pemodelan sem dengan stata - institut tazkia
TRANSCRIPT
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 1
PEMODELAN SEM DENGAN STATA
I. PENGANTAR PEMODELAN SEM
▪ Structural Equation Modeling (SEM) merupakan teknik analisis multivariat yang dikembangkan untuk
menutupi keterbatasan yang dimiliki oleh model-model seperti: analisis regresi, path analysis (analisis
jalur), dan confirmatory factor analysis (analisis faktor konfirmatori) (Hox dan Bechger, 1998).
▪ SEM merupakan teknik statistik yang digunakan untuk membangun dan menguji model statistik yang
umumnya dalam bentuk model hubungan kausalitas. SEM merupakan teknik perpaduan (hybrid) yang
menggabungkan aspek dari analisis regresi, path analysis (analisis jalur), dan confirmatory factor
analysis (analisis faktor konfirmatori).
▪ Beberapa Istilah Dasar. Beberapa istilah umum yang berkaitan dengan SEM Hair et al. (1995):
1) Variabel atau konstrak laten merupakan variabel-variabel yang tidak diobservasi (unobserved
variables). Pengertian konstrak adalah semacam konsep yang membuat peneliti mendefinisikan
ketentuan konseptual, namun tidak secara langsung (bersifat laten), melainkan diukur berdasarkan
perkiraan atas indikator-indkator terukur tertentu. Dengan demikian, konstrak merupakan suatu proses
atau kejadian dari suatu amatan yang diformulasikan dalam bentuk konseptual dan memerlukan
indikator untuk memperjelasnya.
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2) Variabel-variabel yang diobservasi (observed variable), kadang disebut sebagai variabel manifes
(manifest variables) atau variabel referensi (reference variables).
3) Variabel Eksogen, Variabel Endogen, dan Variabel Error.
o Variabel eksogen adalah variabel penyebab atau variabel yang tidak dipengaruhi oleh variabel
lainnya. Variabel eksogen memberikan efek kepada variabel lainnya. Dalam diagram jalur,
variabel eksogen secara eksplisit ditandai sebagai variabel dimana tidak ada panah tunggal
yang menuju ke arahnya.
o Variabel endogen adalah variabel yang dijelaskan oleh variabel eksogen. Variabel endogen
adalah efek dari variabel eksogen. Dalam diagram jalur, variabel endogen secara eksplisit
ditandai oleh kepala panah yang menuju ke arahnya.
o Variabel error didefinisikan sebagai kumpulan variabel-variabel eksogen lainnya yang tidak
dimasukkan dalam model yang dimungkinkan masih memiliki pengaruh terhadap variabel
endogen.
4) Diagram jalur adalah sebuah diagram yang menggambarkan hubungan kausal antar variabel.
Pembangunan diagram jalur dimaksudkan untuk menvisualisasikan keseluruhan alur hubungan
antara variabel. Sebagai contoh, diberikan diagram jalur dari pengaruh Internal dan Eksternal
(variabel eksogen) terhadap Kinerja variabel endogen).
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Tanda anak panah (→) menunjukkan pengaruh antara konstrak laten eksogen terhadap konstrak laten
endogen).
5) Koefesien struktural atau jalur. Koefisien jalur adalah suatu koefisien regresi terstandardisasi
(standardized) yang menunjukkan besarnya pengaruh dari suatu variabel eksogen terhadap variabel
endogen dalam diagram jalur. Koefisien jalur disebut juga standardized solution. Standardized
solution yang menghubungkan antara konstrak laten dan variabel indikatornya disebut sebagai loading
factor.
▪ Notasi/simbol dalam Analisis SEM. Secara umum, analisis SEM mengunakan beberapa notasi atau
simbol yang masing-masing memiliki makna tersendiri.
• Untuk mengenalkan notasi-notasi yang sering digunakan dalam SEM, berikut ini akan dijelaskan melalui
diagram jalur sebagai berikut:
Faktor Internal
Faktor Eksternal
Kinerja
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Keterangan:
• (KSI) : konstrak/variabel laten eksogen.
• (ETA) : konstrak/variabel laten endogen.
• (GAMMA) : hubungan langsung variabel eksogen terhadap variabel endogen.
• (BETA) : hubungan langsung variabel endogen terhadap variabel endogen.
• (LAMBDA) : hubungan langsung variabel eksogen ataupun endogen terhadap indikatornya
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• (PHI) : kovarian/korelasi antara variabel eksogen
• (DELTA) : measurement error (kesalahan pengukuran) dari indikator variabel eksogen
• (EPSILON) : measurement error dari indikator variabel endogen
• (ZETA) : kesalahan dalam persamaan, yaitu antara variabel eksogen/endogen dan variabel
endogen
• (PSI) : kovarian di antara struktural residu
• (THETA-DELTA) : matriks kovarian simetris di antara kesalahan pengukuran pada indikator-
indikator dari variabel eksogen
• (THETA-EPSILON): matriks kovarian simetris di antara kesalahan pengukuran pada indikator-
indikator dari variabel endogen
▪ Persamaan Matematis dalam SEM. Secara umum ada dua persamaan matematis dalam SEM, yakni
persamaan pengkuran (measurement model) dan persamaan struktural (structural model).
▪ Melalui gambar diagram jalur di atas akan diilustrasikan kedua persamaan tersebut:
1) Persamaan model struktural
2) Persamaan model pengukuran variabel eksogen
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3) Persamaan model pengukuran variabel endogen
▪ Efek Dekomposisi (Pengaruh Total dan Pengaruh Tak Langsung). Efek dekomposisi terjadi
berdasarkan pembentukan diagram jalur yang dibangun berdasarkan teori. Pengaruh antara konstrak
laten dibagi berdasarkan kompleksitas hubungan variabel, yaitu:
1) pengaruh langsung (direct effects)
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2) pengaruh tak langsung (indirect effects)
3) pengaruh total (total effects)
▪ Pengaruh variabel eksogen ( ) terhadap variabel endogen kedua (2), adalah sebagai berikut:
• pengaruh langsung terhadap 2 = r1
• pengaruh tak langsung terhadap 2 = pengaruh langsung terhadap 1 x pengaruh langsung 1
terhadap 2 = r2 x r3
• pengaruh total terhadap 2 = pengaruh langsung terhadap 2 + pengaruh tak langsung terhadap
2 = r1 + (r2 x r3)
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II. PEMODELAN SEM DENGAN STATA
▪ Secara umum, command yang digunakan untuk pemodelan SEM dalam STATA adalah sem dan
gsem. Di sini gsem adalah singkatan dari Generalized SEM.
▪ Persamaan dan perbedaan penggunaan antara sem dan gsem dalam STATA.
• Command sem digunakan untuk pemodelan SEM linier standar, sementara gsem digunakan
untuk pemodelan SEM yang lebih umum atau perluasan dari model SEM linier standar.
• Command sem umumnya digunakan untuk data atau respons yang bersifat kontinu, dan
spesifikasi model yang digunakan umumnya adalah regresi linier. Sementara command gsem,
digunakan tidak hanya untuk data atau respons kontinu, tetapi bisa juga biner, ordinal, count, atau
multinomial. Pada gsem, spsesifikasi model yang digunakan adalah regresi linier, regresi gamma,
logit, probit, ordinal logit, ordinal probit, Poisson, binomial negatif, multinomial logit, dan banyak
lagi.
• Command sem umumnya digunakan untuk mengestimasi model dengan single-level data.
Sementara command gsem umumnya digunakan untuk mengestimasi model dengan single-level
data maupun multilevel data. Di sini, variabel laten dapat disertakan di tingkat mana pun.
• Comand gsem, juga dapat memenuhi model dengan efek campuran (mixed-effect), termasuk
random-effect seperti unobserved effect (misal heterogenitas individu yang tidak teramati
(unobserved)), nested effect (misal unobserved effect antara pasien dengan dokter), atau cross
effect (misal unobserved effect antara pekerjaan dan lokasi)
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III. STUDI KASUS 1. MEASUREMENT MODEL
▪ Pada studi kasus ini akan diilustrasikan tahapan estimasi model pengukuran (measurement model)
dengan menggunakan STATA.
▪ Spesifikasi model pengukuran:
▪ Path Diagram:
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 10
▪ Tahapan estimasi model:
1) Buka StudiKasus1.dta
2) Menyajikan statistik deskriptif variabel. . summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
x1 | 500 99.518 14.35402 60 137
x2 | 500 99.954 14.1939 52 140
x3 | 500 99.052 14.26395 59 150
x4 | 500 94.474 70.11603 -113 295
3) Uji Multvariate Normal . mvtest normality x1 x2 x3 x4, univariate stats(all)
Test for univariate normality
---------------------------------------------------------------------
| ------- joint ------
Variable | Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
-------------+-------------------------------------------------------
x1 | 0.9260 0.5037 0.46 0.7947
x2 | 0.6652 0.6008 0.46 0.7926
x3 | 0.2869 0.8902 1.16 0.5604
x4 | 0.9667 0.5058 0.45 0.7993
---------------------------------------------------------------------
Test for multivariate normality
Mardia mSkewness = .2078419 chi2(20) = 17.466 Prob>chi2 = 0.6225
Mardia mKurtosis = 23.26195 chi2(1) = 1.419 Prob>chi2 = 0.2336
Henze-Zirkler = .7781454 chi2(1) = 1.989 Prob>chi2 = 0.1584
Doornik-Hansen chi2(8) = 5.468 Prob>chi2 = 0.7066
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 11
4) Klik Statistics → SEM → Model Building and Estimation.
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5) Klik ikon pada menu bar di samping. Kemudian klik di sembarang tempat pada area
layar.
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Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 14
6) Klik Estimation → estimate
. sem (X -> x1, ) (X -> x2, ) (X -> x3, ) (X -> x4, ), latent(X ) nocapslatent
Endogenous variables
Measurement: x1 x2 x3 x4
Exogenous variables
Latent: X
Fitting target model:
Iteration 0: log likelihood = -8487.5905
Iteration 1: log likelihood = -8487.2358
Iteration 2: log likelihood = -8487.2337
Iteration 3: log likelihood = -8487.2337
Structural equation model Number of obs = 500
Estimation method = ml
Log likelihood = -8487.2337
( 1) [x1]X = 1
------------------------------------------------------------------------------
| OIM
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 15
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Measurement |
x1 |
X | 1 (constrained)
_cons | 99.518 .6412888 155.18 0.000 98.2611 100.7749
-----------+----------------------------------------------------------------
x2 |
X | 1.033249 .0723898 14.27 0.000 .8913676 1.17513
_cons | 99.954 .6341354 157.62 0.000 98.71112 101.1969
-----------+----------------------------------------------------------------
x3 |
X | 1.063876 .0729725 14.58 0.000 .9208526 1.2069
_cons | 99.052 .6372649 155.43 0.000 97.80298 100.301
-----------+----------------------------------------------------------------
x4 |
X | 7.276754 .4277638 17.01 0.000 6.438353 8.115156
_cons | 94.474 3.132547 30.16 0.000 88.33432 100.6137
-------------+----------------------------------------------------------------
var(e.x1)| 115.6865 7.790423 101.3823 132.0089
var(e.x2)| 105.0445 7.38755 91.51873 120.5692
var(e.x3)| 101.2572 7.17635 88.12499 116.3463
var(e.x4)| 144.0406 145.2887 19.94838 1040.069
var(X)| 89.93921 11.07933 70.64676 114.5001
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(2) = 1.46, Prob > chi2 = 0.4827
• Secara keseluruhan, Goodness of fit model dapat dilihat dari hasil uji LR: Model vs Saturated.
Bentuk Hipotesis Nol dari uji ini adalah tidak adanya perbedaan yang signifikan antara sample
covariance matrix dengan fitted covariance matrix atau model dikatakan fit.
• Nilai Chi Square umumnya diharapkan kecil untuk menghasilkan nilai probabilitas uji yang
tidak signifikan agar hipotesis nol tidak dapat ditolak. Hasil uji di atas menghasilkan nilai uji
Chi Square sebesar 1.46 dan p-value di atas 5%, yang mengindikasikan model fit.
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 16
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 17
7) Menyajikan output dalam bentuk standardized. Klik View → Standardized Estimate
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Bagaimaan jika uji normalitas pada data tidak dapat dipenuhi?
. sem (X -> x1, ) (X -> x2, ) (X -> x3, ) (X -> x4, ), vce(sbentler) latent(X ) nocaps
> latent
Endogenous variables
Measurement: x1 x2 x3 x4
Exogenous variables
Latent: X
Fitting target model:
Iteration 0: log pseudolikelihood = -8487.5905
Iteration 1: log pseudolikelihood = -8487.2358
Iteration 2: log pseudolikelihood = -8487.2337
Iteration 3: log pseudolikelihood = -8487.2337
Structural equation model Number of obs = 500
Estimation method = ml
Log pseudolikelihood = -8487.2337
( 1) [x1]X = 1
------------------------------------------------------------------------------
| Satorra-Bentler
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Measurement |
x1 |
X | 1 (constrained)
_cons | 99.518 .6419311 155.03 0.000 98.25984 100.7762
-----------+----------------------------------------------------------------
x2 |
X | 1.033249 .0767608 13.46 0.000 .8828006 1.183698
_cons | 99.954 .6347705 157.46 0.000 98.70987 101.1981
-----------+----------------------------------------------------------------
x3 |
X | 1.063876 .0751028 14.17 0.000 .9166773 1.211075
_cons | 99.052 .6379032 155.28 0.000 97.80173 100.3023
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 19
-----------+----------------------------------------------------------------
x4 |
X | 7.276754 .4386592 16.59 0.000 6.416998 8.13651
_cons | 94.474 3.135684 30.13 0.000 88.32817 100.6198
-------------+----------------------------------------------------------------
var(e.x1)| 115.6865 7.744173 101.4617 131.9055
var(e.x2)| 105.0445 6.499187 93.04833 118.5872
var(e.x3)| 101.2572 7.00047 88.42551 115.9509
var(e.x4)| 144.0406 145.6607 19.84766 1045.347
var(X)| 89.93921 11.2763 70.34416 114.9927
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(2) = 1.46, Prob > chi2 = 0.4827
Satorra-Bentler scaled test: chi2(2) = 1.59, Prob > chi2 = 0.4526
IV. STUDI KASUS 2. MEMBUAT MATRIKS KOVARIAN
▪ Salah satu kelebihan dari pemodelan SEM adalah proses estimasi dapat dilakukan dengan
menggunakan matriks kovarian dari sekumpulan variabel yang digunakan dalam pemodelan.
Dengan demikian kita dapat melakukan pemodelan tanpa menggunakan dataset dari setiap variabel,
melainkan cukup dengan matriks kovarian yang dihasilkan.
▪ Berikut ini akan diberikan studi kasus membuat matriks kovarian dari dataset variabel yang
digunakan pada Studi Kasus 1.
. ssd build x1 x2 x3 x4
(data in memory now summary statistics data; you can use ssd describe and ssd list
to describe and list results.)
. ssd desc
Summary statistics data
obs: 500
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 20
vars: 4
(_dta has notes)
------------------------------------------------------------------------
variable name variable label
------------------------------------------------------------------------
x1 Measure of X, test 1
x2 Measure of X, test 2
x3 Measure of X, test 3
x4 Measure of X, test 4
------------------------------------------------------------------------
. ssd list
Observations = 500
Means:
x1 x2 x3 x4
99.518 99.954 99.052 94.474
Variances implicitly defined; they are the diagonal of the covariance matrix.
Covariances:
x1 x2 x3 x4
206.03775
92.434697 201.46682
91.960986 103.06452 203.46022
656.72392 676.86554 697.55646 4916.2578
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 21
V.STUDI KASUS 3. PEMODELAN SEM
▪ Sebagai ilustrasi awal dalam pemodelan SEM, diberikan studi kasus yang mengkaji pengaruh KSI terhadap
ETA1 dan ETA2. Di sini, KSI, ETA1, dan ETA2 adalah variabel laten sembarang yang dapat
mencerminkan hubungan antar variabel dalam konteks penelitian di berbagai aspek.
▪ Secara lebih terinci, permsalahan yang hendak di jawab dalam kajian ini adalah:
1. Bagaimanakah pengaruh KSI terhadap ETA1?
2. Bagaimanakah pengaruh KSI dan ETA 1 dan ETA2?
▪ Kerangka pemikiran dalam kajian ini, disajikan dalam diagram jalur sebagai berikut:
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 22
▪ Dari diagram jalur diatas dapat dituliskan dua persamaan model, yakni: model persamaan pengukuran
(measurement model) dan model persamaan struktural.
• Persamaan model pengukuran:
o Pengukuran variabel laten eksogen KSI
1 1
2 2
3 3
1
2
3
X KSI
X KSI
X KSI
= +
= +
= +
o Pengukuran variabel laten endogen ETA1
4 4
5 5
6 6
1 1
2 1
3 1
Y ETA
Y ETA
Y ETA
= +
= +
= +
o Pengukuran variabel laten endogen ETA2
7 7
8 8
9 9
4 2
5 2
6 2
Y ETA
Y ETA
Y ETA
= +
= +
= +
• Persamaan model struktural:
o Model struktural ETA1:
1 11ETA KSI = +
o Model struktural ETA2:
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2 22 1ETA KSI ETA = + +
▪ Hipotesis penelitian ini beserta statistik uji yang digunakan dalam penelitian ini dirangkum dalam tabel
berikut:
Model Hipotesis Statistik Uji Kriteria Uji
Overall
Model
Fit
H0: S = : Matriks kovariansi variabel X1 sampai Y6
berdasarkan data sampel tidak berbeda dengan
matriks kovariansi populasi yang diestimasi.
Nilai P-
value,
RMSEA,
dan
CFI
Diharapkan H0
diterima, jika:
P-value > 0,05
RMSEA < 0,08
atau
CFI > 0,90
H1 : S : Matriks kovariansi variabel X1 sampai Y6
berdasarkan data sampel berbeda dengan matriks
kovariansi populasi yang diestimasi.
Model
ETA1
H0: 1= 0 : KSI tidak mempengaruhi ETA1.
H1: 1 > 0: KSI berpengaruh positif terhadap ETA1.
Uji-t Diharapkan H0
Ditolak, jika
nilai
t-hitung >1,96
Model
ETA2
Ho : 2 = = 0: KSI atau ETA1 tidak mempengaruhi
ETA2.
H1 : 2> 0: KSI berpengaruh positif terhadap ETA2.
H1 : > 0: ETA1 berpengaruh positif terhadap ETA2.
Uji-t Diharapkan H0
Ditolak, jika
nilai
t-hitung >1,96
▪ Tahapan estimasi model dengan STATA adalah sebagai berikut:
1) Menyiapkan data matriks kovarian
. ssd init y1 y2 y3 y4 y5 y6 x1 x2 x3
Summary statistics data initialized. Next use, in any order,
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ssd set observations (required)
It is best to do this first.
ssd set means (optional)
Default setting is 0.
ssd set variances or ssd set sd (optional)
Use this only if you have set or will set correlations and, even then, this
is optional but highly recommended. Default setting is 1.
ssd set covariances or ssd set correlations (required)
. ssd set obs 100
(value set)
Status:
observations: set
means: unset
variances or sd: unset
covariances or correlations: unset (required to be set)
. ssd set cov 2.5672\ 1.1806 2.7452\ 0.7867 0.8039 1.6278\ 0.1759 0.4987 0.4122
2.3386\ 0.5491 0.6382 0.1042 1.5607 2.3307\ 0.4531 0.6491 0.1747 1.4589
1.5658 2.3080\ 1.3798 1.1590 0.7150 0.1121 0.3509 0.3003 3.2784\ 1.2069
0.8239 0.2754 -0.0960 0.3032 0.2802 2.6512 3.5732\ 1.4242 1.1898 0.9296
0.3185 0.4241 0.4513 3.1494 3.1353 4.5507
(values set)
Status:
observations: set
means: unset
variances or sd: set
covariances or correlations: set
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 25
2) Uji Normalitas Data
. mvtest normality y1 y2 y3 y4 y5 y6 x1 x2 x3 , univariate stats(all)
Test for univariate normality
---------------------------------------------------------------------
| ------- joint ------
Variable | Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
-------------+-------------------------------------------------------
y1 | 0.2947 0.9575 1.24 0.5378
y2 | 0.0423 0.4884 4.66 0.0973
y3 | 0.3117 0.8253 1.20 0.5474
y4 | 0.3122 0.2893 2.57 0.2770
y5 | 0.3013 0.2926 2.61 0.2714
y6 | 0.2663 0.3332 2.60 0.2721
x1 | 0.6901 0.0344 4.68 0.0963
x2 | 0.5477 0.0446 4.52 0.1045
x3 | 0.3621 0.2251 2.77 0.2502
---------------------------------------------------------------------
Test for multivariate normality
Mardia mSkewness = 72 chi2(165) = 165.000 Prob>chi2 = 0.4854
Mardia mKurtosis = 81 chi2(1) = 4.091 Prob>chi2 = 0.0431
Henze-Zirkler = .9670836 chi2(1) = 3.512 Prob>chi2 = 0.0609
Doornik-Hansen chi2(18) = 141.980 Prob>chi2 = 0.0000
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3) Membuat Diagram
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4) Hasil Estimasi dalam bentuk Standardized
. sem (KSI -> x1, ) (KSI -> x2, ) (KSI -> x3, ) (KSI -> ETA1, ) (KSI -> ETA2, ) (ETA1
> -> y1, ) (ETA1 -> y2, ) (ETA1 -> y3, ) (ETA1 -> ETA2, ) (ETA2 -> y4, ) (ETA2 -> y5,
> ) (ETA2 -> y6, ), standardized latent(KSI ETA1 ETA2 ) nocapslatent
Endogenous variables
Measurement: x1 x2 x3 y1 y2 y3 y4 y5 y6
Latent: ETA1 ETA2
Exogenous variables
Latent: KSI
Fitting target model:
Iteration 0: log likelihood = -1507.8452
Iteration 1: log likelihood = -1505.9347
Iteration 2: log likelihood = -1505.3006
Iteration 3: log likelihood = -1505.2938
Iteration 4: log likelihood = -1505.2938
Structural equation model Number of obs = 100
Estimation method = ml
Log likelihood = -1505.2938
( 1) [y1]ETA1 = 1
( 2) [y4]ETA2 = 1
( 3) [x1]KSI = 1
------------------------------------------------------------------------------
| OIM
Standardized | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural |
ETA1 |
KSI | .6315544 .0895749 7.05 0.000 .4559909 .8071179
-----------+----------------------------------------------------------------
ETA2 |
ETA1 | .4161629 .1812472 2.30 0.022 .060925 .7714009
KSI | -.1265192 .1678472 -0.75 0.451 -.4554937 .2024553
-------------+----------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 28
Measurement |
x1 |
KSI | .9111997 .0271385 33.58 0.000 .8580092 .9643903
-----------+----------------------------------------------------------------
x2 |
KSI | .8498162 .0336755 25.24 0.000 .7838135 .9158189
-----------+----------------------------------------------------------------
x3 |
KSI | .9019249 .0279972 32.21 0.000 .8470514 .9567983
-----------+----------------------------------------------------------------
y1 |
ETA1 | .718899 .0794774 9.05 0.000 .5631263 .8746718
-----------+----------------------------------------------------------------
y2 |
ETA1 | .6460855 .0844512 7.65 0.000 .4805641 .8116068
-----------+----------------------------------------------------------------
y3 |
ETA1 | .5321133 .0918729 5.79 0.000 .3520457 .7121808
-----------+----------------------------------------------------------------
y4 |
ETA2 | .784327 .0518489 15.13 0.000 .682705 .8859489
-----------+----------------------------------------------------------------
y5 |
ETA2 | .8500983 .0468639 18.14 0.000 .7582468 .9419498
-----------+----------------------------------------------------------------
y6 |
ETA2 | .7977964 .0510079 15.64 0.000 .6978227 .8977701
-------------+----------------------------------------------------------------
var(e.x1)| .1697151 .0494572 .0958669 .3004498
var(e.x2)| .2778124 .0572359 .1855172 .4160248
var(e.x3)| .1865316 .0505027 .1097218 .3171113
var(e.y1)| .4831842 .1142724 .3039518 .7681052
var(e.y2)| .5825736 .1091254 .403559 .8409972
var(e.y3)| .7168554 .0977736 .5486991 .9365456
var(e.y4)| .3848312 .081333 .2543146 .5823302
var(e.y5)| .2773329 .0796778 .157925 .4870256
var(e.y6)| .3635208 .0813879 .234399 .5637713
var(e.ETA1)| .6011391 .1131428 .4156875 .8693267
var(e.ETA2)| .8773073 .0910713 .7157979 1.075259
var(KSI)| 1 . . .
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(24) = 35.48, Prob > chi2 = 0.0616
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 29
5) Hasil Estimasi dalam bentuk Path Diagram
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 30
6) Menyajikan Goodness of Fit Model
Convergent and Discriminant Validity Assessment
--------------------------------------------------------------------------------------
Squared correlations (SC) among latent variables
--------------------------------------------------------------------------------------
KSI
KSI 1.000
--------------------------------------------------------------------------------------
Average variance extracted (AVE) by latent variables
--------------------------------------------------------------------------------------
AVE_KSI . No problem with discriminant validity
No problem with convergent validity
--------------------------------------------------------------------------------------
Note: when AVE values >= SC values there is no problem with discriminant validity
when AVE values >= 0.5 there is no problem with convergent validity
. estat eqgof
Equation-level goodness of fit
------------------------------------------------------------------------------
| Variance |
depvars | fitted predicted residual | R-squared mc mc2
-------------+---------------------------------+------------------------------
observed | |
x1 | 3.245616 2.694786 .5508299 | .8302849 .9111997 .8302849
x2 | 3.537468 2.554715 .9827526 | .7221876 .8498162 .7221876
x3 | 4.505193 3.664832 .8403607 | .8134684 .9019249 .8134684
y1 | 2.541528 1.313502 1.228026 | .5168158 .718899 .5168158
y2 | 2.717748 1.13446 1.583288 | .4174264 .6460855 .4174264
y3 | 1.611522 .4562937 1.155228 | .2831446 .5321133 .2831446
y4 | 2.315214 1.424247 .8909666 | .6151688 .784327 .6151688
y5 | 2.307393 1.667477 .639916 | .7226671 .8500983 .7226671
y6 | 2.28492 1.454304 .830616 | .6364792 .7977964 .6364792
latent | |
ETA1 | 1.313502 .5239046 .7895973 | .3988609 .6315544 .3988609
ETA2 | 1.424247 .1747448 1.249503 | .1226927 .3502752 .1226927
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 31
-------------+---------------------------------+------------------------------
overall | | .9261151
------------------------------------------------------------------------------
mc = correlation between depvar and its prediction
mc2 = mc^2 is the Bentler-Raykov squared multiple correlation coefficient
. estat gof, stats(all)
----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Likelihood ratio |
chi2_ms(24) | 35.480 model vs. saturated
p > chi2 | 0.062
chi2_bs(36) | 464.746 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.069 Root mean squared error of approximation
90% CI, lower bound | 0.000
upper bound | 0.115
pclose | 0.242 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Information criteria |
AIC | 3052.588 Akaike's information criterion
BIC | 3107.296 Bayesian information criterion
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 0.973 Comparative fit index
TLI | 0.960 Tucker-Lewis index
---------------------+------------------------------------------------------
Size of residuals |
SRMR | 0.050 Standardized root mean squared residual
CD | 0.926 Coefficient of determination
----------------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 32
. estat stable
Stability analysis of simultaneous equation systems
Eigenvalue stability condition
+----------------------------------------+
| Eigenvalue | Modulus |
|--------------------------+-------------|
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
| 0 | 0 |
+----------------------------------------+
stability index = 0
All the eigenvalues lie inside the unit circle.
SEM satisfies stability condition.
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 33
VI. STUDI KASUS 4. PEMODELAN SEM
▪ Berikut akakan diberikan ilustrasi Pemodelan SEM Studi Pengaruh Home Backround (HOME) dan Ability
(ABILITY) terhadap Aspirations of Educations (ASPIRE) dan Achievment (ACHIEVE).
• Permasalahan yang ingin coba dijawab dalam penelitian ini adalah:
• Apakah HOME dan ABILITY mempengaruhi ASPIRE?
• Apakah HOME, ABILITY, dan ASPIRE mempengaruhi ACHIEVE?
• Hubungan antar variabel dalam permasalahan ini disajikan dalam diagram jalur sebagai berikut:
HOME
ABILITY
ASPIRE
ACHIEVE
FAMIINC
FAED
MOED
VERBAB
QUANTAB
EDASP
OCCASP
VERBACH
QUANTACH
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 34
• Variabel-variabel di atas dijelaskan sebagai berikut:
Variabel Indikator
Aspirations of Education (HOME) Skala home background dengan indikator skor:
• Family income (FAMINC)
• Father's education (FAED)
• Mother's education (MOED)
Ability (ABILITY) Skala ability dengan indikator skor:
• Verbal ability (VERBAB)
• Quantitative ability (QUANTAB)
Aspirations of Education (ASPIRE) Skala aspirations of education dengan indikator skor:
• Educational aspiration (EDASP)
• Occupational aspiration (ACCASP)
Achievement (ACHIEVE) Skala achievement dengan indikator skor:
• Verbal achievement (VERBACH)
• Quantitative achievement (QUANTACH)
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 35
▪ Tahapan estimasi model dengan STATA adalah sebagai berikut:
1) Menyiapkan dataset dalam bentuk matriks kovarian.
. ssd init edasp occasp verbach quantach faminc faed moed verbab quantab
Summary statistics data initialized. Next use, in any order,
ssd set observations (required)
It is best to do this first.
ssd set means (optional)
Default setting is 0.
ssd set variances or ssd set sd (optional)
Use this only if you have set or will set correlations and, even then, this
is optional but highly recommended. Default setting is 1.
ssd set covariances or ssd set correlations (required)
. ssd set obs 200
(value set)
Status:
observations: set
means: unset
variances or sd: unset
covariances or correlations: unset (required to be set)
. ssd set cov 1.024\0.792 1.077\1.027 0.919 1.844\0.756 0.697 1.244 1.286\0.567 0.537 0.876 0.632
0.852\0.445 0.424 0.677 0.526 0.518 0.670\0.434 0.389 0.635 0.498 0.475 0.545 0.716\0.580 0.564 0.893 0.716
0.546 0.422 0.373 0.851\0.491 0.499 0.888 0.646 0.508 0.389 0.339 0.629 0.871
(values set)
Status:
observations: set
means: unset
variances or sd: set
covariances or correlations: set
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 36
2) Uji normalitas data
. mvtest normality edasp-quantab
Test for multivariate normality
Doornik-Hansen chi2(18) = 103.358 Prob>chi2 = 0.0000
. mvtest normality edasp-quantab, univariate stats(all)
Test for univariate normality
---------------------------------------------------------------------
| ------- joint ------
Variable | Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2
-------------+-------------------------------------------------------
edasp | 0.6399 0.0830 3.70 0.1574
occasp | 0.4900 0.2502 2.12 0.3459
verbach | 0.0868 0.7219 3.58 0.1672
quantach | 0.2368 0.2853 3.08 0.2144
faminc | 0.2288 0.2427 3.39 0.1833
faed | 0.6192 0.1511 2.78 0.2492
moed | 0.4942 0.3260 1.66 0.4368
verbab | 0.9350 0.3197 1.11 0.5737
quantab | 0.6429 0.2087 2.12 0.3467
---------------------------------------------------------------------
Test for multivariate normality
Mardia mSkewness = 72 chi2(165) = 165.000 Prob>chi2 = 0.4854
Mardia mKurtosis = 81 chi2(1) = 4.091 Prob>chi2 = 0.0431
Henze-Zirkler = .9670836 chi2(1) = 3.512 Prob>chi2 = 0.0609
Doornik-Hansen chi2(18) = 103.358 Prob>chi2 = 0.0000
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 37
3) Membuat Diagram
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 38
4) Hasil Estimasi dalam bentuk Standardized
. sem (home -> faminc, ) (home -> faed, ) (home -> moed, ) (home -> aspire, ) (home ->
> achieve, ) (ability -> verbab, ) (ability -> quantab, ) (ability -> aspire, ) (abil
> ity -> achieve, ) (aspire -> edasp, ) (aspire -> occasp, ) (aspire -> achieve, ) (ac
> hieve -> verbach, ) (achieve -> quantach, ), covstruct(_lexogenous, diagonal) standa
> rdized latent(home ability aspire achieve ) nocapslatent
Endogenous variables
Measurement: faminc faed moed verbab quantab edasp occasp verbach quantach
Latent: aspire achieve
Exogenous variables
Latent: home ability
Fitting target model:
Iteration 0: log likelihood = -1910.3916
Iteration 1: log likelihood = -1900.5346
Iteration 2: log likelihood = -1897.3189
Iteration 3: log likelihood = -1897.2426
Iteration 4: log likelihood = -1897.2424
Iteration 5: log likelihood = -1897.2424
Structural equation model Number of obs = 200
Estimation method = ml
Log likelihood = -1897.2424
( 1) [edasp]aspire = 1
( 2) [verbach]achieve = 1
( 3) [faminc]home = 1
( 4) [verbab]ability = 1
--------------------------------------------------------------------------------
| OIM
Standardized | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
aspire |
home | .4175084 .0849721 4.91 0.000 .2509662 .5840506
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 39
ability | .5749619 .0801156 7.18 0.000 .4179382 .7319856
-------------+----------------------------------------------------------------
achieve |
aspire | .4124486 .0764183 5.40 0.000 .2626715 .5622258
home | .2308143 .0704332 3.28 0.001 .0927679 .3688608
ability | .5186624 .0771056 6.73 0.000 .3675381 .6697867
---------------+----------------------------------------------------------------
Measurement |
faminc |
home | .7472384 .0364223 20.52 0.000 .6758519 .8186248
-------------+----------------------------------------------------------------
faed |
home | .917876 .0226953 40.44 0.000 .873394 .9623581
-------------+----------------------------------------------------------------
moed |
home | .8488434 .0265828 31.93 0.000 .796742 .9009447
-------------+----------------------------------------------------------------
verbab |
ability | .8738324 .0307141 28.45 0.000 .8136339 .9340308
-------------+----------------------------------------------------------------
quantab |
ability | .8360816 .032568 25.67 0.000 .7722495 .8999136
-------------+----------------------------------------------------------------
edasp |
aspire | .9007962 .0281454 32.01 0.000 .8456323 .9559602
-------------+----------------------------------------------------------------
occasp |
aspire | .7881665 .0319218 24.69 0.000 .7256008 .8507321
-------------+----------------------------------------------------------------
verbach |
achieve | .9263459 .0198632 46.64 0.000 .8874147 .965277
-------------+----------------------------------------------------------------
quantach |
achieve | .8214097 .02555 32.15 0.000 .7713326 .8714868
---------------+----------------------------------------------------------------
var(e.faminc)| .4416348 .0544323 .3468575 .5623096
var(e.faed)| .1575036 .041663 .0937842 .2645156
var(e.moed)| .2794649 .0451293 .2036439 .3835158
var(e.verbab)| .236417 .0536779 .151501 .3689282
var(e.quantab)| .3009676 .054459 .2111047 .4290831
var(e.edasp)| .1885661 .0507066 .1113193 .3194161
var(e.occasp)| .3787936 .0503194 .2919631 .4914478
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 40
var(e.verbach)| .1418833 .0368004 .0853404 .2358893
var(e.quantach)| .3252862 .0419741 .2525974 .4188923
var(e.aspire)| .4951055 .0630957 .385675 .6355856
var(e.achieve)| .1821139 .0433261 .1142448 .2903016
var(home)| 1 . . .
var(ability)| 1 . . .
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(22) = 156.06, Prob > chi2 = 0.0000
5) Hasil Estimasi dalam bentuk Path Diagram
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 41
6) Menyajikan Goodness of Fit Model
. estat gof, stats(all)
----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Likelihood ratio |
chi2_ms(22) | 156.060 model vs. saturated
p > chi2 | 0.000
chi2_bs(36) | 1414.169 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.175 Root mean squared error of approximation
90% CI, lower bound | 0.149
upper bound | 0.201
pclose | 0.000 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Information criteria |
AIC | 3840.485 Akaike's information criterion
BIC | 3916.346 Bayesian information criterion
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 0.903 Comparative fit index
TLI | 0.841 Tucker-Lewis index
---------------------+------------------------------------------------------
Size of residuals |
SRMR | 0.275 Standardized root mean squared residual
CD | 0.989 Coefficient of determination
----------------------------------------------------------------------------
7) Memperbaiki model dengan informasi Modification Indices
. estat mindices
Modification indices
-----------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 42
| Standard
| MI df P>MI EPC EPC
------------------------+----------------------------------------------
Structural |
aspire |
faminc | 5.974 1 0.01 .2183548 .2432504
verbab | 4.543 1 0.03 .4774207 .5315412
quantab | 4.543 1 0.03 -.3546798 -.3994997
----------------------+----------------------------------------------
achieve |
verbab | 4.543 1 0.03 -.5774205 -.4794934
quantab | 4.543 1 0.03 .4289705 .3603812
------------------------+----------------------------------------------
Measurement |
faminc |
moed | 5.357 1 0.02 -.4938034 -.4526795
verbab | 24.112 1 0.00 .2466853 .2465405
quantab | 21.525 1 0.00 .2303805 .2329352
edasp | 20.358 1 0.00 .2510349 .2501594
occasp | 17.542 1 0.00 .2169202 .2263453
verbach | 33.561 1 0.00 .2464692 .3202167
quantach | 16.481 1 0.00 .1974226 .2194224
aspire | 28.460 1 0.00 .3634645 .3262656
achieve | 34.883 1 0.00 .2885936 .3473291
ability | 31.117 1 0.00 .3431561 .2996849
----------------------+----------------------------------------------
faed |
moed | 9.310 1 0.00 .9167559 .9477043
----------------------+----------------------------------------------
moed |
faminc | 5.357 1 0.02 -.2625981 -.2864539
faed | 9.310 1 0.00 1.738316 1.68155
----------------------+----------------------------------------------
verbab |
faminc | 12.245 1 0.00 .1636819 .163778
faed | 16.492 1 0.00 .2199733 .1951833
moed | 11.324 1 0.00 .1745402 .1600985
edasp | 13.161 1 0.00 .2504617 .2497348
occasp | 10.335 1 0.00 .1843567 .1924799
verbach | 4.335 1 0.04 .1866009 .2425773
quantach | 13.436 1 0.00 .2685767 .2986809
aspire | 19.553 1 0.00 .4162918 .3739058
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 43
achieve | 17.624 1 0.00 .4397051 .5295063
home | 17.538 1 0.00 .2847413 .2128946
----------------------+----------------------------------------------
quantab |
faed | 5.026 1 0.02 .1231016 .1079672
verbach | 10.181 1 0.00 .2887298 .3710083
achieve | 4.737 1 0.03 .2306148 .2745064
home | 4.786 1 0.03 .1504819 .1112127
----------------------+----------------------------------------------
edasp |
quantab | 4.545 1 0.03 -.1417093 -.1437822
------------------------+----------------------------------------------
cov(e.faminc,e.moed)| 5.357 1 0.02 -.0983145 -.3600997
cov(e.faminc,e.verbach)| 5.035 1 0.02 .0668431 .2424459
cov(e.faminc,e.aspire)| 5.974 1 0.01 .0817502 .2297398
cov(e.faed,e.moed)| 9.310 1 0.00 .1825228 1.262383
cov(e.verbab,e.verbach)| 8.273 1 0.00 -.0996683 -.4943821
cov(e.verbab,e.aspire)| 4.543 1 0.03 .0955724 .3673055
cov(e.verbab,e.achieve)| 4.543 1 0.03 -.1155909 -.5463243
cov(e.quantab,e.edasp)| 5.102 1 0.02 -.0576928 -.2835274
cov(e.quantab,e.verbach)| 6.352 1 0.01 .0876025 .3806777
cov(e.quantab,e.aspire)| 4.543 1 0.03 -.0925119 -.311478
cov(e.quantab,e.achieve)| 4.543 1 0.03 .1118893 .4632874
cov(home,ability)| 76.265 1 0.00 .390776 .7063711
-----------------------------------------------------------------------
EPC = expected parameter change
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 44
8) Proses model dengan informasi Modification Indices
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 45
. sem (home -> faminc, ) (home -> faed, ) (home -> moed, ) (home -> aspire, ) (home ->
> achieve, ) (ability -> verbab, ) (ability -> quantab, ) (ability -> aspire, ) (abil
> ity -> achieve, ) (aspire -> edasp, ) (aspire -> occasp, ) (aspire -> achieve, ) (ac
> hieve -> verbach, ) (achieve -> quantach, ), covstruct(_lexogenous, diagonal) standa
> rdized latent(home ability aspire achieve ) cov( home*ability) nocapslatent
Endogenous variables
Measurement: faminc faed moed verbab quantab edasp occasp verbach quantach
Latent: aspire achieve
Exogenous variables
Latent: home ability
Fitting target model:
Iteration 0: log likelihood = -1851.6515
Iteration 1: log likelihood = -1848.9346
Iteration 2: log likelihood = -1847.9544
Iteration 3: log likelihood = -1847.9396
Iteration 4: log likelihood = -1847.9396
Structural equation model Number of obs = 200
Estimation method = ml
Log likelihood = -1847.9396
( 1) [edasp]aspire = 1
( 2) [verbach]achieve = 1
( 3) [faminc]home = 1
( 4) [verbab]ability = 1
----------------------------------------------------------------------------------
| OIM
Standardized | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
Structural |
aspire |
home | .3218167 .0965197 3.33 0.001 .1326415 .5109919
ability | .516915 .0956452 5.40 0.000 .3294539 .7043761
---------------+----------------------------------------------------------------
achieve |
aspire | .3976658 .0765917 5.19 0.000 .2475489 .5477828
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 46
home | .1381605 .0730718 1.89 0.059 -.0050577 .2813786
ability | .4774988 .085068 5.61 0.000 .3107686 .6442291
-----------------+----------------------------------------------------------------
Measurement |
faminc |
home | .7904294 .03446 22.94 0.000 .7228891 .8579698
---------------+----------------------------------------------------------------
faed |
home | .8977966 .0236671 37.93 0.000 .85141 .9441832
---------------+----------------------------------------------------------------
moed |
home | .8308087 .0281366 29.53 0.000 .7756621 .8859553
---------------+----------------------------------------------------------------
verbab |
ability | .8827228 .0256478 34.42 0.000 .832454 .9329916
---------------+----------------------------------------------------------------
quantab |
ability | .8276609 .0293267 28.22 0.000 .7701816 .8851403
---------------+----------------------------------------------------------------
edasp |
aspire | .9185566 .0234102 39.24 0.000 .8726735 .9644398
---------------+----------------------------------------------------------------
occasp |
aspire | .8210345 .0295191 27.81 0.000 .7631782 .8788909
---------------+----------------------------------------------------------------
verbach |
achieve | .9427998 .0156268 60.33 0.000 .9121718 .9734277
---------------+----------------------------------------------------------------
quantach |
achieve | .8568405 .0224502 38.17 0.000 .812839 .9008419
-----------------+----------------------------------------------------------------
var(e.faminc)| .3752213 .0544764 .2822968 .498734
var(e.faed)| .1939612 .0424964 .1262463 .2979964
var(e.moed)| .3097569 .0467522 .2304346 .4163844
var(e.verbab)| .2208005 .0452798 .1477213 .3300327
var(e.quantab)| .3149774 .0485452 .2328573 .4260581
var(e.edasp)| .1562537 .0430072 .0911058 .2679875
var(e.occasp)| .3259023 .0484724 .2434926 .4362034
var(e.verbach)| .1111286 .0294659 .0660889 .1868628
var(e.quantach)| .2658244 .0384724 .2001712 .3530109
var(e.aspire)| .3875595 .0601281 .2859424 .5252891
var(e.achieve)| .1372188 .0345873 .083726 .2248885
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 47
var(home)| 1 . . .
var(ability)| 1 . . .
-----------------+----------------------------------------------------------------
cov(home,ability)| .7263921 .0476731 15.24 0.000 .6329546 .8198295
----------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(21) = 57.45, Prob > chi2 = 0.0000
. estat mindices
Modification indices
-----------------------------------------------------------------------
| Standard
| MI df P>MI EPC EPC
------------------------+----------------------------------------------
Structural |
aspire |
faminc | 5.175 1 0.02 .2296598 .2280599
----------------------+----------------------------------------------
achieve |
verbab | 4.007 1 0.05 -.4554521 -.3281766
quantab | 4.007 1 0.05 .2959015 .2157031
------------------------+----------------------------------------------
Measurement |
faminc |
faed | 7.929 1 0.00 -.6977387 -.6187431
moed | 10.552 1 0.00 -.4449144 -.4078619
verbab | 22.250 1 0.00 .3223561 .3221669
quantab | 17.398 1 0.00 .264972 .2679102
edasp | 14.202 1 0.00 .2350482 .2576838
occasp | 11.317 1 0.00 .1853222 .2083606
verbach | 30.942 1 0.00 .2997435 .4409714
quantach | 9.890 1 0.00 .1770939 .2175726
aspire | 23.039 1 0.00 .4006307 .4034413
achieve | 35.954 1 0.00 .4125705 .5722402
ability | 35.635 1 0.00 .6201628 .5471105
----------------------+----------------------------------------------
faed |
faminc | 7.929 1 0.00 -.2836318 -.3198433
moed | 40.378 1 0.00 .9245283 .955739
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 48
edasp | 5.669 1 0.02 -.1277926 -.1579859
verbach | 8.585 1 0.00 -.1425147 -.2364301
aspire | 7.342 1 0.01 -.2029286 -.2304421
achieve | 10.041 1 0.00 -.2058037 -.3218961
ability | 5.797 1 0.02 -.2288601 -.2276784
----------------------+----------------------------------------------
moed |
faminc | 10.552 1 0.00 -.308662 -.3367026
faed | 40.378 1 0.00 1.577846 1.526319
verbab | 6.618 1 0.01 -.1568568 -.1710061
quantab | 6.263 1 0.01 -.1407219 -.155208
verbach | 4.899 1 0.03 -.1084669 -.1740689
achieve | 6.269 1 0.01 -.1589567 -.240504
ability | 9.878 1 0.00 -.297911 -.2866943
----------------------+----------------------------------------------
verbab |
verbach | 6.480 1 0.01 -.3344116 -.4922627
aspire | 4.120 1 0.04 .2689298 .2709755
----------------------+----------------------------------------------
quantab |
edasp | 5.530 1 0.02 -.1826227 -.1980139
verbach | 5.134 1 0.02 .2977285 .4332034
aspire | 4.120 1 0.04 -.2551005 -.2540728
----------------------+----------------------------------------------
edasp |
quantab | 5.872 1 0.02 -.1819019 -.1677631
----------------------+----------------------------------------------
verbach |
verbab | 5.580 1 0.02 -.291198 -.1978211
------------------------+----------------------------------------------
cov(e.faminc,e.faed)| 7.929 1 0.00 -.0902205 -.4448603
cov(e.faminc,e.moed)| 10.552 1 0.00 -.0981823 -.3705781
cov(e.faminc,e.verbach)| 5.028 1 0.02 .0637494 .2503202
cov(e.faminc,e.aspire)| 5.175 1 0.02 .0730525 .2244003
cov(e.faed,e.moed)| 40.378 1 0.00 .2040221 1.207792
cov(e.verbab,e.verbach)| 7.705 1 0.01 -.0858602 -.4397551
cov(e.verbab,e.achieve)| 4.007 1 0.05 -.0851521 -.4162946
cov(e.quantab,e.edasp)| 4.977 1 0.03 -.0560661 -.2689451
cov(e.quantab,e.verbach)| 6.203 1 0.01 .0785418 .3329166
cov(e.quantab,e.achieve)| 4.007 1 0.05 .0807733 .3268054
-----------------------------------------------------------------------
EPC = expected parameter change
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 49
. sem (home -> faminc, ) (home -> faed, ) (home -> moed, ) (home -> aspire, ) (home ->
> achieve, ) (ability -> verbab, ) (ability -> quantab, ) (ability -> aspire, ) (abil
> ity -> achieve, ) (aspire -> edasp, ) (aspire -> occasp, ) (aspire -> achieve, ) (ac
> hieve -> verbach, ) (achieve -> quantach, ), covstruct(_lexogenous, diagonal) standa
> rdized latent(home ability aspire achieve ) cov( home*ability e.faed*e.moed) nocapsl
> atent
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 50
Endogenous variables
Measurement: faminc faed moed verbab quantab edasp occasp verbach quantach
Latent: aspire achieve
Exogenous variables
Latent: home ability
Fitting target model:
Iteration 0: log likelihood = -1851.6515
Iteration 1: log likelihood = -1836.1875
Iteration 2: log likelihood = -1829.4211
Iteration 3: log likelihood = -1828.8469
Iteration 4: log likelihood = -1828.8449
Iteration 5: log likelihood = -1828.8449
Structural equation model Number of obs = 200
Estimation method = ml
Log likelihood = -1828.8449
( 1) [edasp]aspire = 1
( 2) [verbach]achieve = 1
( 3) [faminc]home = 1
( 4) [verbab]ability = 1
-----------------------------------------------------------------------------------
| OIM
Standardized | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------+----------------------------------------------------------------
Structural |
aspire |
home | .4428709 .1280987 3.46 0.001 .1918021 .6939397
ability | .3915325 .1285507 3.05 0.002 .1395778 .6434872
----------------+----------------------------------------------------------------
achieve |
aspire | .3799922 .078289 4.85 0.000 .2265486 .5334358
home | .1910696 .1014238 1.88 0.060 -.0077174 .3898567
ability | .4340808 .0983848 4.41 0.000 .2412501 .6269114
------------------+----------------------------------------------------------------
Measurement |
faminc |
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 51
home | .8813234 .0283077 31.13 0.000 .8258413 .9368055
----------------+----------------------------------------------------------------
faed |
home | .7773148 .0351298 22.13 0.000 .7084617 .8461679
----------------+----------------------------------------------------------------
moed |
home | .6918227 .0436489 15.85 0.000 .6062725 .7773729
----------------+----------------------------------------------------------------
verbab |
ability | .8824514 .0253081 34.87 0.000 .8328484 .9320543
----------------+----------------------------------------------------------------
quantab |
ability | .8279155 .0290346 28.51 0.000 .7710087 .8848223
----------------+----------------------------------------------------------------
edasp |
aspire | .9180499 .0233404 39.33 0.000 .8723036 .9637962
----------------+----------------------------------------------------------------
occasp |
aspire | .8214877 .0294266 27.92 0.000 .7638127 .8791628
----------------+----------------------------------------------------------------
verbach |
achieve | .9463121 .0153722 61.56 0.000 .9161832 .976441
----------------+----------------------------------------------------------------
quantach |
achieve | .8536602 .022711 37.59 0.000 .8091474 .898173
------------------+----------------------------------------------------------------
var(e.faminc)| .2232691 .0498965 .144079 .3459844
var(e.faed)| .3957817 .0546138 .3019943 .5186958
var(e.moed)| .5213814 .0603945 .4154856 .654267
var(e.verbab)| .2212796 .0446663 .148978 .3286702
var(e.quantab)| .314556 .0480764 .2331316 .4244189
var(e.edasp)| .1571844 .0428552 .0921161 .2682154
var(e.occasp)| .3251579 .0483471 .2429577 .435169
var(e.verbach)| .1044934 .0290937 .0605468 .1803375
var(e.quantach)| .2712643 .038775 .2049841 .3589756
var(e.aspire)| .3691189 .0594812 .2691538 .5062115
var(e.achieve)| .1378646 .0341446 .0848472 .2240102
var(home)| 1 . . .
var(ability)| 1 . . .
------------------+----------------------------------------------------------------
cov(e.faed,e.moed)| .5483746 .0577886 9.49 0.000 .435111 .6616381
cov(home,ability)| .8115672 .0407974 19.89 0.000 .7316056 .8915287
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 52
-----------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(20) = 19.26, Prob > chi2 = 0.5047
. estat gof, stats(all)
----------------------------------------------------------------------------
Fit statistic | Value Description
---------------------+------------------------------------------------------
Likelihood ratio |
chi2_ms(20) | 19.265 model vs. saturated
p > chi2 | 0.505
chi2_bs(36) | 1414.169 baseline vs. saturated
p > chi2 | 0.000
---------------------+------------------------------------------------------
Population error |
RMSEA | 0.000 Root mean squared error of approximation
90% CI, lower bound | 0.000
upper bound | 0.058
pclose | 0.898 Probability RMSEA <= 0.05
---------------------+------------------------------------------------------
Information criteria |
AIC | 3707.690 Akaike's information criterion
BIC | 3790.148 Bayesian information criterion
---------------------+------------------------------------------------------
Baseline comparison |
CFI | 1.000 Comparative fit index
TLI | 1.000 Tucker-Lewis index
---------------------+------------------------------------------------------
Size of residuals |
SRMR | 0.015 Standardized root mean squared residual
CD | 0.968 Coefficient of determination
----------------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 53
. estat teffects, standardized
Direct effects
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| Std. Coef.
-------------+----------------------------------------------------------------
Measurement |
faminc |
home | 1 (constrained) .8813234
-----------+----------------------------------------------------------------
faed |
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 54
home | .7821305 .0640357 12.21 0.000 .7773148
-----------+----------------------------------------------------------------
moed |
home | .7196084 .0694513 10.36 0.000 .6918227
-----------+----------------------------------------------------------------
verbab |
ability | 1 (constrained) .8824514
-----------+----------------------------------------------------------------
quantab |
ability | .9491602 .0677554 14.01 0.000 .8279155
-----------+----------------------------------------------------------------
edasp |
aspire | 1 (constrained) .9180499
home | 0 (no path) 0
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
occasp |
aspire | .917683 .0641823 14.30 0.000 .8214877
home | 0 (no path) 0
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
verbach |
aspire | 0 (no path) 0
achieve | 1 (constrained) .9463121
home | 0 (no path) 0
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
quantach |
aspire | 0 (no path) 0
achieve | .7533394 .0415301 18.14 0.000 .8536602
home | 0 (no path) 0
ability | 0 (no path) 0
-------------+----------------------------------------------------------------
Structural |
aspire |
home | .5057528 .1541702 3.28 0.001 .4428709
ability | .4468158 .1494357 2.99 0.003 .3915325
-----------+----------------------------------------------------------------
achieve |
aspire | .5256217 .1131072 4.65 0.000 .3799922
home | .3018224 .1625909 1.86 0.063 .1910696
ability | .6852197 .1641088 4.18 0.000 .4340808
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 55
------------------------------------------------------------------------------
Indirect effects
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| Std. Coef.
-------------+----------------------------------------------------------------
Measurement |
faminc |
home | 0 (no path) 0
-----------+----------------------------------------------------------------
faed |
home | 0 (no path) 0
-----------+----------------------------------------------------------------
moed |
home | 0 (no path) 0
-----------+----------------------------------------------------------------
verbab |
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
quantab |
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
edasp |
aspire | 0 (no path) 0
home | .5057528 .1541702 3.28 0.001 .4065775
ability | .4468158 .1494357 2.99 0.003 .3594464
-----------+----------------------------------------------------------------
occasp |
aspire | 0 (no path) 0
home | .4641207 .1423248 3.26 0.001 .363813
ability | .4100353 .1395913 2.94 0.003 .3216392
-----------+----------------------------------------------------------------
verbach |
aspire | .5256217 .1131072 4.65 0.000 .3595912
achieve | 0 (no path) 0
home | .567657 .1759505 3.23 0.001 .340064
ability | .9200758 .1785483 5.15 0.000 .5515675
-----------+----------------------------------------------------------------
quantach |
aspire | .3959715 .0864075 4.58 0.000 .3243842
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 56
achieve | 0 (no path) 0
home | .4276384 .1332787 3.21 0.001 .3067689
ability | .6931293 .1377876 5.03 0.000 .4975644
-------------+----------------------------------------------------------------
Structural |
aspire |
home | 0 (no path) 0
ability | 0 (no path) 0
-----------+----------------------------------------------------------------
achieve |
aspire | 0 (no path) 0
home | .2658346 .0997929 2.66 0.008 .1682875
ability | .2348561 .0896786 2.62 0.009 .1487793
------------------------------------------------------------------------------
Total effects
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| Std. Coef.
-------------+----------------------------------------------------------------
Measurement |
faminc |
home | 1 (constrained) .8813234
-----------+----------------------------------------------------------------
faed |
home | .7821305 .0640357 12.21 0.000 .7773148
-----------+----------------------------------------------------------------
moed |
home | .7196084 .0694513 10.36 0.000 .6918227
-----------+----------------------------------------------------------------
verbab |
ability | 1 (constrained) .8824514
-----------+----------------------------------------------------------------
quantab |
ability | .9491602 .0677554 14.01 0.000 .8279155
-----------+----------------------------------------------------------------
edasp |
aspire | 1 (constrained) .9180499
home | .5057528 .1541702 3.28 0.001 .4065775
ability | .4468158 .1494357 2.99 0.003 .3594464
-----------+----------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 57
occasp |
aspire | .917683 .0641823 14.30 0.000 .8214877
home | .4641207 .1423248 3.26 0.001 .363813
ability | .4100353 .1395913 2.94 0.003 .3216392
-----------+----------------------------------------------------------------
verbach |
aspire | .5256217 .1131072 4.65 0.000 .3595912
achieve | 1 (constrained) .9463121
home | .567657 .1759505 3.23 0.001 .340064
ability | .9200758 .1785483 5.15 0.000 .5515675
-----------+----------------------------------------------------------------
quantach |
aspire | .3959715 .0864075 4.58 0.000 .3243842
achieve | .7533394 .0415301 18.14 0.000 .8536602
home | .4276384 .1332787 3.21 0.001 .3067689
ability | .6931293 .1377876 5.03 0.000 .4975644
-------------+----------------------------------------------------------------
Structural |
aspire |
home | .5057528 .1541702 3.28 0.001 .4428709
ability | .4468158 .1494357 2.99 0.003 .3915325
-----------+----------------------------------------------------------------
achieve |
aspire | .5256217 .1131072 4.65 0.000 .3799922
home | .567657 .1759505 3.23 0.001 .3593571
ability | .9200758 .1785483 5.15 0.000 .5828601
------------------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 58
VII. MODEL PLS SEM
▪ Umumnya terdapat dua jenis pemodelan SEM yang dapat digunakan untuk menganalisis hubungan antara
variabel laten, yaitu Covariance-Based SEM (CB-SEM) yang dikembangkan oleh Jöreskog (1969) dan
Partial Least Squares SEM (PLS-SEM) yang dikembangkan oleh Wold (1974).
▪ Model CB-SEM sering disebut hard modeling karena memerlukan beberapa asumsi yang ketat, seperti
normalitas dan menggunakan sampel berukuran besar. Di sisi lain, PLS-SEM sering disebut soft modeling
karena menggunakan asumsi yang lebih lunak dan dapat menggunakan ukuran sampel yang kecil.
▪ Kelemahan lain dalam pemodelan CB-SEM adalah bahwa variabel manifes (indikator) hanya
dimungkinkan bersifat reflektif, yaitu variabel laten menjelaskan (merefleksikan) variabel manifes, dan
tidak dapat bersifat formatif di mana variabel manifes menjelaskan variabel laten. PLS-SEM dapat
mengatasi kelemahan ini karena dimungkinkan untuk menggunakan indikator yang reflektif atau formatif.
▪ PLS-SEM dikembangkan untuk menguji teori yang lemah dan data yang lemah, seperti ukuran sampel
kecil atau masalah dengan normalitas data (Wold, 1985). Selain digunakan untuk menjelaskan ada atau
tidak adanya hubungan antara variabel laten, PLS-SEM juga dapat digunakan untuk mengkonfirmasi teori
(Chin & Newsted, 1999).
▪ Prosedur Estimasi SEM-PLS
1) Merancang spesifikasi model. Tahap ini melibatkan spesifikasi teoretis dari hubungan antara variabel
laten dan bagaimana masing-masing variabel laten akan diukur. Spesifikasi model dapat dicapai
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 59
berdasarkan pengalaman di bidang tertentu, tinjauan teori dan literatur. Dalam penelitian ini, spesifikasi
model dikembangkan mengadopsi berbagai literatur secara terpisah.
2) Menggambar diagram jalur.
3) Mengestimasi parameter model.
Metode pendugaan parameter (estimasi) dalam PLS adalah metode kuadrat terkecil (least square
methods). Proses perhitungan dilakukan dengan cara iterasi, dimana iterasi akan berhenti jika telah
mencapai kondisi konvergen. Pendugaan parameter di dalam PLS meliputi 3 hal, yaitu
• Estimasi bobot (weight estimate) untuk membuat bobot atau menciptakan skor (score factor) pada
variabel laten.
• Estimasi jalur (path estimate) dilakukan untuk menghubungkan antar variabel laten (koefisien
jalur) yaitu koefisien beta (β) dan gamma (γ) dan antara variabel laten dengan indikatornya yaitu
estimasi loading factor yang merupakan koefisien outer model yaitu lambda (λ).
• Estimasi rata-rata (mean) dan parameter lokasi (nilai konstanta regresi) untuk indikator dan
variabel laten. Estimasi dilakukan dengan algoritma PLS yang berlangsung dalam tiga tahap.
• Langkah pertama dalam estimasi PLS terdiri dari prosedur iterasi regresi sederhana atau regresi
berganda dengan memperhitungkan hubungan model struktural/inner model, model
pengukuran/outer model dan estimasi bobot/weight relation.
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 60
• Kemudian hasil dari estimasi satu set bobot digunakan untuk menghitung nilai skor variabel laten,
yang mana merupakan kombinasi linier dari variabel indikator/manifest.
• Setelah estimasi skor variabel laten diperoleh, maka langkah kedua dan ketiga melibatkan estimasi
koefisien model struktural (inner model) dan koefisien dari masing-masing model pengukuran
(outer model). Pada dasarnya algoritma PLS merupakan serangkaian regresi sederhana dan
berganda dengan estimasi ordinary least square (Tenenhaus, 2005).
4) Mengevaluasi model. Evaluasi model dalam PLS-SEM mencakup evaluasi terhadap outer model dan
inner model.
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 61
VIII. STUDI KASUS 5. PEMODELAN PLS SEM
▪ Pada studi kasus ini akan diilustrasikan pemodelan dengan hubungan struktural sebagai berikut:
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 62
▪ Estimasi parameter model.
. plssem (Attractive > face fit) (Appearance > body appear attract) (Muscle > muscle s
> trength endur) (Weight > lweight calories cweight), structural(Appearance Attractive
> , Muscle Appearance, Weight Appearance)
Iteration 1: outer weights rel. diff. = 6.31e-01
Iteration 2: outer weights rel. diff. = 1.49e-02
Iteration 3: outer weights rel. diff. = 1.34e-03
Iteration 4: outer weights rel. diff. = 7.76e-05
Iteration 5: outer weights rel. diff. = 6.80e-06
Iteration 6: outer weights rel. diff. = 4.00e-07
Iteration 7: outer weights rel. diff. = 3.49e-08
Partial least squares path modeling Number of obs = 187
Average R-squared = 0.15795
Weighting scheme: path Average communality = 0.79165
Tolerance: 1.00e-07 GoF = 0.35361
Initialization: indsum Average redundancy = 0.11941
Measurement model - Standardized loadings
--------------------------------------------------------------------------
| Reflective: Reflective: Reflective: Reflective:
| Attractive Appearance Muscle Weight
--------------+-----------------------------------------------------------
face | 0.908
fit | 0.919
body | 0.899
appear | 0.949
attract | 0.923
muscle | 0.886
strength | 0.873
endur | 0.623
lweight | 0.916
calories | 0.937
cweight | 0.911
--------------+-----------------------------------------------------------
Cronbach | 0.801 0.914 0.734 0.912
DG | 0.909 0.946 0.842 0.944
rho_A | 0.803 0.917 0.849 0.931
--------------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 63
Discriminant validity - Squared interfactor correlation vs. Average variance extracted
> (AVE)
--------------------------------------------------------------------------
| Attractive Appearance Muscle Weight
--------------+-----------------------------------------------------------
Attractive | 1.000 0.080 0.021 0.002
Appearance | 0.080 1.000 0.217 0.177
Muscle | 0.021 0.217 1.000 0.041
Weight | 0.002 0.177 0.041 1.000
--------------+-----------------------------------------------------------
AVE | 0.834 0.854 0.645 0.849
--------------------------------------------------------------------------
Structural model - Standardized path coefficients
-----------------------------------------------------------
Variable | Appearance Muscle Weight
--------------+--------------------------------------------
Attractive | 0.283
| (0.000)
Appearance | 0.466 0.420
| (0.000) (0.000)
--------------+--------------------------------------------
r2_a | 0.075 0.213 0.172
-----------------------------------------------------------
p-values in parentheses
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 64
▪ Plot Inner model
. plssemplot, innermodel
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 65
▪ Plot Outer Weight
. plssemplot, loadings
Loadings
--------------------------------------------------------------------------
| Reflective: Reflective: Reflective: Reflective:
| Attractive Appearance Muscle Weight
--------------+-----------------------------------------------------------
face | 0.9077
fit | 0.9186
body | 0.8989
appear | 0.9492
attract | 0.9230
muscle | 0.8856
strength | 0.8730
endur | 0.6225
lweight | 0.9156
calories | 0.9366
cweight | 0.9111
--------------------------------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 66
. plssemplot, crossloadings
Cross loadings
----------------------------------------------------
| Attra~e Appea~e Muscle Weight
------------+---------------------------------------
face | 0.9077 0.2504 0.1515 -0.0167
fit | 0.9186 0.2660 0.1118 -0.0576
body | 0.3434 0.8989 0.4354 0.3642
appear | 0.1901 0.9492 0.4386 0.4593
attract | 0.2490 0.9230 0.4152 0.3364
muscle | 0.1645 0.4941 0.8856 0.1253
strength | 0.1222 0.3320 0.8730 0.1033
endur | 0.0096 0.2190 0.6225 0.3620
lweight | -0.1211 0.4148 0.2122 0.9156
calories | 0.0544 0.4191 0.1947 0.9366
cweight | -0.0523 0.3086 0.1439 0.9111
----------------------------------------------------
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 67
Disusun oleh: Dr. Indra, S.Si, M.Si ([email protected]/[email protected]) 68
. estat indirect, effects(Muscle Appearance Attractive, Weight Appearance Attractive)
Significance testing of (standardized) indirect effects
--------------------------------------------------------------
| Muscle <- | Weight <-
Statistics | Appearance <- | Appearance <-
| Attractive | Attractive
------------------------+------------------+------------------
Indirect effect | 0.132 | 0.119
Standard error | 0.038 | 0.035
Z statistic | 3.501 | 3.385
P-value | 0.000 | 0.001
Conf. interval | (0.058, 0.206) | (0.050, 0.188)
--------------------------------------------------------------
confidence level: 95%
. estat total
Direct, Indirect (overall) and Total Effects
-----------------------------------------------------------------
Effect | Direct Indirect Total
--------------------------+--------------------------------------
Attractive -> Appearance | 0.283 0.283
Attractive -> Muscle | 0.132 0.132
Attractive -> Weight | 0.119 0.119
Appearance -> Muscle | 0.466 0.466
Appearance -> Weight | 0.420 0.420
----------------------------------------------------------------
. estat vif
Structural model - Multicollinearity check (VIFs)
-----------------------------------------------------
Variable | Appearance Muscle Weight
--------------+--------------------------------------
Attractive | 1.000
Appearance | 1.000 1.000
-----------------------------------------------------