--- title: "MEMORE_vs_wsMed" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{MEMORE_vs_wsMed} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # introduction This document presents a comparison between the **MEMORE 3.0 (SPSS Plugin)** and **wsMed (R Package)** outputs. ## parallel mediation We analyze a **three-mediator parallel mediation model**, comparing the results obtained from both methods. ### MEMORE 3.0 Analysis Report ```` ## ``` ## ## 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 ----- ## ## ``` ```` ### wsMed Analysis Report ``` ## ## ## *************** 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_indirect ``` ## chained/serial mediation We analyze a **Serial Mediation (`Serial = 1`)**. ### MEMORE 3.0 Analysis Report ```` ## ``` ## ## 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 ----- ## ## ``` ```` ### wsMed Analysis Report ``` r 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| ``` ``` r printGM(result2) ``` ``` ## ## 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 ```