Subject Index (Work in Progress)

This index is designed assuming you will search for the word(s) of interest to you using the Ctrl-F search option. Click on a link to go to the chapter text on the topic. Click the ‘back’ left pointing arrow icon on your browser to return to this page. More generally, to open the hyperlink in the current window, click on it. To open it in a new background tab, press Ctrl+click (for Macs, Cmd+click). To bring it to immediate focus in a new tab when you activate it, press Ctrl+shift+click (or Cmd+Shift+click for Macs) on the link. If you are not on the internet, go to the listed chapter and search on the index term of your interest. Terms are being added as I complete each chapter, in turn.

A

Adaptive design - Chapter 4

Adaptive randomization - Chapter 4

Adjustable constants - Chapter 6

Atheoretical partialling - Chapter 2

Average marginal effects - Chapter 5

Average marginal effects for binary outcomes - Chapter 12

Average marginal effects: Calculation in Mplus - Chapter 12

Average marginal effects for GAM - Chapter 15

Average marginal effects for quadratic models - Chapter 15

B

Baseline assessments - Chapter 3

Bayes SEM - Chapter 8

Bayes SEM: Credible interval - Chapter 8

Bayes SEM: Deviance Information Criterion (DIC) - Chapter 8

Bayes SEM: Diffuse prior - Chapter 8

Bayes SEM: Equal tail interval - Chapter 8

Bayes SEM: Highest posterior density interval (HPD) - Chapter 8

Bayes SEM: Hyperparameters - Chapter 8

Bayes SEM: Informative prior - Chapter 8

Bayes SEM: Kolmogorov-Smirnov (KS) test of convergence - Chapter 8

Bayes SEM: Markov Chain Monte Carlo (MCMC) - Chapter 8

Bayes SEM: Model fit indices in BSEM - Chapter 8

Bayes SEM: Parameter estimates - Chapter 8

Bayes SEM: Posterior predictive p-value - Chapter 8

Bayes SEM: Posterior probability distribution - Chapter 8

Bayes SEM: Potential scale reduction (PSR) - Chapter 8

Bayes SEM: Prior probability distribution - Chapter 8

Bayes SEM: Uninformative prior - Chapter 8

Berkson’s paradox - Chapter 2

Binary outcome analysis: Broader perspectives - Chapter 12

Binary outcome analysis: Covariates in RETs - Chapter 12

Binary regression: The basics - Chapter 5

Binary regression and the generalized linear model - Chapter 5

Binary regression and the modeling of conditional probabilities - Chapter 5

Binary regression: Interpreting coefficients - Chapter 5

Binary regression: The latent response representation of logistic regression - Chapter 5

Binary regression: The log binomial model - Chapter 5

Binary regression: Logistic - Chapter 5

Binary regression: Probit - Chapter 5

Binary regression: Modified linear probability model basics - Chapter 5

Binary regression: Modified linear probability model: Additional perspectives - Chapter 5

Binary regression: Sequential least squares (SLS) - Chapter 5

Bonferroni method - Chapter 6

Bootstrapping - Chapter 5

C

Causality: The nature of - Chapter 2

Centering variables - Chapter 6

Confidence intervals: Controversies - Chapter 6

Change scores: Within-condition analysis - Chapter 4

Clinical trial phases - Chapter 4

Cluster randomized trials: Bias-reduced linearization - Chapter 25

Cluster randomized trials: Cluster populations - Chapter 25

Cluster randomized trials: Clusters as a nuisance or as theoretically meaningful - Chapter 25

Cluster randomized trials: Clustering as a nuisance analyses using Mplus - Chapter 25

Cluster randomized trials: Design effects - Chapter 25

Cluster randomized trials: Generalized estimation equations - Chapter 25

Cluster randomized trials: The intraclass correlation coefficient - Chapter 25

Cluster randomized trials: Methodological issues - Chapter 25

Collider variables - Chapter 2

Composites: Breadth versus depth of coverage - Chapter 3

Composites: Higher order models - Chapter 3

Composites: Reflective and formative measurement - Chapter 3

Concept-measurement mapping - Chapter 3

Conceptual logic models - Chapter 1

Conditional means - Chapter 5

Confidence interval: Realized - Chapter 7

Confidence intervals: Simultaneous - Chapter 6

Confounders: Common determinants - Chapter 2

Confounders: Correlated but not a common cause - Chapter 2

Confounders: Guidelines for confounder control - Chapter 2

Confounders: Identification of - Chapter 1 Chapter 2

Confounders: Proximal and distal - Chapter 2

CONSORT checklist - Chapter 4

Counterfactual - Chapter 8

Count/discrete regression: The basics - Chapter 5

Count/discrete regression: Hurdle models - Chapter 5

Count/discrete regression: Models with offsets Chapter 5

Count/discrete regression: Interpreting coefficients - Chapter 5

Count/discrete regression: The negative binomial distribution - Chapter 5

Count/discrete regression: The Poisson distribution - Chapter 5

Count/discrete regression: Zero inflated regression models - Chapter 5

Count/discrete regression: Zero truncated models - Chapter 5

Covariate control that creates bias - Chapter 2

Covariate control that reduces bias - Chapter 2

Covariate control: Frugal - Chapter 2

Credible intervals - Chapter 6

Cronbach’s alpha - Chapter 3

Cross-over design - Chapter 4

D

Demand characteristic - Chapter 4

Dichotomization of measures - Chapter 3

Discriminant validity - Chapter 3

Directed acyclic graphs (DAGs) - Chapter 2

Discrete outcome regression models - Chapter 14

Dismantling designs - Chapter 1

Disturbance variables - Chapter 1

Double-blind study - Chapter 4

Dummy variable - Chapter 5

E

Effect size: Latitude of effect ambiguity - Chapter 2 Chapter 10

Effect size: Latitude of meaningfulness - Chapter 2 Chapter 10

Effect size: Latitude of no effect - Chapter 2 Chapter 10

Effect sizes in RETs - Chapter 10

Effect size index: Cohen’s d - Chapter 10

Effect size index: Common language effect size - Chapter 10

Effect size index: Exceptions to the rule - Chapter 10

Effect size index: Hedge’s g - Chapter 10

Effect size index: Number needed to treat - Chapter 10

Effect size index: Odds ratios - Chapter 10

Effect size index: Omega squared - Chapter 10

Effect size index: p Values - Chapter 10

Effect size index: Partially standardized coefficients - Chapter 10

Effect size index: Probability of exceptions - Chapter 10

Effect size index: Probability of superiority - Chapter 10

Effect size index: Relative risks - Chapter 10

Effect size index: Risk differences - Chapter 10

Effect size index: Squared semi-part correlation - Chapter 10

Effect size index: Standardized path coefficient - Chapter 10

Effect size index: Unstandardized regression coefficients - Chapter 10

Effect size index: Variances of disturbance terms - Chapter 10

Effect size indices for omnibus mediation effects - Chapter 10

Effect size standards - Chapter 10

Effect size standards: Anchor-based methods - Chapter 10

Effect size standards: Clinically meaningful change - Chapter 10

Effect size standards: Delphi method - Chapter 10

Effect size standards: Distribution-based approaches - Chapter 10

Effect size standards: Benchmarks - Chapter 10

Effect size standards: Expert-based approaches - Chapter 10

Effectiveness trial - Chapter 4 Chapter 27

Efficacy trial - Chapter 4 Chapter 27

Endogenous variable - Chapter 2

Endogeneity - Chapter 6

Estimands - Chapter 27

Exogenous variable - Chapter 2

F

Factorial RETs - Chapter 1

Familywise errors - Chapter 6

Familywise errors: A tentative approach - Chapter 6

Familywise errors: Bonferroni method - Chapter 6

Familywise errors: False discovery rate - Chapter 6

Familywise errors - Chapter 6

Familywise error: Holm modified Bonferroni method - Chapter 6

Finite sample break down point - Chapter 5

G

Graph theory - Chapter 2

H

History effects - Chapter 4

Huber-White robust estimator - Chapter 5

Hybrid designs - Chapter 4
I

Identity matrix - Chapter 7

Implementation trials - Chapter 27

Independence model - Chapter 7

Influence diagrams as a conceptual tool for RETs - Chapter 2

Influence diagrams - Chapter 2

Influence diagrams with many variables - Chapter 2

Instantaneous change - Chapter 6

Instrument change - Chapter 4

Intent to treat analysis - Chapter 27

Interchangeable indicators - Chapter 2

Intent-to-treat (ITT) analysis - Chapter 4

J

Just-identified models - Chapter 7

K

Kitchen sink approach - Chapter 1

L

Latent variables in SEM - Chapter 7

Leverage - Chapter 6

Leverage: Minimum volume ellipsoid - Chapter 6

Limited information estimation: Bollen’s SEM approach - Chapter 8

Limited information estimation: Independence relations - Chapter 8

Limited information structural equation modeling - Chapter 8

Limited information estimation: Comparison of limited and full information SEM - Chapter 8

Limited information estimation: C statistic - Chapter 8

Limited information estimation: D-separation - Chapter 8

Linear regression: The basics - Chapter 5

Linear regression with nominal predictors - Chapter 5

Linear regression with quantitative predictors - Chapter 5

Linear regression: Population assumptions - Chapter 5

Linear regression: Random versus fixed predictor regression - Chapter 5

Logistic regression properties: Inflection point - Chapter 12

Logistic regression properties: Non-collapsibility - Chapter 12

Logistic regression properties: Odds ratios versus probabilities - Chapter 12

Logistic regression properties: Treatment probability differences and covariates - Chapter 12

Longitudinal modeling: RETs with follow-ups - Chapter 16

Longitudinal modeling: Optimal time lag definitions - Chapter 16

Longitudinal modeling: Time-specific direct and indirect effects - Chapter 16

Longitudinal modeling: Shortintudinal research - Chapter 16

Longitudinal modeling: Facets of causal inference - Chapter 16

Longitudinal modeling: Loops and feedback dynamics - Chapter 16

Longitudinal modeling: Perpetual cycling assumptions - Chapter 16

Longitudinal modeling: Exogeneity restriction in instruments - Chapter 16

Longitudinal modeling: Instrumental variable analysis limitations - Chapter 16

Longitudinal modeling: System explosion diagnostic - Chapter 16

Longitudinal modeling: Inertia and carry-over effects - Chapter 16

Longitudinal modeling: Lagged dependent variable controversy - Chapter 16

Longitudinal modeling: Autoregressive distributed lag (ADL) modeling - Chapter 16

Longitudinal modeling: Lagged sign reversals - Chapter 16

Longitudinal modeling: Partial measurement invariance over time - Chapter 16

Longitudinal modeling: Between-person and within-person analytical frameworks - Chapter 16

Longitudinal modeling: Varying/random coefficients - Chapter 16

Longitudinal modeling: Interventional effects - Chapter 16

Longitudinal modeling: Multi-group approach versus dummy variable approach - Chapter 16

Longitudinal modeling: Time-to-event outcomes and survival analysis - Chapter 16

Longitudinal modeling: Survival functions and hazard rates - Chapter 16

Longitudinal modeling: Accelerated failure time (AFT) - Chapter 16

Longitudinal modeling: Dynamic structural equation modeling - Chapter 16

Longitudinal modeling: Scale-location modeling - Chapter 16

Longitudinal modeling: Stepped-care designs - Chapter 16

Longitudinal modeling: Stationarity, stability, and equilibrium - Chapter 16

Longitudinal modeling: Instrumenal variable validation criteria - Chapter 16

Longitudinal modeling: Intrumental variable test for weak instruments - Chapter 16

Longitudinal modeling: Sargan specification tests - Chapter 16

Longitudinal modeling: Heterogenous banded constraints - Chapter 16

Longitudinal modeling: Proportional hazard assumption - Chapter 16

Longitudinal modeling: Schoenfeld residuals diagnostics - Chapter 16

Longitudinal modeling: Partial autocorrelation function checks - Chapter 16

Longitudinal modeling: Contemporaneous and longitudinal causal modeling - Chapter 16

Longitudinal modeling: Instrumental Variable Analysis - Chapter 16

Longitudinal modeling: Conditional Instrumental Variables (CIVs) - Chapter 16

Longitudinal modeling: Autocorrelation and Autoregression - Chapter 16

Longitudinal modeling: Simplex Model Structure - Chapter 16

Longitudinal modeling: Linear Fixed Effects Panel Models - Chapter 16

Longitudinal modeling: Random Effects Panel Models - Chapter 16

Longitudinal modeling: Within-Between Generalized Linear Models - Chapter 16

Longitudinal modeling: General Estimating Equations (GEE) - Chapter 16

Longitudinal modeling: Latent Growth Curve (LGC) Modeling - Chapter 16

Longitudinal modeling: Discrete-time survival analysis - Chapter 16

Longitudinal modeling: Continuous-time survival analysis (Cox Regression) - Chapter 16

Longitudinal modeling: Intensive longitudinal modeling (DSEM) - Chapter 16

Longitudinal modeling: Latent change score models (LCSM) - Chapter 16

Longitudinal modeling: Linear mixed models - Chapter 16

Longitudinal modeling: Restricted mean survival time (RMST) - Chapter 16

Longitudinal modeling: Kaplan-Meier survival function - Chapter 16

Longitudinal modeling: Frailty in survival analysis - Chapter 16

Longitudinal modeling: Lüdtke's bias corrections - Chapter 16

Longitudinal modeling: Detrended path coefficients - Chapter 16

Longitudinal modeling: Response-conditional adaptive structures - Chapter 16

Longitudinal modeling: Mee and Chua regression to the mean test - Chapter 16

Longitudinal modeling: SMART designs - Chapter 16

M

Margins of error - Chapter 6

Maturation - Chapter 4

Maximum likelihood estimation - Chapter 7

Meta-populations - Chapter 4

Measurement: Aggregation bias - Chapter 3

Measurement error - Chapter 3

Measurement: The facets of measurement - Chapter 3

Measurement fundamentals for RETs - Chapter 3

Measurement-intervention correspondence - Chapter 3

Measurement invariance - Chapter 3

Measurement metrics - Chapter 3

Measurement models - Chapter 3

Measurement and latent variables - Chapter 3

Measurement: Naming fallacy - Chapter 3

Measurement pilot studies - Chapter 3

Measurement: Random error - Chapter 3

Measurement: Systematic error - Chapter 3

Measurement: Unidimensionality and minor factors - Chapter 3

Measurement: Uniform versus differential weighting - Chapter 3

Mediated moderation - Chapter 1

Mediation analysis in RETs vs traditional mediation analysis - Chapter 1

Mediation analysis: Core assumptions - Chapter 9

Mediation analysis with count outcomes - Chapter 14

Mediation analysis with count outcomes: Definition of count outcomes - Chapter 14

Mediation analysis with count outcomes: The quotient rule - Chapter 14

Mediation analysis with count outcomes: Incident rate ratios - Chapter 14

Mediation analysis with count outcomes: Measurement model challenges with mixed indicators - Chapter 14

Mediation analysis with count outcomes: Overdispersion and underdispersion - Chapter 14

Mediation analysis with count outcomes: Observed versus predicted frequency distributions - Chapter 14

Mediation analysis with count outcomes: McFadden pseudo squared R - Chapter 14

Mediation analysis with count outcomes: To-the-right jittering - Chapter 14

Mediation analysis with count outcomes: The Poisson Distribution - Chapter 14

Mediation analysis with count outcomes: The Negative Binomial Distribution - Chapter 14

Mediation analysis with count outcomes: Models with Offsets - Chapter 14

Mediation analysis with count outcomes: Fractional logit/probit modeling - Chapter 14

Mediation analysis with count outcomes: Zero-Truncated Models - Chapter 14

Mediation analysis with count outcomes: Zero-Inflated Poisson Model - Chapter 14

Mediation analysis with count outcomes: Zero-Inflated Negative Binomial Regression - Chapter 14

Mediation analysis with count outcomes: Hurdle Models - Chapter 14

Mediation analysis with count outcomes: Quasi-likelihood Poisson regression - Chapter 14

Mediation analysis with count outcomes: Random intercept Poisson model - Chapter 14

Mediation analysis with count outcomes: Quantile Regression Perspectives on counts - Chapter 14

Mediation analysis with count outcomes: Latent Variables with Count Indicators - Chapter 14

Mediation analysis with ordinal outcomes: Alternative parameterizations - Chapter 13

Mediation analysis with ordinal outcomes: Appropriateness of logit/probit modeling - Chapter 13

Mediation analysis with ordinal outcomes: Average marginal effects - Chapter 13

Mediation analysis with ordinal outcomes: Leverages - Chapter 13

Mediation analysis with ordinal outcomes: Nominal mediators and latent variables - Chapter 13

Mediation analysis with ordinal outcomes: Ordinal mediators and latent variables - Chapter 13

Mediation analysis with ordinal outcomes: The outcome distribution - Chapter 13

Mediation analysis with ordinal outcomes: Parallel coefficient assumption - Chapter 13

Mediation analysis with ordinal outcomes: Preliminary analyses - Chapter 13

Mediation analysis with ordinal outcomes: Cumulative link ordinal regression model - Chapter 13

Mediation example: Binary outcome - Chapter 12

Mediation example: Binary outcome: Bayesian modeling - Chapter 12

Mediation example: Binary outcome: The modified linear probability model - Chapter 12

Mediation example: Binary outcome: The probit model - Chapter 12

Mediation example: Binary outcome using LISEM: The modified linear probability model - Chapter 12

Mediation example: Binary outcome using LISEM: The probit model - Chapter 12

Mediation example: Continuous outcomes - Chapter 11

Mediation example: Continuous outcome with Bayesian SEM - Chapter 11

Mediation example: Continuous outcome and correlated disturbances - Chapter 11

Mediation example: Continuous outcome with full information SEM analysis - Chapter 11

Mediation example: Continuous outcome with LISEM: Bollen’s MIIV-SEM - Chapter 11

Mediation example: Continuous outcome with LISEM: Ordinary Least Squares Regression - Chapter 11

Mediation example: Continuous outcome with LISEM: Quantile Regression - Chapter 11

Mediation example: Continuous outcome with LISEM: Robust Regression - Chapter 11

Mediation example: Continuous outcome and meaningfulness standard for the program total effect - Chapter 11

Mediation example: Continuous outcome and meaningfulness standards for mediator effects - Chapter 11

Mediation example: Continuous outcome and meaningfulness standards for program effects on mediators - Chapter 11

Mediation example: Continuous outcome and Pearl’s causal mediation analysis - Chapter 11

Mediation example: Continuous outcome with profile analysis - Chapter 11

Mediation example: Continuous outcome with sensitivity analyses - Chapter 11

Mediation example: Nominal outcomes - Chapter 13

Mediation example: Ordinal outcomes - Chapter 13

Mediation example: Ordinal outcomes using the latent response approach - Chapter 13

Mediation example: Ordinal outcomes using the probability approach - Chapter 13

Mediation methods: Baron and Kenny method - Chapter 9

Mediation methods: Coefficient product method - Chapter 9

Mediation methods: Causal mediation analysis - Chapter 9

Mediation methods: Hayes conditional process analysis - Chapter 9

Mediation methods: Joint significance test - Chapter 9

Mediation methods: MacArthur network model - Chapter 9

Mediation methods: Structural equation modeling - Chapter 9

Mediation types: Serial, sequential parallel, and consequential mediation - Chapter 9

Mediator mapping - Chapter 1 Chapter 2

Mediator reduction: When the number of mediators is large: data reduction - Chapter 17

Mediator reduction: Relative importance of omnibus mediation effects - Chapter 17

Mediator reduction and relative importance: All possible regressions - Chapter 17

Mediator reduction and relative importance: Best subset analysis - Chapter 17

Mediator reduction and relative importance: Choosing mediators based on the M→Y link - Chapter 17

Mediator reduction and relative importance: Choosing mediators based on the omnibus T→M→Y link - Chapter 17

Mediator reduction and relative importance: Correlational indices - Chapter 17

Mediator reduction and relative importance: Dominance analysis - Chapter 17

Mediator reduction and relative importance: Factor analysis and principal components analysis - Chapter 17

Mediator reduction and relative importance: Generalized additive models - Chapter 17

Mediator reduction and relative importance: Indices of treatment impact on the mediators - Chapter 17

Mediator reduction and relative importance: Lasso regression - Chapter 17

Mediator reduction and relative importance: Omnibus indirect effects using raw metrics - Chapter 17

Mediator reduction and relative importance: Omnibus indirect effects indexed by percents - Chapter 17

Mediator reduction and relative importance: Squared zero order correlations - Chapter 17

Mediator reduction and relative importance: Standardized regression coefficients and part correlations - Chapter 17

Mediator reduction and relative importance: Stepwise regression - Chapter 17

Mediator reduction and relative importance: Using p values as indicators of mediator relative importance - Chapter 17

Mediator specificity and mediator abstractness - Chapter 1

Meta-populations - Chapter 4

Missing data: Assessment of bias - Chapter 26

Missing data: Bayesian full information approaches - Chapter 26

Missing data: Bayesian multiple imputation - Chapter 26

Missing data bias is not always bad - Chapter 26

Missing data: Full information maximum likelihood (FIML) - Chapter 26

Missing data: FIML and non-normality - Chapter 26

Missing data: FIML and auxiliary variables - Chapter 26

Missing data: Hot deck imputation - Chapter 26

Missing data: Little’s MCAR test - Chapter 26

Missing data: Maximum likelihood approaches - Chapter 26

Missing data mechanisms - Chapter 26

Missing data: Missing at random - Chapter 26

Missing data: Missing at random is a matter of degree - Chapter 26

Missing data: Missing completely at random - Chapter 26

Missing data: Missing not at random - Chapter 26

Missing data: Modern strategies for dealing with - Chapter 26

Missing data: Multiple imputation approaches - Chapter 26

Missing data: Patterns of missing data - Chapter 26

Missing data: Random recursive partitioning imputation strategy - Chapter 26

Missing data: Single imputation approaches - Chapter 26

Missing data: Traditional approaches - Chapter 26

Mixed method RETs - Chapter 1 Chapter 2

Model comparisons: Approximate fit approach - Chapter 7

Model comparisons: Chi square difference test - Chapter 7

Model comparisons: Equivalent and non-nested models - Chapter 7

Model comparisons: Information theory indices - Chapter 7

Model comparisons: The comparative fit index - Chapter 7

Model comparisons: Comparing nested models - Chapter 7

Model fit: Chi square test of fit - Chapter 7

Model fit: Comparative fit index - Chapter 7

Model fit: Global indices of model fit - Chapter 7

Model fit: Localized fit indices - Chapter 7

Model fit: Modification index - Chapter 7

Model fit: Residual matrix - Chapter 7

Model fit: Root mean square of approximation - Chapter 7

Model fit: Standardized root mean square residual - Chapter 7

Model fit: Tautological predicted and observed covariances - Chapter 7

Model implied instrumental variable SEM - Chapter 8

Model revisions: Backward searching - Chapter 7

Model revisions: Forward searching - Chapter 7

Model revisions: Theory revisions based on data - Chapter 7

Model revisions: Theory trimming - Chapter 7

Model revisions: To make or not make model modifications - Chapter 7

Moderator variables in RETs - Chapter 1 Chapter 18

Moderated mediation - Chapter 1

Moderated moderation - Chapter 18

Moderation graphing - Chapter 18

Moderator contrasts: Continuous moderator and a continuous focal independent variable - Chapter 18

Moderator contrasts: Continuous moderator and a nominal focal independent variable - Chapter 18

Moderator contrasts: Nominal moderator and a continuous focal independent variable - Chapter 18

Moderator contrasts: Nominal moderator and a nominal focal independent variable - Chapter 18

Moderator mapping - Chapter 1

Moderation parameterization - Chapter 18

Moderation symmetry - Chapter 18

Moderation types: Ordinal and disordinal moderation - Chapter 18

Moderation versus interaction - Chapter 18

Moderation versus interaction revisited - Chapter 18

Monte Carlo confidence intervals - Chapter 8

Multilevel models - Chapter 25

Multilevel SEM - Chapter 25

Multilevel SEM: Assumptions - Chapter 25

Multilevel SEM: Between-cluster variance - Chapter 25

Multilevel SEM: Cross-level moderation - Chapter 25

Multilevel SEM: Example 1 - Chapter 25

Multilevel SEM: Example 2 - Chapter 25

Multilevel SEM: Global versus contextual level-2 variables - Chapter 25

Multilevel SEM: Influence diagrams - Chapter 25

Multilevel SEM: Lüdtke’s bias - Chapter 25

Multilevel SEM: Within-cluster variance - Chapter 25

Multilevel SEM: Varying slopes versus non-varying slopes - Chapter 25

Multinomial regression: The basics - Chapter 5

Multinomial conditional probabilities - Chapter 5

N

Non-inferiority trial - Chapter 4

Non-linear mediation analysis: Bayes additive regression trees - Chapter 15

Non-linear mediation analysis: Cluster analysis - Chapter 15

Non-linear mediation analysis: Consensus clustering - Chapter 15

Non-linear mediation analysis: Generalized additive models - Chapter 15

Non-linear mediation analysis: Latent profile/class analysis - Chapter 15

Non-linear mediation analysis: Multiplicative treatment effects and log-log regression - Chapter 15

Non-linear mediation analysis: Quadratic regression - Chapter 15

Non-linear mediation analysis: Recursive partitioning (CART) models - Chapter 15

Non-linear mediation analysis: Smoothers - Chapter 15

Non-linear mediation analysis: Spline regression - Chapter 15

Non-linear mediation analysis: Traditional non-linear regression - Chapter 15

Non-linear mediation analysis: Trimmed K-means cluster analysis in RETs - Chapter 15

Non-linear regression - Chapter 6

Non-linear regression : Cubic regression - Chapter 6

Non-linear regression: Quadratic regression - Chapter 6

Non-linear regression: Spline regression - Chapter 6

O

Observer drift - Chapter 4

Odds ratio - Chapter 5

Opposing mediation - Chapter 9

Ordinal regression: The basics - Chapter 5

Ordinal regression: Local conditional odds - Chapter 5

Ordinal regression: Parallel coefficient assumption - Chapter 5

Outliers - Chapter 6

Outlier masking - Chapter 6

Overfitting - Chapter 6

Over-identified models - Chapter 7

P

Partial residual plot - Chapter 6 Chapter 15

Passive control group - Chapter 4

Path analysis - Chapter 7

Per-protocol analysis - Chapter 4 Chapter 27

Per protocol analysis: Complier average causal effects and instrumental variable analysis - Chapter 27

Per protocol analysis: Direct covariate approach - Chapter 27

Per protocol analysis example - Chapter 27

Per protocol analysis: Inverse probability treatment weighting - Chapter 27

Per protocol analysis: Imbalance - Chapter 27

Per protocol analysis: Stabilized inverse probability treatment weight - Chapter 27

Piecewise SEM - Chapter 8

Populations in randomized trials - Chapter 4

Pragmatic randomized trials - Chapter 1 Chapter 4

Profile analysis - Chapter 6

Profile Analysis: Significance tests - Chapter 6

Profile analysis: Standard errors and confidence intervals - Chapter 6

Q

Quantile regression - Chapter 6

Quantile regression: Conditional - Chapter 6

Quantile regression: Marginal - Chapter 6

Quantile treatment effects - Chapter 6 Chapter 8

Quantile regression: Unconditional - Chapter 6

R

Random Assignment: Block randomization - Chapter 4

Random assignment: Fixed allocation - Chapter 4

Random assignment and imbalance - Chapter 4

Random assignment: Imbalance and sample size - Chapter 4

Random assignment: Stratified - Chapter 4

Randomization strategies - Chapter 4

Randomized explanatory trials: General - Chapter 1

Reference indicator - Chapter 7

Regression to the mean - Chapter 4

RET key facets - Chapter 1

RETs as thought experiments - Chapter 1

Robust regression - Chapter 5 Chapter 6

Robust regression: MM regression - Chapter 6

Robust regression: Trimmed mean regression - Chapter 6

S

Sample size analysis: Factors affecting statistical power other than sample size - Chapter 28

Sample size analysis: Post hoc power analysis - Chapter 28

Sample size decisions: Asymptotic theory - Chapter 28

Sample size decisions: Effect size sensitivity - Chapter 28

Sample size decisions: Factors affecting sampling error - Chapter 28

Sample size and margins of error - Chapter 28

Sample size decisions: The mechanics of power analysis - Chapter 28

Sample size decisions: Power analysis for the chi square difference test - Chapter 28

Sample size decisions: Power analysis for the global chi square test - Chapter 28

Sample size decisions: Power analysis for a logistic coefficient - Chapter 28

Sample size decisions: Power analysis for a regression/path coefficient - Chapter 28

Sample size decisions: Power analysis for group administered interventions - Chapter 28

Sample size decisions: Power analysis for mean differences between independent groups - Chapter 28

Sample size decisions: Power analysis for robust statistics - Chapter 28

Sample size decisions: Power analysis for selected SEM tests - Chapter 28

Sample size and properties of estimators - Chapter 28

Sample size decisions: Reducing model complexity for small sample analysis - Chapter 28

Sample size decisions: Role of pilot studies and past research in power analysis - Chapter 28

Sample size decisions: Sampling distributions and standard errors - Chapter 28

Sample size decisions: Sampling error - Chapter 28

Sample size and statistical power - Chapter 28

Sample size decisions: Specifying target population effect sizes - Chapter 28

Sample size decisions: The strength of the effect in the population relative to population variability - Chapter 28

Sample size simulations: Choosing parameter values - Chapter 28

Sample size simulations: Double checking the parameter values - Chapter 28

Sample size simulations: Exploring sample sizes, effect sizes, and model parameter values - Chapter 28

Sample size simulations: Localized simulations for sample size decisions - Chapter 28

Sample size simulations: Output for global fit indices - Chapter 28

Sample size simulations: Output for model parameters - Chapter 28

Sample size simulations: Post hoc localized simulations - Chapter 28

Saturated model - Chapter 7

Selection effects - Chapter 4

SEM basics - Chapter 7

Sensitivity analyses - Chapter 6

Sequential ignorability - Chapter 1

Small sample full information SEM - Chapter 28

Small sample statistical tests - Chapter 28

Smoothers: B splines - Chapter 6

Smoothers: Bandwidth - Chapter 6

Smoothers: Binning - Chapter 6

Smoothers: Exploratory analyses - Chapter 6

Smoothers: Running interval smoother - Chapter 6

Specification error: Avoiding in RETs - Chapter 7

Structural causal modeling: Average causal effect - Chapter 8

Structural causal modeling: Causal mediation analysis - Chapter 8

Structural causal modeling: Conditional probabilities - Chapter 8

Structural causal modeling: Do operator - Chapter 8

Structural causal modeling: Judea Pearl’s framework - Chapter 8

Structural causal model mediation approach: Cross-world counterfactual - Chapter 9

Structural causal model mediation approach: Controlled direct effect - Chapter 9

Structural causal model mediation approach: Pure natural direct effect (PNDE) - Chapter 9 Chapter 10

Structural causal model mediation approach: Pure natural indirect effect (PNIE) - Chapter 9 Chapter 10

Structural causal model mediation approach: Three types of direct effects - Chapter 9

Structural causal model mediation approach: Total natural direct effect (TNDE) - Chapter 9 Chapter 10

Structural causal model mediation approach: Total natural indirect effect (TNIE) - Chapter 9 Chapter 10

Structural causal model mediation approach: Two types of indirect effects - Chapter 9

T

Temporal dynamics - Chapter 1

Testing effects - Chapter 4

Treatment integrity - Chapter 4

Treatment as usual - Chapter 4

Trial design: Adaptive designs - Chapter 4

Trial design: Clustered - Chapter 4

Trial design: Parallel groups trial - Chapter 4

Trial design: Two-group pretest-posttest - Chapter 4

Trial design: Two-group, posttest only design - Chapter 4

Trial design: Solomon four group design - Chapter 4

Trial design: Wait list and cross-over designs - Chapter 4

Trimmed means - Chapter 6

U

Under-identified models - Chapter 7

W

Weight of the evidence perspective on model fit - Chapter 7