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PRELIS 2, PRELIS 2, Model Pengukuran dan Model Pengukuran dan Model Struktural Model Struktural Dr. Setyo Hari Wijanto <[email protected]>
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PRELIS 2, Model Pengukuran dan Model Struktural

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Page 1: PRELIS 2, Model Pengukuran dan Model Struktural

PRELIS 2,PRELIS 2,Model Pengukuran Model Pengukuran dan Model Strukturaldan Model Struktural

Dr. Setyo Hari Wijanto<[email protected]>

Page 2: PRELIS 2, Model Pengukuran dan Model Struktural

Bab 5Bab 5Data Input dan Data Input dan PRELIS2PRELIS2

Dr. Setyo Hari Wijanto<[email protected]>

Page 3: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran3

Jenis Raw Data

Salah satu data input adalah raw data yang merupakan data dari variabel-variabel teramati (observed variables) yang ada di dalam model.

Raw data ini bisa diperoleh dari survei (yang dikenal sebagai data primer) atau dari berbagai sumber data (data sekunder).

LISREL 8 mempunyai 3 jenis data input yang dapat digunakan, yaitu: Ordinal (ordinal) Continue (kontinu) Censored (sensor)

Page 4: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran4

ORDINAL VARIABLE Ketika data dikumpulkan melalui wawancara atau

kuesioner observed variables adalah ordinal, yaitu, respon-respon diklasifikasikan ke dalam kategori-kategori yang berurutan.

Sebuah ordinal variabel z bisa dianggap sebagai ukuran mentah/kasar dari unobserved atau unobservable continuous variable z* yang mendasarinya.

Misalkan 4 point ordinal scale dapat dituliskan: If z* <= T1 , z is scored 1

If τ1 < z* <= τ2, z is scored 2

If τ2 < z* <= τ3, z is scored 3

If τ3 > z* , z is scored 4

Dimana τ1 < τ2 < τ3 adalah threshold values untuk z* . Sering diasumsikan bahwa z* mempunyai distribusi normal.

Page 5: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran5

ORDINAL VARIABLE Dengan mengasumsikan setiap pasang z*

mempunyai bivariate normal distribution, maka jenis koefisien korelasi yang dapat dihitung adalah sebagai berikut:

Polychoric – Kedua z variables mempunyai sebuah skala ordinal

Tetrachoric - Kedua z variables mempunyai sebuah skala dikotomi

Polyserial – Satu z variable mempunyai skala ordinal dan yang lainnya mempunyai skala interval

Biserial - Satu z variable mempunyai skala interval dan yang lainnya mempunyai skala dikotomi

Page 6: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran6

ORDINAL VARIABLE

Page 7: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran7

Variabel Kontinu dan Skor Normal

Perlunya Skor Normal Untuk variabel kontinu yang tidak normal. Jika

metode estimasi Maximum Likelihood (ML) yang digunakan, kesalahan standar dan chi-squares bisa agak tidak tepat. Secara teoritis, Weighted Least Square (WLS atau ADF) dengan weight matrix yang tepat akan menghasilkan kesalahan standar dan chi-squares tepat, tetapi hal ini memerlukan sampel yang besar.

Salah satu solusi atas ketidak-normalan dari variabel kontinu jika sampel tidak terlalu besar adalah melakukan normalisasi variabel sebelum melakukan analisis (Joreskog et.al. 1999). Salah satu fitur dari LISREL 8.8 adalah Normal Scores yang menawarkan cara efektif untuk menormalisasikan variabel kontinu.

Page 8: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran8

Variabel Kontinu dan Skor Normal

Page 9: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran9

Variabel Kontinu dan Skor Normal

Page 10: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran10

Variabel Kontinu dan Skor Normal

Page 11: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran11

Variabel Sensor

Suatu variabel sensor adalah variabel yang mempunyai bagian dari observasi yang cukup banyak pada nilai minimum dan maksimum.

Sensor di bawah (censored below)

y = c jika y* ≤ c = y* selain itu

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April 2009Bab 6 Model Pengukuran12

Variabel Sensor MAINTNCE = Rata-rata banyaknya waktu (dalam menit) setiap

hari yang dihabiskan untuk pemeliharaan (sebagai variabel dependen)

AGE = dalam tahun HOUSE = 1, jika responden tinggal di sebuah rumah, = 0 selain

itu RECHOUSE = 1, jika responden mempunyai sebuah rumah

rekreasi, = 0 selain itu CAR =1, jika responden mempunyai mobil, = 0 selain itu SCHOOLYR = lamanya responden bersekolah (dalam tahun) INCOME = disposable income dari responden (dalam mata

uang Swedia SEK) MARGTAX = Respondent’s marginal tax rate dalam %

SY= MAINTENANCE.PSFCR MAINTNCE on AGE – MARGTAXOU

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April 2009Bab 6 Model Pengukuran13

Variabel Sensor

Variable MAINTNCE is censored below It has 1329 (65.76%) values = 0.000 Estimated Mean and Standard Deviation based on 2021 complete cases. Mean = -61.951 (0.028) Standard Deviation = 143.009 (0.000) Estimated Censored Regression based on 2021 complete cases. MAINTNCE = - 202.106 + 1.124*AGE + 50.517*HOUSE + 10.932*RECHOUSE Standerr (25.958) (0.308) (8.380) (8.875) Z-values -7.786 3.653 6.028 1.232 P-values 0.000 0.000 0.000 0.218 + 42.790*CAR - 4.130*SCHOOLYR + 0.357*INCOME + 0.838*MARGTAX (11.703) (1.276) (0.167) (0.407) 3.656 -3.236 2.137 2.060 0.000 0.001 0.033 0.039 + Error, R² = 0.0820

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April 2009Bab 6 Model Pengukuran14

PRELIS2 PRELIS merupakan singkatan dari

preprocessor for LISREL telah tersedia sejak tahun 1986 yaitu pada LISREL 7.

Fungsi utama dari PRELIS adalah perhitungan berbagai statistik untuk digunakan sebagai input data bagi program LISREL.

Pada LISREL 7 dan LISREL 8 versi awal kita harus membuat sintaks PRELIS sebelum menjalankannya, maka pada LISREL 8.8, penggunaan PRELIS dapat dilakukan secara:

• interaktif • melalui pembuatan sintak

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April 2009Bab 6 Model Pengukuran15

PRELIS2 Interaktif

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April 2009Bab 6 Model Pengukuran16

PRELIS2 Interaktif

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April 2009Bab 6 Model Pengukuran17

PRELIS2 Interaktif

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April 2009Bab 6 Model Pengukuran18

PRELIS2 Sintak PRELIS untuk Input PSF

TI

<string>

SY=<psfname>

<commands>

OU <options>

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April 2009Bab 6 Model Pengukuran19

PRELIS2 Sintak PRELIS untuk Input PSF Contoh

TI

Contoh Censored Regression

SY = MAINTENANCE.PSF

CR MAINTNCE on AGE – MARGTAX

OU

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April 2009Bab 6 Model Pengukuran20

PRELIS2 Sintak PRELIS untuk Input Text File

TI

<string>

DA=<data specifications>

LA

<labels>

RA=<filename>

<commands>

OU <options>

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April 2009Bab 6 Model Pengukuran21

PRELIS2 Sintak PRELIS untuk Input Text File Contoh

TI

Creating a merge data file

DA NI=3 NO=350,259

LA

ASC MSC ESC

RA=ACADSCM.DAT, ACADSCF.DAT FO

(3F1.0)

(3F1.0)

OU RA=ACADSC.DAT WI=1 ND=0

Page 22: PRELIS 2, Model Pengukuran dan Model Struktural

Bab 6Bab 6Model PengukuranModel Pengukuran

Dr. Setyo Hari Wijanto<[email protected]>

Page 23: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran23

Confirmatory Factor Analysis

Iktisar Prosedur Spesifikasi Model Pengumpulan Data Pembuatan Program SIMPLIS Menjalankan Program SIMPLIS dan Analisis

Keluarannya• Offending Estimate Negative Error

Variance• Analisis Validitas Standardized Loading

≥0.50 atau ≥ 0.70• Uji Kecocokan Keseluruhan Model• Analisis Reliabilitas

Respesifikasi Modification Index

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April 2009Bab 6 Model Pengukuran24

Confirmatory Factor Analysis

Spesifikasi

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April 2009Bab 6 Model Pengukuran25

Confirmatory Factor Analysis

Pengumpulan Data dikonversikan ke Normal Score

Pembuatan Program SIMPLIS

System File from File TTFINPUT.DSF Latent Variable: Ttf Utility Performance Relationship: LEVEL= 1 * Ttf ACCURACY = Ttf LOCATABI = Ttf ACCESSIB = Ttf MEANING = Ttf ASSISTAN = Ttf EASE = Ttf CURRENCY = Ttf PRESENTA = Ttf P1= 1 * Performance P2 = Performance UT1= 1 * Utility UT2= Utility UT3= Utility Method: Weighted Least Square Options: SC Path Diagram End of Problem

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April 2009Bab 6 Model Pengukuran26

Confirmatory Factor Analysis

Menjalankan Program SIMPLIS dan analisis keluaran

Page 27: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran27

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan analisis

keluaran

ACCURACY = 0.63*Ttf, Errorvar.= -0.021 , R² = 1.04 (0.019) (0.051) 33.45 -0.41 W_A_R_N_I_N_G : Error variance is negative. . . P1 = 1.00*Performa, Errorvar.= -0.016, R² = 1.01 (0.26) -0.060 W_A_R_N_I_N_G : Error variance is negative.

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April 2009Bab 6 Model Pengukuran28

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan analisis

keluaran

Completely Standardized Solution LAMBDA-X Ttf Utility Performa -------- -------- -------- LEVEL 0.87 - - - - ACCURACY 1.02 - - - - LOCATABI 0.81 - - - - ACCESSIB 0.89 - - - - MEANING 0.93 - - - - ASSISTAN 0.92 - - - - EASE 0.86 - - - - CURRENCY 0.97 - - - - PRESENTA 0.87 - - - - UT1 - - 0.96 - - UT2 - - 0.37 - - UT3 - - 0.88 - - P1 - - - - 1.00 P2 - - - - 0.85

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Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaranGoodness of Fit Statistics

Degrees of Freedom = 63

Minimum Fit Function Chi-Square = 156.74 (P = 0.00) Estimated Non-centrality Parameter (NCP) = 93.74

90 Percent Confidence Interval for NCP = (60.71 ; 134.47)

Minimum Fit Function Value = 1.02 Population Discrepancy Function Value (F0) = 0.61

90 Percent Confidence Interval for F0 = (0.40 ; 0.88) Root Mean Square Error of Approximation (RMSEA) = 0.099

90 Percent Confidence Interval for RMSEA = (0.079 ; 0.12) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00

Expected Cross-Validation Index (ECVI) = 1.39

90 Percent Confidence Interval for ECVI = (1.17 ; 1.66) ECVI for Saturated Model = 1.19

ECVI for Independence Model = 22.06

Chi-Square for Independence Model with 78 Degrees of Freedom = 3349.04

Independence AIC = 3375.04 Model AIC = 212.74

Saturated AIC = 182.00 Independence CAIC = 3427.52

Model CAIC = 325.78 Saturated CAIC = 549.36

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Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaran

Normed Fit Index (NFI) = 0.95 Non-Normed Fit Index (NNFI) = 0.96

Parsimony Normed Fit Index (PNFI) = 0.77 Comparative Fit Index (CFI) = 0.97 Incremental Fit Index (IFI) = 0.97 Relative Fit Index (RFI) = 0.94

Critical N (CN) = 90.81

Root Mean Square Residual (RMR) = 2.24

Standardized RMR = 0.35 Goodness of Fit Index (GFI) = 0.97

Adjusted Goodness of Fit Index (AGFI) = 0.96 Parsimony Goodness of Fit Index (PGFI) = 0.67

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April 2009Bab 6 Model Pengukuran31

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaranThe Modification Indices Suggest to Add the

Path to from Decrease in Chi-Square New Estimate LEVEL Utility 20.2 1.06 MEANING Performa 8.7 0.24 ASSISTAN Utility 23.9 -2.96 CURRENCY Utility 12.0 0.48

The Modification Indices Suggest to Add an Error

Covariance Between and Decrease in Chi-Square New Estimate LOCATABI ACCURACY 9.1 0.73 ACCESSIB ACCURACY 12.1 -0.13 ACCESSIB LOCATABI 17.8 1.87 CURRENCY LEVEL 9.5 -0.12 CURRENCY ASSISTAN 10.6 0.39 PRESENTA ACCURACY 9.9 0.10 UT1 LEVEL 14.6 0.15 UT3 ACCESSIB 9.0 0.14 P1 LOCATABI 14.4 -1.40 P2 ACCESSIB 9.4 -0.33

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April 2009Bab 6 Model Pengukuran32

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaran

System File from File TTFINPUT.DSF Latent Variable: Ttf Utility Performance Relationship: LEVEL= 1 * Ttf ACCURACY = Ttf LOCATABI = Ttf ACCESSIB = Ttf MEANING = Ttf ASSISTAN = Ttf EASE = Ttf CURRENCY = Ttf PRESENTA = Ttf UT1= 1 * Utility UT3 = Utility P1= 1 * Performance P2 = Performance

Page 33: PRELIS 2, Model Pengukuran dan Model Struktural

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Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaran

Set Error Variance of ACCURACY to 0.01 Set Error Variance of P1 to 0.01 Let Error Covariance of ACCESSIB and LOCATABI Free Let Error Covariance of ACCESSIB and ACCURACY Free Let Error Covariance of CURRENCY and ASSISTAN Free Let Error Covariance of PRESENTA and ACCURACY Free Let Error Covariance of CURRENCY and LEVEL Free Let Error Covariance of LOCATABI and ACCURACY Free Method: Weighted Least Square Options: SC Path Diagram End of Problem

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April 2009Bab 6 Model Pengukuran34

Confirmatory Factor Analysis

Menjalankan Program SIMPLIS dan analisis keluaran

Page 35: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran35

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaranGoodness of Fit Statistics Degrees of Freedom = 58

Minimum Fit Function Chi-Square = 113.50 (P = 0.00) Estimated Non-centrality Parameter (NCP) = 55.50

90 Percent Confidence Interval for NCP = (29.08 ; 89.71) Minimum Fit Function Value = 0.74

Population Discrepancy Function Value (F0) = 0.36 90 Percent Confidence Interval for F0 = (0.19 ; 0.58)

Root Mean Square Error of Approximation (RMSEA) = 0.079 90 Percent Confidence Interval for RMSEA = (0.057 ; 0.10)

P-Value for Test of Close Fit (RMSEA < 0.05) = 0.017 .

Normed Fit Index (NFI) = 0.97 Non-Normed Fit Index (NNFI) = 0.98

Parsimony Normed Fit Index (PNFI) = 0.72 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98 Relative Fit Index (RFI) = 0.96

Critical N (CN) = 117.62

Root Mean Square Residual (RMR) = 1.76 Standardized RMR = 0.30

Goodness of Fit Index (GFI) = 0.98 Adjusted Goodness of Fit Index (AGFI) = 0.97 Parsimony Goodness of Fit Index (PGFI) = 0.62

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April 2009Bab 6 Model Pengukuran36

Confirmatory Factor Analysis Menjalankan Program SIMPLIS dan

analisis keluaran Ttf

(ΣSLF)2 = (0.84+0.99+0.74+0.86+0.91+0.88+0.87+0.90+0.81)2

= (7.8)2 = 60.84

ΣSLF2 = 0.842+0.992+0.742+0.862+0.912+0.882+0.872+0.902+0.812 = 6.80

Σerrors = 0.30+0.02+0.45+0.26+0.18+0.22+0.25+0.18+0.35 = 2.21

Costruct Reliability (CR) = 60.84/(60.84+2.21) = 0.96

Variance Extracted (VE) = 6.8/(6.8+2.21) = 0.75

Utility

(ΣSLF)2 = (0.92+0.98)2 = 3.61 ΣSLF2 = 0.922+0.982 = 1.81

Σerrors = 0.14+0.04 = 0.18

CR = 3.61/(3.61+0.18) =0.95 VE = 1.81/(1.81+0.18) = 0.91

Performance

(ΣSLF)2 = (1.00+0.84)2 = 3.39 ΣSLF2 = 1.002+0.842 = 1.71

Σerrors = 0.00+0.30 = 0.30

CR = 3.39/(3.39+0.30) =0.92 VE = 1.71/(1.71+0.30) = 0.85

Catatan: SLF = Standardized Loading Factors

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April 2009Bab 6 Model Pengukuran37

Second Order Confirmatory Factor Analysis

Page 38: PRELIS 2, Model Pengukuran dan Model Struktural

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Second Order Confirmatory Factor AnalysisSystem File from File DATAKBR.DSF Latent Variables: Social External Combina Internal Kbr Relationships KBRS1 = 1* Social KBRS2 KBRS3 = Social KBRE1 = 1* External KBRE2 KBRE3 = External KBRC1 = 1* Combina KBRC2 KBRC3 = Combina KBRI1 = 1* Internal KBRI2 KBRI3 = Internal Social External Combina Internal = Kbr Set Error Variance of Social to 0.01 Let Error Between KBRE3 and KBRE1 Correlate Let Error Between KBRC1 and KBRE1 Correlate Let Error Between KBRC1 and KBRE3 Correlate Let Error Between KBRC3 and KBRC2 Correlate Let Error Between KBRI1 and KBRS3 Correlate Let Error Between KBRI2 and KBRE1 Correlate Let Error Between KBRS2 and KBRS1 Correlate Let Error Between KBRC3 and KBRE2 Correlate Method: Weighted Least Square Options: SC Path Diagram End Of Problem

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April 2009Bab 6 Model Pengukuran39

Second Order Confirmatory Factor Analysis

Page 40: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran40

Second Order Confirmatory Factor Analysis

Variabel Standardized Loading Factors

Errors Reliabilitas Keterangan

≥ 0.50 CR≥0.70 VE≥0.50 1stCFA Social

KBRS1 KBRS2 KBRS3

External

KBRE1 KBRE2 KBRE3

Combina

KBRC1 KBRC2 KBRC3

Internal

KBRI1 KBRI2 KBRI3

0.80 0.75 0.65

0.81 0.93 0.64

0.78 0.87 0.82

0.69 0.90 0.80

0.36 0.43 0.58

0.34 0.13 0.58

0.40 0.24 0.33

0.53 0.19 0.36

0.78

0.84

0.86

0.84

0.54

0.65

0.68

0.64

Reliabilitas baik Validitas baik Validitas baik Validitas baik Reliabilitas baik Validitas baik Validitas baik Validitas baik Reliabilitas baik Validitas baik Validitas baik Validitas baik Reliabilitas baik Validitas baik Validitas baik Validitas baik

2ndCFA Kbr

Social External

Combina Internal

0.99 0.79 0.95 0.97

0.02 0.37 0.10 0.06

0.96

0.86

Reliabilitas baik Validitas baik Validitas baik Validitas baik Validitas baik

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April 2009Bab 6 Model Pengukuran41

Latent Variable Score (LVS)

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Latent Variable Score (LVS)

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Latent Variable Score (LVS)

Program SIMPLIS untuk menghitung Latent Variable Score (LVS)

System File from File Self.DSF

Latent Variables Self

Relationships

SELF1 - SELF5 = Self

Let Error Covariance of SELF5 and SELF1 Free

Let Error Covariance of SELF4 and SELF1 Free

!Statemen untuk menghitung Latent Variable Score

PSFFile Self.PSF

Path Diagram End of Problem

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April 2009Bab 6 Model Pengukuran44

Latent Variable Score (LVS)

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Latent Variable Score (LVS)

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April 2009Bab 6 Model Pengukuran46

Latent Variable Score (LVS)

Self

Gambar 6.22.a.

Self Selfest

Gambar 6.22.b.

1

0

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April 2009Bab 6 Model Pengukuran47

Latent Variable Score (LVS) Penyederhanaan Model

Infoacq Infodis Sharint Decmem Procmem

IT_Comp

Org_Learn

Firm_Perf

ITKnow ITOps ITObj

IA1 IA6 ID1 ID6 SI1 SI5 DM1 DM7 PM1

FP1

FP4

IK1 IK4 IO1 IO6 IT1 IT5

PM5

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April 2009Bab 6 Model Pengukuran48

Latent Variable Score (LVS) Penyederhanaan Model

Infoacq Infodis Sharint Decmem

ITknow ITops ITobj

Procmem

FirmLVS

Org_learn

It_comp

Firm_Perf

10

Page 49: PRELIS 2, Model Pengukuran dan Model Struktural

Bab 7Bab 7Model StrukturalModel Struktural

Dr. Setyo Hari Wijanto<[email protected]>

Page 50: PRELIS 2, Model Pengukuran dan Model Struktural

April 2009Bab 6 Model Pengukuran50

Model Struktural Rekursif

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Model Struktural RekursifSystem File from File TTFINPUT.DSF Latent Variable: Ttf Utility Performance Relationship: LEVEL= 1 * Ttf ACCURACY = Ttf LOCATABI = Ttf ACCESSIB = Ttf MEANING = Ttf ASSISTAN = Ttf EASE = Ttf CURRENCY = Ttf PRESENTA = Ttf UT1= 1 * Utility UT3 = Utility P1= 1 * Performance P2 = Performance Performance = Ttf Utility Utility = Ttf Set Error Variance of ACCURACY to 0.01 Set Error Variance of P1 to 0.01 Let Error Covariance of ACCESSIB and LOCATABI Free Let Error Covariance of ACCESSIB and ACCURACY Free Let Error Covariance of CURRENCY and ASSISTAN Free Let Error Covariance of PRESENTA and ACCURACY Free Let Error Covariance of CURRENCY and LEVEL Free Let Error Covariance of LOCATABI and ACCURACY Free Method: Weighted Least Square Options: SC Path Diagram End of Problem

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Model Struktural Rekursif

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Model Struktural Rekursif

Goodness of Fit Statistics

Degrees of Freedom = 58 Minimum Fit Function Chi-Square = 113.50 (P = 0.00)

Estimated Non-centrality Parameter (NCP) = 55.50 90 Percent Confidence Interval for NCP = (29.08 ; 89.71)

Minimum Fit Function Value = 0.74

Population Discrepancy Function Value (F0) = 0.36 90 Percent Confidence Interval for F0 = (0.19 ; 0.58)

Root Mean Square Error of Approximation (RMSEA) = 0.079 90 Percent Confidence Interval for RMSEA = (0.057 ; 0.10)

P-Value for Test of Close Fit (RMSEA < 0.05) = 0.017

Normed Fit Index (NFI) = 0.97 Non-Normed Fit Index (NNFI) = 0.98

Parsimony Normed Fit Index (PNFI) = 0.72 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98

Relative Fit Index (RFI) = 0.96

Critical N (CN) = 117.62 Root Mean Square Residual (RMR) = 1.76

Standardized RMR = 0.30 Goodness of Fit Index (GFI) = 0.98

Adjusted Goodness of Fit Index (AGFI) = 0.97 Parsimony Goodness of Fit Index (PGFI) = 0.62

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April 2009Bab 6 Model Pengukuran54

Model Struktural Rekursif

Structural Equations Utility = 0.46*Ttf, Errorvar.= 0.23 , R² = 0.55 (0.024) (0.031) 19.09 7.32 Performa = - 0.33*Utility + 0.96*Ttf, Errorvar.= 1.53 , R² = 0.37 (0.25) (0.14) (0.22) -1.34 6.72 7.02 Reduced Form Equations Utility = 0.46*Ttf, Errorvar.= 0.23, R² = 0.55 (0.024) 19.09 Performa = 0.81*Ttf, Errorvar.= 1.56, R² = 0.36 (0.071) 11.32

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April 2009Bab 6 Model Pengukuran55

Model Struktural Rekursif

Path Estimasi Nilai - t Kesimpulan

1 Ttf Utility 0.46 19.09 Signifikan

2 Utility Performance -0.33 -1.34 Tdk Signifikan

3 Ttf Performance 0.96 6.72 Signifikan