Guten Morgen,
ich habe die Moderatorenanalyse mit SPSS Plugin von Hayes (PROCESS) gerechnet. So wie ich es verstehe, ist das Modell einfach nicht signifikant. Mich hatte nur gewundert, dass sich die beiden Moderatorenanalysen grafisch so unterscheiden.
X=Belastung
Y=Somatische Beschwerden
M= Coping (Moderator)
Meine Stichproben haben ein n von 53. Das ist der Output von "Schule"
Model = 1
Y = ysom
X = Belast
M = CopDefMW
Sample size
53
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Outcome: ysom
Model Summary
R R-sq MSE F df1 df2 p
,2385 ,0569 1,9385 ,5966 3,0000 49,0000 ,6203
Model
coeff se t p LLCI ULCI
constant 1,8332 ,2208 8,3022 ,0000 1,3895 2,2769
CopDefM ,0952 ,1569 ,6067 ,5468 -,2202 ,4106
Belast ,0846 ,0683 1,2378 ,2217 -,0527 ,2218
int_1 ,0483 ,0549 ,8795 ,3834 -,0620 ,1585
Conditional effect of X on Y at values of the moderator(s):
CopDefMW Effect se t p LLCI ULCI
-1,5123 ,0116 ,0874 ,1326 ,8950 -,1640 ,1871
,0000 ,0846 ,0683 1,2378 ,2217 -,0527 ,2218
1,5123 ,1575 ,1244 1,2664 ,2113 -,0924 ,4075
Values for quantitative moderators are the mean and plus/minus one SD from mean.
Values for dichotomous moderators are the two values of the moderator.
********************* JOHNSON-NEYMAN TECHNIQUE **************************
There are no statistical significance transition points within the observed
range of the moderator.
Hier der Output von "Klinik"
Model = 1
Y = YSOMATIC
X = LEBER
M = COPDEFMW
Sample size
53
**************************************************************************
Outcome: YSOMATIC
Model Summary
R R-sq MSE F df1 df2 p
,3785 ,1433 8,5905 5,3611 3,0000 49,0000 ,0028
Model
coeff se t p LLCI ULCI
constant 4,8870 ,4071 12,0044 ,0000 4,0689 5,7052
COPDEFMW,5000 ,2747 1,8200 ,0749 -,0521 1,0521
LEBER ,1478 ,0495 2,9838 ,0044 ,0483 ,2474
int_1 ,0003 ,0342 ,0074 ,9942 -,0685 ,0690
Product terms key:
int_1 LEBER X COPDEFMW
R-square increase due to interaction(s):
R2-chng F df1 df2 p
int_1 ,0000 ,0001 1,0000 49,0000 ,9942
*************************************************************************
Conditional effect of X on Y at values of the moderator(s):
COPDEFMW Effect se t p LLCI ULCI
-1,4224 ,1475 ,0592 2,4896 ,0162 ,0284 ,2665
,0000 ,1478 ,0495 2,9838 ,0044 ,0483 ,2474
1,4224 ,1482 ,0784 1,8908 ,0646 -,0093 ,3057
Values for quantitative moderators are the mean and plus/minus one SD from mean.
Values for dichotomous moderators are the two values of the moderator.
********************* JOHNSON-NEYMAN TECHNIQUE **************************
Moderator value(s) defining Johnson-Neyman significance region(s)
Value % below % above
1,2479 83,0189 16,9811
-2,0221 9,4340 90,5660
Conditional effect of X on Y at values of the moderator (M)
COPDEFMW Effect se t p LLCI ULCI
-3,2579 ,1470 ,1090 1,3490 ,1835 -,0720 ,3660
-2,9579 ,1471 ,0998 1,4731 ,1471 -,0536 ,3477
-2,6579 ,1472 ,0910 1,6180 ,1121 -,0356 ,3300
-2,3579 ,1472 ,0824 1,7872 ,0801 -,0183 ,3128
-2,0579 ,1473 ,0742 1,9841 ,0529 -,0019 ,2965
-2,0221 ,1473 ,0733 2,0096 ,0500 ,0000 ,2947
-1,7579 ,1474 ,0667 2,2098 ,0318 ,0134 ,2814
-1,4579 ,1475 ,0600 2,4592 ,0175 ,0270 ,2680
-1,1579 ,1475 ,0544 2,7146 ,0091 ,0383 ,2568
-,8579 ,1476 ,0502 2,9388 ,0050 ,0467 ,2486
-,5579 ,1477 ,0480 3,0776 ,0034 ,0513 ,2441
-,2579 ,1478 ,0479 3,0852 ,0033 ,0515 ,2440
,0421 ,1478 ,0500 2,9594 ,0047 ,0474 ,2482
,3421 ,1479 ,0539 2,7428 ,0085 ,0395 ,2563
,6421 ,1480 ,0594 2,4901 ,0162 ,0286 ,2674
,9421 ,1481 ,0661 2,2407 ,0296 ,0153 ,2809
1,2421 ,1481 ,0736 2,0137 ,0495 ,0003 ,2960
1,2479 ,1481 ,0737 2,0096 ,0500 ,0000 ,2963
1,5421 ,1482 ,0817 1,8150 ,0756 -,0159 ,3123
1,8421 ,1483 ,0902 1,6441 ,1066 -,0330 ,3296
2,1421 ,1484 ,0991 1,4977 ,1406 -,0507 ,3475
2,4421 ,1485 ,1082 1,3722 ,1763 -,0690 ,3659
2,7421 ,1485 ,1175 1,2641 ,2122 -,0876 ,3846
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Liebe Grüße
Anna
