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雷达卡
做一个有调节的中介模型，拿了一组数据测试，结果如表2：自变量SQ，中介SC，因变量Loylty，调节变量CS，
模型是process中模型58，SQ--SC--Loylty，CS调节中介过程，第一阶段的调节是否显著是不是看int_1的置信区间是否包含0(橙色标注)，第二阶段的调节看int_2的置信区间是否包含0（橙色标注），是不是第一阶段和第二阶段的调节都显著就可以说明调节变量调节了整个中介过程？
还有就是想要像一片英文文章一样呈现如表1这样的结果，但是不知道怎么看，把调节变量分为高低两组，process执行过程应该有自动分组了，但是分组以后的indirect effect 和置信区间应该看哪里啊？求解答
表1
Independent variable Mediator Moderator Indirect effect 95%bootstrappedCI
CEO transformational TMT trust High ED (+1SD) .03* [.005, .071]
leadership climate Low ED (−1SD) .06* [.023, .124]
表2
Run MATRIX procedure:
************** PROCESS Procedure for SPSS Release 2.15 *******************
Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2013). www.guilford.com/p/hayes3
**************************************************************************
Model = 58
Y = loyalty
X = SQmean
M = SCmean
W = CSmean
Statistical Controls:
CONTROL= gender age
Sample size
295
**************************************************************************
Outcome: SCmean
Model Summary
R R-sq MSE F df1 df2 p
.4017 .1613 1.0427 11.1185 5.0000 289.0000 .0000
Model
coeff se t p LLCI ULCI
constant 3.4010 1.9381 1.7548 .0804 -.4137 7.2157
SQmean .0181 .3962 .0458 .9635 -.7616 .7979
CSmean -.2713 .3912 -.6936 .4885 -1.0412 .4986
int_1 .0888 .0763 1.1644 .2452 -.0613 .2389
gender .0069 .1280 .0539 .9570 -.2450 .2588
age -.0065 .0058 -1.1139 .2662 -.0180 .0050
Product terms key:
int_1 SQmean X CSmean
**************************************************************************
Outcome: loyalty
Model Summary
R R-sq MSE F df1 df2 p
.5882 .3460 .6873 25.3954 6.0000 288.0000 .0000
Model
coeff se t p LLCI ULCI
constant 2.2590 .9842 2.2954 .0224 .3220 4.1961
SCmean -.1651 .2166 -.7624 .4464 -.5915 .2612
SQmean .4192 .0911 4.6001 .0000 .2398 .5985
CSmean .0261 .1910 .1368 .8913 -.3499 .4022
int_2 .0631 .0421 1.4992 .1349 -.0198 .1460
gender -.1297 .1039 -1.2488 .2128 -.3342 .0747
age -.0064 .0047 -1.3474 .1789 -.0157 .0029
Product terms key:
int_2 SCmean X CSmean
******************** DIRECT AND INDIRECT EFFECTS *************************
Direct effect of X on Y
Effect SE t p LLCI ULCI
.4192 .0911 4.6001 .0000 .2398 .5985
Conditional indirect effect(s) of X on Y at values of the moderator(s):
Mediator
CSmean Effect Boot SE BootLLCI BootULCI
SCmean 4.1040 .0360 .0455 -.0459 .1407
SCmean 5.0176 .0703 .0354 .0103 .1475
SCmean 5.9313 .1141 .0574 .0269 .2812
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.
******************** ANALYSIS NOTES AND WARNINGS *************************
Number of bootstrap samples for bias corrected bootstrap confidence intervals:
1000
Level of confidence for all confidence intervals in output:
95.00
------ END MATRIX -----
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