Newsom 1 Structural Equation Modeling Winter 2015 Path Analysis Example Mplus (output excerpts)

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1 Newsom 1 Path Analysis Example Mplus (output excerpts) Note: I use the bootstrap approach here for testing the indirect effect. The bootstrap approach will generally produce preferable standard errors for the indirect effect test, but we will come back to this technique later. Mplus VERSION 7.3 MUTHEN & MUTHEN 01/14/2015 9:32 PM INPUT INSTRUCTIONS title: Data from social exchanges pretest; data: file=path1.dat; format=free; listwise=on; variable: names = negaff; missing = sex-negaff (-99);! analysis: type=basic; analysis: type=general; estimator=ml; matrix=covariance; bootstrap = 1000;! at least 500 bootstrap samples are recommended; model: hostile on sex; negaff on hostile sex; Model indirect: negaff ind sex; output: stdyx ;!* cinterval can be added to the output line to obtain confidence intervals for indirect effects but I omitted here to save space. cinterval(bcbootstrap) obtains bias-corrected bootstrap intervals for nonsymmetric sampling distribution*! INPUT READING TERMINATED NORMALLY Data from social exchanges pretest; SUMMARY OF ANALYSIS Number of groups 1 Number of observations 271 Number of dependent variables 2 Number of independent variables 1 Number of continuous latent variables 0 Estimator ML THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 7 Loglikelihood H0 Value H1 Value Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) Chi-Square Test of Model Fit Value Degrees of Freedom 0 P-Value

2 Newsom 2 MODEL RESULTS Two-Tailed S.E. Est./S.E. P-Value HOSTILE ON SEX NEGAFF ON HOSTILE SEX Intercepts HOSTILE NEGAFF Residual Variances HOSTILE NEGAFF STANDARDIZED MODEL RESULTS StdYX HOSTILE ON SEX NEGAFF ON HOSTILE SEX Intercepts HOSTILE NEGAFF R-SQUARE Observed Variable HOSTILE NEGAFF TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS Two-Tailed S.E. Est./S.E. P-Value Effects from SEX to NEGAFF Total Total indirect Specific indirect NEGAFF HOSTILE SEX Direct NEGAFF SEX Sample write-up: To investigate whether hostility mediates the relation between gender and negative affect, a path model was tested using Mplus Version 7.3 (Muthen & Muthen, ). Results indicated that, although hostility significantly predicted negative affect (β =.344, SE =.090, β* =.297, p <.001), gender was not significantly related to hostility (β =.006, SE =.077, β* =.005, ns). As would be expected from these results, the indirect effect tested using bootstrapped standard errors was also nonsignficant (β =.002, SE =.028, β* =.001, ns). (Note: The standardized indirect coefficient was deleted from the output). These findings do not support the hypothesized mediational model. Muthén, L.K. and Muthén, B.O. ( ). Mplus User s Guide. Seventh Edition. Los Angeles, CA: Muthén & Muthén

3 Newsom 3 lavaan (output excerpts) Input library(lavaan) # always call lavaan library first # first time use on the computer, install the lavaan package with the following command # install.packages("lavaan", dependencies=true) ## Path Example for Newsom's SEM Class path1 = read.table ("c:/jason/r/lavaan/semclass/path1.dat", header=false) # read the raw data names(path1) = c("sex", "hostile", "negaff") # name the variables (must be in the correct order) path1[path1 == -99] <- NA # tells R that -99 is missing (NA is the missing designation) model = ' negaff ~ c*sex hostile ~ a*sex negaff ~ b*hostile # note that all the model commands are enclosed single quotes #indirect effect ab := a*b #total effect total := c + (a*b) ' fit = sem(model, data = path1, missing = 'listwise',se = 'bootstrap') summary(fit,fit.measures=true, rsquare=true, standardized=true) Output lavaan (0.5-17) converged normally after 14 iterations Used Total Number of observations Estimator ML Minimum Function Test Statistic Degrees of freedom 0 Parameter estimates: Information Observed Standard Errors Bootstrap Number of requested bootstrap draws 1000 Number of successful bootstrap draws 1000 Std.err Z-value P(> z ) Std.lv Std.all Regressions: negaff ~ sex (c) hostile ~ sex (a) negaff ~ hostile (b) Variances: negaff hostile Defined parameters: ab total R-Square: negaff hostile 0.000

4 Newsom 4 Amos (excerpts) Note: this analysis, conducted with Version 22. s (Group number 1 - Default model) Analysis Summary Date and Time Date: Wednesday, January 14, 2015 Time: 10:55:45 PM Title path1_15: Wednesday, January 14, :55 PM Scalar s (Group number 1 - Default model) Maximum Likelihood s Regression Weights: (Group number 1 - Default model) S.E. C.R. P Label hostile <--- sex negaff <--- hostile *** negaff <--- sex Standardized Regression Weights: (Group number 1 - Default model) hostile <--- sex.005 negaff <--- hostile.297 negaff <--- sex Means: (Group number 1 - Default model) S.E. C.R. P Label sex *** Intercepts: (Group number 1 - Default model) S.E. C.R. P Label hostile *** negaff *** Variances: (Group number 1 - Default model) S.E. C.R. P Label sex *** e *** e *** Squared Multiple Correlations: (Group number 1 - Default model) hostile.000 negaff.095

5 Newsom 5 Matrices (Group number 1 - Default model) Total Effects (Group number 1 - Default model) hostile negaff Standardized Total Effects (Group number 1 - Default model) hostile negaff Direct Effects (Group number 1 - Default model) hostile negaff Standardized Direct Effects (Group number 1 - Default model) hostile negaff Indirect Effects (Group number 1 - Default model) hostile negaff Standardized Indirect Effects (Group number 1 - Default model) hostile negaff Iteration Negative Smallest Condition # eigenvalues eigenvalue Diameter F NTries Ratio 0 e e e e e e

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