This document presents a comparison between the MEMORE 3.0 (SPSS Plugin) and wsMed (R Package) outputs.
We analyze a three-mediator parallel mediation model, comparing the results obtained from both methods.
## ```
##
## Run MATRIX procedure:
##
## *********************** MEMORE Procedure for SPSS Version 3.0 ***********************
##
## Written by Amanda Montoya
##
## Documentation available at github.com/akmontoya/MEMORE
##
## **************************** ANALYSIS NOTES AND WARNINGS ****************************
##
## Bootstrap confidence interval method used: Percentile bootstrap.
##
## Number of bootstrap samples for bootstrap confidence intervals:
## 5000
##
## The following variables were mean centered prior to analysis:
## ( A2 + A1 ) /2
## ( B2 + B1 ) /2
## ( C2 + C1 ) /2
##
## Level of confidence for all confidence intervals in output:
## 95.00
##
## **************************************************************************************
##
## Model:
## 1
##
## Variables:
## Y = D2 D1
## M1 = A2 A1
## M2 = B2 B1
## M3 = C2 C1
##
## Computed Variables:
## Ydiff = D2 - D1
## M1diff = A2 - A1
## M2diff = B2 - B1
## M3diff = C2 - C1
## M1avg = ( A2 + A1 ) /2 Centered
## M2avg = ( B2 + B1 ) /2 Centered
## M3avg = ( C2 + C1 ) /2 Centered
##
## Sample Size:
## 100
##
## **************************************************************************************
## Outcome: Ydiff = D2 - D1
##
## Model
## Coef SE t p LLCI ULCI
## constant -.0316 .0170 -1.8586 .0661 -.0653 .0021
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: M1diff = A2 - A1
##
## Model
## Coef SE t p LLCI ULCI
## constant -.0271 .0176 -1.5385 .1271 -.0621 .0079
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: M2diff = B2 - B1
##
## Model
## Coef SE t p LLCI ULCI
## constant .0145 .0181 .8024 .4243 -.0213 .0503
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: M3diff = C2 - C1
##
## Model
## Coef SE t p LLCI ULCI
## constant .0153 .0163 .9404 .3493 -.0170 .0477
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: Ydiff = D2 - D1
##
## Model Summary
## R R-sq MSE F df1 df2 p
## .1978 .0391 .0296 .6312 6.0000 93.0000 .7049
##
## Model
## Coef SE t p LLCI ULCI
## constant -.0315 .0176 -1.7875 .0771 -.0665 .0035
## M1diff -.0501 .1009 -.4968 .6205 -.2505 .1502
## M2diff -.0844 .1022 -.8252 .4114 -.2874 .1187
## M3diff -.0167 .1084 -.1536 .8782 -.2320 .1987
## M1avg -.0305 .1015 -.3002 .7647 -.2320 .1711
## M2avg -.0060 .0945 -.0633 .9497 -.1936 .1816
## M3avg -.1552 .1090 -1.4239 .1578 -.3716 .0612
##
## Degrees of freedom for all regression coefficient estimates:
## 93
##
## ************************* TOTAL, DIRECT, AND INDIRECT EFFECTS *************************
##
## Total effect of X on Y
## Effect SE t df p LLCI ULCI
## -.0316 .0170 -1.8586 99.0000 .0661 -.0653 .0021
##
## Direct effect of X on Y
## Effect SE t df p LLCI ULCI
## -.0315 .0176 -1.7875 93.0000 .0771 -.0665 .0035
##
## Indirect Effect of X on Y through M
## Effect BootSE BootLLCI BootULCI
## Ind1 .0014 .0030 -.0049 .0078
## Ind2 -.0012 .0029 -.0088 .0035
## Ind3 -.0003 .0028 -.0072 .0050
## Total -.0001 .0054 -.0130 .0092
##
## Indirect Key
## Ind1 'X' -> M1diff -> Ydiff
## Ind2 'X' -> M2diff -> Ydiff
## Ind3 'X' -> M3diff -> Ydiff
##
## Pairwise Contrasts Between Specific Indirect Effects
## Effect BootSE BootLLCI BootULCI
## (C1) .0026 .0039 -.0053 .0113
## (C2) .0016 .0043 -.0067 .0114
## (C3) -.0010 .0038 -.0089 .0068
##
## Contrast Key:
## (C1) Ind1 - Ind2
## (C2) Ind1 - Ind3
## (C3) Ind2 - Ind3
##
## ------ END MATRIX -----
##
## ```##
##
## *************** VARIABLES ***************
## Outcome (Y):
## Condition 1: D1
## Condition 2: D2
## Mediators (M):
## M1:
## Condition 1: A1
## Condition 2: A2
## M2:
## Condition 1: B1
## Condition 2: B2
## M3:
## Condition 1: C1
## Condition 2: C2
## Sample size (rows kept): 100
##
##
## *************** MODEL FIT ***************
##
##
## |Measure | Value|
## |:---------|------:|
## |Chi-Sq | 16.275|
## |df | 12.000|
## |p | 0.179|
## |CFI | 0.000|
## |TLI | -1.829|
## |RMSEA | 0.060|
## |RMSEA Low | 0.000|
## |RMSEA Up | 0.126|
## |SRMR | 0.072|
##
##
## ************* TOTAL / DIRECT / TOTAL-IND (MC) *************
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|--------:|------:|---------:|----------:|
## |Total effect | -0.0316| 0.0169| -0.0645| 0.0016|
## |Direct effect | -0.0315| 0.0170| -0.0644| 0.0019|
## |Total indirect | -0.0001| 0.0046| -0.0096| 0.0094|
##
## Indirect effects:
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----|--------:|------:|---------:|----------:|
## |ind_1 | 0.0014| 0.0031| -0.0043| 0.0087|
## |ind_2 | -0.0012| 0.0026| -0.0078| 0.0031|
## |ind_3 | -0.0003| 0.0023| -0.0055| 0.0044|
##
## Indirect-effect key:
##
##
## |Ind |Path |
## |:-----|:--------------------|
## |ind_1 |X -> M1diff -> Ydiff |
## |ind_2 |X -> M2diff -> Ydiff |
## |ind_3 |X -> M3diff -> Ydiff |
##
##
## *************** MODERATION EFFECTS (d-paths, MC) ***************
##
##
## |Coefficient | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----------|--------:|------:|---------:|----------:|
## |d1 | -0.0305| 0.0983| -0.2217| 0.1623|
## |d2 | -0.0060| 0.0894| -0.1805| 0.1685|
## |d3 | -0.1552| 0.1047| -0.3621| 0.0490|
##
##
## *************** MODERATION KEY (d-paths) ***************
##
##
## |Coefficient |Path |Moderated |
## |:-----------|:--------------|:---------------|
## |d1 |M1avg -> Ydiff |M1diff -> Ydiff |
## |d2 |M2avg -> Ydiff |M2diff -> Ydiff |
## |d3 |M3avg -> Ydiff |M3diff -> Ydiff |
##
##
## *************** CONTRAST INDIRECT EFFECTS (No Moderator) ***************
##
##
## |Contrast | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-------------------------|--------:|------:|---------:|----------:|
## |indirect_2 - indirect_1 | -0.0030| 0.0040| -0.0120| 0.0050|
## |indirect_3 - indirect_1 | -0.0020| 0.0040| -0.0100| 0.0060|
## |indirect_3 - indirect_2 | 0.0010| 0.0030| -0.0060| 0.0090|
##
##
## *************** C1-C2 COEFFICIENTS (No Moderator) ***************
##
##
## |Coeff | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----|--------:|------:|---------:|----------:|
## |X1_b1 | -0.0650| 0.1060| -0.2730| 0.1440|
## |X0_b1 | -0.0350| 0.1070| -0.2440| 0.1740|
## |X1_b2 | -0.0870| 0.1020| -0.2860| 0.1140|
## |X0_b2 | -0.0800| 0.1020| -0.2770| 0.1230|
## |X1_b3 | -0.0940| 0.1140| -0.3180| 0.1300|
## |X0_b3 | 0.0610| 0.1150| -0.1630| 0.2880|
##
##
## *************** REGRESSION PATHS (MC) ***************
##
##
## |Path |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff ~ M1diff |b1 | -0.0501| 0.0945| -0.2337| 0.1359|
## |Ydiff ~ M1avg |d1 | -0.0305| 0.0983| -0.2217| 0.1623|
## |Ydiff ~ M2diff |b2 | -0.0844| 0.0916| -0.2629| 0.0996|
## |Ydiff ~ M2avg |d2 | -0.0060| 0.0894| -0.1805| 0.1685|
## |Ydiff ~ M3diff |b3 | -0.0167| 0.1023| -0.2173| 0.1843|
## |Ydiff ~ M3avg |d3 | -0.1552| 0.1047| -0.3621| 0.0490|
##
##
## *************** INTERCEPTS (MC) ***************
##
##
## |Intercept |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~1 |cp | -0.0315| 0.0170| -0.0644| 0.0019|
## |M1diff~1 |a1 | -0.0271| 0.0175| -0.0608| 0.0076|
## |M2diff~1 |a2 | 0.0145| 0.0180| -0.0207| 0.0502|
## |M3diff~1 |a3 | 0.0153| 0.0163| -0.0170| 0.0471|
## |M1avg~1 | | -0.0000| 0.0185| -0.0361| 0.0361|
## |M2avg~1 | | 0.0000| 0.0202| -0.0396| 0.0400|
## |M3avg~1 | | -0.0000| 0.0175| -0.0342| 0.0342|
##
##
## *************** VARIANCES (MC) ***************
##
##
## |Variance |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~~Ydiff | | 0.0275| 0.0039| 0.0199| 0.0351|
## |M1diff~~M1diff | | 0.0308| 0.0044| 0.0221| 0.0391|
## |M2diff~~M2diff | | 0.0323| 0.0046| 0.0232| 0.0413|
## |M3diff~~M3diff | | 0.0264| 0.0037| 0.0191| 0.0337|
## |M1avg~~M1avg | | 0.0339| 0.0048| 0.0245| 0.0433|
## |M2avg~~M2avg | | 0.0407| 0.0058| 0.0294| 0.0520|
## |M3avg~~M3avg | | 0.0306| 0.0043| 0.0221| 0.0390|
##
##
## *************** STANDARDIZED (MC) ***************
##
##
## |Parameter | Estimate| SE| R| 2.5%| 97.5%|
## |:--------------|--------:|------:|----------:|-------:|------:|
## |cp | -0.1858| 0.0992| 20000.0000| -0.3775| 0.0106|
## |b1 | -0.0519| 0.0950| 20000.0000| -0.2366| 0.1367|
## |d1 | -0.0331| 0.1037| 20000.0000| -0.2335| 0.1716|
## |b2 | -0.0894| 0.0941| 20000.0000| -0.2669| 0.1038|
## |d2 | -0.0071| 0.1035| 20000.0000| -0.2093| 0.1937|
## |b3 | -0.0160| 0.0953| 20000.0000| -0.2012| 0.1716|
## |d3 | -0.1601| 0.1041| 20000.0000| -0.3586| 0.0502|
## |a1 | -0.1546| 0.1019| 20000.0000| -0.3560| 0.0432|
## |a2 | 0.0806| 0.1015| 20000.0000| -0.1168| 0.2840|
## |a3 | 0.0945| 0.1014| 20000.0000| -0.1045| 0.2976|
## |Ydiff~~Ydiff | 0.9578| 0.0468| 20000.0000| 0.7977| 0.9770|
## |M1diff~~M1diff | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M2diff~~M2diff | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M3diff~~M3diff | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~~M1avg | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~~M2avg | 0.2898| 0.0931| 20000.0000| 0.0992| 0.4640|
## |M1avg~~M3avg | 0.3395| 0.0906| 20000.0000| 0.1521| 0.5080|
## |M2avg~~M2avg | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M2avg~~M3avg | 0.3240| 0.0916| 20000.0000| 0.1347| 0.4941|
## |M3avg~~M3avg | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~1 | -0.0000| 0.1016| 20000.0000| -0.1996| 0.1997|
## |M2avg~1 | 0.0000| 0.1012| 20000.0000| -0.1967| 0.1997|
## |M3avg~1 | -0.0000| 0.1011| 20000.0000| -0.1967| 0.1977|
## |indirect_1 | 0.0080| 0.0180| 20000.0000| -0.0250| 0.0497|
## |indirect_2 | -0.0072| 0.0149| 20000.0000| -0.0443| 0.0178|
## |indirect_3 | -0.0015| 0.0131| 20000.0000| -0.0320| 0.0254|
## |total_indirect | -0.0007| 0.0267| 20000.0000| -0.0552| 0.0540|
## |total_effect | -0.1865| 0.0991| 20000.0000| -0.3788| 0.0093|##
## Outcome Difference Model (Ydiff):
## Ydiff ~ cp*1 + b1*M1diff + d1*M1avg + b2*M2diff + d2*M2avg + b3*M3diff + d3*M3avg
##
## Mediator Difference Model (Chained Mediator - M1diff):
## M1diff ~ a1*1
##
## Mediator Difference Model (Other Mediators):
## M2diff ~ a2*1
## M3diff ~ a3*1
##
## Indirect Effects:
## indirect_1 := a1 * b1
## indirect_2 := a2 * b2
## indirect_3 := a3 * b3
##
## Total Indirect Effect:
## total_indirect := indirect_1 + indirect_2 + indirect_3
##
## Total Effect:
## total_effect := cp + total_indirectWe analyze a Serial Mediation
(Serial = 1).
## ```
##
## Run MATRIX procedure:
##
## *********************** MEMORE Procedure for SPSS Version 3.0 ***********************
##
## Written by Amanda Montoya
##
## Documentation available at github.com/akmontoya/MEMORE
##
## **************************** ANALYSIS NOTES AND WARNINGS ****************************
##
## Bootstrap confidence interval method used: Percentile bootstrap.
##
## Number of bootstrap samples for bootstrap confidence intervals:
## 5000
##
## The following variables were mean centered prior to analysis:
## ( A2 + A1 ) /2
## ( B2 + B1 ) /2
##
## Level of confidence for all confidence intervals in output:
## 95.00
##
## **************************************************************************************
##
## Model:
## 1
##
## Variables:
## Y = C2 C1
## M1 = A2 A1
## M2 = B2 B1
##
## Computed Variables:
## Ydiff = C2 - C1
## M1diff = A2 - A1
## M2diff = B2 - B1
## M1avg = ( A2 + A1 ) /2 Centered
## M2avg = ( B2 + B1 ) /2 Centered
##
## Sample Size:
## 100
##
## **************************************************************************************
## Outcome: Ydiff = C2 - C1
##
## Model
## Coef SE t p LLCI ULCI
## constant .0153 .0163 .9404 .3493 -.0170 .0477
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: M1diff = A2 - A1
##
## Model
## Coef SE t p LLCI ULCI
## constant -.0271 .0176 -1.5385 .1271 -.0621 .0079
##
## Degrees of freedom for all regression coefficient estimates:
## 99
##
## **************************************************************************************
## Outcome: M2diff = B2 - B1
##
## Model Summary
## R R-sq MSE F df1 df2 p
## .2351 .0553 .0314 2.8371 2.0000 97.0000 .0635
##
## Model
## coeff SE t p LLCI ULCI
## constant .0208 .0179 1.1603 .2488 -.0148 .0564
## M1diff .2333 .1010 2.3086 .0231 .0327 .4338
## M1avg -.0578 .0963 -.6002 .5498 -.2489 .1333
##
## Degrees of freedom for all regression coefficient estimates:
## 97
##
## **************************************************************************************
## Outcome: Ydiff = C2 - C1
##
## Model Summary
## R R-sq MSE F df1 df2 p
## .1720 .0296 .0269 .7238 4.0000 95.0000 .5778
##
## Model
## Coef SE t p LLCI ULCI
## constant .0160 .0167 .9563 .3413 -.0172 .0492
## M1diff -.0360 .0962 -.3744 .7089 -.2270 .1549
## M2diff -.1123 .0967 -1.1607 .2487 -.3043 .0798
## M1avg -.0617 .0931 -.6619 .5097 -.2466 .1233
## M2avg -.0733 .0875 -.8374 .4044 -.2469 .1004
##
## Degrees of freedom for all regression coefficient estimates:
## 95
##
## ************************* TOTAL, DIRECT, AND INDIRECT EFFECTS *************************
##
## Total effect of X on Y
## Effect SE t df p LLCI ULCI
## .0153 .0163 .9404 99.0000 .3493 -.0170 .0477
##
## Direct effect of X on Y
## Effect SE t df p LLCI ULCI
## .0160 .0167 .9563 95.0000 .3413 -.0172 .0492
##
## Indirect Effect of X on Y through M
## Effect BootSE BootLLCI BootULCI
## Ind1 .0010 .0031 -.0045 .0084
## Ind2 -.0023 .0036 -.0111 .0033
## Ind3 .0007 .0010 -.0008 .0034
## Total -.0006 .0048 -.0109 .0095
##
## Indirect Key
## Ind1 'X' -> M1diff -> Ydiff
## Ind2 'X' -> M2diff -> Ydiff
## Ind3 'X' -> M1diff -> M2diff -> YDiff
##
## Pairwise Contrasts Between Specific Indirect Effects
## Effect BootSE BootLLCI BootULCI
## (C1) .0033 .0042 -.0042 .0128
## (C2) .0003 .0034 -.0063 .0083
## (C3) -.0030 .0041 -.0132 .0035
##
## Contrast Key:
## (C1) Ind1 - Ind2
## (C2) Ind1 - Ind3
## (C3) Ind2 - Ind3
##
## ------ END MATRIX -----
##
## ```result2 <- wsMed(
data = example_data, #dataset
M_C1 = c("A1","B1"), # A1/B1 is A/B mediator variable in condition 1
M_C2 = c("A2","B2"), # A2/B2 is A/B mediator variable in condition 2
Y_C1 = "C1", # C1 is outcome variable in condition 1
Y_C2 = "C2", # C2 is outcome variable in condition 2
form = "CN", # Parallel mediation
standardized = TRUE,
)
print(result2,digits=4)##
##
## *************** VARIABLES ***************
## Outcome (Y):
## Condition 1: C1
## Condition 2: C2
## Mediators (M):
## M1:
## Condition 1: A1
## Condition 2: A2
## M2:
## Condition 1: B1
## Condition 2: B2
## Sample size (rows kept): 100
##
##
## *************** MODEL FIT ***************
##
##
## |Measure | Value|
## |:---------|------:|
## |Chi-Sq | 5.751|
## |df | 3.000|
## |p | 0.124|
## |CFI | 0.494|
## |TLI | -0.518|
## |RMSEA | 0.096|
## |RMSEA Low | 0.000|
## |RMSEA Up | 0.214|
## |SRMR | 0.050|
##
##
## ************* TOTAL / DIRECT / TOTAL-IND (MC) *************
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|--------:|------:|---------:|----------:|
## |Total effect | 0.0153| 0.0163| -0.0169| 0.0469|
## |Direct effect | 0.0160| 0.0162| -0.0161| 0.0479|
## |Total indirect | -0.0006| 0.0045| -0.0100| 0.0083|
##
## Indirect effects:
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-------|--------:|------:|---------:|----------:|
## |ind_1 | 0.0010| 0.0031| -0.0050| 0.0081|
## |ind_2 | -0.0023| 0.0032| -0.0103| 0.0025|
## |ind_1_2 | 0.0007| 0.0009| -0.0005| 0.0031|
##
## Indirect-effect key:
##
##
## |Ind |Path |
## |:-------|:------------------------------|
## |ind_1 |X -> M1diff -> Ydiff |
## |ind_2 |X -> M2diff -> Ydiff |
## |ind_1_2 |X -> M1diff -> M2diff -> Ydiff |
##
##
## *************** MODERATION EFFECTS (d-paths, MC) ***************
##
##
## |Coefficient | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----------|--------:|------:|---------:|----------:|
## |d1 | -0.0617| 0.0914| -0.2433| 0.1203|
## |d2 | -0.0733| 0.0835| -0.2367| 0.0913|
## |d_1_2 | -0.0578| 0.0943| -0.2440| 0.1290|
##
##
## *************** MODERATION KEY (d-paths) ***************
##
##
## |Coefficient |Path |Moderated |
## |:-----------|:---------------|:----------------|
## |d1 |M1avg -> Ydiff |M1diff -> Ydiff |
## |d2 |M2avg -> Ydiff |M2diff -> Ydiff |
## |d_1_2 |M1avg -> M2diff |M1diff -> M2diff |
##
##
## *************** CONTRAST INDIRECT EFFECTS (No Moderator) ***************
##
##
## |Contrast | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------------------------|--------:|------:|---------:|----------:|
## |indirect_2 - indirect_1 | -0.0030| 0.0040| -0.0130| 0.0040|
## |indirect_1_2 - indirect_1 | -0.0000| 0.0030| -0.0070| 0.0070|
## |indirect_1_2 - indirect_2 | 0.0030| 0.0040| -0.0020| 0.0120|
##
##
## *************** C1-C2 COEFFICIENTS (No Moderator) ***************
##
##
## |Coeff | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------|--------:|------:|---------:|----------:|
## |X1_b1 | -0.0660| 0.1030| -0.2700| 0.1340|
## |X0_b1 | -0.0060| 0.1050| -0.2130| 0.2000|
## |X1_b2 | -0.1480| 0.1000| -0.3450| 0.0470|
## |X0_b2 | -0.0750| 0.1000| -0.2720| 0.1210|
## |X1_b_1_2 | 0.2030| 0.1110| -0.0170| 0.4200|
## |X0_b_1_2 | 0.2620| 0.1110| 0.0440| 0.4770|
##
##
## *************** REGRESSION PATHS (MC) ***************
##
##
## |Path |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff ~ M1diff |b1 | -0.0360| 0.0930| -0.2178| 0.1460|
## |Ydiff ~ M1avg |d1 | -0.0617| 0.0914| -0.2433| 0.1203|
## |Ydiff ~ M2diff |b2 | -0.1123| 0.0911| -0.2890| 0.0662|
## |Ydiff ~ M2avg |d2 | -0.0733| 0.0835| -0.2367| 0.0913|
## |M2diff ~ M1diff |b_1_2 | 0.2333| 0.1002| 0.0358| 0.4305|
## |M2diff ~ M1avg |d_1_2 | -0.0578| 0.0943| -0.2440| 0.1290|
##
##
## *************** INTERCEPTS (MC) ***************
##
##
## |Intercept |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~1 |cp | 0.0160| 0.0162| -0.0161| 0.0479|
## |M1diff~1 |a1 | -0.0271| 0.0176| -0.0618| 0.0073|
## |M2diff~1 |a2 | 0.0208| 0.0177| -0.0134| 0.0562|
## |M1avg~1 | | -0.0000| 0.0186| -0.0366| 0.0362|
## |M2avg~1 | | 0.0000| 0.0202| -0.0396| 0.0392|
##
##
## *************** VARIANCES (MC) ***************
##
##
## |Variance |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~~Ydiff | | 0.0256| 0.0036| 0.0186| 0.0327|
## |M2diff~~M2diff | | 0.0305| 0.0043| 0.0220| 0.0389|
## |M1diff~~M1diff | | 0.0308| 0.0044| 0.0222| 0.0394|
## |M1avg~~M1avg | | 0.0339| 0.0048| 0.0244| 0.0433|
## |M2avg~~M2avg | | 0.0407| 0.0058| 0.0293| 0.0519|
##
##
## *************** STANDARDIZED (MC) ***************
##
##
## |Parameter | Estimate| SE| R| 2.5%| 97.5%|
## |:--------------|--------:|------:|----------:|-------:|------:|
## |cp | 0.0983| 0.0989| 20000.0000| -0.0974| 0.2916|
## |b1 | -0.0388| 0.0983| 20000.0000| -0.2299| 0.1548|
## |d1 | -0.0697| 0.1012| 20000.0000| -0.2673| 0.1335|
## |b2 | -0.1239| 0.0988| 20000.0000| -0.3132| 0.0726|
## |d2 | -0.0908| 0.1010| 20000.0000| -0.2852| 0.1108|
## |a1 | -0.1546| 0.1020| 20000.0000| -0.3591| 0.0415|
## |a2 | 0.1159| 0.0987| 20000.0000| -0.0757| 0.3112|
## |b_1_2 | 0.2278| 0.0946| 20000.0000| 0.0346| 0.4082|
## |d_1_2 | -0.0592| 0.0956| 20000.0000| -0.2468| 0.1299|
## |Ydiff~~Ydiff | 0.9656| 0.0419| 20000.0000| 0.8320| 0.9891|
## |M2diff~~M2diff | 0.9446| 0.0461| 20000.0000| 0.8186| 0.9930|
## |M1diff~~M1diff | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~~M1avg | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~~M2avg | 0.2898| 0.0938| 20000.0000| 0.0944| 0.4651|
## |M2avg~~M2avg | 1.0000| 0.0000| 20000.0000| 1.0000| 1.0000|
## |M1avg~1 | -0.0000| 0.1020| 20000.0000| -0.2020| 0.2000|
## |M2avg~1 | 0.0000| 0.1012| 20000.0000| -0.1985| 0.1984|
## |indirect_1 | 0.0060| 0.0187| 20000.0000| -0.0302| 0.0485|
## |indirect_2 | -0.0144| 0.0192| 20000.0000| -0.0611| 0.0147|
## |indirect_1_2 | 0.0044| 0.0056| 20000.0000| -0.0032| 0.0189|
## |total_indirect | -0.0040| 0.0269| 20000.0000| -0.0605| 0.0493|
## |total_effect | 0.0943| 0.0991| 20000.0000| -0.1023| 0.2878|##
## Outcome Difference Model (Ydiff):
## Ydiff ~ cp*1 + b1*M1diff + d1*M1avg + b2*M2diff + d2*M2avg
##
## Mediator Difference Model (Chained Mediator - M1diff):
## M1diff ~ a1*1
##
## Mediator Difference Model (Other Mediators):
## M2diff ~ a2*1 + b_1_2*M1diff + d_1_2*M1avg
##
## Indirect Effects:
## indirect_1 := a1 * b1
## indirect_2 := a2 * b2
## indirect_1_2 := a1 * b_1_2 * b2
##
## Total Indirect Effect:
## total_indirect := indirect_1 + indirect_2 + indirect_1_2
##
## Total Effect:
## total_effect := cp + total_indirect