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# (p. 759) Index

(p. 759) Index

AAdvanced mixture modeling, 603–606

Aiken, Leona S., 26–51

Alternating least squares scaling (ALSCAL), 238

Alternative models for binary outcomes, 35–36

Anderson, Rawni A., 718–758

Anselin, Luc, 154–174

Approximate discrete model, 427–428

Assumptions, violations of, 28–29

B
Baraldi, Amanda N., 635–664

Bayesian configural frequency analysis, 86–87

Bayesian models for fMRI, 189–191

Beauchaine, Theodore P., 612–634

Binary classification tree, 682

Binary logistic regression, 33–37

Binary variables, 58–59

Binomial test, 108–109

Blokland, Gabriëlla A. M., 198–218

Bootstrap methods, 126–131

Brose, Annette, 441–457

Brown, Timothy A., 257–280

Buskirk, Trent D., 106–141

C
Card, Noel A., 701–717

Case diagnostics, 47–49

Casper, Deborah M., 701–717

Categorical methods, 52–73

categorical variables, 52–61

measuring strength of association between, 58–61

testing for significant association between, 52–58

conclusions and future directions, 71–72

effect sizes, 64–66

key terms, 55

symbols used, 53–55

Categorical variables

measuring strength of association between, 58–61

testing for significant association between, 52–58

Cell frequencies, and configural frequency analysis, 87

Chi-Square test, 119–122

Classical statistical approaches, overview of, 7–25

Classification and regression trees, 683–690

cut-point and variable selection bias, 686–687

instability of trees, 690

interpretation, 688–690

prediction and interpretation, 688

recursive partitioning, 683–684

split selection criteria, 684–686

stopping and pruning, 687

Classification techniques

Cluster analysis and MDS, 239–240

Clustering and classification techniques, 517–550

chapter notation, 522

concluding remarks, 543

finite mixture and latent class models, 530–543

absolute fit assessment, 542

class-specific item response probabilities, 533

instrument calibration

*vs.*respondent scaling, 533investigating relative performance, 540

item-fit assessment, 542–543

item response probabilities for five assessment items, 539

latent classes as attribute profiles, 535

local/conditional independence assumption, 533

model-data fit at different levels, 540–541

for multiple quantitative response variables, 532–533

parameter constraints via the Q-matrix, 535–536

parameter values for five assessment items, 538

person-fit assessment, 543

relative fit assessment, 541

for single quantitative response variables, 531–532

software packages for, 540

statistical structure of unrestricted latent class model, 534–535

unconstrained latent class models, 533–535

foundational terminology, 519–522

exploratory

*vs.*confirmatory techniques, 520–521nonparametric

*vs.*parametric model-based techniques, 520observations

*vs.*variables, 519variable types

*vs.*measurement scales, 519–520glossary of key terms, 544–546

introduction to, 517–518

nonparametric techniques, 522–530

additional example, 530

agglomerative

*vs.*divisive approaches, 522basic concepts, 522

distance measures for multivariate space, 525–526

graphical representation, 523

*K*-means clustering, 527–529

measures of intercluster distance, 526–527

numerical representation, 522–523

partitioning cluster methods, 527–529

pre-processing choices for hierarchical techniques, 523–527

software for, 529–530

range of applications, 518

standardization formulas for cluster analysis, 524

The column problem, 145–146

Configural frequency analysis (CFA), 74–105

appropriate questions for, 75–76

base models, 78–80

future directions, 102

null hypothesis, 80–81

sample models and applications, 89–102

longitudinal CFA, 93–97

mediation configural frequency analysis, 97–102

prediction configural frequency analysis, 89–91

two-group CFA, 91–93

six steps of, 86–89

symbols and definitions, 103

technical elements of, 76–81

Contextual variable fallacies, 720–725

avoiding hierarchically nested data structures, 724–725

confusing moderation with additive effects, 724

direct effect and evidence of mediation, 721–722

mistaking mediation for moderation, 720–721

testing mediation with constituent paths, 721

using cross-sectional models to test mediation, 722–724

Continuous time, models of, 416–426

conclusions and future directions, 426–427

first-order differential equation model, 420–423

second-order differential equation model, 423–426

Control variables, associations with, 61–64

Coombs’ contribution to MDS, 238

Correspondence analysis, 142–153

application to other data types, 151

canonical correspondence analysis, 151–152

correspondence analysis displays, 146–147

introductory example, 143

measure of fit, 150–151

multiple correspondence analysis, 147–150

principal component analysis and multidimensional scaling, 143–147

statistical inference, 152

Coxe, Stefany, 26–51

Curve estimation methods, 131–137

D
Data mining, 678–700

binary classification tree, 682

conclusion, 698

other techniques, literature, and software, 697–698

Deboeck, Pascal R., 411–431

Dellinger, Anne, 4

Density estimation, nonparametric, 131–135

Deviation, concepts of, 87–88

Diagnostics, model and case, 47–49

Differential item functioning, 65–66

Ding, Cody S., 235–256

Discrete-time survival factor mixture model, diagram, 605

Distance measures, 48–49

Donnellan, M. Brent, 665–677

Dowsett, Chantelle, 4

Dynamic causal models, 192

Dynamic factor analysis, 441–457

background, 442–444

five steps for conducting

person-specific models, 448–449

research questions, 446

study design and data collection, 446–447

variable selection and data preprocessing, 447–448

future directions, 451–454

adaptive guidance, 453–454

idiographic filters, 454

non-stationarity, 452–453

glossary, 455

synopsis, 454–455

technical background, 444–445

Dynamical systems, 411–431

E
approximate discrete model, 427–428

attractors and self-regulation, 414–416

concept of, 412–413

conclusions and future directions, 426–427

language of, 413–414

latent differential equation modeling, 428–430

Edgeworth, Francis Y., 8

Edwards, Michael, 4

Effect sizes

and categorical methods, 64–66

introduction of concept, 9

recommendations for best practice, 23–24

Eigenvalues, 21–22

Electroencephalography, and statistical parametric mapping, 177

Enders, Craig K., 635–664

Ensemble methods, of data mining, 690–697

bagging, 690–691

predictions from ensembles, 693–695

random forests, 691–693

randomness, 696–697

variable importance, 695–696

Error covariance matrix, 184–185

Estimation theory, 12–13

Event history data analysis, 486–516

conclusion, 514

continuous state space, 511–514

continuous time formulation, 493–499

basic concepts, 493–494

examples, 497–498

rate and probability, 499

specifications and estimation, 496–497

discrete state space, 509–511

discrete time formulation, 492–493

hazard-rate framework, 492–493

motivation, 488, 490–492

censoring and time-varying covariates, 488

initial statement of the solution, 491–492

observability of the dependent variable, 506–507

problems created for standard techniques, 489

repeated events, 507–509

Excess zeros, concept of, 43–44

Extensions to space-time, 160–162

F
Factor analysis

fallacies, 739–743

default use of orthogonal rotation, 741

misuse of principal components, 739–740

number of factors retained in EFA, 740–741

other issues in factor analysis, 742

summary, 742–743

using CFA analysis to confirm EFA analysis, 741–742

and MDS, 239

Finite mixture modeling, 551–611

advanced mixture modeling, 603–606

conclusion, 606–607

future directions, 607

history of mixture modeling, 554–557

finite mixture modeling, 554–555

latent class analysis, 555–556

the more recent past, 556–557

as latent variable models, 552

list of abbreviations, 607–608

as a person-centered approach, 552–554

Fisher, Ronald A., 8

Fisherian school of statistics, 8

Fisher’s exact test, 119–122

Frequentist configural frequency analysis, 86–87

Friedman’s test, 115–117

Functional magnetic resonance imaging

and statistical parametric mapping, 177

Functional magnetic resonance imaging (fMRI)

Bayesian models for, 189–191

G
Gaussian processes, 460

General linear model (GLM)

overview of, 9

three classes of, 13–20

times series model at the voxel level, 185

Generalized linear models (GLiM), 26–51

diagnostics, 47–49

introduction to, 26–27

maximum likelihood estimation, 30–33

multiple regression, 27–29

pseudo-R-squared measures of fit, 46–47

summary and conclusions, 49–50

three components of a GLiM, 29–30

Genes, quantitative analysis of, 219–234

association analysis, 227–233

case-control association tests, 227–229

family-based association tests, 230–232

genome-wide association studies, 232–233

population stratification, 229

quality control and prior data cleaning, 227

significance of linkage, 226–227

summary, 233

Genetics, twin studies, 198–218

classical twin model, 202–215

assumptions of the model, 205–208

extensions to the model, 208–211

multivariate modeling, 211–215

structural equation modeling, 203–205

introduction and overview, 198–202

twin studies and beyond, 215

Global autocorrelation, 156–158

Global configural frequency analysis, 79

Gossett, William S., 8

Gottschall, Amanda C., 338–360

Greenacre, Michael J., 142–153

Growth mixture model, diagram, 605

H
Harshman, R.A., 8

Hau, Kit-Tai, 361–386

Heteroscedasticity, 28

Hierarchical linear model, 185–186

History of traditional statistics, 8–9

Hox, Joop J., 281–294

Hurdle regression models, 44–45

I
Imaging data, analysis of

analytic models and designs

functional magnetic imaging models, 183–184

positron emission tomography, 182

Bayesian methods of analysis, 186–187

Bayesian models for fMRI, 189–191

classic frequentist probability, 187–189

conclusion and future directions, 195

dynamic causal models, 192

early approaches based on general linear method, 176–177

history of imaging methods and analyses, 176

multivariate autoregressive models, 192–193

parameter estimation, 182

spatial normalization and topological influence, 177–182

statistical parametric mapping, 179–180

steps from image acquisition to analysis, 180

statistical parametric mapping, 177

structural equation modeling, 193–195

Imaging data, analysis of, 175–197

Individual differences MDS models, 238

Influence measures, 49

Intensive longitudinal data

Interaction

*See*Moderation

Interpretation, recommendations for best practice, 23–24

Introduction, 1–6

J
Johnson, David, 4

K
*K*-means clustering, 527–529

Kadlec, Kelly M., 295–337

Kendall’s

*t*, 117–119Kisbu-Sakarya, Yasemin, 338–360

Kruskal-Wallis test, 113–115

Kruskal’s contribution to MDS, 237–238

L
Land use planning models, 170–171

Latent class analysis, 557–584

a brief history of, 555–556

mediation model, 605

missing data, 573–584

model building, 565–573

model estimation, 561–565

model formulation, 557–558

model interpretation, 558–561

Latent differential equation modeling, 428–430

Latent mixture modeling, diagram, 605

Latent profile analysis, 584–606

example of, 592–600

example of latent class regression, 602–603

latent class regression, 600–601

model estimation, 590

model interpretation, 587–590

*post hoc*class comparisons, 603

Latent transition model, diagram, 605

Latent variable interpretation, 35

Latent variable measurement models, 257–280

conclusion, 276–277

confirmatory factor analysis, 260–266

exploratory factor analysis, 258–260

extensions of confirmatory factor analysis, 269–273

future directions, 277–279

higher-order models, 273–276

hybrid latent variable measurement models, 266–269

selected output for confirmatory factor analysis, 263

selected output for exploratory structural equation modeling, 268–269

Lee, Jason and Steve, 4

Leverage measures, 48

Limited dependent variables, 28–29

Linear regression model, 163–165

Linearity, 28–29

Local autocorrelation, 158–159

Location models, 169–170

Longitudinal configural frequency analysis, 93–97

Longitudinal data, intensive, 432–440

challenges and opportunities, 438–439

idiographic-nomothetic continuum, 434–437

reactivity, 436–437

statistical models, 437–438

Longitudinal data analysis, 387–410

advances in modeling, 406–407

conclusion and discussion, 406–407

multilevel modeling approach, 389–397

curvilinear growth curve model, 392–393

error structures, 396

linear growth curve model, 389–392

nonlinear growth curve model, 393–394

spline curve models, 395

time-constant covariates, 396–397

time-varying covariates, 397

structural equation modeling approaches, 397–406

autoregressive cross-lagged models, 401–402

curvilinear latent curve model, 398–399

general assumptions, 405–406

latent difference score models, 403

linear latent curve model, 397–398

nonlinear latent curve model, 399–401

parallel process latent curve model, 403–404

second-order latent curve model, 404–405

Longtitudinal mediation, 351–353

Lucas, Richard E., 665–677

M
MacKinnon, David P., 338–360

Magnetic resonance imaging

and analytic models and designs, 183–184

Bayesian models for, 189–191

and statistical parametric mapping, 177

Mair, Patrick, 74–105

Marsh, Herbert W., 361–386

Masyn, Katherine E., 551–611

McArdle, John J., 295–337

McNemar’s test, 110–113

Mean, estimation of, 460

Measure of fit, 150–151

Measurement error fallacies, 725–730

ignorance of latent mixture and multilevel structure, 728–729

individual items and composite scores, 726–728

the myth about numbers, 725–726

reliability and test length, 728

unreliability and attenuated effects, 729–730

Mediation analysis, 338–360

causal inference in, 348–351

experimental designs, 350–351

principal stratification, 350

sequential ignorability assumption, 348–350

estimating the mediated effect, 340–342

assumptions, 341

coefficients approach, 340–341

covariates, 341

multiple mediators, 341–342

point estimation, 340–342

standard error, 342

history, 339

longtitudinal mediation, 351–353

autoregressive models, 352

latent change score models, 352–353

latent growth curve models, 352

person-centered approaches, 353

three (or more)-wave models, 351–353

two-wave models, 351

mediation analysis in groups, 345–348

moderation and mediation, 346–347

multilevel mediation, 347–348

modern appeal, 339–340

significance testing and confidence interval estimation, 342–345

Bayesian methods, 343

categorical and count outcomes, 343–344

effect size measures, 343

non-normality, 344

small samples, 344–345

summary and future directions, 353–354

Mediation configural frequency analysis, 97–102

decisions concerning type, 98–102

four base models for, 97–98

Medical imaging

Bayesian models for fMRI, 189–191

connectivity of brain regions, 191–192

issues in neuroimaging, 181

and statistical parametric mapping, 177

Meta-analysis, 701–717

advanced topics, 715–716

alternative effect sizes, 715

artifact corrections, 715–716

multivariate meta-analysis, 716

analysis of mean effect sizes, 710–713

fixed-effects means, 711

heterogeneity, 711–712

random-effects means, 712–713

coding effect sizes, 707–710

computing effect sizes, 709–710

correlation coefficient, 708

odds ratios, 709

standardized mean differences, 708–709

coding study characteristics, 707

introduction to, 701–702

moderator analyses, 713–715

categorical moderators, 714–715

limitations to, 715

single categorical moderator, 713–714

single continuous moderator, 714

Metric MDS model, 237

MIMIC data, 323–334

Missing data fallacies, 730–732

attempting to prepare for missing data, 732

missing-data treatments and notion of “cheating,” 730–732

Missing data methods, 635–664

artificial data example, 636–637

atheoretical missing data handling methods, 639–641

averaging available items, 639–640

last observation carried forward imputation, 640

mean imputation, 639

similar response pattern imputation, 640–641

conclusion, 661–662

data analysis examples, 653–657

complete data, 653–654

not missing at random-based approaches revisited, 656–657

not missing at random data, 655–656

improving missing at random-based analyses, 650–653

dealing with non-normal data, 652–653

role of auxiliary variables, 650–651

missing at random (MAR), 642–648

maximum likelihood estimation, 645–648

multiple imputation, 642–645

stochastic regression imputation, 642

missing completely at random (MCAR), 641–642, 654

deletion methods, 641–642

regression imputation, 641

missing data mechanisms, 637–639

not missing at random, 648–650

Model diagnostics, 47

Modeling

*See*individual types of modeling

Models

*See*individual model types

Moderation, 361–386

analysis of variance, 364–365

classic definition of, 362–363

confounding nonlinear and interaction effects, 379–380

distribution-analytic approaches, 377–378

further research, 379

graphs of interaction effects, 363–364

interactions with more than two continuous variables, 381–382

moderated multiple regression approaches, 365–373

disordinal interactions, 371–372

interactions with continuous observed variables, 369–371

multicollinearity involved with product terms, 372–373

power in detecting interactions, 372

standardized solutions for models with interactions terms, 368

tests of statistical significance of interaction effects, 368–369

multiple group SEM approach to interaction, 375

non-latent approaches

for observed variables, 364

traditional approaches to interaction effects, 373–374

separate group multiple regression, 365

summary, 378–379

tests of measurement invariance, 382–383

*vs.*causal ordering, 380–381

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 models, 240–254

computer programs for MDS analysis, 251–252

individual differences models, 246–250

metric model, 242–243

new applications, 252–254

nonmetric model, 243–246

using maximum likelihood estimation, 250–251

variety of data, 240–242

a brief description of MDS(X) programs, 253

and cluster analysis, 239–240

conclusion, 254

future directions, 254–255

and principal component analysis, 143–147

terminology and symbols, 236

Multilevel regression modeling

conclusion, 291

introduction, 281–282

key terms and symbols, 292

Multilevel structural equation modeling

conclusion, 291

introduction, 281–282

key terms and symbols, 292

Multivariate statistics, 20–23

Mun, Eun-Young, 74–105

Murray, Alan T., 154–174

N
Nagengast, Benjamin, 361–386

Neyman, Jerzy, 8

Neyman-Pearson school of statistics, 8

Nonmetric MDS model, 237–238

Nonparametric statistical techniques, 106–141

classical nonparametric methods, 108–122

comparing more than two samples, 113–117

comparing two dependent samples, 110–113

comparing two independent samples, 109–110

methods based on a single sample, 108–109

nonparametric analysis of nominal data, 119–122

nonparametric correlation coefficients, 117–119

curve estimation methods, 131–137

density estimation, 131–135

extensions to multiple regression, 137

simple nonparametric regression, 135–137

future directions, 138–139

glossary of terms, 139–140

modern resampling-based methods, 122–131

applying permutation tests to one sample, 122–

bootstrap confidence interval methods, 129–130

bootstrap methods, 126–129

bootstrap methods and permutation tests, 131

general permutation tests, 122

other applications of bootstrap methods, 130–131

statistical software for conducting, 137

*vs.*parametric methods, 137–138

Null hypothesis

O
in configural frequency analysis (CFA), 80–81

Optimization modeling, 168–171

Ordinal variables, 59–61

Organization of

*Handbook of Quantitative Methods*, 2–4Orthogonal rotation, 741

Overdispersed Poisson regression, 42–43

P
*P*calculated values

introduction of, 8

Parameter estimates and fit statistics, 450

Pearson, Karl and Egon S., 8

Pearson’s computations, 10–11

Petersen, Trond, 486–516

Population stratification, 229

Positron emission tomography

and analytic models and designs, 182–183

and statistical parametric mapping, 177

Preacher, Kris, 4

Prediction configural frequency analysis, 89–91

Preference MDS models, 247

Price, Larry R., 175–197

Principal component analysis, 143–147

Proportional odds model, 69–71

Pseudo-R-squared measures of fit, 46–47

Q
Quantitative research methodology, common fallacies in, 718–758

R
contextual variable fallacies, 720–725

avoiding hierarchically nested data structures, 724–725

confusing moderation with additive effects, 724

direct effect and evidence of mediation, 721–722

mistaking mediation for moderation, 720–721

testing mediation with constituent paths, 721

using cross-sectional models to test mediation, 722–724

factor analysis fallacies, 739–743

default use of orthogonal rotation, 741

misuse of principal components, 739–740

number of factors retained in EFA, 740–741

other issues in factor analysis, 742

summary, 742–743

using CFA analysis to confirm EFA analysis, 741–742

introduction to, 718–720

measurement error fallacies, 725–730

ignorance of latent mixture and multilevel structure, 728–729

individual items and composite scores, 726–728

the myth about numbers, 725–726

reliability and test length, 728

unreliability and attenuated effects, 729–730

(p. 764)
missing data fallacies, 730–732

attempting to prepare for missing data, 732

missing-data treatments and notion of “cheating,” 730–732

statistical power fallacies, 736–739

lack of retrospective power and null hypothesis, 737

nonsignificance and null hypothesis, 736–737

statistical power as a single, unified concept, 736

summary and recommendations, 737–739

statistical significance fallacies, 732–735

alternative paradigms, 735

alternatives and solutions, 734–735

*p*-values and strength of effect, 733

*p*-values reflect replicabililty, 734

relationship between significant findings and study success, 734

significance of

*p*-value and research hypothesis, 733statistical significance and practical importance, 733–734

summary checklist, 743–748

R Code, 332–334

Raju, N.S., 8

Ram, Nilam, 441–457

Regional configural frequency analysis, 79–80

Regression analysis, spatial, 162–168

Regression mixture model, diagram, 605

Regression specification, 163

Regression time series models, 475–478

Rey, Sergio J., 154–174

Rhemtulla, Mijke, 4

The row problem, 145

Rupp, André A., 517–550

S
Scatterplot smoothing, 135–137

Secondary data analysis, 665–677

advantages and disadvantages, 667–668

conclusion, 675

measurement concerns in existing data sets, 671–673

missing data in existing data sets, 673–674

primary research

*vs.*secondary research, 666–667sample weighting in existing data sets, 674–675

steps for beginning, 669–671

Selig, James P., 387–410

SEM-CALIS, 325–329

SEM-Mplus, 329–332

Serial correlation, modeling, 184–185

Sign test, 110–113

Significant association, testing for, 52–58

Significant difference, introduction of term, 8

Snedecor, George W., 8

Space-time, extensions to, 160–162

Spatial analysis, 154–174

autocorrelation analysis, 156–162

conclusion, 171–172

Spatial data, 155–156

Spearman’s

*p*, 117–119Statistical approaches, overview of traditional methods, 7–25

ANOVA computations, 12–13

brief history of traditional statistics, 8–9

variance partitions, 9–12

Statistical estimation theory, 12–13

Statistical inference, 152

Statistical parametric mapping, and medical imaging, 177

Statistical power fallacies, 736–739

lack of retrospective power and null hypothesis, 737

nonsignificance and null hypothesis, 736–737

statistical power as a single, unified concept, 736

summary and recommendations, 737–739

Statistical significance, 15–18

fallacies, 732–735

alternative paradigms, 735

alternatives and solutions, 734–735

*p*-values and strength of effect, 733

*p*-values reflect replicabililty, 734

relationship between significant findings and study success, 734

significance of

*p*-value and research hypothesis, 733statistical significance and practical importance, 733–734

*p*calculated values, 8

recommendations for best practice, 23

*vs.*practical significance, 9

Strobl, Carolin, 678–700

Structural equation modeling, 193–195

common factors and latent variables

benefits and limitations of including common factors, 315

common factors with cross-sectional observations, 315–316

common factors with longitudinal observations, 316–317

common factors with multiple longitudinal observations, 317–319

the future of, 319–321

and longtitudinal data analysis, 397–406

as a tool, 311–315

creating expectations, 312

estimating linear multiple regression, 313–315

as general data analysis technique, 311–312

statistical indicators, 312–313

*See also*Structural equation models

Structural equation models, 295–337

(p. 765)
T
appendix: notes and computer programs, 321–334

example of structural equation model fitting, 322–334

fitting simulated MIMIC data with R Code, 332–334

fitting simulated MIMIC data with SEM-CALIS, 325–329

fitting simulated MIMIC data with SEM-Mplus, 329–332

fitting simulated MIMIC data with standard modeling software, 323–325

reconsidering simple linear regression, 321–322

common factors and latent variables, 302–311

benefits and limitations of including common factors, 315

common factor models, 303–304

common factor models within latent path regression, 305

common factors with cross-sectional observations, 315–316

common factors with longitudinal observations, 316–317

common factors with multiple longitudinal observations, 317–319

invariant common factors, 305–307

multiple repeated measures, 307–311

concept of, 298–302

issues with means and covariances, 302

missing predictors, 300

path analysis diagrams, 299–300

true feedback loops, 302

unreliability of both predictors and outcomes, 301–302

unreliable outcomes, 301

unreliable predictors, 300–301

confirmatory factor analysis, 296–297

current state of research, 298

currently available SEM programs, 311

definition of, 295–296

the future of, 319–321

linear structural equation model (LISREL), 297

with product indicators, 375–377

as a tool, 311–315

creating expectations, 312

estimating linear multiple regression, 313–315

and general data analysis, 311–312

statistical indicators, 312–313

*See also*Structural equation modeling

*T*-test, introduction of, 8

Taxometrics, 612–634

conclusion, 630

other important considerations, 627–630

number of indicators, 628

other approaches, 629–630

replication, 628–629

sample size, 628

skew, 628

performing a taxometric analysis, 617–627

assessing fit, 623–626

interpreting results, 626–627

selecting suitable indicators, 617–620

winnowing indicators, 620–623

problems with imprecise measures, 613–614

Testing for significant association, 52–58

Thompson, Bruce, 7–25

Time series analysis, 458–485

commonly used terms, notations, and equations, 483–484

concluding remarks and future directions, 482–484

fundamental concepts, 459–461

autocorrelation, 459

estimating mean, variance, and autocorrelation, 460

moving average and autoregressive representations, 460–461

partial autocorrelation, 459–460

strictly and weakly stationary processes, 459

white noise and Gaussian processes, 460

intervention and outlier analysis, 471–473

regression time series models, 475–478

regression with autocorrelated errors, 476

regression with heteroscedasticity, 477–478

time series model building, 464–469

diagnostic checking, 466

illustrative example of, 467–469

model identification, 464–465

model selection, 466–467

parameter estimation, 466

transfer function models, 473–475

Tomazic, Terry J., 106–141

Traditional statistical approaches, overview of, 7–25

Transfer function models, 473–475

Trees

Truncated zeros, 44

Twin model, classical, 202–215

assumptions of the model, 205–208

degrees of genetic similarity, 206

equal environments, 206–207

generalizability, 205

genotype-environment correlation, 207–208

genotype-environment interaction, 207

random mating, 205–206

extensions to the model

data from additional family members, 210–211

liability threshold model, 209–210

sex limitation, 208–209

multivariate modeling

causal model, 214

common pathway model, 211–213

cross-sectional cohort and longitudinal designs, 213–214

independent pathway model, 213

latent class analysis, 214–215

structural equation modeling, 203–205

*See also*Genetics, twin studies

Two-group configural frequency analysis, 91–93

Two-part models, 44–46

U
V
Variables

*See*individual variable types

Variance, estimation of, 460

Vector time series models, 478–482

Verweij, Karin J. H., 198–218

Von Eye, Alexander, 74–105

Von Weber, Stefan, 74–105

W
Walls, Theodore A., 432–440

Wang, Lihshing Leigh, 718–758

Watts, Amber S., 718–758

Wei, William W. S., 458–485

Wen, Zhonglin, 361–386

West, Stephen G., 26–51

*What if There Were No Significance Tests*(Mulaik, Raju, Harshman), 8

White noise process, 460

Wilcoxon Mann Whitney test, 109–110

Wilcoxon signed rank test, 110–113

Willoughby, Lisa M., 106–141

Woods, Carol M., 52–73

Wu, Wei, 387–410

Z
Zero-inflated regression models, 45–46

Zimmerman, Chad, 4