Somehow my Computer can not run the model. I don't know why and I tried everything.
Error in `[[<-.data.frame`(`*tmp*`, ov.int.names[iv], value = integer(0)) :
replacement has 0 rows, data has 157
lavaan ERROR: missing observed variables in dataset: H1_total H2_total DV1_mean SL_mean T1_mean T2_mean
Does that mean that instead of listing all the single items in my syntax, I just take the mean of the scale like in the following?
What did you mean by using scale sums?
And using scale sums would mean I work with manifest variables instead, right?
I read about building item parcels, would that also be an option?
I get the lavaan Error:lavaan ERROR: missing observed variables in dataset: H1_total H2_total DV1_mean SL_mean T1_mean T2_mean
So I need to specify my means in my syntax of the sem model, right?But how do I do this while not using all the items as indicators?
I used the function select() and cbind(), but still it can't find the variables. Do you have an idea, why R can not find the variables?
DV1.names <- c("DV02_01","DV02_02","DV02_03","DV02_04","DV02_05","DV02_06",
"DV02_07","DV02_08","DV02_09","DV02_10","DV02_11","DV02_12")
SL1.names <- c("SL01_01","SL01_02","SL01_03","SL01_04","SL01_05","SL01_06","SL01_07","SL01_08","SL01_09","SL01_10",
"SL01_11","SL01_12","SL01_13","SL01_14","SL01_15","SL01_16","SL01_17","SL01_18","SL01_19","SL01_20")
## be cognizant of how you deal with missing scale items
masterarbeit$DV1 <- rowMeans(masterarbeit[ , DV1.names], na.rm = TRUE) # this option assumes MCAR!
masterarbeit$SL1 <- rowMeans(masterarbeit[ , SL1.names], na.rm = FALSE) # this returns NA if any item is NA
Warning messages:
1: In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
2: In lav_test_yuan_bentler(lavobject = NULL, lavsamplestats = lavsamplestats, :
lavaan WARNING: could not invert information matrix needed for robust test statistic
When I use estimator = "ML" I only get the ones with the standard errors.
My Syntax:
SEMModel_mean <- '
H1_total =~ H1_ment_total+H1_phys_total
H1 =~ H1_total
H2_total =~ H2_ment_total+H2_phys_total
H2 =~ H2_total
DV1_mean =~ DV1_teamdistribution_mean+DV1_workplacemobility_mean+DV1_varietyofpractices_mean
DV1 =~ DV1_mean
SL1_mean =~ SL1_taskleadership_mean+SL1_relationleadership_mean+SL1_changeleadership_mean+SL1_micropoliticalleadership_mean
SL1 =~ SL1_mean
T1 =~ T1_mean
T2 =~ T2_mean
## Interaktion von DV1 und SL1
interaction =~ DV1:SL1
## Regressionen
H1 ~ c*DV1 + b*T1
H2 ~ c2*DV1 + b2*T2
T1 ~ a*DV1 + SL1 + interaction
T2 ~ a2*DV1 + SL1 + interaction
indirect :=a*b
indirect2 :=a2*b2
direct := c
direct2 :=c2
total := c + (a*b)
total2 := c2 + (a2*b2)'
Is there anything important missing in my model?
Thank you so so much!
Due to the fact that SL1, DV1 and H1/H1 have subdimensions,
I took their means to describe the total mean.
H1_total =~ H1_ment_total+H1_phys_totalH1 =~ H1_total
## Interaktion von DV1 und SL1
interaction =~ DV1:SL1
indicator1 = intercept1 + loading1*factor + residual1
indicator2 = intercept2 + loading2*factor + residual2
indicator3 = intercept3 + loading3*factor + residual3
ScaleSum = indicator1 + indicator2 + indicator3 # (no residual)
T1 ~ a*DV1 + SL1 + DV1:SL1
T1 ~ DV1 (a) -0.008 0.148 -0.052 0.959 -0.004 -0.004 SL1 1.749 0.269 6.506 0.000 0.868 0.868 DV1:SL1 0.000 149.078 0.000 1.000 0.000 0.000
But when I first define the interaction in my syntax like the following:
masterarbeit$ModVar_nonc <- DV1_mean*SL1_mean
Then I get this output:
T1 ~ DV1 (a) 0.680 0.871 0.780 0.435 0.257 0.257 SL1 2.232 0.789 2.830 0.005 0.843 0.843 ModVr_nnc -0.121 0.151 -0.804 0.421 -0.046 -0.285
So probably second version is the correct one, right? I'm a bit confused.
Is there nessecarity to center the moderator variable like I do in regression?
Thank you so much.
This is the current syntax:
SEMModel_mean <- '
H1 =~ H1_phys_total+H1_ment_total
H2 =~ H2_phys_total+H2_ment_total
DV1 =~ DV1_teamdistribution_mean+DV1_workplacemobility_mean+DV1_varietyofpractices_mean
SL1 =~ SL1_taskleadership_mean+SL1_relationleadership_mean+SL1_changeleadership_mean+SL1_micropoliticalleadership_mean
T1 =~ T102_01+T102_02+T102_03+T102_04+T102_05+T102_06+T102_07+T102_08
T2 =~ T202_01+T202_02+T202_03+T202_04+T202_05+T202_06+T202_07+T202_08
## Regressionen
H1 ~ c*DV1 + b*T1
H2 ~ c2*DV1 + b2*T2
T1 ~ a*DV1 + SL1 + DV1:SL1
T2 ~ a2*DV1 + SL1 + DV1:SL1
indirect :=a*b ## Mediation MZP 1
indirect2 :=a2*b2 ## Mediation MZP 2
direct := c ## Direkter Effekt von DV1 auf Health1
direct2 :=c2 ## Direkter Effekt von DV1 auf Health"
total := c + (a*b) ## Totaler Effekt: Direkter + Indirekter Effekt (MZP1)
total2 := c2 + (a2*b2) ## Totaler Effekt: Direkter + Indirekter Effekt (MZP2)
H1 ~~ H1
H2 ~~ H2
H1 ~~ H2
H1_phys_total ~~ H2_phys_total
H1_ment_total ~~ H2_ment_total
T1 ~~ T1
T2 ~~ T2
T1 ~~ T2
T102_01 ~~ T202_01
T102_02 ~~ T202_02
T102_03 ~~ T202_03
T102_04 ~~ T202_04
T102_05 ~~ T202_05
T102_06 ~~ T202_06
T102_07 ~~ T202_07
T102_08 ~~ T202_08
SL1 ~~ SL1
DV1 ~~ DV1'
fitSEMModel_mean <- sem(SEMModel_mean, data=masterarbeit, estimator='ML', missing = "fiml", meanstructure=TRUE, std.lv=TRUE, orthogonal=TRUE, check.gradient = FALSE)
summary(fitSEMModel_mean, fit.measure=TRUE, standardized=TRUE, rsq=TRUE)
Thank you for being so helpful!
Although you need to think more carefully about your defined parameters. If your mediation path(s) are moderated, you need to define simple/conditional indirect effects to probe the interaction. See related links here:
Also, testing causation within the same timepoint is dubious. You have 2 occasions, so you could specify a more appropriate model for your "half-longitudinal" design. See Slide 13: https://slideplayer.com/slide/4448764/
(2)If you are defining SUBscale composites, which you then use as multiple indicators of a common factor, then you cannot use the colon operator (i.e., DV1:SL1 only works if both variables are observed, not latent). If this is the route you want to take, you would then use indProd() as before, but the observed indicators would be SUBscale composites (i.e., the mean of items within subdimensions). Then you would use the product indicators to define a latent interaction, as shown in the syntax examples on the ?probe2WayMC help page. (Note that unfortunately, probe2WayMC() is not designed to help probe interactions involving indirect effects, only direct effects.)It is always best to start simple and build up from there. So I would recommend you try (1) first, then (2).
In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, :
lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= 4.574149e-15) is close to zero. This may be a symptom that the
model is not identified.
When I leave it out there is no warning.
As T1/T2 is the only construct that doesn't have subdimension, I used the items as indicators. Is this a possible way to deal with it? (see orange marker)
I tried to find a solution for this in the syntax. Is the way I did it now correct to probe the interaction?
Thank you for the advice! I tried to specify it more appropriate and marked my changes yellow. Is that what you meant? Or am I totally wrong?
## mediator model
T2 ~ T1 + a*DV1 + w*SL1 + aw*DV1xSL1
## outcome model
H2 ~ H1 + b*T1 + c*DV1
## if model fit is poor, try adding the effects you hypothesize are zero:
# H2 ~ SL1 + DV1xSL1 # potentially, the c path could also be moderated
# T2 ~ H1 # reciprocal effect is also possible
## cfa() automatically estimates all variances (no need to specify them)
## Time-1 covariances should also be automatically estimated
DV1 ~~ SL1 + DV1xSL1 + T1 + H1
SL1 ~~ DV1xSL1 + T1 + H1
DV1xSL1 ~~ T1 + H1
T1 ~~ H1
## Time-2 endogenous covariance should also be automatically estimated
H2 ~~ T2
## define conditional indirect effects
ind_m2 := (a - 2*aw)*b
ind_m1 := (a - aw)*b
ind0 := a*b
ind_p1 := (a + aw)*b
ind_p2 := (a + 2*aw)*b
## define total effects (not sure this is makes sense in moderated mediation)
tot_m2 := ind_m2 + c
...
tot_p2 := ind_p2 + c
## define proportion
prop_m2 := ind_m2 / tot_m2
...
prop_p2 := ind_p2 / tot_p2
Last thing where I am unsure is the green marked part. When I leave it in I get the following warning:In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, : lavaan WARNING: The variance-covariance matrix of the estimated parameters (vcov) does not appear to be positive definite! The smallest eigenvalue (= 4.574149e-15) is close to zero. This may be a symptom that the model is not identified.
When I leave it out there is no warning.
So even if my model looks different I don't need the yellow parts, right?
Thank you very much, Terrence! These articels are perfect for understanding it. Thank you!
Using the indProd() function and the indicators I did in my syntax, I am back at latent modeling and not manifest modeling, right?
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
DV1 ~~
SL1 0.261 0.090 2.901 0.004 0.261 0.261
DV1xSL1 0.068 0.148 0.460 0.646 0.068 0.068
T1 0.233 0.097 2.406 0.016 0.233 0.233
H1 ~~
DV1 0.231 0.103 2.249 0.024 0.231 0.231
SL1 ~~
DV1xSL1 -0.295 0.155 -1.909 0.056 -0.295 -0.295
T1 0.847 0.038 22.494 0.000 0.847 0.847
H1 ~~
SL1 0.445 0.105 4.256 0.000 0.445 0.445
T1 ~~
DV1xSL1 -0.267 0.163 -1.645 0.100 -0.267 -0.267
H1 ~~
DV1xSL1 -0.171 0.138 -1.235 0.217 -0.171 -0.171
T1 0.535 0.119 4.487 0.000 0.535 0.535
.H2 ~~
.T2 98.180 40.588 2.419 0.016 98.180 98.180
Is it alright that the yellow marked is so high? How can that happen?
> summary(fitSEMModel, fit.measure=TRUE, standardized=TRUE, rsq=TRUE)
lavaan 0.6-5 ended normally after 645 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 135
Number of observations 157
Number of missing patterns 26
Model Test User Model:
Standard Robust
Test Statistic 1936.189 1913.671
Degrees of freedom 684 684
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.012
for the Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 4841.276 4364.296
Degrees of freedom 741 741
P-value 0.000 0.000
Scaling correction factor 1.109
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.695 0.661
Tucker-Lewis Index (TLI) 0.669 0.632
Robust Comparative Fit Index (CFI) 0.690
Robust Tucker-Lewis Index (TLI) 0.665
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -7796.566 -7796.566
Scaling correction factor 1.854
for the MLR correction
Loglikelihood unrestricted model (H1) NA NA
Scaling correction factor 1.151
for the MLR correction
Akaike (AIC) 15863.131 15863.131
Bayesian (BIC) 16275.724 16275.724
Sample-size adjusted Bayesian (BIC) 15848.396 15848.396
Root Mean Square Error of Approximation:
RMSEA 0.108 0.107
90 Percent confidence interval - lower 0.102 0.101
90 Percent confidence interval - upper 0.114 0.113
P-value RMSEA <= 0.05 0.000 0.000
Robust RMSEA 0.108
90 Percent confidence interval - lower 0.102
90 Percent confidence interval - upper 0.113
Standardized Root Mean Square Residual:
SRMR 0.088 0.088
Parameter Estimates:
Information Observed
Observed information based on Hessian
Standard errors Robust.huber.white
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
H1 =~
H1_phys_ttl_rw 1.113 0.766 1.453 0.146 1.113 0.209
H1_ment_ttl_rw 8.111 1.064 7.623 0.000 8.111 0.822
H2 =~
H2_phys_ttl_rw 0.006 0.002 3.550 0.000 2.326 0.389
H2_ment_ttl_rw 0.019 0.002 12.494 0.000 7.253 0.783
DV1 =~
DV1_tmdstrbtn_ 1.041 0.117 8.896 0.000 1.041 0.706
DV1_wrkplcmbl_ 0.880 0.094 9.342 0.000 0.880 0.756
DV1_vrtyfprct_ 1.112 0.125 8.889 0.000 1.112 0.738
SL1 =~
SL1_tskldrshp_ 0.631 0.088 7.174 0.000 0.631 0.752
SL1_rltnldrsh_ 1.014 0.076 13.391 0.000 1.014 0.937
SL1_chngldrsh_ 0.911 0.072 12.649 0.000 0.911 0.944
SL1_mcrpltcll_ 0.793 0.071 11.250 0.000 0.793 0.801
T1 =~
T102_01 0.580 0.072 8.071 0.000 0.580 0.608
T102_02 0.494 0.060 8.170 0.000 0.494 0.693
T102_03 0.509 0.079 6.427 0.000 0.509 0.710
T102_04 0.677 0.090 7.502 0.000 0.677 0.749
T102_05 0.491 0.075 6.537 0.000 0.491 0.711
T102_06 -0.634 0.105 -6.030 0.000 -0.634 -0.570
T102_07 -0.727 0.085 -8.536 0.000 -0.727 -0.720
T102_08 0.708 0.055 12.918 0.000 0.708 0.791
T2 =~
T202_01 0.316 0.049 6.439 0.000 0.604 0.566
T202_02 0.300 0.054 5.515 0.000 0.574 0.754
T202_03 0.342 0.055 6.171 0.000 0.654 0.835
T202_04 0.389 0.063 6.198 0.000 0.744 0.781
T202_05 0.269 0.050 5.343 0.000 0.513 0.715
T202_06 -0.341 0.060 -5.655 0.000 -0.652 -0.635
T202_07 -0.369 0.060 -6.156 0.000 -0.705 -0.682
T202_08 0.334 0.050 6.688 0.000 0.639 0.775
DV1xSL1 =~
DV1_tmd_.SL1__ 0.875 0.186 4.698 0.000 0.875 0.659
DV1_tmd_.SL1__ 1.039 0.229 4.541 0.000 1.039 0.685
DV1_tmd_.SL1__ 0.932 0.212 4.398 0.000 0.932 0.701
DV1_tmd_.SL1__ 1.021 0.219 4.653 0.000 1.021 0.672
DV1_wrk_.SL1__ 0.625 0.173 3.611 0.000 0.625 0.645
DV1_wrk_.SL1__ 0.803 0.187 4.295 0.000 0.803 0.689
DV1_wrk_.SL1__ 0.727 0.189 3.852 0.000 0.727 0.674
DV1_wrk_.SL1__ 0.735 0.157 4.674 0.000 0.735 0.679
DV1_vrt_.SL1__ 1.113 0.184 6.043 0.000 1.113 0.823
DV1_vrt_.SL1__ 1.454 0.215 6.775 0.000 1.454 0.852
DV1_vrt_.SL1__ 1.384 0.200 6.915 0.000 1.384 0.870
DV1_vrt_.SL1__ 1.287 0.152 8.482 0.000 1.287 0.831
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
T2 ~
T1 1.680 0.518 3.243 0.001 0.879 0.879
DV1 (a) -0.289 0.135 -2.134 0.033 -0.151 -0.151
SL1 (w) 0.050 0.285 0.176 0.860 0.026 0.026
DV1xSL1 (aw) 0.223 0.159 1.404 0.160 0.117 0.117
H2 ~
H1 378.770 49.762 7.612 0.000 1.009 1.009
T1 (b) 12.074 76.586 0.158 0.875 0.032 0.032
DV1 (c) -66.938 41.960 -1.595 0.111 -0.178 -0.178
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
DV1 ~~
SL1 0.261 0.090 2.901 0.004 0.261 0.261
DV1xSL1 0.068 0.148 0.460 0.646 0.068 0.068
T1 0.233 0.097 2.406 0.016 0.233 0.233
H1 ~~
DV1 0.231 0.103 2.249 0.024 0.231 0.231
SL1 ~~
DV1xSL1 -0.295 0.155 -1.909 0.056 -0.295 -0.295
T1 0.847 0.038 22.494 0.000 0.847 0.847
H1 ~~
SL1 0.445 0.105 4.256 0.000 0.445 0.445
T1 ~~
DV1xSL1 -0.267 0.163 -1.645 0.100 -0.267 -0.267
H1 ~~
DV1xSL1 -0.171 0.138 -1.235 0.217 -0.171 -0.171
T1 0.535 0.119 4.487 0.000 0.535 0.535
.H2 ~~
.T2 98.180 40.588 2.419 0.016 98.180 98.180
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.H1_phys_ttl_rw -3.159 0.431 -7.327 0.000 -3.159 -0.592
.H1_ment_ttl_rw -11.644 0.794 -14.669 0.000 -11.644 -1.180
.H2_phys_ttl_rw -3.996 0.510 -7.840 0.000 -3.996 -0.669
.H2_ment_ttl_rw -11.562 0.778 -14.861 0.000 -11.562 -1.248
.DV1_tmdstrbtn_ 2.831 0.120 23.678 0.000 2.831 1.919
.DV1_wrkplcmbl_ 3.429 0.095 36.087 0.000 3.429 2.944
.DV1_vrtyfprct_ 3.303 0.129 25.619 0.000 3.303 2.192
.SL1_tskldrshp_ 4.583 0.067 68.177 0.000 4.583 5.463
.SL1_rltnldrsh_ 4.516 0.087 52.022 0.000 4.516 4.170
.SL1_chngldrsh_ 4.623 0.077 59.808 0.000 4.623 4.789
.SL1_mcrpltcll_ 4.235 0.082 51.825 0.000 4.235 4.280
.T102_01 4.032 0.076 52.964 0.000 4.032 4.227
.T102_02 4.185 0.057 73.619 0.000 4.185 5.875
.T102_03 4.293 0.057 75.111 0.000 4.293 5.994
.T102_04 4.102 0.072 56.840 0.000 4.102 4.536
.T102_05 4.382 0.055 79.406 0.000 4.382 6.337
.T102_06 2.153 0.089 24.246 0.000 2.153 1.935
.T102_07 1.987 0.081 24.668 0.000 1.987 1.969
.T102_08 4.038 0.071 56.533 0.000 4.038 4.512
.T202_01 3.815 0.090 42.439 0.000 3.815 3.573
.T202_02 4.081 0.063 64.890 0.000 4.081 5.361
.T202_03 4.089 0.065 63.343 0.000 4.089 5.223
.T202_04 3.868 0.079 48.720 0.000 3.868 4.059
.T202_05 4.243 0.060 71.137 0.000 4.243 5.910
.T202_06 2.066 0.086 24.013 0.000 2.066 2.015
.T202_07 1.947 0.087 22.437 0.000 1.947 1.884
.T202_08 3.919 0.068 57.370 0.000 3.919 4.756
.DV1_tmd_.SL1__ -0.006 0.107 -0.053 0.957 -0.006 -0.004
.DV1_tmd_.SL1__ 0.017 0.125 0.135 0.893 0.017 0.011
.DV1_tmd_.SL1__ 0.008 0.108 0.071 0.943 0.008 0.006
.DV1_tmd_.SL1__ -0.034 0.127 -0.268 0.789 -0.034 -0.022
.DV1_wrk_.SL1__ -0.007 0.081 -0.085 0.932 -0.007 -0.007
.DV1_wrk_.SL1__ 0.010 0.097 0.109 0.913 0.010 0.009
.DV1_wrk_.SL1__ 0.012 0.089 0.137 0.891 0.012 0.011
.DV1_wrk_.SL1__ -0.029 0.095 -0.305 0.761 -0.029 -0.027
.DV1_vrt_.SL1__ -0.009 0.115 -0.081 0.936 -0.009 -0.007
.DV1_vrt_.SL1__ 0.034 0.147 0.228 0.820 0.034 0.020
.DV1_vrt_.SL1__ 0.033 0.138 0.240 0.810 0.033 0.021
.DV1_vrt_.SL1__ -0.041 0.135 -0.306 0.759 -0.041 -0.027
H1 0.000 0.000 0.000
.H2 0.000 0.000 0.000
DV1 0.000 0.000 0.000
SL1 0.000 0.000 0.000
T1 0.000 0.000 0.000
.T2 0.000 0.000 0.000
DV1xSL1 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.H1_phys_ttl_rw 27.237 5.632 4.836 0.000 27.237 0.956
.H1_ment_ttl_rw 31.571 13.259 2.381 0.017 31.571 0.324
.H2_phys_ttl_rw 30.309 5.215 5.812 0.000 30.309 0.849
.H2_ment_ttl_rw 33.183 11.299 2.937 0.003 33.183 0.387
.DV1_tmdstrbtn_ 1.093 0.177 6.164 0.000 1.093 0.502
.DV1_wrkplcmbl_ 0.582 0.153 3.800 0.000 0.582 0.429
.DV1_vrtyfprct_ 1.033 0.238 4.334 0.000 1.033 0.455
.SL1_tskldrshp_ 0.306 0.054 5.666 0.000 0.306 0.435
.SL1_rltnldrsh_ 0.144 0.023 6.206 0.000 0.144 0.123
.SL1_chngldrsh_ 0.102 0.022 4.674 0.000 0.102 0.109
.SL1_mcrpltcll_ 0.350 0.046 7.635 0.000 0.350 0.358
.T102_01 0.574 0.215 2.670 0.008 0.574 0.630
.T102_02 0.263 0.037 7.175 0.000 0.263 0.519
.T102_03 0.254 0.060 4.272 0.000 0.254 0.496
.T102_04 0.359 0.045 8.049 0.000 0.359 0.439
.T102_05 0.237 0.029 8.080 0.000 0.237 0.495
.T102_06 0.835 0.174 4.793 0.000 0.835 0.675
.T102_07 0.490 0.096 5.122 0.000 0.490 0.481
.T102_08 0.299 0.142 2.110 0.035 0.299 0.374
.T202_01 0.774 0.290 2.672 0.008 0.774 0.679
.T202_02 0.250 0.051 4.925 0.000 0.250 0.432
.T202_03 0.185 0.029 6.292 0.000 0.185 0.302
.T202_04 0.355 0.060 5.947 0.000 0.355 0.391
.T202_05 0.252 0.041 6.119 0.000 0.252 0.489
.T202_06 0.627 0.153 4.099 0.000 0.627 0.596
.T202_07 0.572 0.144 3.980 0.000 0.572 0.535
.T202_08 0.271 0.097 2.788 0.005 0.271 0.399
.DV1_tmd_.SL1__ 0.998 0.239 4.179 0.000 0.998 0.566
.DV1_tmd_.SL1__ 1.224 0.322 3.797 0.000 1.224 0.531
.DV1_tmd_.SL1__ 0.898 0.238 3.770 0.000 0.898 0.508
.DV1_tmd_.SL1__ 1.266 0.300 4.216 0.000 1.266 0.548
.DV1_wrk_.SL1__ 0.548 0.296 1.849 0.064 0.548 0.583
.DV1_wrk_.SL1__ 0.712 0.302 2.353 0.019 0.712 0.525
.DV1_wrk_.SL1__ 0.634 0.319 1.991 0.047 0.634 0.545
.DV1_wrk_.SL1__ 0.632 0.196 3.222 0.001 0.632 0.539
.DV1_vrt_.SL1__ 0.589 0.150 3.912 0.000 0.589 0.322
.DV1_vrt_.SL1__ 0.797 0.288 2.764 0.006 0.797 0.274
.DV1_vrt_.SL1__ 0.614 0.237 2.592 0.010 0.614 0.243
.DV1_vrt_.SL1__ 0.742 0.177 4.184 0.000 0.742 0.309
H1 1.000 1.000 1.000
.H2 1.000 0.000 0.000
DV1 1.000 1.000 1.000
SL1 1.000 1.000 1.000
T1 1.000 1.000 1.000
.T2 1.000 0.274 0.274
DV1xSL1 1.000 1.000 1.000
R-Square:
Estimate
H1_phys_ttl_rw 0.044
H1_ment_ttl_rw 0.676
H2_phys_ttl_rw 0.151
H2_ment_ttl_rw 0.613
DV1_tmdstrbtn_ 0.498
DV1_wrkplcmbl_ 0.571
DV1_vrtyfprct_ 0.545
SL1_tskldrshp_ 0.565
SL1_rltnldrsh_ 0.877
SL1_chngldrsh_ 0.891
SL1_mcrpltcll_ 0.642
T102_01 0.370
T102_02 0.481
T102_03 0.504
T102_04 0.561
T102_05 0.505
T102_06 0.325
T102_07 0.519
T102_08 0.626
T202_01 0.321
T202_02 0.568
T202_03 0.698
T202_04 0.609
T202_05 0.511
T202_06 0.404
T202_07 0.465
T202_08 0.601
DV1_tmd_.SL1__ 0.434
DV1_tmd_.SL1__ 0.469
DV1_tmd_.SL1__ 0.492
DV1_tmd_.SL1__ 0.452
DV1_wrk_.SL1__ 0.417
DV1_wrk_.SL1__ 0.475
DV1_wrk_.SL1__ 0.455
DV1_wrk_.SL1__ 0.461
DV1_vrt_.SL1__ 0.678
DV1_vrt_.SL1__ 0.726
DV1_vrt_.SL1__ 0.757
DV1_vrt_.SL1__ 0.691
H2 1.000
T2 0.726
Defined Parameters:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
indirect -3.490 22.341 -0.156 0.876 -0.005 -0.005
direct -66.938 41.960 -1.595 0.111 -0.178 -0.178
total -70.428 49.743 -1.416 0.157 -0.183 -0.183
ind_SDbelow2 -8.881 57.030 -0.156 0.876 -0.012 -0.012
ind_SDbelow1 -6.186 39.668 -0.156 0.876 -0.009 -0.009
ind_0 -3.490 22.341 -0.156 0.876 -0.005 -0.005
ind_SDabove1 -0.794 5.393 -0.147 0.883 -0.001 -0.001
ind_SDabove2 1.902 12.787 0.149 0.882 0.003 0.003
tot_SDbelow2 -75.819 74.835 -1.013 0.311 -0.191 -0.191
tot_SDbelow1 -73.123 61.116 -1.196 0.232 -0.187 -0.187
tot_0 -70.428 49.743 -1.416 0.157 -0.183 -0.183
tot_SDabove1 -67.732 42.638 -1.589 0.112 -0.179 -0.179
tot_SDabove2 -65.036 42.025 -1.548 0.122 -0.176 -0.176
prop_SDbelow2 -7.998 57.687 -0.139 0.890 0.923 0.923
prop_SDbelow1 -5.270 40.159 -0.131 0.896 0.945 0.945
prop_0 -2.539 22.639 -0.112 0.911 0.969 0.969
prop_SDabove1 0.194 5.472 0.035 0.972 0.993 0.993
prop_SDabove2 2.931 12.986 0.226 0.821 1.018 1.018
index.mod.med 2.696 17.384 0.155 0.877 0.004 0.004
But could the scaling be the reason for that?
standardizedSolution(fitSEMModel, cov.std = TRUE)
standardizedSolution(fitSEMModel, cov.std = FALSE)