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Analisis Faktor (8 Proksi IOS)

  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 Approx. Chi-Square 1689.661 Sphericity 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

  Extraction Method: Principal Component Analysis.

  a Component Matrix

  Component

  1

  2 MVABVA .963 .223

  MVEBVE .947 .233 TOBINQ .766 .522

  VPPE .858 -.248

  VDEP .881 .122 CAPBVA

  • .299 .903 CAPMVA
  • .630 .652

  VARRET -.135 .368 Extraction Method: Principal Component Analysis.

  a.

  2 components extracted.

  a Rotated Component Matrix

  Component

  1

  2 MVABVA .980 -.132 MVEBVE

  .968 -.116 TOBINQ

  .901 .217

  VPPE .715 -.535

  VDEP .868 -.197

  CAPBVA .040 .951

  CAPMVA -.359 .833

  VARRET .003 .392 Extraction Method: Principal Component Analysis.

  Rotation Method: Varimax with Kaiser Normalization.

  a.

  Rotation converged in 3 iterations.

  Analisis Faktor (7 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 CAPMVA .299605 .2333384 179

  VARRET 3.125724 20.4352661 179 KMO and Bartlett's Test

  Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

  .796 Bartlett's Test of Approx. Chi-Square 1310.298 Sphericity 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

  Extraction Method: Principal Component Analysis.

  a Component Matrix

  Component

  1

  2 MVABVA .977 .127

  MVEBVE .961 .140 TOBINQ .809 .245

  VPPE .834 -.154

  VDEP .885 .129 CAPMVA

  • .579 .593

  VARRET

  • .119 .835 Extraction Method: Principal Component Analysis.

  a.

  2 components extracted.

  a Rotated Component Matrix

  Component

  1

  2 MVABVA .978 -.127 MVEBVE

  .965 -.110 TOBINQ

  .845 .030

  VPPE .767 -.362

  VDEP .889 -.102

  CAPMVA

  • .408 .722

  VARRET .098 .838 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

  a.

  Rotation converged in 3 iterations.

  Analisis Faktor (6 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 CAPMVA .299605 .2333384 179

  KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

  .803 Bartlett's Test of Approx. Chi-Square 1293.574 Sphericity 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

  Extraction Method: Principal Component Analysis.

  a Component Matrix

  Compone nt

  1 MVABVA .979

  MVEBVE .963

  TOBINQ .810

  VPPE .834

  VDEP .887

  CAPMVA

  • .572 Extraction Method: Principal Component Analysis.

  a.

  1 components extracted.

  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

  Interval of the Difference t-test for Equality of Means

  Hasil Estimasi Akrual Model Jones Dimodifikasi Sebelum Outlier Descriptive Statistics N Minimum Maximum Mean Std. Deviation

  V1 179 -.2616 .3797 -.016186 .1065912

V2 179 -.6215 5.4631 .227536 .4787412

  V3 179 .0471 1.0844 .383521 .1892645 Valid N (listwise) 179 b

  Variables Entered/Removed Variables Variables Model Entered Removed Method a

1 V3, V2 . Enter a.

  All requested variables entered.

  b.

  Dependent Variable: V1

b

  Model Summary Adjusted Std. Error of Durbin- Model R R Square R Square the Estimate Watson a

  1

.246 .060 .050 .1039124 2.074

a.

  Predictors: (Constant), V3, V2 b. Dependent Variable: V1 b ANOVA

  Sum of Model Squares df Mean Square F Sig. a

  1 Regression .122 2 .061 5.648 .004 Residual

  

1.900 176 .011

Total 2.022 178 a.

  Predictors: (Constant), V3, V2 b. Dependent Variable: V1 a

  Coefficients Unstandardized Standardized Coefficients Coefficients Collinearity Statistics

  Model B Std. Error Beta t Sig. Tolerance

  VIF 1 (Constant)

  • .009 .018 -.525 .600 V2 .052 .016 .233 3.185 .002 .999 1.001

  V3

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

  Dependent Variable: V1 Keterangan :

  TA / A it it −

  1 V1 : ( / / ) Δ REV A − Δ REC A it it 1 it it

  1 V2 : − − V3 :

  PPE / A it it

  1 −

  Tests of Normality a

  Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual

  .102 179 .000 .949 179 .000 a. Lilliefors Significance Correction

  Hasil Estimasi Akrual Model Jones Dimodifikasi Setelah Outlier Descriptive Statistics

  N Minimum Maximum Mean Std. Deviation

  V1 171 -.2616 .2920 -.023896 .0941815 V2 171 -.6215 5.4631 .216474 .4815970 V3 171 .0471 1.0844 .380574 .1856277 Valid N (listwise)

  171

  b Variables Entered/Removed

  Variables Variables Model Entered Removed Method

  a

  1 V3, V2 . Enter a.

  All requested variables entered.

  b.

  Dependent Variable: V1

  

b

Model Summary

  Adjusted Std. Error of Durbin- Model R R Square R Square the Estimate Watson

  a

  1 .226 .051 .040 .0922984 2.063 a.

  Predictors: (Constant), V3, V2 b. Dependent Variable: V1

  

b

ANOVA

  Sum of Model Squares df Mean Square F Sig.

  a

  1 Regression .077 2 .038 4.504 .012 Residual

  1.431 168 .009 Total

  1.508 170 a. Predictors: (Constant), V3, V2 b. Dependent Variable: V1

  a

Coefficients

  Unstandardized Standardized Coefficients Coefficients Collinearity Statistics

  Model B Std. Error Beta t Sig. Tolerance

  VIF 1 (Constant)

  • .016 .016 -.977 .330

  V2 .041 .015 .211 2.801 .006 .999 1.001

  V3

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

  Dependent Variable: V1

  Dari tabel dapat diperoleh hasilnya sebagai berikut : , 016 , 041 , 044

  α = − β = β = −

  1

  1

2 Tests of Normality

  a

  Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual

  .067 171 .055 .982 171 .023 a. Lilliefors Significance Correction

  Hasil Uji Glejser untuk Pengujian Heteroskedastisitas a

Coefficients

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

  1 (Constant) -.021 .016 -1.273 .205

  V2 .008 .015 .044 .570 .570

  V3 .032 .038 .065 .848 .398 a.

  Dependent Variable: Absolute_Residual

  Hasil Uji Autokorelasi DW DW Tabel Hitung

  Keterangan d d 4 − d 4 − d

  L U U

L

2,063 1,724 1,772 2,276 2,228 Tidak terjadi autokorelasi Keterangan :

  TA / A it it

  1 − V1 : ( Δ REV / A − Δ REC / A ) V2 : it it − 1 it it −

1 V3 :

  PPE / A it it

  1

  Normal P-P Plot of Regression Standardized Residual Dependent Variable: V1

  1.0

  0.8 ob Pr

  0.6

0.4 Expected Cum

  0.2

  0.0

  0.0

  0.2

  0.4

  0.6

  0.8

  1.0 Observed Cum Prob Scatterplot 3 4 Dependent Variable: V1 idual 2 ized Res

  • -1
    • 1

    • 2

      Regression Student

    • -3 -2
      • 2 4 6 8 10 Regression Standardized Predicted Value

        Hasil Pengujian Regresi Awal Variabel Dummy IOS, LEV dan SIZE terhadap DA Negatif Descriptive Statistics N Minimum Maximum Mean Std. Deviation DA

        89 -.2546 -.0023 -.066665 .0572827 DUMMY_IOS 89 1 .51 .503 LEV 89 .1473 .8452 .465008 .1628394 SIZE 89 22.3872 31.7830 27.094570 1.8022828 Valid N (listwise)

        89

      b

        Model Summary

        Adjusted Std. Error of Durbin- Model R R Square R Square the Estimate Watson

        a

        1 .155 .024 -.010 .0575775 .638 a.

        Predictors: (Constant), SIZE, LEV, DUMMY_IOS b. Dependent Variable: DA

        b ANOVA

        Sum of Model Squares df Mean Square F Sig.

        a

        1 Regression .007 3 .002 .700 .554 Residual

        .282 85 .003 Total .289

        88 a. Predictors: (Constant), SIZE, LEV, DUMMY_IOS b. Dependent Variable: DA

        a

      Coefficients

        Unstandardized Standardized Coefficients Coefficients Collinearity Statistics

        Model B Std. Error Beta t Sig. Tolerance

        VIF 1 (Constant) -.182 .109 -1.673 .098 DUMMY_IO

      • .018 .015 -.156 -1.193 .236 .670 1.492 LEV
      • .023 .038 -.065 -.597 .552 .982 1.018 SIZE .005 .004 .157 1.193 .236 .664 1.507 a.

        Dependent Variable: DA

        Tests of Normality a

        Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual .133 89 .001 .875 89 .000 a.

        Lilliefors Significance Correction

        Hasil Pengujian Regresi Setelah Penghilangan Outlier Descriptive Statistics N Minimum Maximum Mean Std. Deviation DA

        58 -.1359 -.0120 -.063885 .0322200 DUMMY_IOS 58 1 .48 .504 LEV 58 .1608 .8452 .473847 .1613268 SIZE

        58

      2.3E+10

        6.4E+13

        4.8E+12 1.188E+13 Valid N (listwise)

        58 b Variables Entered/Removed

        Variables Variables Model Entered Removed Method

        1 SIZE, LEV, DUMMY_ . Enter a

        IOS a. All requested variables entered.

        b.

        Dependent Variable: DA b Model Summary

        Adjusted Std. Error of Durbin- Model R R Square R Square the Estimate Watson a

        1 .117 .014 -.004 .0908973 2.009 a.

        Predictors: (Constant), SIZE, LEV, DUMMY_IOS b. Dependent Variable: DA b

        

      ANOVA

      Sum of Model Squares df Mean Square F Sig. a

        1 Regression .008 3 .003 2.689 .055 Residual .051

        54 .001 Total .059

        

      57

      a. Predictors: (Constant), SIZE, LEV, DUMMY_IOS b. Dependent Variable: DA a

        

      Coefficients

      Unstandardized Standardized Coefficients Coefficients Collinearity Statistics

        Model B Std. Error Beta t Sig. Tolerance

        VIF 1 (Constant)

      • .203 .069 -2.931 .005 DUMMY_IOS
      • .010 .010 -.161 -1.054 .297 .689 1.451 LEV
      • .049 .026 -.248 -1.929 .059 .977 1.023 SIZE .006 .003 .358 2.319 .024 .677 1.478 a.

        Dependent Variable: DA Dari tabel dapat diperoleh hasilnya sebagai berikut : α , 203 β , 010 β , 049 , 006

        = − = − = − β =

        1

        1

        2

        3

        Tests of Normality a

        Kolmogorov-Smirnov Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residua

        .116 58 .051 .929 58 .002 a. Lilliefors Significance Correction

        Hasil Uji Glejser untuk Pengujian Heteroskedastisitas a

      Coefficients

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

        1 (Constant)

      • .017 .070 -.245 .808 DUMMY_IOS

        .001 .010 .020 .125 .901 LEV

      • .020 .026 -.109 -.793 .431 SIZE

        .001 .003 .066 .401 .690 a. Dependent Variable: Absolute_Residual

        Hasil Uji Autolorelasi DW DW Tabel Hitung

        Keterangan d d 4 − d

        4 − d L U L U

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

        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
        • 1 2 Regression Standardized Predicted Value -2 -1 1 2 Regr essio n Stud entized Residu al Dependent Variable: DA Scatterplot

          Uji Beda DA antara Perusahaan Bertumbuh dan Tidak Bertumbuh Paired Samples Statistics

          Std. Error Mean N Std. Deviation Mean

          Pair DA Perusahaan

        • .063171 28 .0344481 .0065101

          1 Bertumbuh DA Perusahaan

        • .063425 28 .0310476 .0058675

          Tidak Bertumbuh

          Paired Samples Correlations N Correlation Sig.

          Pair DA Perusahaan

          1 Bertumbuh & DA 28 -.230 .239 Perusahaan Tidak Bertumbuh

          

        Paired Samples Test

        Paired Differences 95% Confidence

          Interval of the Difference Std. Error Mean Std. Deviation Mean Lower Upper t df Sig. (2-tailed)

          Pair DA Perusahaan

          1 Bertumbuh - DA

        .0002536 .0514105 .0097157 -.0196813 .0201885 .026

        27 .979 Perusahaan

          Tidak Bertumbuh

          MVABVA MVEBVE TOBINQ

          VPPE

          VDEP CAPBVA CAPMVA

          VARRET Anti-image MVABVA

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

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

          Correlation 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

          Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component 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

          8 .020 .247 100.000 Extraction Method: Principal Component Analysis.

        MVABVA MVEBVE TOBINQ

          VPPE

        VDEP CAPMVA

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

          VARRET Anti-image Covariance MVABVA

        • .028 .050 .000 .012 -.007 .006 -.007 TOBINQ
        • .042 .000 .197 .068 .086 -.056 .020

          VPPE

        • .035 .012 .068 .296 .015 .164 -.042
        • .034 -.007 .086 .015 .188 -.032 -.004 CAPMVA .008 .006 -.056 .164 -.032 .540 -.215

          VDEP

          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)

          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 4.363 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 7 .020 .288 100.000

          VARRET .001 -.007 .020 -.042 -.004 -.215 .896 Anti-image Correlation

          Anti-image Matrices (6 Proksi IOS)

        MVABVA MVEBVE TOBINQ

        • .028 .051 .000 .012 -.007 .005 TOBINQ
        • .042 .000 .197 .070 .086 -.056
        • .035 .012 .070 .298 .015 .171
        • .034 -.007 .086 .015 .188 -.037 CAPMVA .010 .005 -.056 .171 -.037 .597 Anti-image Correlation MVABVA .726(a) -.732 -.557 -.381 -.459 .073
        • .732 .848(a) -.001 .099 -.070 .028 TOBINQ
        • .557 -.001 .770(a) .289 .446 -.164
        • .381 .099 .289 .843(a) .062 .406
        • .459 -.070 .446 .062 .853(a) -.109 CAPMVA .073 .028 -.164 .406 -.109 .825(a)

          a Measures of Sampling Adequacy(MSA)

          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

          Extraction Method: Principal Component Analysis.

          VPPE

          VDEP CAPMVA Anti-image Covariance MVABVA

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

          VPPE

          VDEP

          MVEBVE

          VPPE

          VDEP

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