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雷达卡
求救救孩子！感谢各位大佬做调节变量在层2，其他变量层1的多水平调节中介模型。调节变量同时调节前半段和直接效应不显著，但单独做调节前半段显著，这是怎么回事啊
结果在下面：
调节前半段
MODEL FIT INFORMATION
Number of Free Parameters 26
Loglikelihood
H0 Value -2996.928
H0 Scaling Correction Factor 1.1257
for MLR
Information Criteria
Akaike (AIC) 6045.856
Bayesian (BIC) 6172.853
Sample-Size Adjusted BIC 6090.277
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Means
X 0.000 0.000 999.000 999.000
Intercepts
M 0.000 0.000 999.000 999.000
Variances
X 0.747 0.041 18.208 0.000
Residual Variances
M 0.203 0.013 15.680 0.000
Y 0.619 0.027 22.814 0.000
Between Level
S_A ON
W 0.108 0.042 2.582 0.010
MM ON
XM 0.040 0.069 0.586 0.558
AGEM 0.032 0.015 2.157 0.031
Y ON
XM 0.000 0.183 -0.001 0.999
MM 1.044 0.348 3.000 0.003
AGEM 0.001 0.045 0.013 0.989
INCM -0.069 0.074 -0.927 0.354
Y WITH
S_A 0.008 0.009 0.919 0.358
S_B 0.003 0.028 0.110 0.912
S_CP 0.002 0.011 0.208 0.835
S_A WITH
S_B 0.006 0.015 0.422 0.673
S_CP 0.006 0.011 0.509 0.610
S_B WITH
S_CP 0.009 0.015 0.592 0.554
Means
S_B 0.459 0.070 6.540 0.000
S_CP 0.201 0.043 4.622 0.000
Intercepts
MM 2.416 0.397 6.090 0.000
Y 0.120 2.031 0.059 0.953
S_A -0.248 0.162 -1.527 0.127
Variances
S_B 0.021 0.037 0.558 0.577
S_CP 0.017 0.017 0.989 0.323
Residual Variances
MM 0.016 0.004 4.629 0.000
Y 0.013 0.015 0.898 0.369
S_A 0.008 0.006 1.266 0.206
New/Additional Parameters
INDH 0.117 0.021 5.468 0.000
INDL 0.049 0.031 1.557 0.119
INDM 0.083 0.023 3.536 0.000
IND1 -0.034 0.013 -2.623 0.009
IND2 -0.068 0.026 -2.623 0.009
IND3 -0.034 0.013 -2.623 0.009
INDB 0.042 0.076 0.556 0.578
DB 0.000 0.183 -0.001 0.999
DW 0.201 0.043 4.622 0.000
调节前半+直接
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 27
Loglikelihood
H0 Value -2994.371
H0 Scaling Correction Factor 2.8045
for MLR
Information Criteria
Akaike (AIC) 6042.743
Bayesian (BIC) 6174.624
Sample-Size Adjusted BIC 6088.872
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Within Level
Means
X 0.000 0.000 999.000 999.000
Intercepts
M 0.000 0.000 999.000 999.000
Variances
X 0.747 0.041 18.208 0.000
Residual Variances
M 0.203 0.013 15.698 0.000
Y 0.618 0.226 2.728 0.006
Between Level
S_A ON
W 0.095 0.024 3.948 0.000
S_CP ON
W -0.108 0.635 -0.169 0.866
MM ON
XM 0.040 0.069 0.586 0.558
AGEM 0.032 0.015 2.157 0.031
Y ON
XM 0.000 0.986 0.000 1.000
MM 1.045 1.220 0.856 0.392
AGEM 0.001 0.165 0.008 0.994
INCM -0.068 0.148 -0.461 0.645
Y WITH
S_A 0.008 0.010 0.813 0.416
S_B 0.003 0.209 0.015 0.988
S_CP 0.001 0.076 0.019 0.985
S_A WITH
S_B 0.008 0.024 0.330 0.741
S_CP 0.004 0.026 0.163 0.871
S_B WITH
S_CP 0.014 0.528 0.026 0.979
Means
S_B 0.473 0.295 1.607 0.108
Intercepts
MM 2.416 0.397 6.090 0.000
Y 0.104 8.534 0.012 0.990
S_A 0.170 0.021 7.936 0.000
S_CP 0.191 0.135 1.415 0.157
Variances
S_B 0.029 0.937 0.031 0.976
Residual Variances
MM 0.016 0.004 4.629 0.000
Y 0.014 0.048 0.283 0.777
S_A 0.008 0.006 1.228 0.219
S_CP 0.009 0.342 0.027 0.978
New/Additional Parameters
INDH 0.120 0.090 1.334 0.182
INDL 0.057 0.057 1.013 0.311
INDM 0.089 0.072 1.225 0.220
IND1 -0.031 0.020 -1.541 0.123
IND2 -0.062 0.040 -1.541 0.123
IND3 -0.031 0.020 -1.541 0.123
INDB 0.042 0.098 0.427 0.669
DB 0.000 0.986 0.000 1.000
DL 0.265 0.567 0.467 0.640
DM 0.191 0.135 1.415 0.157
DH 0.117 0.314 0.371 0.710
D1 0.074 0.438 0.169 0.866
D2 0.148 0.877 0.169 0.866
D3 0.074 0.438 0.169 0.866
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