pengabdian masyarakat analisis structural equation modeling sem dengan lisrel 8 windows 12 agustus 2

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DEPARTEMEN PENDI DI KAN NASI ONAL

UNI VERSI TAS NEGERI YOGYAKARTA

LEMBAGA PENELI TI AN

Alamat: Karangmalang, Yogyakarta. 55281. Telp. (0274) 550839

.

(0274) 518617. E-mail: lem litunv@vahoo.com : Sekreta@telkom .net

Nom or

Lam pir an

Hal

271 / H 3 4 .2 1 / TU/ 2 0 0 9

Jad w al Pelat ih an

Per m oh o n an seb agai Tu t o r

6 Ju lii2 0 0 9

Kepada Yt h. : Bp. DR. Sam sul Hadi, MT, M.Pd.

Dosen PT. Elek t r o FT UNY

Di Yog y ak ar t a

Den g an h or m at , m en in dak lan j u t i pada p er t em u an pan it ia Pelak san aan

Pelat ihan An alisis SEM dan PLS, ber sam a ini kam i

m oh on k esediaan Bapak

u nt u k m enj adi Tu t o r

p elat ihan An alisis SEM dan PLS y an g ak an kam i

lak san ak an pada:

Hari

: Sen in s.d Selasa

Ta n g g al

: 10 dan 11 Ag u st u s 2009

Jam

: 0 8. 00 s.d 16.00 WI B.

Tem p a t

: Ru an g Sidang Lem bag a Penelit ian UNY

At as Kesediaan dan k er j asam an y a d iu cap k an t er im a kasih

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Ja d w a l Ke g ia t a n ( I m p le m e n t a t if )

Waktu

K egi atan

Pengampu

K eterangan

Jum at , 7 Agu

12.45- 13.15

stus 2 0 0 9

1. Regist rasi Pesert a dan

Pelunasan Biaya Pelat ihan

1. Nardiyant o, SI P

2. Suyud, S.Pd

Disediakan cam ilan

dan minum:

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

3. Siswa PKL

2. Penyerahan CD Soft ware,

dan Form ulir Klarifikasi

Nam a Lengkap

1. Sugeng Sut art o, S.Pd

2. Ant . Hedy Ari P, SI P

13.15- 15.00

I nst alasi Soft ware LI SREL,

AMOS, dan Sm art PLS

1. Dr. Samsul Hadi

0 3. Ali Muhson, M.Pd

2. Dr. Heri Ret nowat i

Senin, 10 Agi

07.30- 08.00

j stus 2 0 0 9

Regist rasi Pesert a

1. Sastri Sihati, A.Md

2. Siswa PKL

08.00- 08.30

Pem bukaan:

1. Laporan Ketua Panit ia

2. Sam but an Ketua Lem baga

Penelit ian

Mast er Cerem onv:

Sukardi, SI P

08.30- 10.00

Konsep Dasar SEM dan Berbagai

Model Analisis SEM

Prof. Dr. I m am Ghozali

Moderat or:

Dr. Samsul Hadi

10.00-10.15

Istirahat

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan minum

dan cam i ia n

_{. . .}

10.30-12.00

Aplikasi PLS unt uk Model

Pengukuran I ndikat or Reflekt if

dan For m at if

Prof. Dr. I m am Ghozali

Moderat or:

Dr. Samsul Hadi

12.00-13.00

Istirahat, Sholat, dan Makan

Siang

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan makan

siang dan minum

13.00- 14.30

Aplikasi PLS unt uk Pat h Analysis

Prof. Dr. I m am Ghozali

Moderat or:

Ali Muhson, M.Pd

14.30-14.45

Istirahat

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan minum

dan cam Ha n

14.45-16.15

Aplikasi PLS unt uk Analisis Full

Model St rukt ural ( SEM)

Prof. Dr. I mam Ghozali

Moderat or:

Ali Muhson, M.Pd

16.15-16.30

Penj elasan Panit ia t ent ang

Rencana Kegiat an Pelat ihan

Esok hari

Mast er Cerem onv:

Sukardi, SI P

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Waktu

K egi atan

Pengampu

K eterangan

Selasa, 11 Ag ustus 2 0 0 9

07.30- 08.00

Regist rasi Pesert a

1. Sastri Sihati, A.Md

2. Siswa PKL

08.00- 10.00

Aplikasi LI SR

EL unt uk

Confirm at ory Fact or Analysis

( CFA)

1. Dr. Samsul Hadi

2. Dr. Heri Ret nowat i

3. Ali Muhson, M.Pd

Dilaksanakan dalam

3 kelas

Fasilitator:

Sugeng Sutarto, S.Pd

10.00-10.15

I stirahat

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan minum

dan cam ilan

10.30- 12.00

Aplikasi LI SREL unt uk Path

Analysis dan Full Model

1. Dr. Samsul Hadi

2. Dr. Heri Ret nowat i

3. Ali Muhson, M.Pd

Dilaksanakan dalam

3 kelas

Fasilitator:

Sugeng Sutarto, S.Pd

12.00-13.00

Istirahat, Shoiat, dan Makan

Siang

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan makan

siang dan minum

13.00-14.30

Aplikasi AMOS unt uk

Confirm at ory Fact or Analysis

(CFA)

1. Dr. Samsul Hadi

2. Dr. Heri Ret nowat i

3. Ali Muhson, M.Pd

Dilaksanakan dalam

3 kelas

Fasilitator:

Sugeng Sutarto, S.Pd

14.30-14.45

I stirahat

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

Disediakan minum

dan cam ilan

14.30- 16.00

Aplikasi AMOS unt uk Path

Analysis dan Full Model

1. Dr. Samsul Hadi

2. Dr. Heri Ret nowat i

3. Ali Muhson, M.Pd

Dilaksanakan dalam

3 kelas

Fasilitator:

Sugeng Sutarto, S.Pd

16.15- 16.30

Penj elasan Panit ia t ent ang

Rencana Kegiat an Pelat ihan

Esok hari

Mast er Cerem onv:

Sukardi, SI P

Rabu, 12 Agust us 2 0 0 9

07.30- 08.00

Regist rasi Pesert a

1. Sastri Sihati, A.Md

2. Siswa PKL

Disediakan cam ilan

dan minum:

1. Nur Wahyu K, SE

2. Sastri Sihati, A.Md

08.00- 10.00

Tugas/ Berlat ih Mandiri

Tim :

1. Dr. Samsul Hadi

2. Dr. Heri Ret nowat i

3. Ali Muhson, M.Pd

10.00- 11.30

Tut orial

11.30- 12.00

Penut upan:

Sam but an Ket ua Lem baga

Penelit ian

Mast er Cerem onv:

Sukardi, SI P

Penyerahan

Sertifikat:

1. Suhardi, S.Pd

2. Ant. Hedy AP, SI P

3. Siswa PKL

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S E RTIFIKAT

N

O: 339/H34.21/PL.2009

DEPARTEMEN PEN D ID IK A N NASIONAL

U N IVER SITA S NEGERI YOGYAKARTA

LEMBAGA P E N ELITIA N

D

I B E R I K A N K E P A D A

DR. SAMSUL HADI, MT

S E B A G A I

IN S T R U K T U R

P A D A P E L A T I H A N A N A L I S I S

STRUCTURAL EQUATION MODELLING (SEM)

D E N G A N L I S R E L , A M O S , D A N S M A R T P L S Y A N G D I S E L E N G G A R A K A N

T A N G G A L 7 - 1 2 A G U S T U S 2 0 0 9 , D I L E M B A G A P E N E L I T I A N

U N I V E R S I T A S N E G E R I Y O G Y A K A R T A

Y o g y a k a r t a , 1 2 A g u s t u s 2 0 0 9

^l^li^-Ketua

r d i, P h .D .

N IP . 1 4 3 5 3 0 5 1 9 1 9 7 8 1 1 1 0 0 1

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D aftar Materi Pelatihan Analisis Structural Equation Modelling (SEM) dengan

LISREL, AMOS dan SmartPLS

Materi

Ju mlah Jam

Instali dan Pengenalan LISREL, AMOS, dan SmartPLS

2 Konsep D asar SEM dan B erb agai Model Analisis SEM

2,5

Aplikasi PLS untuk Model Pengu ku ran Indikator Reflektif dan Formatif

2,5

Aplikasi PLS untuk Path Analysis

2 Aplikasi PLS untuk Analisis Full Model Stru ktu ral (SEM)

2 Aplikasi LISRE L untuk C onfirmatory Factor Analysis (CFA)

2,5

Aplikasi LISRE L untuk Path Analysis

2 Aplikasi LISRE L u ntu k Full Model

2 Aplikasi AMOS u ntu k Confirmatory Factor Analysis (CFA)

2 Aplikasi AMOS u ntu k Path Analysis

2 Aplikasi AMO S u ntu k Full Model

2 Tu torial & Tu gas Mandiri

10,5

Ju mlah

34

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ANALISIS

STRUCTURAL EQUATION M ODELING

_(SEM)

DENGAN LISREL 8 FOR WINDOWS

O l e h :

S a m s u l H a d i

UNIVERSITAS NEGERI YOGYAKARTA

2 0 0 9

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ANALISIS

STRUCTURAL EQUATION M ODELING

_(SEM)

DENGAN LISREL 8 FOR WINDOWS

A. Pendahuluan

St r u c t u r a l E q u a t i o n M o d e l i n g (SEM ) m er u p akan gab u n gan an t ar a an alisis f akt o r

ko n f ir m at o r i d en gan an alisis jalu r yan g d ilaksan akan se car a sim u lt an . An alisis an alisis f akt o r ko n f ir m at o r i ( c o n f i r m a t o r y f a c t o r a n a l y s i s , CFA) d igu n akan u n t u k m en gu n gkap

m o d el k o n st r u k in st r u m e n . An alisis jalu r ( p a t h a n a l y s i s ) d igu n akan u n t u k m en get ah u i

e f e k lan gsu n g d an / at au t id ak lan gsu n g d ari var iab el ekso gen ke var iab el e n d o gen m au p u n var iab el e n d o gen ke e n d o gen . Var iab e l e kso gen ad alah var iab el d alam m o d el yan g t id ak p er n ah d ip e n gar u h i var iab el lain , sed an gkan var iab el e n d o gen ad alah var iab el yan g d ip e n gar u h i o leh var iab el ekso gen .

Pen gu jian m o d el d en gan SEM d ap at m en gh asilkan p er sam aan p en gu k u r an , b aik u n t u k var iab el e kso gen m au p u n en d o gen , se r t a p er sam aan st r u kt u r al. Ru m u s u m um p er sam aan p en gu ku r an var iab el ekso gen ad alah : X = A x T| +8 (Jo r esk o g & So r b o m , 1996: 2 d an Su p r an t o , 2 0 04: 296). Pe r sam aan p en gu ku r an var iab el en d o gen secar a u m um d in yat akan d en gan Y = A Y T| + e (Jo r esk o g & So r b o m , 1996: 2 d an Su p r an t o , 2004: 296). Per sam aan st r u kt u r al secar a u m u m d in yat akan d en gan r| = F2, + ^ (Jo r esk o g & So r b o m , 1996: 205). Per sam aan t e r se b u t d ap at d p er o leh secar a lan gsu n g d en gan LISREL.

B. Pengujian Model Persamaan Struktural

Pen gu jian m o d el d en gan LISREL d ap at d ilaku kan d en gan t iga p en d ekat an , yait u : 1) S t r i c k l y C o n f i r m a t o r y , 2 ) A l t e r n a t i v e M o d e l at au C o m p e t i n g M o d e l , d an 3) M o d e l G e n e r a t i n g (Jo r esk o g & So r b o m , 2 003).

Pe n d e kat an S t r i c k l y C o n f i r m a t o r y m en u n t u t p en elit i u n t u k m en et ap k an sat u

m o d el d an m e n gu m p u lk an d at a e m p ir ik u n t u k m en gu ji m o d el yan g ad a. Hasil an alisis ko n f ir m at o r i b er d asar kan d at a e m p ir ik d ap at m en e r im a at au m e n o lak m o d el yan g ad a. Pen d ekat an A l t e r n a t i v e M o d e l at au C o m p e t i n g M o d e l m en u n t u t p en elit i m e n ge m

b an gkan b eb er ap a m o d el alt e r n at if d an m en gu jin ya m en gggu n akan d at a yan g sam a u n t u k m em p er o le h m o d el yan g p alin g b aik. Pe n d e kat an M o d e l G e n e r a t i n g m en u n t u t

p en elit i m em b u at m o d el t e n t at if d an m en gu jin ya. Jik a m o d el t id ak f it , m o d el h aru s d im o d if ik asi d an d iu ji lagi m en ggu n akan d at a yan g sam a. M o d if ikasi m o d el t er seb u t m u n gkin h ar u s d ilaku kan b er kali-kali sam p ai d it e m u k an m o d el yan g f it d an r asio n al.

C. Langkah-langkah Analisis SEM dengan Lisrel

1. Bu at m o d el ko n sep t u al b e r d asar kan kajian t eo r i, h asil p en elit ian , d an rasio n al (d alam p r o p o sal p en elit ian ). M isalkan p en gar u h d ep r esi t e r h ad ap p er caya diri dan t in d akan d im o d e lk an se car a ko n sep t u al sbb:

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2. En t r y d at a yan g d ip er o leh d ari lap an gan m en ggu n akan p r o gr am Excel, SPSS, at au lain n ya. Pen u lis m e r eko m e n d asikan u n t u k m en ggu n akan Excel at au SPSS. Jika m e n ggu n akan Excel, e n t r y m u d ah , kerja k o m p u t er r in gan , d an p r o gr am b iasan ya su d ah t er in st al p ad a se t iap ko m p u t er . Jik a m e n ggu n akan SPSS, im p o r t d at a ke Lisrel leb ih m u d ah , t et ap i kerj a k o m p u t er leb ih b er at d an t id ak sem u a ko m p u t er t er in st al SPSS. M isal m en ggu n akan Excel d en gan n am a f ile M o d el DP.xls sbb :

t a p

d “ j - p * * M o d e l D P [C o m p a t ib il it y M o d e ] - M i c r o s o f t E x c e l

Home In»e*1 P a g e L a y o u t F o r m u la s Data n o n e w W c w A d d -I n s

& C u t

Calibn * ; 1 1 a' — = = 9 - i=3J W r a p T e x t General *

1 ^ J|

fcaste * c + n . + B / U 1 y j * ■ 3* • £ • m m m I E ^ M e r g e & C e n t e r ~ $ * % * *.• 00 .M « 6 C o n d i t i o r F o r m a tt im

C li p b o a r d c-t A l i g n m e n t ftjmt-ff

P12 - j.

A B C 0 E F 6 H K L

1 DPI DP2 DP3 DP4 PDI PD2 PD3 PD4 PD5 TK1 TK2 TK3

2 19 23 68 55 35 59 70 87 44 39 20 86

3 36 81 46 94 14 36 90 13 11 30 39 95

4 85 S 31 62 35 65 48 50 32 25 68 64

5 67 11 6 19 75 92 86 6 S O 69 56 61

b 23 S 36 18 58 99 58 34 62 80 45 77

7 95 74 45 29 45 33 45 61 32 2 51 31

Ko lo m m en u n ju kk an in d ikat o r at au var iab el, b ar is m en u n ju kk an ju m lah sam p el.

3. Jalan k an p r o gr am LISREL

O I

File View Help

LI SREL W in d o w s A p p lica t io n

* ■ 1

*

* | -

# | j l ] f j

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4. Pilih m en u File su b m en u

Import External Data in Other Formats

§ LI SREL W in d o w s Ap p lica t io n

File View Help

New C trl+ N

O p e n ... C trl+ O

Im p o rt D a ta in F ree F o rm at

□

f

Im p o rt E x te rn a l D ata in O th e r Form ats P rin t 5 e tu p ..,

5. Pilih f ile d at a Excel p ad a d ialo g b o x sb b :

I n p u t D a t a b a se

_?| xj

File name: Files of t ype:

□ p en

Exce l 97/ 2000 (“ .xls) Can cel

6. Pad a lan gkah 5, t ek an t o m b o l

OK

d an sim p an file d en gan n am a sam a d e n gan file Excel t et ap i d en gan e kst en si PSF seh in gga t am p il d at a ed it o r LIS REL sbb :

L I S REL W i n d o w s A p p l i c at i o n M o d el D P

File Edit D a ta T ra n s fo rm a tio n Statis tics G rap h s M u ltilevel V ie w W in d o w H elp

□ |cg|ig|> I N a l #l#| s|

I

■ ?

I

D P1 D P 2 I D P 3 D P 4 | P D 1 I P D 2 | P D 3 | P D 4 ! P D 5 | T K 1 | T K 2 | T K 3

1 ^ K i c i u i ] 3.00 5 3.0 0 8 7.0 0 8 .00 96.0 0 5 .0 0 7 2 .0 0 5 .00 3 0.0 0 3 1.0 0 97.0 0

2 8 8 .0 0 5 9.0 0 76.0 0 2 4.0 0 1 3.0 0 92 00 2 7 .0 0 1 6 .0 0 24 DC 5 1.0 0 1 5.0 0 17 00

3 8-1 00 8 4.0 0 6 7.0 0 7 2.0 0 5 5.0 0 29.00 7 .0 0 3 8 .0 0 71.0 0 8 4.0 0 2 3.0 0 39.0 0

A 2 1 .0 0 9 9.0 0 8 9.0 0 4 1.0 0 9 6.0 0 85.0 0 2 7 .0 0 1 4 .0 0 74.0 0 8 8.0 0 5 6.0 0 17.0 0

7. Tu n j u k salah sat u n am a in d ikat o r / var iab e l, klik kan an , d an pilih m en u

Define

Variables

M o d e l D P

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m LI SREL W in d o w s A p p lica t io n - M o d e l DP

File Edit D a ta T ra n sfo rm a tio n S ta tistics G raphs M u ltilevel View W in do w Help

D

p M o d e l DP

Delel In se i

D efine Variables. D ele te Variables In s e rt Variable

u n .u u

P 3

DP4

| P D 1 P D 2 |

5 3 . 0 0 8 7 . 0 0 8.00 9 6 . 0 0

7 6 . 0 0 2 4 . 0 0 1 3 . 0 0 9 2 . 0 0

6 7 . 0 0 7 2 . 0 0 55.00 2 9 . 0 0

8. Tu n j u k salah sat u in d ikat o r at au var iab el d an klik m en u

Variable Type

D e f in e V a r ia b le s

_x|

D

P2

DP3 DP4

PD1 PD2 PD3 PD4 PD5

TK1

TK2

TK3

Insert

Ren am e

Variable Typ e

Cat egory Lab els

M issing Valu es

Can cel To select more t han one variable at a t ime,hold dow n t he CT R L key w hile clicking on t he variables to be select ed

9. Pilih t ip e d at a u n t u k var iab el t er seb u t , jik a t ip e d at a t e r se b u t b er laku u n t u k sem u a b er i t an d a cek

Apply to all.

Kem u d ian save file d at a (klik gam b ar d isket ).

* ]

C

Ordinal U K

<•

_{Cont inuous} _{Can ce |}

f * Censored abo ve Censored below

C

_{Censored ab o ve and belo^} _{P ' App ly to all} a ria h lp Typ p s [n r D PI

10. U n t u k m elih at ko n d isi d at a d an m en yiap kan m at r ik s yan g akan d ian alisis, pilih m enu

Statistics,

su b m en u

Output Options

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§ LI SREL W in d o w s A p p lica t io n - M o d e l D P

File 1Edit D a ta T ra n sfo rm a tio n S ta tistics G raphs M ultilevel VietV

^ 1

\gi

l * j n 1 * J M

1 m D a ta S creening

Tm m ihp i i p c

W ▼ | M

l l l i p U L C 1 II 1I I V U l U C J i i i

M ultiple Im p u ta tio n ... 1

lo d e l DP

DP1

_□

1 53.00

2 88.00

4

i K n r i m

5 76.00 6 11.00 7 5.00 0 36.00 9 38.00 10 52.00

II 11 1 L_

Equal T h re sh o ld s... Fix T h re sh o ld s.,. H om oge ne ity T e st ,. Norm al S co re s... F a cto r A n a ly sis,.. C en so re d R e g re ss io n s ... Logistic R eg re ss io n s ... P rob it R eg re ss io n s ... R eg re ss io n s ...

T w o -S ta g e L e a s t-5 q u a re s ... B o o ts tra p p in g ..

O u tp u t O ptions

11. Kem u d ian cek

LISREL system data

u n t u k m en yiap kan m at r ik s ko var ian s (d ef au lt ),

Perform tests of multivariate normality

u n t u k m elih at n o r m alit as m u lt ivar iat d at a, d an

Asymptotic Covariance Matrix

u n t u k m en yiap kan m at r ik s ko var ian s asim t o t ik (jika d ip e r lu k an ), kem u d ian t ekan OK.

O u t p u t

• M oment Matrix

| Co var ian ces

zl

Sa v e to file: V LI SR EL syst em dat a

r

M eans

V~

_{Sa v e to file:}

St and ard Deviat ions Sa v e to file:

Asym pt ot ic Co var ian ce M atrix f Sa v e to file1 ' Print in out put I---Asym pt ot ic Var ian ce

s-I- Sa v e to file: I- Print in out put

*

Dat a' —

Sa v e t he t ransformed dat a to file:

W idt h of fields: 15 Num ber of decim als: 6

Num ber of repet it ions:

pj

I- Rew in d dat a after each repetition I- Print bivariat e frequency t ables

V

_{Print t est s of underlying bivariat e normality}

P Perform t est s of multivariat e normality I- W id e print

• Ran dom seed

C

Set seed to | l 2345G

OK Can cel

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12. Siap kan d iagr am jalu r m elalu i m en u

File,

New, Path Diagram

seb agai b er iku t :

p LISREl W in d o w s A ppl i cation - M odel DP

File Edit D a ta T ra n sfo rm a tio n S ta tistics G raphs M ultilevel

1 D l o c l i f l H l a | n | f |

| : a ▼ z i h ► m KT < e ¥

■

| 5 E

|N e w _{* J}

N ew

■—

_{P R ELIS D ata}

11—

r i

p. .

1 □ K D P 4

SIM PLIS P roject L IS R E L Proiect

J t i

- 1

C ancel

87.0 _24.0

Path Diagram

72.0

41.0

84.0

Beri n am a d iagr am j alu r sam a d en gan n am a d at a.

13. Siap kan in d ikat o r / valiab el yan g akan d igam b ar d alam d iagr am m elalu i m en u

Setup,

su b m en u

Variables.

Kem u d ian klik

Add/ Read Variables

p ad a b agian

Observed

Variables.

*1

Observed Variables

Name i VAR 1 ? VAR 2

Lat ent Variables Nam e

< Previous

Next >

Can cel

Ad d / Read Variables _{Add Lat ent Variables}

14. Klik

Brows

d an pilih f ile DSF yan g se su ai. Kem u d ian len gkap i n am a var iab el lat en t d en gan m e n gklik t o m b o l

Add Latent Variables.

Se t e lah it u klik t o m b o l

Next.

R e a d from file: | L I S R E L S jis le m File 3

C A d d list o f v a r ia b le s (e. g ., v a r1 -v a r 5 ):

r In fo

---S e le c t o n e o f th e tw o s ystem files. T h e L IS R E L d a ta system file h a s a D S F ex te n s io n a n d th e P R E L IS s p r e a d s h e e t a P S F e x te n s io n .

*1

Ca n e d |

SSlwcCElW’.DSF

J *1

Ftenarr*

Files o f type: | L IS R E L S y s te m D a ta (*.d s f)

| O

®

» | Lancd j

(13)

La b e ls _{* 1}

Obser ved Variab les Lat ent Variables Nam e

1 DP1

-? DP2

3 DP3

4 DP4

5 PD1

6 PD2

7 PD3

8 PD4

s

PD5

±LL

TK1 ▼

Nam e

1 D EPRES

?

3 TIN DA KA N

< Previo us

Next >

Can cel

A d d / Re ad Variab les _{Ad d Lat ent Var iables}

15 .

Isi ju m lah sam p el sesu ai d en gan d at a p ad a d ialo g b o x sb b , kem u d ian klik

OK.

Groups:

~ 3 r

s

am e acr o ss groups

Sum m ary st at ist ics

St at ist ics from: File t ype: Edit

New.

| Co var ian ce s d I LI SR EL Syst em Dat a

Full matrix I- Fort ran format t ed File name: Brow se...

Next >

OK | D:\ IRT_Tr ain in g\ Lisr el\ M ODEL C

St at ist ics included:

Can cel I M ean included in t he dat a

W eight

In clud e w eight matrix

L

_ l

Num ber of observat ions

| 150

16. Ten t u k an var iab el m an a yan g ekso gen d an m an a yan g en d o gen , ju ga in d ikat o r yan g t er kait . Kem u d ian b u at d iagr am jalu r n ya:

(14)

. . File Ed it S etup D ra w View Im a g e Oubpub W in d o w Help * » l - i di n t ?

Cwudt | - J M o d e l) | £ / E i . k * k l E i to M t M V i W l l f f i l W W M B i * |

17. Pilih men u

Setup

su b m en u

Build LISREL Syntax.

Jika d iin gin kan o u t p u t yan g m u d ah d ib aca, pilih ju ga su b m en u

Build SIM PLIS Syntax.

§ LI SREL W in d o w s A p p lica t io n - M o d e lD P

File Edit 5 e tu p D raw View Im age O u tp u t W indow Help

Title and Com m ents ... G ro u p s..,

V ariables.. D a ta ...

A D ?

Groups: M odels: B asic M odel

Observe

Ri liW 1 TCD Cl FJ

DP1

DR2fx

Build 5IM PLI5 S y n ta x F8

18. Jalan kan

LISREL Syntax

at au

SIM PLIS Syntax

yan g ad a d en gan m en gklik t o m b o l

'J

F ile E d i t S e tu p M o d e l O u t p u t O p t io n s W in d o w H e lp

_d

( £ &

y| jtN a l &|#| a|o|f I_________________________________

T I

IDA NI=12 N0=150 WG=1 HA=CH SY='C:\XXX.dsf1 NG=1 SE

5 6 7 8 9 10 11 12 2 1 3 4 /

HO NX=4 NY=8 NK=1 NE=2 LY=FU,FI LX=FU,FI BE=FU,FI GA=FU,FI PH=SY,FR PS=DI,FR TE=DI,FR TD=DI,FR

TINDAKAN PD LK

DEPRES

FR L Y (2,2) L Y (3,2) LY(4,2) LY(5,2) LY(7,1) LY(8,1) LX(1,1) LX(3,1) LX(4,1) FR BE(1,2) G A (1,1) GA(2,1)

V A 1.000 L Y (1,2) L Y (6,1) LX(2,1) PD

OU AH ND=3 AD=OFF

(15)

Co n t o h h asil est im asi p ar am et e r o leh LISREL sb b :

- 875.

Chi-Square=46.67, df=51, P-value=D.64634, RMSEA=0.000

(Est im at es)

0.210—_ /

S 7

o.uo /

l>^

-0.781

/

- - - -

\ V°

0.034

0 34“

.056

Y

_{\ V}

0.134

-0.462

\

V ' — ^

£ . 2 2 5 ^ T U f D A K A H

0 . 0 0 3

Ch±-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(St an d ar d ized So lu t io n )

067

9 8 6

0 0 2

9 5 0

121

.•»1

0 6$

Oil

O i l

0 3 1

1 76

O O ff

0 2 1

(16)

6 . Z 0 5 +»

1 . 0 9 5 ^

Chi-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(T-valu es)

Chi-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(M id if icat io n In d ices)

(17)

METODE ESTIM

_{ASI DALAM SEM}

Ad a b an yak m et o d e est im asi yan g d ap at d igu n akan d alam SEM (Jo r esk o g & So r b o m , 1996: 17 d an Jo r e sk o g & So r b o m , 2 0 03). M et o d e e st im asi t e r se b u t ad alah :

1. I n s t r u m e n t a l V a r i abl e s (N) , 2 . T w o - S t a g e L e a s t S q u a r e s (TSLS),

3. U n w e i g h t e d L e a s t S q u a r e s (U LS),

4. G e n e r a l i z e d L e a s t S q u a r e s (GLS),

5. M a x i m u m L i k e l i h o o d (M L),

6. G e n e r a l l y W e i g h t e d L e a s t S q u a r e s (W LS),

7. D i a g o n a l l y W e i g h t e d L e a s t S q u a r e s ( DW LS).

Pad a t ah u n 1987 Br o w n e m e n ge m b an gkan m et o d e R obu s t M a x i m u m L i k e l i h o o d

(RM L) d an se t ah u n ke m u d ian , yait u t ah u n 19 88, Sat o r r a d an Ben t ler m en ye m p u r n ak an m et o d e RM Ld e n ga n m em p er b aik i r u m u s %2 (M els, 20 04: 13 d an M els, 2 0 0 6 :1 2 ).

Te r k ait d en gan b an yakn ya m et o d e est im asi yan g d ap at d igu n akan d alam SEM , Jo r e sk o g d an So r b o m (200 3) m em b er i t u n t u n an p r akt is u n t u k m em ilih m et o d e e st im asi yan g t ep at . Tu n t u n an t e r se b u t ad alah seb agai b er ik u t .

1. Jik a d at a ko n t in u d an b er d ist r ib u si n o r m al m u lt ivar iat , m aka m et o d e M L p er lu d igu n akan .

2. Jik a d at a ko n t in u t et ap i t id ak b er d ist r ib u si n o r m al m u lt ivar iat ser t a u ku ran sam p eln ya t id ak b esar , m aka p en ggu n aan m et o d e RM L d ir ek o m en d asikan ; n am u n jik a u ku r an sam p el b esar , m aka m et o d e W LS p er lu d igu n akan . 3. Jika d at a o r d in al, kat e go r ikal at au cam p u r an , m aka m et o d e W LS d en gan

m at r ik s ko r elasi p o lik o r ik at au p o liser ial p er lu d igu n akan .

DAFTAR PUSTAKA

Jo r e sk o g, K. G. & So r b o m , D. (1 9 9 6 ). L i s r e l 8 : u s e r ' s r e f e r e n c e g u i d e . Ch icago : Scie n t if ic

So f t w ar e In t e r n at io n al.

Jo r e sk o g, K. G. & So r b o m , D. (2 0 0 3 ). L i s r e l 8 . 5 4 h e l p . Ch icago : Scie n t if ic So f t w ar e

In t er n at io n al.

M els, G. (2 0 0 4 ). L i s r e l f o r w i n d o w s : G e t t i n g s t a r t e d g u i d e . Lin co ln w o o d : Scie n t if ic

So f t w ar e In t e r n at io n al.

M els, G. (2 0 0 6 ). G e t t i n g s t a r t e d w i t h t h e s t u d e n t e d i t i o n o f L i s r e l 8 . 5 4 f o r w i n d o w s .

Lin co ln w o o d : Scie n t if ic So f t w ar e In t er n at io n al.

(1)

12. Siap kan d iagr am jalu r m elalu i m en u

File,

New, Path Diagram seb agai b er iku t :

p LISREl W in d o w s A ppl i cation - M odel DP

File Edit D a ta T ra n sfo rm a tio n S ta tistics G raphs M ultilevel

1 D l o c l i f l H l a | n | f |

| : a ▼ z i h ► m KT < e ¥

■

| 5 E

|N e w _{* J}

N ew

■—

_{P R ELIS D ata}

11—

r i

p. .

1 □ K D P 4 SIM PLIS P roject

L IS R E L Proiect

J t i

- 1

C ancel

87.0 _24.0

Path Diagram

72.0

41.0

84.0

Beri n am a d iagr am j alu r sam a d en gan n am a d at a.

13. Siap kan in d ikat o r / valiab el yan g akan d igam b ar d alam d iagr am m elalu i m en u

Setup,

su b m en u

Variables.

Kem u d ian klik

Add/ Read Variables

p ad a b agian

Observed

Variables.

*1

Observed Variables Name i VAR 1 ? VAR 2

Lat ent Variables Nam e

< Previous

Next >

Can cel

Ad d / Read Variables _{Add Lat ent Variables}

14. Klik

Brows

d an pilih f ile DSF yan g se su ai. Kem u d ian len gkap i n am a var iab el lat en t d en gan m e n gklik t o m b o l

Add Latent Variables.

Se t e lah it u klik t o m b o l

Next.

R e a d from file: | L I S R E L S jis le m File 3 C A d d list o f v a r ia b le s (e. g ., v a r1 -v a r 5 ):

*1

SSlwcCElW’.DSF

(2)

La b e ls _{* 1}

Obser ved Variab les Lat ent Variables

Nam e

1 DP1

-? DP2

3 DP3

4 DP4

5 PD1

6 PD2

7 PD3

8 PD4

s

PD5

±LL

TK1 ▼

Nam e

1 D EPRES

?

3 TIN DA KA N

< Previo us

Next >

Can cel

A d d / Re ad Variab les _{Ad d Lat ent Var iables}

15 .

Isi ju m lah sam p el sesu ai d en gan d at a p ad a d ialo g b o x sb b , kem u d ian klik

OK.

Groups:

~ 3 r

s

am e acr o ss groups

Sum m ary st at ist ics

St at ist ics from: File t ype: Edit

New.

| Co var ian ce s d I LI SR EL Syst em Dat a

Full matrix I- Fort ran format t ed File name: Brow se...

Next >

OK | D:\ IRT_Tr ain in g\ Lisr el\ M ODEL C

St at ist ics included:

Can cel

I M ean included in t he dat a

W eight

In clud e w eight matrix

L

_ l

Num ber of observat ions

| 150

16. Ten t u k an var iab el m an a yan g ekso gen d an m an a yan g en d o gen , ju ga in d ikat o r yan g t er kait . Kem u d ian b u at d iagr am jalu r n ya:

(3)

. . File Ed it S etup D ra w View Im a g e Oubpub W in d o w Help * » l - i di n t ?

Cwudt | - J M o d e l) | £ / E i . k * k l E i to M t M V i W l l f f i l W W M B i * |

17. Pilih men u

Setup su b m en u

Build LISREL Syntax. Jika d iin gin kan o u t p u t yan g m u d ah

d ib aca, pilih ju ga su b m en u

Build SIM PLIS Syntax.

§ LI SREL W in d o w s A p p lica t io n - M o d e lD P

File Edit 5 e tu p D raw View Im age O u tp u t W indow Help Title and Com m ents ...

G ro u p s.., V ariables.. D a ta ...

A D ?

Groups: M odels: B asic M odel

Observe

Ri liW 1 TCD Cl FJ

DP1

DR2fx

Build 5IM PLI5 S y n ta x F8

18. Jalan kan

LISREL Syntax at au

SIM PLIS Syntax

yan g ad a d en gan m en gklik t o m b o l

'J

F ile E d i t S e tu p M o d e l O u t p u t O p t io n s W in d o w H e lp

_d

( £ &

y| jtN a l &|#| a|o|f I_________________________________

T I

IDA NI=12 N0=150 WG=1 HA=CH SY='C:\XXX.dsf1 NG=1 SE

5 6 7 8 9 10 11 12 2 1 3 4 /

HO NX=4 NY=8 NK=1 NE=2 LY=FU,FI LX=FU,FI BE=FU,FI GA=FU,FI PH=SY,FR PS=DI,FR TE=DI,FR TD=DI,FR

TINDAKAN PD LK

DEPRES

(4)

Co n t o h h asil est im asi p ar am et e r o leh LISREL sb b :

- 875.

Chi-Square=46.67, df=51, P-value=D.64634, RMSEA=0.000

(Est im at es)

0.210—_ /

S 7

o.uo /

l>^

-0.781

/

- - - -

\ V°

0.034

0 34“

.056

Y

_{\ V}

0.134

-0.462

\

V ' — ^

£ . 2 2 5 ^ T U f D A K A H

0 . 0 0 3

Ch±-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(St an d ar d ized So lu t io n )

067

9 8 6

0 0 2

9 5 0

121

.•»1

0 6$

Oil

O i l

0 3 1

1 76

O O ff

0 2 1

(5)

6 . Z 0 5 +»

1 . 0 9 5 ^

Chi-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(T-valu es)

Chi-Square=46.67, df=51, P-value=0.64634, RMSEA=0.000

(6)

METODE ESTIM

_{ASI DALAM SEM}

1. I n s t r u m e n t a l V a r i abl e s (N) , 2 . T w o - S t a g e L e a s t S q u a r e s (TSLS), 3. U n w e i g h t e d L e a s t S q u a r e s (U LS), 4. G e n e r a l i z e d L e a s t S q u a r e s (GLS), 5. M a x i m u m L i k e l i h o o d (M L),

6. G e n e r a l l y W e i g h t e d L e a s t S q u a r e s (W LS), 7. D i a g o n a l l y W e i g h t e d L e a s t S q u a r e s ( DW LS).

Pad a t ah u n 1987 Br o w n e m e n ge m b an gkan m et o d e R obu s t M a x i m u m L i k e l i h o o d (RM L) d an se t ah u n ke m u d ian , yait u t ah u n 19 88, Sat o r r a d an Ben t ler m en ye m p u r n ak an m et o d e RM Ld e n ga n m em p er b aik i r u m u s %2 (M els, 20 04: 13 d an M els, 2 0 0 6 :1 2 ).

1. Jik a d at a ko n t in u d an b er d ist r ib u si n o r m al m u lt ivar iat , m aka m et o d e M L p er lu d igu n akan .

m at r ik s ko r elasi p o lik o r ik at au p o liser ial p er lu d igu n akan .

DAFTAR PUSTAKA

Jo r e sk o g, K. G. & So r b o m , D. (1 9 9 6 ). L i s r e l 8 : u s e r ' s r e f e r e n c e g u i d e . Ch icago : Scie n t if ic So f t w ar e In t e r n at io n al.

Jo r e sk o g, K. G. & So r b o m , D. (2 0 0 3 ). L i s r e l 8 . 5 4 h e l p . Ch icago : Scie n t if ic So f t w ar e In t er n at io n al.

M els, G. (2 0 0 4 ). L i s r e l f o r w i n d o w s : G e t t i n g s t a r t e d g u i d e . Lin co ln w o o d : Scie n t if ic So f t w ar e In t e r n at io n al.

M els, G. (2 0 0 6 ). G e t t i n g s t a r t e d w i t h t h e s t u d e n t e d i t i o n o f L i s r e l 8 . 5 4 f o r w i n d o w s . Lin co ln w o o d : Scie n t if ic So f t w ar e In t er n at io n al.