Regression and mediation analysis using Mplus:
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| Format: | Buch |
| Sprache: | English |
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Los Angeles, CA
Muthén & Muthén
[2016]
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XVI, 519 Seiten Diagramme |
| ISBN: | 9780982998311 |
Internformat
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| 100 | 1 | |a Muthén, Bengt O. |e Verfasser |0 (DE-588)1106564189 |4 aut | |
| 245 | 1 | 0 | |a Regression and mediation analysis using Mplus |c Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov |
| 264 | 1 | |a Los Angeles, CA |b Muthén & Muthén |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a XVI, 519 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 700 | 1 | |a Muthén, Linda K. |e Verfasser |0 (DE-588)1106564731 |4 aut | |
| 700 | 1 | |a Asparouhov, Tihomir |e Verfasser |0 (DE-588)110656488X |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028991280&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804176255385337856 |
|---|---|
| adam_text | Contents
Preface xv
1 Linear regression analysis 1
1.1 Linear regression assumptions................................. 1
1.2 Linear regression estimation.................................. 3
1.2.1 Residuals.............................................. 7
1.2.2 Outliers............................................... 7
1.3 Linear regression R-square.................................... 8
1.4 Linear regression standardization............................. 8
1.5 Example: Regression with one covariate..................... 10
1.5.1 Individual residuals and outliers .................... 13
1.5.2 Reporting results .................................... 15
1.6 Multiple covariates.......................................... 17
1.6.1 Linear regression with two continuous covariates ... 17
Interaction between two continuous covariates......... 18
1.6.2 Linear regression with one binary and one continuous
covariate ............................................ 20
Interaction between a binary and a continuous covariate 22
1.7 Example: Regression with two covariates...................... 23
1.7.1 Reporting results..................................... 25
1.8 Example: Regression with an interaction..................... 27
1.8.1 Reporting results..................................... 29
Presenting parameter estimates........................ 30
Presenting results graphically........................ 30
1.9 Special topics............................................... 34
1.9.1 Standardized coefficients greater than one....... 34
1.9.2 Standardized coefficients differing in significance from
unstandardized coefficients .......................... 35
1.9.3 Two-group regression analysis......................... 36
v
VI
CONTENTS
Example: Two-group regression analysis of an inter-
vention study....................................... 37
1.9.4 Bringing covariates into the model................. 39
Missing data on a; ................................ 40
Example: Bringing a covariate into the model for
the intervention example using a two-group
analysis................................... 40
1.9.5 The Bayesian Information Criterion (BIC) and the
Akaike Information Criterion (AIC) .................. 44
1.9.6 Heteroscedasticity modeling.......................... 45
Example: Heteroscedasticity modeling of LSAY math
data......................................... 45
1.9.7 Random coefficient regression........................ 48
Example: Random coefficient regression for LSAY
math data.................................... 50
2 Mediation analysis 57
2.1 A prototypical mediation model............................... 57
2.2 Mediation modeling techniques................................ 59
2.2.1 Estimation........................................... 51
Indirect effect standard errors and confidence intervals 61
2.2.2 Standardization for mediation models................. 63
2.2.3 Model testing.................................* ■ • 65
2.3 Example: Sex discrimination.................................. 66
2.3.1 Inspecting the data and reporting results ....... 68
2.4 Example: Head circumference.................................. 72
2.4.1 Reporting results................................ 75
2.4.2 The saturated model.................................. 80
2.5 Multiple mediators......................................... 82
2.5.1 Example: Parallel mediators for media influence ... 83
2.5.2 Example: Sequential mediators of socioeconomic status 87
2.6 Moderated mediation.......................................... 91
2.6.1 Case 1 (wz): Regression of y on x. m on both
moderated by z................................... 91
2.6.2 Case 2 (mz): Regression of y on m moderated by z . 93
2.6.3 Case 3 (mx): Regression of y on m moderated by x . 95
2.6.4 Combined moderation case............................ 97
2.6.5 Example: Case 1 moderated mediation in an interven-
tion of aggressive behavior in the classroom................ 98
CONTENTS vii
Testing significance of effects at specific moderator
values........................................ 99
Creating a plot with bootstrap confidence intervals for
the effects at a range of moderator values . . 102
Combination of significance of effect at specific mod-
erator values and plot of confidence intervals
using MODEL INDIRECT..........................106
2.6.6 Example: Case 2 moderated mediation for work team
behavior............................................ 108
2.6.7 Example: Case 3 moderated mediation of simulated
data ............................................... Ill
2.6.8 Example: Combined moderated mediation for vSex
discrimination .......................................117
3 Special topics in mediation analysis 121
3.1 Monte Carlo simulation study of mediation....................121
3.1.1 Example: Monte Carlo study of indirect effects .... 123
3.2 Monte Carlo studies of moderation............................130
3.2.1 Example: Moderation of the regression of m on x . . . 130
3.2.2 Example: Moderation of the regression of y on m . . . 136
3.3 Model misspecification..................................... 138
3.3.1 Example: Omitted moderator . ....................... 138
3.3.2 Example: Omitted mediators.....................141
3.3.3 Example: Confounders .................................147
3.4 Instrumental variable estimation....................... . 153
3.4.1 Example: IV estimation with mediator-outcome con-
founding ...............................................155
Bias and coverage of IV estimation of the indirect effect 156
Comparing IV and ML standard errors and power . . 157
IV estimation dependence on the size of the x. rn
correlation ................................ 158
Comparison of IV and maximum-likelihood estima-
tion when the assumptions behind both ap-
proaches are violated . ................. 159
3.5 Sensitivity analysis ...................................... 159
3.5.1 Example: Sensitivity analysis for an experimental
study of sex discrimination in the workplace ...... 162
3.5.2 Example: Sensitivity analysis in a Monte Carlo stud} 165
3.6 Multiple-group mediation analysis ............... 169
CONTENTS
3.6.1 Relating multiple-group parameters to interaction pa-
rameters ..................................................170
3.6.2 Modification indices...................................171
3.6.3 Example: Two-group analysis of moderated media-
tion for sex discrimination................................172
3.7 Measurement errors and latent variables.......................176
3.7.1 Measurement error in an independent variable .... 176
3.7.2 Measurement error in a mediator........................178
3.7.3 Example: Monte Carlo simulation study of measure-
ment error in the mediator ................................179
3.7.4 Known reliability..................................182
3.7.5 Multiple indicators................................182
Reliability of a sum of indicators.....................183
Structural equation modeling with a factor analysis
measurement model............................ 185
4 Causal inference for mediation 187
4.1 Causal assumptions............................................188
4.2 Potential outcomes and counterfactuals .......................189
4.2.1 Example: Hypothetical potential outcome data .... 190
4.3 Basics of counterfactually-defined effects....................191
4.3.1 Example: Hypothetical mediation potential outcomes 193
4.4 Direct and indirect effects...............................196
4.4.1 Direct effects.....................................197
4.4.2 Indirect effects..................................... 198
4.4.3 Total effect decomposition................ ........199
4.4.4 Example: Hypothetical mediation data analysis .... 199
4.5 Causal effect formulas....................................200
4.5.1 Example: Effects in the simple mediation case .... 203
4.5.2 Example: Effects with moderation of Y regressed on
M (case 3).........................................204
4.5.3 Example: Effects in the combined moderation case . . 206
4.5.4 Example: Effects combining case 1 and case 2 moder-
ation ................................................. 207
4.5.5 Multiple mediators................................ . 208
4.6 Summary .................................................... 209
5 Categorical dependent variable 211
5.1 Basic concepts for categorical variables......................211
5.1.1 Binary variables ......................................214
CONTENTS
ix
5.2
5.3
5.4
Binary dependent variable....................................216
5.2.1 Example: OLS, logistic, and probit regression of coal
miner respiratory problems .............................217
5.2.2 Modeling with a logistic regression function............220
5.2.3 Modeling with a probit regression function .............222
5.2.4 Estimation of the logistic and probit regressions . . . 224
5.2.5 Probability curve formulation versus a latent response
variable formulation....................................224
5.2.6 R2 and standardization..................................226
R2 for a binary outcome ................................227
Standardization.........................................227
5.2.7 Example: Logistic and probit regression of British coal
miner data............................................ 228
Logistic regression.....................................228
Probit regression..................................... 232
Computation of estimated probabilities..................232
Comparing logistic and probit regression coefficients . 234
Comparing the logistic and probit regression models
by BIG...................................... 234
5.2.8 Logistic and probit regression with one binary and one
continuous x ...........................................234
5.2.9 Logistic regression and adjusted odds ratios..........235
5.2.10 Example: Adjusted odds ratios for alcohol survey data 237
5.2.11 Example: Adjusted odds ratios for educational achieve-
ment data................................................ 239
Ordinal dependent variable...................................240
5.3.1 Probability curve formulation of ordinal dependent
variable regression...................................240
5.3.2 Latent response variable formulation of ordinal depen-
dent variable regression...................................243
5.3.3 Example: Sample probits for drinking related to age
and income ......................................... 245
5.3.4 Testing the parallel probability curve assumption
behind the ordinal regression model ................ . 245
5.3.5 Example: Ordinal logistic regression of mental impair-
ment ...................................................... 247
Estimated odds ratio................................. 248
Estimated probabilities ............................. 249
Odds ratio with an interaction.......................250
Nominal dependent variable................................ 252
X
CONTENTS
5.4.1 Example: Multinomial logistic regression of antisocial
behavior...........................................253
6 Count dependent variable 259
6.1 Poisson model..............................................259
6.2 Poisson model with a random intercept .....................261
6.3 Zero-inflated Poisson model................................261
6.4 Negative binomial model....................................262
6.5 Zero-inflated negative binomial model......................262
6.6 Two-part (hurdle) model with zero-truncation...............263
6.7 Varying-exposure model.....................................263
6.8 Comparing models..................................... 264
6.9 Example: Count regression of marital affairs...............264
6.9.1 Poisson. Poisson with a random intercept, and nega-
tive binomial models.......................................266
Negative binomial model ............................268
6.9.2 Zero-inflated Poisson and zero-inflated negative bino-
mial models...............................................269
6.9.3 Two-part (hurdle) modeling......................... 272
6.9.4 Conclusion for marital affairs analyses.............276
6.10 Example: Poisson with varying exposure.....................276
7 Censored dependent variable 279
7.1 Basic concepts for a censored variable .....................279
7.2 Censored-normal (tobit) regression........................ . 280
7.3 Censored-inflated regression................................282
7.4 Sample selection (Heckman) regression.......................283
7.4.1 Example: Simulated sample selection data.............285
7.5 Two-part regression ........................................288
7.6 Example: Methods comparison on alcohol data.................290
7.6.1 Analysis results for the four models ................293
Loglikelihood and BIC comparisons of the four models 294
Comparing the results for the censored-normal (tobit)
and censored-inflated models...............295
Comparing the results for the sample selection (Heck-
man) and two-part models.............................296
Comparing the results for the censored-inflated and
two-part models............................297
CONTENTS
xi
Comparing the fit for estimated probabilities and
means for the censored-inflated and two-part
models...............................................300
7.7 Switching regressions........................................302
7.7.1 Example: Monte Carlo simulation of switching regres-
sions .......................................................302
8 Mediation non-continuous variables 307
8.1 Binary outcome, continuous mediator .........................307
8.1.1 A simple hypothetical mediation model...............309
8.1.2 Total, indirect, and direct effects in terms of differ-
ences in probabilities..................................... 310
8.1.3 Causal effect formulas for a continuous M and a
binary Y ........................................... 311
8.1.4 Causal effect formulas applied to a simple mediation
model with a binary outcome and a continuous mediator314
8.1.5 Causal effect formulas defined on the odds ratio scale 315
Odds ratio effects assuming a rare outcome............315
8.1.6 Causal effects with multiple mediators and a binary
outcome...............................................316
8.1.7 Example: Intention to use cigarettes..................316
Probit regression................................... 318
Logistic regression................................. 321
8.1.8 Example: HPV vaccination trial........................323
No intervention-mediator interaction..................324
Intervention-mediator interaction ....................324
Analysis results......................................327
8.2 Count outcome, continuous mediator...........................330
8.2.1 Causal effect formulas for a count outcome............330
8.2.2 Example: Aggressive behavior and school removal . . 332
Estimated count probabilities.........................333
8.3 Two-part outcome, continuous mediator...................... 337
8.3.1 Causal effect formulas for a two-part outcome.......337
8.3.2 Example: Two-part mediation analysis of economic
stress data ...................................... 339
Causal effects for two-part modeling................ 340
Causal effects for regular modeling with log y........343
Causal effects for regular modeling without log y . . . 344
8.4 Binary and ordinal mediator................................ 346
8.4.1 Causal effect formulas for a binary mediator..........346
CONTENTS
xii
8.4.2 Ordinal mediator.....................................348
8.4.3 Latent response variable mediator....................348
8.4.4 Estimation...........................................349
8.4.5 Example: Hypothetical data from the potential out-
come example with a binary mediator and a continu-
ous outcome .........................................350
8.4.6 Example: Ordinal mediator for intention to use cigarettes352
8.4.7 Example: Pearl’s artificial 2x2x2 example............357
8.5 Nominal mediator ...........................................361
8.5.1 Causal effect formulas for a nominal mediator........361
8.5.2 Estimation...........................................362
8.5.3 Example: Hypothetical data with a nominal mediator
and a binary outcome.................................362
8.6 Mediator with measurement error.............................368
8.6.1 Example: A Monte Carlo simulation study for a
mediator measured with error . ......................369
8.6.2 Example: An intervention study of aggressive behav-
ior in the classroom and juvenile court record..............374
9 Bayesian analysis 381
9.1 Prior, likelihood, and posterior............................381
9.1.1 Posterior distribution for the mean of a normal distri-
bution ..............................* *...................383
9.1.2 Types of priors......................................385
9.1.3 Non-normality of parameter distributions.............386
9.2 Markov Chain Monte Carlo (MCMC) ............................387
9.2.1 Example: Bayesian estimation of a mean with missing
data for LSAY math...................................388
Input for Bayesian analysis..........................391
9.2.2 Plots.............................................. 393
Trace plot ........................................ 393
Autocorrelation plot.................................395
Posterior distribution plot........................ 397
9.2.3 Convergence checking.................................399
9.3 Model fit ................................................ 400
9.4 Bayes versus ML intervals ................................. 402
9.5 Example: Mediation model for media influence................404
The Potential Scale Reduction (PSR) convergence
criterion................................. 406
Trace plot....................................... 406
CONTENTS
xiii
Autocorrelation plot.................................406
Inspecting model fit and parameter estimates.........408
9.6 Example: Mediation model for firefighter data.............413
9.6.1 Non-informative priors..............................413
9.6.2 Informative priors..................................413
9.7 Example: Model testing of direct effects...................416
9.8 Example: High school dropout and missing data..............418
9.8.1 Missing data on the mediator (n = 2,213)............418
Bayesian analysis....................................419
Maximum-likelihood analysis..........................422
9.8.2 Missing data on the control variables (n = 2,898) . . . 423
Assuming normality for all covariates ...............424
Acknowledging that some control variables are binary 425
10 Missing data 427
10.1 Example: Missing data information . ......................427
10.2 MGAR, MAR, and NMAR ......................................429
10.2.1 MCAR: Missing completely at random.................430
10.2.2 MAR: Missing at random.............................430
10.2.3 NMAR: Not missing at random.......................431
10.3 MAR, for bivariate normal variables (H case).............431
10.3.1 List wise versus ML................................ . 432
10.3.2 Maximum-likelihood estimation in the bivariate case
with missing on one variable ........................434
10.3.3 The EM algorithm.................................. 436
10.3.4 Multiple imputation............................... 438
10.3.5 Example: Estimating sample statistics for interven-
tion data................................................. 440
10.4 MAR for regression (Hq case)............................ 443
10.4.1 Missing data and selection on x or y................443
10.4.2 Regression analysis with missing data............. 445
10.4.3 Technical aspects of ML assuming MAR............... . 448
10.4.4 Example: MAR simulated data analysis ........ 449
10.4.5 Missing data correlates......................... 456
Example: Simulation study with missing data corre-
late of missing on y . .............................456
10.5 NMAR ......................................................464
10.5.1 Example: Simulated NMAR data with missing influ-
enced by the latent outcome........................ 465
XIV
CONTENTS
10.5.2 Example: Selection modeling versus ML assuming
MAR when MAR holds..................................470
10.6 Example: Comparing missing data methods...................472
10.7 Missing data on covariates................................476
10.7.1 Example: Simulation study of missing on binary
covariates..........................................476
Inputs for generating and analyzing data assuming MAR476
Inputs for generating and analyzing data under NMAR480
Simulation results..................................480
Appendices 489
A Covariance algebra 491
A.l Definition of an expectation...............................491
A.1.1 Rules for an expectation.............................492
A.2 Definition of covariance and variance......................492
A.2.1 Rules for variance................................. 493
A.3 Functions of random variables..............................493
A.4 Example: Covariance algebra rules applied to linear regression 493
A.5 Example: Derivation of the slope attenuation...............494
A.6 Example: Variable measured with error......................495
References
497
|
| any_adam_object | 1 |
| author | Muthén, Bengt O. Muthén, Linda K. Asparouhov, Tihomir |
| author_GND | (DE-588)1106564189 (DE-588)1106564731 (DE-588)110656488X |
| author_facet | Muthén, Bengt O. Muthén, Linda K. Asparouhov, Tihomir |
| author_role | aut aut aut |
| author_sort | Muthén, Bengt O. |
| author_variant | b o m bo bom l k m lk lkm t a ta |
| building | Verbundindex |
| bvnumber | BV043576552 |
| classification_rvk | MR 2100 MR 2200 QH 234 ST 601 |
| classification_tum | DAT 306f |
| ctrlnum | (OCoLC)953841736 (DE-599)BVBBV043576552 |
| discipline | Informatik Soziologie Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV043576552 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:29:16Z |
| institution | BVB |
| isbn | 9780982998311 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028991280 |
| oclc_num | 953841736 |
| open_access_boolean | |
| owner | DE-739 DE-355 DE-BY-UBR DE-188 DE-19 DE-BY-UBM DE-703 DE-91 DE-BY-TUM DE-N32 DE-29 DE-473 DE-BY-UBG DE-11 DE-20 |
| owner_facet | DE-739 DE-355 DE-BY-UBR DE-188 DE-19 DE-BY-UBM DE-703 DE-91 DE-BY-TUM DE-N32 DE-29 DE-473 DE-BY-UBG DE-11 DE-20 |
| physical | XVI, 519 Seiten Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Muthén & Muthén |
| record_format | marc |
| spelling | Muthén, Bengt O. Verfasser (DE-588)1106564189 aut Regression and mediation analysis using Mplus Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov Los Angeles, CA Muthén & Muthén [2016] © 2016 XVI, 519 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Statistik (DE-588)4056995-0 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Mplus (DE-588)7716737-5 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Mplus (DE-588)7716737-5 s Statistik (DE-588)4056995-0 s DE-604 Muthén, Linda K. Verfasser (DE-588)1106564731 aut Asparouhov, Tihomir Verfasser (DE-588)110656488X aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028991280&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Muthén, Bengt O. Muthén, Linda K. Asparouhov, Tihomir Regression and mediation analysis using Mplus Statistik (DE-588)4056995-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd Mplus (DE-588)7716737-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4129903-6 (DE-588)7716737-5 |
| title | Regression and mediation analysis using Mplus |
| title_auth | Regression and mediation analysis using Mplus |
| title_exact_search | Regression and mediation analysis using Mplus |
| title_full | Regression and mediation analysis using Mplus Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov |
| title_fullStr | Regression and mediation analysis using Mplus Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov |
| title_full_unstemmed | Regression and mediation analysis using Mplus Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov |
| title_short | Regression and mediation analysis using Mplus |
| title_sort | regression and mediation analysis using mplus |
| topic | Statistik (DE-588)4056995-0 gnd Regressionsanalyse (DE-588)4129903-6 gnd Mplus (DE-588)7716737-5 gnd |
| topic_facet | Statistik Regressionsanalyse Mplus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028991280&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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