist743–748
- R Code332–334
- Raju, N.S.8
- Ram, Nilam441–457
- Regional configural frequency analysis79–80
- Regression analysis, spatial162–168
- Regression mixture model, diagram605
- Regression specification163
- Regression time series models475–478
- Rey, Sergio J.154–174
- Rhemtulla, Mijke4
- The row problem145
- Rupp, André A.517–550
- Scatterplot smoothing135–137
- Secondary data analysis665–677
- advantages and disadvantages667–668
- conclusion675
- measurement concerns in existing data sets671–673
- missing data in existing data sets673–674
- primary research vs. secondary research666–667
- sample weighting in existing data sets674–675
- steps for beginning669–671
- Selig, James P.387–410
- SEM-CALIS325–329
- SEM-Mplus329–332
- Serial correlation, modeling184–185
- Sign test110–113
- Significant association, testing for52–58
- Significant difference, introduction of term8
- Snedecor, George W.8
- Space-time, extensions to160–162
- Spatial analysis154–174
- autocorrelation analysis156–162
- conclusion171–172
- Spatial data155–156
- Spearman’s p117–119
- Statistical approaches, overview of traditional methods7–25
- ANOVA computations12–13
- brief history of traditional statistics8–9
- variance partitions9–12
- Statistical estimation theory12–13
- Statistical inference152
- Statistical parametric mapping, and medical imaging177
- Statistical power fallacies736–739
- lack of retrospective power and null hypothesis737
- nonsignificance and null hypothesis736–737
- statistical power as a single, unified concept736
- summary and recommendations737–739
- Statistical significance15–18
- fallacies732–735
- alternative paradigms735
- alternatives and solutions734–735
- p-values and strength of effect733
- p-values reflect replicabililty734
- relationship between significant findings and study success734
- significance of p-value and research hypothesis733
- statistical significance and practical importance733–734
- p calculated values8
- recommendations for best practice23
- vs. practical significance9
- Strobl, Carolin678–700
- Structural equation modeling193–195
- common factors and latent variables
- benefits and limitations of including common factors315
- common factors with cross-sectional observations315–316
- common factors with longitudinal observations316–317
- common factors with multiple longitudinal observations317–319
- the future of319–321
- and longtitudinal data analysis397–406
- Structural equation models295–337
- appendix: notes and computer programs321–334
- example of structural equation model fitting322–334
- fitting simulated MIMIC data with R Code332–334
- fitting simulated MIMIC data with SEM-CALIS325–329
- fitting simulated MIMIC data with SEM-Mplus329–332
- fitting simulated MIMIC data with standard modeling software323–325
- reconsidering simple linear regression321–322
- common factors and latent variables302–311
- benefits and limitations of including common factors315
- common factor models303–304
- common factor models within latent path regression305
- common factors with cross-sectional observations315–316
- common factors with longitudinal observations316–317
- common factors with multiple longitudinal observations317–319
- invariant common factors305–307
- multiple repeated measures307–311
- concept of298–302
- issues with means and covariances302
- missing predictors300
- path analysis diagrams299–300
- true feedback loops302
- unreliability of both predictors and outcomes301–302
- unreliable outcomes301
- unreliable predictors300–301
- confirmatory factor analysis296–297
- current state of research298
- currently available SEM programs311
- definition of295–296
- the future of319–321
- linear structural equation model (LISREL)297
- with product indicators375–377
- T-test, introduction of8
- Taxometrics612–634
- conclusion630
- other important considerations627–630
- number of indicators628
- other approaches629–630
- replication628–629
- sample size628
- skew628
- performing a taxometric analysis617–627
- assessing fit623–626
- interpreting results626–627
- selecting suitable indicators617–620
- winnowing indicators620–623
- problems with imprecise measures613–614
- Testing for significant association52–58
- Thompson, Bruce7–25
- Time series analysis458–485
- commonly used terms, notations, and equations483–484
- concluding remarks and future directions482–484
- fundamental concepts459–461
- autocorrelation459
- estimating mean, variance, and autocorrelation460
- moving average and autoregressive representations460–461
- partial autocorrelation459–460
- strictly and weakly stationary processes459
- white noise and Gaussian processes460
- intervention and outlier analysis471–473
- regression time series models475–478
- regression with autocorrelated errors476
- regression with heteroscedasticity477–478
- time series model building464–469
- diagnostic checking466
- illustrative example of467–469
- model identification464–465
- model selection466–467
- parameter estimation466
- transfer function models473–475
- Tomazic, Terry J.106–141
- Traditional statistical approaches, overview of7–25
- Transfer function models473–475
- Trees
- Truncated zeros44
- Twin model, classical202–215
- assumptions of the model205–208
- degrees of genetic similarity206
- equal environments206–207
- generalizability205
- genotype-environment correlation207–208
- genotype-environment interaction207
- random mating205–206
- extensions to the model
- data from additional family members210–211
- liability threshold model209–210
- sex limitation208–209
- multivariate modeling
- causal model214
- common pathway model211–213
- cross-sectional cohort and longitudinal designs213–214
- independent pathway model213
- latent class analysis214–215
- structural equation modeling203–205 See also Genetics, twin studies
- Two-group configural frequency analysis91–93
- Two-part models44–46
- Variables
- Variance, estimation of460
- Vector time series models478–482
- Verweij, Karin J. H.198–218
- Von Eye, Alexander74–105
- Von Weber, Stefan74–105
- Walls, Theodore A.432–440
- Wang, Lihshing Leigh718–758
- Watts, Amber S.718–758
- Wei, William W. S.458–485
- Wen, Zhonglin361–386
- West, Stephen G.26–51
- What if There Were No Significance Tests (Mulaik, Raju, Harshman)8
- White noise process460
- Wilcoxon Mann Whitney test109–110
- Wilcoxon signed rank test110–113
- Willoughby, Lisa M.106–141
- Woods, Carol M.52–73
- Wu, Wei387–410
- Zero-inflated regression models45–46
- Zimmerman, Chad4
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