Analisis Faktor (8 Proksi IOS)

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Analisis Faktor (8 Proksi IOS)

Descriptive Statistics

Mean Std. Deviation Analysis N

MVABVA 1.538273 1.3289313 179

MVEBVE 1.963958 2.4250088 179

TOBINQ 2.095484 1.7624916 179

VPPE 5.890453 5.6879643 179

VDEP 10.132094 16.1151199 179

CAPBVA .321988 .1635267 179

CAPMVA .299605 .2333384 179

VARRET 3.125724 20.4352661 179

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .733

Bartlett's Test of Sphericity

Approx. Chi-Square 1689.661

df 28

Sig. .000

Communalities

Initial Extraction

MVABVA 1.000 .978

MVEBVE 1.000 .951

TOBINQ 1.000 .859

VPPE 1.000 .798

VDEP 1.000 .792

CAPBVA 1.000 .905

CAPMVA 1.000 .822

VARRET 1.000 .153

(2)

Component Matrixa

.963 .223

.947 .233

.766 .522

.858 -.248

.881 .122

-.299 .903 -.630 .652 -.135 .368 MVABVA

MVEBVE TOBINQ VPPE VDEP CAPBVA CAPMVA VARRET

1 2

Component

Extraction Method: Principal Component Analysis.

2 components extracted. a.

Rotated Component Matrixa

.980 -.132 .968 -.116

.901 .217

.715 -.535 .868 -.197

.040 .951

-.359 .833

.003 .392

MVABVA MVEBVE TOBINQ VPPE VDEP CAPBVA CAPMVA VARRET

1 2

Component

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

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Analisis Faktor (7 Proksi IOS)

MVABVA 1.538273 1.3289313 179

MVEBVE _1.963958 _2.4250088 ₁₇₉

TOBINQ _2.095484 _1.7624916 ₁₇₉

VPPE _5.890453 _5.6879643 ₁₇₉

VDEP _10.132094 _16.1151199 ₁₇₉

CAPMVA .299605 .2333384 179

VARRET 3.125724 20.4352661 179

KMO and Bartlett's Test

Adequacy. .796

df 21

Sig. .000

Communalities

Initial Extraction

MVABVA 1.000 .972

MVEBVE 1.000 .944

TOBINQ 1.000 .714

VPPE 1.000 .720

VDEP 1.000 .800

CAPMVA 1.000 .687

VARRET 1.000 .712

(4)

.977 .127

.961 .140

.809 .245

.834 -.154

.885 .129

-.579 .593 -.119 .835 MVABVA

MVEBVE TOBINQ VPPE VDEP CAPMVA VARRET

1 2

Component

Extraction Method: Principal Component Analysis. 2 components extracted.

a.

Rotated Component Matrixa

.978 -.127 .965 -.110

.845 .030

.767 -.362 .889 -.102 -.408 .722

.098 .838

MVABVA MVEBVE TOBINQ VPPE VDEP CAPMVA VARRET

1 2

Component

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

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Analisis Faktor (6 Proksi IOS)

MVABVA 1.538273 1.3289313 179

MVEBVE 1.963958 2.4250088 179

TOBINQ 2.095484 1.7624916 179

VPPE 5.890453 5.6879643 179

VDEP 10.132094 16.1151199 179

CAPMVA .299605 .2333384 179

KMO and Bartlett's Test

Adequacy. _.803

df ₁₅

Sig. _.000

Communalities

Initial Extraction

MVABVA 1.000 .958

MVEBVE 1.000 .927

TOBINQ 1.000 .657

VPPE 1.000 .696

VDEP 1.000 .787

CAPMVA 1.000 .327

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.979 .963 .810 .834 .887 -.572 MVABVA

MVEBVE TOBINQ VPPE VDEP CAPMVA

1 Compone

nt

Extraction Method: Principal Component Analysis. 1 components extracted.

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Uji Beda Perusahaan Bertumbuh dan Tidak Bertumbuh

Group Statistics

90 2.2062511 1.60928557 .16963359

89 .8627921 .18775042 .01990150

PERUSAHAAN BERTUMBUH TIDAK BERTUMBUH FAC_1

N Mean Std. Deviation

Std. Error Mean

Independent Samples Test

36.221 .000 7.823 177 .000 1.3434590 .17173336 1.004551 1.682367

7.866 91.449 .000 1.3434590 .17079703 1.004214 1.682704 Equal variances

assumed Equal variances not assumed FAC_1

F Sig. Levene's Test for Equality of Variances

t df Sig. (2-tailed) Mean Difference

Std. Error

Difference Lower Upper 95% Confidence

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Hasil Estimasi Akrual Model Jones Dimodifikasi Sebelum Outlier

Descriptive Statistics

179 -.2616 .3797 -.016186 .1065912 179 -.6215 5.4631 .227536 .4787412 179 .0471 1.0844 .383521 .1892645 179

V1 V2 V3

Valid N (listwise)

N Minimum Maximum Mean Std. Deviation

Variables Entered/Removed b

V3, V2 a _. _Enter

Model 1 Variables Entered Variables Removed Method

All requested variables entered. a.

Dependent Variable: V1 b.

Model Summaryb

.246a .060 .050 .1039124 2.074

Model 1

R R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

Predictors: (Constant), V3, V2 a.

ANOVAb

.122 2 .061 5.648 .004a

1.900 176 .011

2.022 178 Regression Residual Total Model 1 Sum of

Squares df Mean Square F Sig.

Coefficientsa

-.009 .018 -.525 .600

.052 .016 .233 3.185 .002 .999 1.001

-.048 .041 -.086 -1.177 .241 .999 1.001

(Constant) V2 V3 Model 1

B Std. Error Unstandardized

Coefficients

Beta Standardized

Coefficients

t Sig. Tolerance VIF Collinearity Statistics

Dependent Variable: V1 a.

Keterangan :

V1

:

V2

:

V3 :

1

/

it₋ it

A

TA

)

/

(

Δ

REV

_it

A

_it₋₁

−

Δ

REC

_it

A

_it₋₁

1

/

it₋ it

A

(9)

Tests of Normality

.102 179 .000 .949 179 .000

Unstandardized Residual

Statistic df Sig. Statistic df Sig.

Kolmogorov-Smirnova Shapiro-Wilk

Lilliefors Significance Correction a.

Hasil Estimasi Akrual Model Jones Dimodifikasi Setelah Outlier

Descriptive Statistics

171 -.2616 .2920 -.023896 .0941815 171 -.6215 5.4631 .216474 .4815970

171 .0471 1.0844 .380574 .1856277

171 V1

V2 V3

Valid N (listwise)

Variables Entered/Removedb

V3, V2a . Enter

Model 1

Variables Entered

Variables

Removed Method

Model Summaryb

.226a .051 .040 .0922984 2.063

Model 1

R R Square

Adjusted R Square

Durbin-Watson

ANOVAb

.077 2 .038 4.504 .012a

1.431 168 .009

1.508 170

Regression Residual Total Model 1

Sum of

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Coefficientsa

-.016 .016 -.977 .330

.041 .015 .211 2.801 .006 .999 1.001

-.044 .038 -.087 -1.158 .249 .999 1.001

Coefficients

Beta Standardized

Coefficients

Dependent Variable: V1 a.

Dari tabel dapat diperoleh hasilnya sebagai berikut :

016

,

0

1

=

−

α

β

₁

=

0

,

041

β

₂

=

−

0

,

044

Tests of Normality

.067 171 .055 .982 171 .023

Hasil Uji Glejser untuk Pengujian Heteroskedastisitas

Coefficientsa

-.021 .016 -1.273 .205

.008 .015 .044 .570 .570

.032 .038 .065 .848 .398

B Std. Error Unstandardized Coefficients Beta Standardized Coefficients t Sig.

Dependent Variable: Absolute_Residual a.

Hasil Uji Autokorelasi

DW

Hitung

DW Tabel

Keterangan

2,063 1,724 1,772 2,276 2,228 Tidak

terjadi autokorelasi

Keterangan :

V1

:

V2

:

V3 :

1

/

_it₋

it

A

TA

L

d

_U

4

−

d

_L

4

−

d

U

)

/

(

Δ

REV

it

A

it₋₁

−

Δ

REC

it

A

it₋₁

1

/

_it₋

it

A

(11)

0.0 0.2 0.4 0.6 0.8 1.0 Observed Cum Prob

0.0 0.2 0.4 0.6 0.8 1.0

Expected Cum

Pr

ob

Dependent Variable: V1

Normal P-P Plot of Regression Standardized Residual

-2 0 2 4 6 8 10

Regression Standardized Predicted Value -3

-2 -1 0 1 2 3 4

Regression Student

ized Res

idual

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Hasil Pengujian Regresi Awal Variabel

Dummy

IOS, LEV dan

SIZE terhadap DA Negatif

Descriptive Statistics

89 -.2546 -.0023 -.066665 .0572827

89 0 1 .51 .503

89 .1473 .8452 .465008 .1628394

89 22.3872 31.7830 27.094570 1.8022828

89 DA

DUMMY_IOS LEV

SIZE

Valid N (listwise)

Model Summaryb

.155a .024 -.010 .0575775 .638

Model 1

R R Square

Adjusted R Square

Durbin-Watson

Predictors: (Constant), SIZE, LEV, DUMMY_IOS a.

Dependent Variable: DA b.

ANOVAb

.007 3 .002 .700 .554a

.282 85 .003

.289 88 Regression Residual Total Model 1 Sum of

Coefficientsa

-.182 .109 -1.673 .098

-.018 .015 -.156 -1.193 .236 .670 1.492

-.023 .038 -.065 -.597 .552 .982 1.018

.005 .004 .157 1.193 .236 .664 1.507

(Constant) DUMMY_IO LEV SIZE Model 1

Coefficients

Beta Standardized

Coefficients

Dependent Variable: DA a.

Tests of Normality

.133 89 .001 .875 89 .000

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Hasil Pengujian Regresi Setelah Penghilangan Outlier

Descriptive Statistics

58 -.1359 -.0120 -.063885 .0322200

58 0 1 .48 .504

58 .1608 .8452 .473847 .1613268

58 2.3E+10 6.4E+13 4.8E+12 1.188E+13

58 DA

DUMMY_IOS LEV

SIZE

Valid N (listwise)

Variables Entered/Removed b

SIZE, LEV, DUMMY_ IOSa . Enter Model 1 Variables Entered Variables Removed Method

Model Summaryb

.117a .014 -.004 .0908973 2.009

Model 1

R R Square

Adjusted R Square

Durbin-Watson

ANOVAb

.008 3 .003 2.689 .055a

.051 54 .001

.059 57 Regression Residual Total Model 1 Sum of

Coefficientsa

-.203 .069 -2.931 .005

-.010 .010 -.161 -1.054 .297 .689 1.451

-.049 .026 -.248 -1.929 .059 .977 1.023

.006 .003 .358 2.319 .024 .677 1.478

(Constant) DUMMY_IOS LEV SIZE Model 1

B Std. Error

Unstandardized Coefficients

Beta Standardized

Coefficients

t Sig. Tolerance VIF

Collinearity Statistics

Dependent Variable: DA a.

Dari tabel dapat diperoleh hasilnya sebagai berikut :

203

,

0

1

=

−

(14)

Tests of Normality

.116 58 .051 .929 58 .002

Unstandardized Residua

Hasil Uji Glejser untuk Pengujian Heteroskedastisitas

Coefficientsa

-.017 .070 -.245 .808

.001 .010 .020 .125 .901

-.020 .026 -.109 -.793 .431

.001 .003 .066 .401 .690

(Constant) DUMMY_IOS LEV

SIZE Model 1

Coefficients

Beta Standardized

Coefficients

t Sig.

Dependent Variable: Absolute_Residual a.

Hasil Uji Autolorelasi

DW

Hitung

DW Tabel

Keterangan

2,009 1,469 1,686 2,531 2,314 Tidak

terjadi autokorelasi

L

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0.0 0.2 0.4 0.6 0.8 1.0 Observed Cum Prob

0.0 0.2 0.4 0.6 0.8 1.0

E

xpected Cum

Prob

Dependent Variable: DA

Normal P-P Plot of Regression Standardized Residual

-3 -2 -1 0 1 2

Regression Standardized Predicted Value -2

-1 0 1 2

Regr

essio

n

Stud

entized Residu

al

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Uji Beda DA antara Perusahaan Bertumbuh dan Tidak

Bertumbuh

Paired Samples Statistics

-.063171 28 .0344481 .0065101

-.063425 28 .0310476 .0058675

DA Perusahaan Bertumbuh DA Perusahaan Tidak Bertumbuh Pair

1

Mean N Std. Deviation

Std. Error Mean

Paired Samples Correlations

28 -.230 .239

DA Perusahaan Bertumbuh & DA Perusahaan Tidak Bertumbuh Pair

1

N Correlation Sig.

Paired Samples Test

.0002536 .0514105 .0097157 -.0196813 .0201885 .026 27 .979 DA Perusahaan

Bertumbuh - DA Perusahaan Tidak Bertumbuh Pair

1

Mean Std. Deviation

Std. Error

Mean Lower Upper 95% Confidence Interval of the

Difference Paired Differences

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MVABVA MVEBVE TOBINQ VPPE VDEP CAPBVA CAPMVA VARRET Anti-image

Covariance

MVABVA

.029 -.028 -.023 -.023 -.032 .006 -.003 .002

MVEBVE -.028 .050 .000 .009 -.007 -.001 .003 -.007

TOBINQ _-.023 _.000 _.089 _-.027 _.027 _-.074 _.069 _.001

VPPE -.023 .009 -.027 .226 .023 .077 -.036 -.023

VDEP _-.032 _-.007 _.027 _.023 _.186 _.017 _-.030 _-.002

CAPBVA .006 -.001 -.074 .077 .017 .112 -.119 .012

CAPMVA -.003 .003 .069 -.036 -.030 -.119 .200 -.092

VARRET _.002 _-.007 _.001 _-.023 _-.002 _.012 _-.092 _.895

Anti-image Correlation

MVABVA

.759(a) -.729 -.447 -.282 -.442 .101 -.040 .013

MVEBVE -.729 .849(a) .004 .084 -.070 -.009 .029 -.032

TOBINQ _-.447 _.004 _.664(a) _-.193 _.210 _-.740 _.517 _.004

VPPE -.282 .084 -.193 .858(a) .112 .486 -.168 -.052

VDEP _-.442 _-.070 _.210 _.112 _.896(a) _.118 _-.154 _-.006

CAPBVA .101 -.009 -.740 .486 .118 .398(a) -.794 .039

CAPMVA _-.040 _.029 _.517 _-.168 _-.154 _-.794 _.615(a) _-.219

VARRET .013 -.032 .004 -.052 -.006 .039 -.219 .702(a)

a Measures of Sampling Adequacy(MSA)

Total Variance Explained

Extraction Method: Principal Component Analysis. Component

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 _4.429 _55.367 _55.367 _4.429 _55.367 _55.367 _4.105 _51.310 _51.310 2 1.829 22.861 78.228 1.829 22.861 78.228 2.153 26.918 78.228

3 _.957 _11.964 _90.191

4 .376 4.697 94.888

5 _.252 _3.150 _98.038

6 .089 1.112 99.150

7 _.048 _.603 _99.753

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MVABVA MVEBVE TOBINQ VPPE VDEP CAPMVA VARRET Anti-image

Covariance

MVABVA

.029 -.028 -.042 -.035 -.034 .008 .001

MVEBVE -.028 .050 .000 .012 -.007 .006 -.007

TOBINQ -.042 .000 .197 .068 .086 -.056 .020

VPPE -.035 .012 .068 .296 .015 .164 -.042

VDEP -.034 -.007 .086 .015 .188 -.032 -.004

CAPMVA .008 .006 -.056 .164 -.032 .540 -.215

VARRET .001 -.007 .020 -.042 -.004 -.215 .896

MVABVA

.726(a) -.732 -.556 -.381 -.459 .067 .009

MVEBVE -.732 .847(a) -.003 .101 -.070 .037 -.032

TOBINQ -.556 -.003 .770(a) .283 .445 -.171 .049

VPPE -.381 .101 .283 .841(a) .063 .410 -.081

VDEP -.459 -.070 .445 .063 .854(a) -.100 -.011

CAPMVA .067 .037 -.171 .410 -.100 .776(a) -.309

VARRET .009 -.032 .049 -.081 -.011 -.309 .485(a)

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 4.363 62.331 62.331 4.363 62.331 62.331 4.155 59.362 59.362

2 1.186 16.945 79.276 1.186 16.945 79.276 1.394 19.914 79.276

3 .746 10.658 89.934

4 .353 5.045 94.980

5 .250 3.578 98.558

6 .081 1.154 99.712

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Anti-image Matrices (6 Proksi IOS)

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 _4.352 _72.537 _72.537 _4.352 _72.537 _72.537

2 _.919 _15.312 _87.849

3 _.372 _6.208 _94.057

4 _.256 _4.259 _98.316

5 _.081 _1.348 _99.664

6 _.020 _.336 _100.000

MVABVA MVEBVE TOBINQ VPPE VDEP CAPMVA

Anti-image Covariance

MVABVA

.029 -.028 -.042 -.035 -.034 .010

MVEBVE -.028 .051 .000 .012 -.007 .005

TOBINQ -.042 .000 .197 .070 .086 -.056

VPPE -.035 .012 .070 .298 .015 .171

VDEP -.034 -.007 .086 .015 .188 -.037

CAPMVA .010 .005 -.056 .171 -.037 .597

MVABVA

.726(a) -.732 -.557 -.381 -.459 .073

MVEBVE -.732 .848(a) -.001 .099 -.070 .028

TOBINQ -.557 -.001 .770(a) .289 .446 -.164

VPPE -.381 .099 .289 .843(a) .062 .406

VDEP -.459 -.070 .446 .062 .853(a) -.109