﻿
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 ****************************

Number of samples for Monte Carlo condifidence intervals:
  5000

The following variables were mean centered prior to analysis:
 (        TSRQ_T2   +       TSRQ_T1  )        /2
 (        WEMBS_T2  +       WEMBS_T1 )        /2

Level of confidence for all confidence intervals in output:
      95.00

**************************************************************************************

Model:
  1

Variables:
Y =   DASSS_T2 DASSS_T1
M1 =  TSRQ_T2  TSRQ_T1
M2 =  WEMBS_T2 WEMBS_T1

Computed Variables:
Ydiff =           DASSS_T2  -       DASSS_T1
M1diff =          TSRQ_T2   -       TSRQ_T1
M2diff =          WEMBS_T2  -       WEMBS_T1
M1avg  = (        TSRQ_T2   +       TSRQ_T1  )        /2                         Centered
M2avg  = (        WEMBS_T2  +       WEMBS_T1 )        /2                         Centered

Sample Size:
  63

**************************************************************************************
Outcome: Ydiff =  DASSS_T2  -       DASSS_T1

Model
               Coef         SE          t          p       LLCI       ULCI
constant    -3.0476      .5400    -5.6441      .0000    -4.1270    -1.9682

Degrees of freedom for all regression coefficient estimates:
  62

**************************************************************************************
Outcome: M1diff = TSRQ_T2   -       TSRQ_T1

Model
               Coef         SE          t          p       LLCI       ULCI
constant     1.2381      .8625     1.4355      .1562     -.4860     2.9621

Degrees of freedom for all regression coefficient estimates:
  62

**************************************************************************************
Outcome: M2diff = WEMBS_T2  -       WEMBS_T1

Model
               Coef         SE          t          p       LLCI       ULCI
constant     1.6349      .6252     2.6152      .0112      .3852     2.8846

Degrees of freedom for all regression coefficient estimates:
  62

**************************************************************************************
Outcome: Ydiff =  DASSS_T2  -       DASSS_T1

Model Summary
          R       R-sq        MSE          F        df1        df2          p
      .5061      .2562    14.6053     4.9939     4.0000    58.0000      .0016

Model
               Coef         SE          t          p       LLCI       ULCI
constant    -2.5543      .5193    -4.9182      .0000    -3.5939    -1.5147
M1diff        .0615      .0712      .8646      .3908     -.0809      .2040
M2diff       -.3484      .1019    -3.4184      .0012     -.5524     -.1444
M1avg        -.0104      .0585     -.1784      .8590     -.1276      .1067
M2avg        -.2557      .1246    -2.0512      .0448     -.5051     -.0062

Degrees of freedom for all regression coefficient estimates:
  58

************************* TOTAL, DIRECT, AND INDIRECT EFFECTS *************************

Total effect of X on Y
     Effect         SE          t         df          p       LLCI       ULCI
    -3.0476      .5400    -5.6441    62.0000      .0000    -4.1270    -1.9682

Direct effect of X on Y
     Effect         SE          t         df          p       LLCI       ULCI
    -2.5543      .5193    -4.9182    58.0000      .0000    -3.5939    -1.5147

Indirect Effect of X on Y through M
          Effect       MCSE     MCLLCI     MCULCI
Ind1       .0762      .1216     -.1163      .3652
Ind2      -.5695      .2807    -1.2010     -.1007
Total     -.4934      .3085    -1.1692      .0665

Indirect Key
Ind1  'X'      ->       M1diff   ->       Ydiff
Ind2  'X'      ->       M2diff   ->       Ydiff

Pairwise Contrasts Between Specific Indirect Effects
          Effect       MCSE     MCLLCI     MCULCI
(C1)       .6457      .3032      .1320     1.3132

Contrast Key:
(C1)  Ind1      -       Ind2

------ END MATRIX -----

