DATA: FILE IS 2.dat; ! Bootstrap 法需要原始数据
VARIABLE: NAMES ARE X M Y W WX WM;
ANALYSIS: Bootstrap=2000; ! Bootstrap 法抽样 2000 次
MODEL:
M on X (a1)
W
WX (a3);
!做 W 对 X,U, UX 的回归
!X 和 UX 的回归系数分别命名为 a1 和 a3
Y on X
W
M (b1)
WM (b2);
!做Y对X,U,W,UW的回归
!W 和 UW 的回归系数分别命名为 b1 和 b2
MODEL CONSTRAINT:
new (H1-H7);
H1= a1*b2;
H2= a3*b1;
H3= a3*b2;
H4=a1*b1;
!a1b2 的估计 ! a3b1 的估计 ! a3b2 的估计
!当 U 等于 0 时的 (a1+a3U)(b1+b2U) !的中介效应的值
H5=H4+H1+H2+H3;
!当 U 等于 1 时的中介效应(a1+a3U)(b1+b2U)的值
H6=H4-H1-H2+H3;
!当 U 等于-1 时的中介效应(a1+a3U)(b1+b2U)的值
H7=H5-H4;
! U 等于 1 和 0 时的 (a1+a3U)(b1+b2U)之差
OUTPUT: cinterval (bcbootstrap);
INPUT READING TERMINATED NORMALLY
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 151
Number of dependent variables 2
Number of independent variables 4
Number of continuous latent variables 0
Observed dependent variables
Continuous
M Y
Observed independent variables
X W WX WM
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.500D-04
Maximum number of steepest descent iterations 20
Number of bootstrap draws
Requested 2000
Completed 2000