Pengujian Outliers Analisis Structural Equation Model
85
Tabel 4.9 Hasil Pengujian Outliers
lanjutan
Observation number Mahalanobis d-squared
p1 p2
118 32.217
.152 .131
76 32.200
.152 .090
28 31.742
.166 .126
67 31.068
.187 .231
88 30.621
.202 .301
114 30.253
.215 .354
113 30.176
.218 .305
122 29.579
.240 .456
111 28.489
.286 .798
22 28.240
.297 .816
95 28.221
.298 .765
85 28.136
.302 .731
78 27.594
.327 .847
71 27.573
.328 .803
87 27.542
.329 .756
63 27.399
.336 .746
112 27.153
.348 .774
64 26.879
.362 .811
117 26.821
.365 .776
82 26.752
.368 .743
48 26.749
.369 .681
3 26.665
.373 .649
9 26.661
.373 .580
61 26.632
.375 .520
86 26.572
.378 .475
107 26.511
.381 .431
81 26.416
.386 .404
58 26.388
.387 .347
56 26.292
.392 .323
110 25.697
.424 .534
121 25.648
.427 .485
25 25.592
.430 .441
41 25.445
.438 .442
103 25.226
.450 .479
124 23.849
.528 .936
17 23.689
.537 .940
125 23.650
.540 .923
102 23.600
.543 .906
86
Tabel 4.9 Hasil Pengujian Outliers
lanjutan
Observation number Mahalanobis d-squared
p1 p2
120 23.407
.554 .917
51 23.372
.556 .895
15 23.337
.558 .869
89 23.318
.559 .834
53 23.121
.571 .853
4 23.073
.573 .825
44 22.992
.578 .806
16 22.754
.592 .841
75 22.425
.611 .896
101 22.288
.619 .897
36 22.205
.624 .884
106 22.097
.630 .877
68 21.814
.646 .913
12 21.800
.647 .884
37 21.744
.650 .862
47 21.724
.652 .824
55 21.688
.654 .787
46 21.611
.658 .763
27 21.591
.659 .711
62 21.054
.690 .864
69 20.914
.697 .865
73 20.872
.700 .782
96 20.002
.747 .963
108 19.985
.748 .947
66 19.935
.750 .931
38 19.923
.751 .904
77 19.904
.752 .871
72 19.625
.766 .906
39 19.592
.768 .876
65 19.365
.779 .897
93 19.158
.790 .910
10 19.152
.790 .873
31 19.151
.790 .824
29 19.100
.792 .785
100 19.069
.794 .731
7 18.788
.807 .782
57 18.762
.808 .724
84 18.754
.809 .649
Sumber: data diolah 2015
87
Nilai mahalanobis distance pada penelitian ini sebesar 52,619 χ
2 20,0.001
. Berdasarkan hasil analisis pada tabel 4.9, nilai paling besar mahalanobis d-squared adalah 51.784. Dengan demikian, dapat dapat
disimpulkan bahwa tidak ada outliers dalam penelitian ini. Angka terbesar dalam mahalanobis d-squared yang menunjukkan tidak adanya outliers memberi
makna bahwa tidak ada data yang bias dari penelitian ini. Data-data yang dianalisis mencerminkan fenomena yang sebenarnya.