The MEMORE reports below are stored reference output, not rerun by this vignette. Compare estimates only after matching difference direction, model structure, missing-data handling and interval settings. Standardized results require matching the SD definition as well: wsMed uses marginal model-implied difference-component SDs. Matching raw estimates does not imply matching standardized estimates or confidence intervals.
For moderation, see Standardized moderated mediation. This vignette does not establish agreement between the programs for moderated standardized effects.
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| 0.0170| -0.0651| 0.0024|
## |Direct effect | -0.0315| 0.0170| -0.0645| 0.0019|
## |Total indirect | -0.0001| 0.0047| -0.0099| 0.0096|
##
## Indirect effects:
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----|--------:|------:|---------:|----------:|
## |ind_1 | 0.0014| 0.0032| -0.0046| 0.0090|
## |ind_2 | -0.0012| 0.0026| -0.0077| 0.0030|
## |ind_3 | -0.0003| 0.0023| -0.0054| 0.0045|
##
## 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| 0.0974| -0.2225| 0.1596|
## |d2 | -0.0060| 0.0885| -0.1801| 0.1655|
## |d3 | -0.1552| 0.1038| -0.3555| 0.0511|
##
##
## *************** MODERATION KEY (d-paths) ***************
##
##
## |Coefficient |Path |Moderated |
## |:-----------|:--------------|:---------------|
## |d1 |M1avg -> Ydiff |M1diff -> Ydiff |
## |d2 |M2avg -> Ydiff |M2diff -> Ydiff |
## |d3 |M3avg -> Ydiff |M3diff -> Ydiff |
##
## UNSTANDARDIZED CONDITIONAL EFFECTS (mc)
## Percentile intervals; moderator probes and centering references are held fixed.
##
##
## *************** 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| -0.0110| 0.0060|
## |indirect_3 - indirect_2 | 0.0010| 0.0030| -0.0050| 0.0090|
##
##
## *************** C1-C2 COEFFICIENTS (No Moderator) ***************
##
##
## |Coeff | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-----|--------:|------:|---------:|----------:|
## |X1_b1 | -0.0650| 0.1060| -0.2740| 0.1450|
## |X0_b1 | -0.0350| 0.1070| -0.2460| 0.1720|
## |X1_b2 | -0.0870| 0.1030| -0.2840| 0.1190|
## |X0_b2 | -0.0810| 0.1040| -0.2820| 0.1280|
## |X1_b3 | -0.0940| 0.1150| -0.3170| 0.1330|
## |X0_b3 | 0.0610| 0.1150| -0.1590| 0.2830|
##
##
## *************** REGRESSION PATHS (MC) ***************
##
##
## |Path |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff ~ M1diff |b1 | -0.0501| 0.0947| -0.2371| 0.1337|
## |Ydiff ~ M1avg |d1 | -0.0305| 0.0974| -0.2225| 0.1596|
## |Ydiff ~ M2diff |b2 | -0.0844| 0.0932| -0.2628| 0.0966|
## |Ydiff ~ M2avg |d2 | -0.0060| 0.0885| -0.1801| 0.1655|
## |Ydiff ~ M3diff |b3 | -0.0167| 0.1024| -0.2159| 0.1852|
## |Ydiff ~ M3avg |d3 | -0.1552| 0.1038| -0.3555| 0.0511|
##
##
## *************** INTERCEPTS (MC) ***************
##
##
## |Intercept |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~1 |cp | -0.0315| 0.0170| -0.0645| 0.0019|
## |M1diff~1 |a1 | -0.0271| 0.0175| -0.0613| 0.0073|
## |M2diff~1 |a2 | 0.0145| 0.0180| -0.0206| 0.0502|
## |M3diff~1 |a3 | 0.0153| 0.0164| -0.0172| 0.0470|
## |M1avg~1 | | -0.0000| 0.0184| -0.0356| 0.0359|
## |M2avg~1 | | 0.0000| 0.0203| -0.0407| 0.0401|
## |M3avg~1 | | -0.0000| 0.0174| -0.0337| 0.0339|
##
##
## *************** VARIANCES (MC) ***************
##
##
## |Variance |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~~Ydiff | | 0.0275| 0.0038| 0.0200| 0.0350|
## |M1diff~~M1diff | | 0.0308| 0.0043| 0.0221| 0.0393|
## |M2diff~~M2diff | | 0.0323| 0.0046| 0.0233| 0.0412|
## |M3diff~~M3diff | | 0.0264| 0.0037| 0.0193| 0.0336|
## |M1avg~~M1avg | | 0.0339| 0.0048| 0.0246| 0.0431|
## |M2avg~~M2avg | | 0.0407| 0.0057| 0.0297| 0.0521|
## |M3avg~~M3avg | | 0.0306| 0.0043| 0.0225| 0.0392|
##
##
## *************** STANDARDIZED (MC) ***************
##
##
## |Parameter | Estimate| SE| R| 2.5%| 97.5%|
## |:--------------|--------:|------:|---------:|-------:|------:|
## |cp | -0.1858| 0.0992| 5000.0000| -0.3786| 0.0107|
## |b1 | -0.0519| 0.0953| 5000.0000| -0.2355| 0.1363|
## |d1 | -0.0331| 0.1029| 5000.0000| -0.2376| 0.1723|
## |b2 | -0.0894| 0.0958| 5000.0000| -0.2695| 0.1021|
## |d2 | -0.0071| 0.1029| 5000.0000| -0.2105| 0.1928|
## |b3 | -0.0160| 0.0956| 5000.0000| -0.1997| 0.1712|
## |d3 | -0.1601| 0.1032| 5000.0000| -0.3552| 0.0511|
## |a1 | -0.1546| 0.1015| 5000.0000| -0.3571| 0.0423|
## |a2 | 0.0806| 0.1014| 5000.0000| -0.1138| 0.2833|
## |a3 | 0.0945| 0.1024| 5000.0000| -0.1072| 0.2954|
## |Ydiff~~Ydiff | 0.9578| 0.0458| 5000.0000| 0.8031| 0.9767|
## |M1diff~~M1diff | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M2diff~~M2diff | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M3diff~~M3diff | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~~M1avg | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~~M2avg | 0.2898| 0.0940| 5000.0000| 0.0942| 0.4589|
## |M1avg~~M3avg | 0.3395| 0.0890| 5000.0000| 0.1530| 0.5019|
## |M2avg~~M2avg | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M2avg~~M3avg | 0.3240| 0.0916| 5000.0000| 0.1318| 0.4911|
## |M3avg~~M3avg | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~1 | -0.0000| 0.1010| 5000.0000| -0.1961| 0.1999|
## |M2avg~1 | 0.0000| 0.1019| 5000.0000| -0.2022| 0.2014|
## |M3avg~1 | -0.0000| 0.1007| 5000.0000| -0.1941| 0.1917|
## |indirect_1 | 0.0080| 0.0183| 5000.0000| -0.0263| 0.0508|
## |indirect_2 | -0.0072| 0.0151| 5000.0000| -0.0444| 0.0176|
## |indirect_3 | -0.0015| 0.0132| 5000.0000| -0.0309| 0.0253|
## |total_indirect | -0.0007| 0.0271| 5000.0000| -0.0558| 0.0550|
## |total_effect | -0.1865| 0.0993| 5000.0000| -0.3793| 0.0135|##
## 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", # Serial mediation
standardized = TRUE,
Na = "DE", ci_method = "mc", R = 5000, seed = 123
)
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| 0.0164| -0.0160| 0.0487|
## |Direct effect | 0.0160| 0.0165| -0.0158| 0.0487|
## |Total indirect | -0.0006| 0.0044| -0.0099| 0.0082|
##
## Indirect effects:
##
##
## |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:-------|--------:|------:|---------:|----------:|
## |ind_1 | 0.0010| 0.0030| -0.0048| 0.0079|
## |ind_2 | -0.0023| 0.0032| -0.0102| 0.0025|
## |ind_1_2 | 0.0007| 0.0010| -0.0005| 0.0033|
##
## 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| 0.0908| -0.2381| 0.1148|
## |d2 | -0.0733| 0.0826| -0.2402| 0.0862|
## |d_1_2 | -0.0578| 0.0950| -0.2449| 0.1272|
##
##
## *************** MODERATION KEY (d-paths) ***************
##
##
## |Coefficient |Path |Moderated |
## |:-----------|:---------------|:----------------|
## |d1 |M1avg -> Ydiff |M1diff -> Ydiff |
## |d2 |M2avg -> Ydiff |M2diff -> Ydiff |
## |d_1_2 |M1avg -> M2diff |M1diff -> M2diff |
##
## UNSTANDARDIZED CONDITIONAL EFFECTS (mc)
## Percentile intervals; moderator probes and centering references are held fixed.
##
##
## *************** 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 | -0.0670| 0.1030| -0.2680| 0.1320|
## |X0_b1 | -0.0050| 0.1040| -0.2040| 0.1980|
## |X1_b2 | -0.1490| 0.1010| -0.3470| 0.0500|
## |X0_b2 | -0.0760| 0.1010| -0.2770| 0.1210|
## |X1_b_1_2 | 0.2040| 0.1100| -0.0120| 0.4170|
## |X0_b_1_2 | 0.2620| 0.1110| 0.0500| 0.4760|
##
##
## *************** REGRESSION PATHS (MC) ***************
##
##
## |Path |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff ~ M1diff |b1 | -0.0360| 0.0930| -0.2178| 0.1468|
## |Ydiff ~ M1avg |d1 | -0.0617| 0.0908| -0.2381| 0.1148|
## |Ydiff ~ M2diff |b2 | -0.1123| 0.0924| -0.2915| 0.0688|
## |Ydiff ~ M2avg |d2 | -0.0733| 0.0826| -0.2402| 0.0862|
## |M2diff ~ M1diff |b_1_2 | 0.2333| 0.0998| 0.0368| 0.4267|
## |M2diff ~ M1avg |d_1_2 | -0.0578| 0.0950| -0.2449| 0.1272|
##
##
## *************** INTERCEPTS (MC) ***************
##
##
## |Intercept |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:---------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~1 |cp | 0.0160| 0.0165| -0.0158| 0.0487|
## |M1diff~1 |a1 | -0.0271| 0.0176| -0.0620| 0.0072|
## |M2diff~1 |a2 | 0.0208| 0.0176| -0.0139| 0.0552|
## |M1avg~1 | | -0.0000| 0.0184| -0.0360| 0.0371|
## |M2avg~1 | | 0.0000| 0.0201| -0.0391| 0.0398|
##
##
## *************** VARIANCES (MC) ***************
##
##
## |Variance |Label | Estimate| SE| 2.5%CI.Lo| 97.5%CI.Up|
## |:--------------|:-----|--------:|------:|---------:|----------:|
## |Ydiff~~Ydiff | | 0.0256| 0.0037| 0.0185| 0.0327|
## |M2diff~~M2diff | | 0.0305| 0.0043| 0.0218| 0.0391|
## |M1diff~~M1diff | | 0.0308| 0.0043| 0.0224| 0.0388|
## |M1avg~~M1avg | | 0.0339| 0.0049| 0.0245| 0.0434|
## |M2avg~~M2avg | | 0.0407| 0.0058| 0.0292| 0.0518|
##
##
## *************** STANDARDIZED (MC) ***************
##
##
## |Parameter | Estimate| SE| R| 2.5%| 97.5%|
## |:--------------|--------:|------:|---------:|-------:|------:|
## |cp | 0.0983| 0.1005| 5000.0000| -0.0947| 0.2980|
## |b1 | -0.0388| 0.0986| 5000.0000| -0.2278| 0.1595|
## |d1 | -0.0697| 0.1004| 5000.0000| -0.2633| 0.1288|
## |b2 | -0.1239| 0.1004| 5000.0000| -0.3148| 0.0758|
## |d2 | -0.0908| 0.1006| 5000.0000| -0.2915| 0.1062|
## |a1 | -0.1546| 0.1016| 5000.0000| -0.3562| 0.0398|
## |a2 | 0.1159| 0.0978| 5000.0000| -0.0779| 0.3045|
## |b_1_2 | 0.2278| 0.0947| 5000.0000| 0.0379| 0.4048|
## |d_1_2 | -0.0592| 0.0963| 5000.0000| -0.2496| 0.1286|
## |Ydiff~~Ydiff | 0.9656| 0.0420| 5000.0000| 0.8312| 0.9897|
## |M2diff~~M2diff | 0.9446| 0.0466| 5000.0000| 0.8192| 0.9929|
## |M1diff~~M1diff | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~~M1avg | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~~M2avg | 0.2898| 0.0931| 5000.0000| 0.0953| 0.4603|
## |M2avg~~M2avg | 1.0000| 0.0000| 5000.0000| 1.0000| 1.0000|
## |M1avg~1 | -0.0000| 0.1011| 5000.0000| -0.1988| 0.2006|
## |M2avg~1 | 0.0000| 0.1010| 5000.0000| -0.1952| 0.2005|
## |indirect_1 | 0.0060| 0.0183| 5000.0000| -0.0298| 0.0483|
## |indirect_2 | -0.0144| 0.0191| 5000.0000| -0.0606| 0.0152|
## |indirect_1_2 | 0.0044| 0.0058| 5000.0000| -0.0033| 0.0196|
## |total_indirect | -0.0040| 0.0265| 5000.0000| -0.0593| 0.0500|
## |total_effect | 0.0943| 0.1005| 5000.0000| -0.0974| 0.2946|##
## 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