mplus培训手册 (1).pdf

Latent Variable Modeling Using Mplus: Day 3Bengt Muth´ en improved unrotated starting values and standard errorsBengt Muth´ en SAVE=FS(300); FACTORS=factornames; This command specifies that 300 imputations will be used to estimate the factor scores and that plausible value distributions are available for plottingPosterior mean, median, confidence intervals, standard error, all imputed values, distribution plot for each factor score for each latent variable for any model estimated with the Bayes estimatorBayes factor score advantages: more accurate than ML factor scores in small sample size, Bayes factor score more accurate in secondary analysis such as for example computing correlations between factorBengt Muth´ en MISSING = ALL(-999); !CLUSTER = hosp; GROUPING = hosp (101 102 104 105 201 301-306 308 310-314 316-320 322 401-403 405-409 412-416 501-503 505-512 602-609 612-613 701 801 901-908); ANALYSIS:ESTIMATOR = ML; PROCESSORS = 8; MODEL:lead BY lead21-lead30; ! specifies measurement invariance PLOT:TYPE = PLOT2; OUTPUT:TECH1 TECH8 MODINDICES(ALL);Bengt Muth´ en 1 factor on each level): yrij= νr+λBrηBj+εBrj+λWijηWij+εWrij(4)Alternative expression often used in 2-level IRT:yrij= νr+λrηij+εrij,(5)ηij= ηBj+ηWij,(6)so that λ is the same for between and within.Bengt Muth´ en MISSING = ALL (-999); CLUSTER = hosp; ANALYSIS:TYPE = TWOLEVEL; ESTIMATOR = ML; PROCESSORS = 8; MODEL:%WITHIN% leadw BY lead21-lead30* (lam1-lam10); leadw@1; %BETWEEN% leadb BY lead21-lead30* (lam1-lam10); leadb; OUTPUT:TECH1 TECH8 MODINDICES(ALL);Bengt Muth´ en VARIABLE: NAMES = y1-y4 x1 x2 w clus; WITHIN = x1 x2; BETWEEN = w; CLUSTER = clus; ANALYSIS: TYPE = TWOLEVEL RANDOM; ESTIMATOR = BAYES; PROCESSORS = 2; BITER = (1000); MODEL: %WITHIN% s1-s4 | f BY y1-y4; f@1; f ON x1 x2; %BETWEEN% f ON w; f; ! defaults: s1-s4; [s1-s4]; PLOT: TYPE = PLOT2; OUTPUT: TECH1 TECH8; Bengt Muth´ en f@1; f ON x1 x2; %BETWEEN% fb BY y1-y4; fb ON w; f@0; is the between-level defaultBengt Muth´ en f@1; f ON x1 x2; %BETWEEN% fb BY y1-y4* (lam1-lam4); fb ON w; [s1-s4*1] (lam1-lam4); Bengt Muth´ en rejects the non-zero variance hypothesis 51% of the timeBITER=100000(5000); rejects the non-zero variance hypothesis 95% of the timeBITER=100000(10000); rejects the non-zero variance hypothesis 100% of the timeConclusion: The variance component test needs good number of iterations due to estimation of tail probabilitiesPower: if we generate data with Var(f)=0.05, the power to detectsignificantly non-zero variance component is 50% comparable to ML T-test of 44%Bengt Muth´ en f@1; %BETWEEN% f; y1-y8 (v1-v8); s1-s8 (v9-v16); MODEL PRIORS: v1-v16∼IG(1, 0.005); OUTPUT: TECH1 TECH16;Bengt Muth´ en s1-s2 | f BY y1-y2; f BY y3*1; s4-s8 | f BY y4-y8; %BETWEEN% f; y1-y8; s1-s8;Bengt Muth´ en f@1; %BETWEEN% y1-y8 s1-s8; [s1-s8*1] (p1-p8); fb BY y1-y8*1 (p1-p8); sigma BY s1-s8*1 (p1-p8); fb sigma;Bengt Muth´ en f@1; %BETWEEN% f ON x; f; s1-s21 jittery-scornful; [s1-s21*1] (lambda1-lambda21); sigma BY s1-s21*1 (lambda1-lambda21); sigma ON x; sigma;Bengt Muth´ en The random slope s has variance on level 2 and level 3Type 2: Defined on the level 2 %BETWEEN level2% s | y ON x; The random slope s has variance on level 3 onlyThe dependent variable can be an observed Y or a factor. Thecovariate X should be specified as WITHIN= for type 1 or BETWEEN=(level2) for type 2, i.e., no variation beyond the level it is used atBengt Muth´ en VARIABLE: NAMES = y x w z level2 level3; CLUSTER = level3 level2; WITHIN = x; BETWEEN =(level2) w (level3) z; ANALYSIS: TYPE = THREELEVEL RANDOM; MODEL: %WITHIN% s1 | y ON x; %BETWEEN level2% s2 | y ON w; s12 | s1 ON w; y WITH s1; %BETWEEN level3% y ON z; s1 ON z; s2 ON z; s12 ON z; y WITH s1 s2 s12; s1 WITH s2 s12; s2 WITH s12; OUTPUT: TECH1 TECH8; Bengt Muth´ en VARIABLE:NAMES = hospital ward wardid nurse age gender experience stress wardtype hospsize expcon zage zgender zexperience zstress zwardtyi zhospsize zexpcon cexpcon chospsize; CLUSTER = hospital wardid; WITHIN = age gender experience; BETWEEN = (hospital) hospsize (wardid) expcon wardtype; USEVARIABLES = stress expcon age gender experience wardtype hospsize; CENTERING = GRANDMEAN(expcon hospsize); ANALYSIS:TYPE = THREELEVEL RANDOM; ESTIMATOR = MLR;Bengt Muth´ en %BETWEEN wardid% s | stress ON expcon; stress ON wardtype; %BETWEEN hospital% s stress ON hospsize; s; s WITH stress; OUTPUT:TECH1 TECH8; SAVEDATA:SAVE = FSCORES; FILE = fs.dat; PLOT:TYPE = PLOT2 PLOT3;Bengt Muth´ en VARIABLE: NAMES = u y2 y y3 x w z level2 level3; CATEGORICAL = u; CLUSTER = level3 level2; WITHIN = x; BETWEEN = y2 (level2) w (level3) z y3; ANALYSIS: TYPE = THREELEVEL; ESTIMATOR = BAYES; PROCESSORS = 2; BITERATIONS = (1000); MODEL: %WITHIN% u ON y x; y ON x; %BETWEEN level2% u ON w y y2; y ON w; y2 ON w; y WITH y2; %BETWEEN level3% u ON y y2; y ON z; y2 ON z; y3 ON y y2; y WITH y2; u WITH y3; OUTPUT: TECH1 TECH8; Bengt Muth´ en VARIABLE: NAMES = y1-y6 x1 x2 w z level2 level3; CLUSTER = leve。