multiple classification analysis (mca) using winidams (widyo pura buana)

12/7/2012

Widyo Pura Buana (MCA using WinIDAMS) 1

MULTIPLE CLASSIFICATION ANALYSIS(MCA)

USING WinIDAMS

WIDYO PURA BUANA

Widyo Pura Buana (MCA using WinIDAMS)

Jalankan Software WinIDAMS


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Tampilan Awal WinIDAMS


Create an Application Environment (1)(Membuat/Menentukan Tempat Kerja)


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Kembali ke Windows Semula


File Data dalam Format Excel(Akan di Import ke dalamWinIDAMS)


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File XLSX Save ke CSV Format


Import ke WinIDAMS


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Pilih File yang akan diimport keWinIDAMS (1)




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Hasil Import ke WinIDAMS (1)


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Variable Definition


Build Dictionary


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Save Dictionary Data


V2, V4, V7 dan V21 sudah masuk


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V2 dirubah menjadi


Pendefinisian Variabel (1)


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Membuat File Setup (SYNTAX)


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Window Setup/Syntax


Syntax MCA ‐WinIDAMS


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OUTPUT MCA WinIDAMS


Output MCA WinIDAMS


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WIDYO PURA BUANAMULTIPLE CLASSIFICATION ANALYSIS

USING WinIDAMS


| 1 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)

*** UNESCO WinIDAMS 1.3 December 2007 *** 7/12/2012

9:27:11

____________________________________________________________________

| |

| Welcome to WinIDAMS 1.3 December 2007 |

| (English Version) |

| |

|____________________________________________________________________|

1 $COMMENT SETUP FILE WITH EXAMPLES

2 $COMMENT OF 1 DATA MANAGEMENT

3 $COMMENT ANOVA AND MULTIPLE CLASSIFICATION ANALYSIS

Listing of setup

1 $RUN MCA

2 $FILES

3 DICTIN = 'MCA WinIDAMS.dic'

>>>>> E:\MCA_IDAMS\data\MCA WinIDAMS.dic

4 DATAIN = 'MCA WinIDAMS.dat'

>>>>> E:\MCA_IDAMS\data\MCA WinIDAMS.dat

7 $SETUP

8 'Analysis of variance and multiple classification analysis'

9 BADDATA=MD1

10 DEPVAR=V4 CONVARS=(V1,V2,V3)

11 DEPVAR=V4 CONVARS=V3


**** MCA **** Multiple Classification Analysis

Source: OSIRIS III.2 (MCA), Univ. of Michigan, U.S.A.

Last updated: UNESCO, 25 August 2005

No main filter

Label: Analysis of variance and multiple classification analysis

Parameters:

BADDATA=MD1

Parameters as interpreted:

Input ddname suffix: IN

All cases (after filtering) will be used from the input file

Bad data will be replaced by MD1

Analysis specifications:

DEPVAR=V4 CONVARS=(V1,V2,V3)

DEPVAR=V4 CONVARS=V3

***W* MCA001 --- Multivariate predictors must be in the range 0-31

After filtering, 101 cases read from the input data file


Analysis - 1 Analysis of variance and multiple classification analysis

Dependent variable

Name #ARTICLES *

Number 4

Max.code 9999999.000

Include MD1? NO

Include MD2? NO

Include outliers? YES

Range: L.T. -26.519

G.T. 35.487

Weight variable? NO

Print frequencies? NO

Iteration maximum 25

Convergence test PCTMEAN

Test for convergence 0.00500

Print coefficients? NO

Number of predictors 3

Predictor list

Variable Name Number of codes

1 CM POSITION IN UNIT * 3

2 SEX * 4

3 SCIENTIFIC DEGREE * 6

Number of cases eliminated

due to dependent variable requirements: 8

due to weight requirements: 0

due to predictor requirements: 0

Number of cases remaining: 93

Number of outlying cases: 0


Results based on test 2 Iteration 14

Dependent variable statistics

Dependent variable (y) = 4: #ARTICLES

M e a n = 4.4838710

Standard deviation = 6.2006536

Coeff. of variation = 138.3

Sum of Y = 417.00000

Sum of Y square = 5407.0000

Total sum of squares = 3537.2258

Explained sum of square = 1173.0288

Residual sum of squares = 2364.1970

Number of cases = 93


Predictor statistics

Predictor: Variable 1 CM POSITION IN UNIT

Unadjusted No of Sum of Per Class deviation from

Class Label cases weights cents mean grand mean Coefficient Adjusted mean Stand dev. C. var.

1 HEAD 8 8 8.6 9.12500 4.6411290 -4.7486949 -0.26482391 4.6425824 50.9

2 S&E 67 67 72.0 5.13433 0.65045738 1.3889226 5.8727937 6.5410024 127.4

3 TS 18 18 19.4 0.00000 -4.4838710 -3.0593460 1.4245250 0.0000000 *******

Eta-square = 0.15903983 Beta-square = 0.13516912

Eta = 0.39879799 Beta = 0.36765352

Eta-square(adj)= 0.14035182

Eta(adj)= 0.37463558

Unadjusted deviation SS = 562.55975

Adjusted deviation SS = 478.12369

Predictor: Variable 2 SEX

Unadjusted

No of Sum of Per Class deviation from


1 MALE 28 28 30.1 5.92857 1.4447002 0.48761001 4.9714808 6.9704592 117.6

2 FEMALE 63 63 67.7 3.93651 -0.54736304 -0.20506388 4.2788072 5.8554442 148.7

4 1 1 1.1 0.00000 -4.4838710 0.43825150E-01 4.5276961 0.0000000 *******

5 1 1 1.1 3.00000 -1.4838710 -0.77786005 3.7060108 0.0000000 0.0

The correction of Eta-squared for the number of subclasses was too large. Value of

eta-squared is 0.28164053E-01

Eta-square = 0.28164053E-01 Beta-square = 0.28026458E-02

Eta = 0.16782150 Beta = 0.52940022E-01


Eta(adj)= 0.0000000




Predictor statistics

Predictor: Variable 3 SCIENTIFIC DEGREE

Unadjusted

No of Sum of Per Class deviation from


1 PROFESS. 6 6 6.5 11.0000 6.5161290 10.777214 15.261086 3.2863353 29.9

2 ASS.PROF 6 6 6.5 13.0000 8.5161285 8.2397604 12.723631 8.4380092 64.9

3 DOCTOR 25 25 26.9 5.00000 0.51612902 -0.23260523 4.2512655 6.6833126 133.7

4 M.A/M.SC 42 42 45.2 3.52381 -0.96006155 -2.0998745 2.3839965 5.0327614 142.8

6 OTHER 13 13 14.0 0.00000 -4.4838710 -1.4517369 3.0321341 0.0000000 *******

9 MD 1 1 1.1 0.00000 -4.4838710 -1.2194612 3.2644098 0.0000000 *******

Eta-square = 0.28744265 Beta-square = 0.37308550

Eta = 0.53613681 Beta = 0.61080724


Eta(adj)= 0.49647868




Multiple classification analysis statistics

R-squared (unadjusted) = proportion of variation explained by fitted model = 0.33162

Adjustment for degrees of freedom = 1.12195

***Multiple R (adjusted) = 0.50011 Multiple R-squared (adjusted) = 0.25011


Dependent variable 4: #ARTICLES

Listing of Betas in descending order

Rank Var. no. Name Beta

1 3 SCIENTIFIC DEGREE 0.61080724

2 1 CM POSITION IN UNIT 0.36765352

3 2 SEX 0.52940022E-01

***Multiple R (adjusted) = 0.50011 Multiple R-squared (adjusted) = 0.25011


Analysis - 2 Analysis of variance and multiple classification analysis

Dependent variable

Name #ARTICLES *

Number 4

Max.code 9999999.000

Include MD1? NO

Include MD2? NO

Include outliers? YES

Range: L.T. -26.519

G.T. 35.487

Weight variable? NO

Number of predictors 1

Predictor list

Variable Name Maximum code

3 SCIENTIFIC DEGREE * 9

One-way analysis of variance only

Number of cases eliminated

due to dependent variable requirements: 8

due to weight requirements: 0

due to predictor requirements: 0

Number of cases remaining: 93

Number of outlying cases: 0


Predictor category statistics

Code Label N Weight-sum % Mean S.D.(est.) C. var. Sum of Y % Sum of Y-square

1 PROFESS. 6 6 6.52 11.0000 3.28634 29.9 66.0000 15.83 780.000

2 ASS.PROF 6 6 6.52 13.0000 8.43801 64.9 78.0000 18.71 1370.00

3 DOCTOR 25 25 27.17 5.00000 6.68331 133.7 125.000 29.98 1697.00

4 M.A/M.SC 42 42 45.65 3.52381 5.03276 142.8 148.000 35.49 1560.00

6 OTHER 13 13 14.13 0.00000 0.00000 ******* 0.00000 0.00 0.00000

9 MD 1 1 0.00000 0.00000 ******* 0.00000 0.00000

Totals 92 92.00 100.0 4.53261 6.216693 137.2 417.0000 100.0 5407.000

***Note: all these statistics exclude codes with fewer than two cases


One-way analysis of variance statistics

***Note: all these statistics exclude codes with fewer than two cases

Eta-squared (unadjusted) = proportion of variation explained for 5 catagories = 0.28332

Adjustment for degrees of freedom = 1.04598

***Eta (adjusted) = 0.50037 Eta-squared (adjusted) = 0.25037

Total sum of squares = 3516.9021

Between means sum of squares = 996.42627

Within groups sum of squares = 2520.4758

F( 4, 87) = 8.598

***** Normal termination of MCA

***** No more RUN statements in SETUP; step terminated


SYNTAX (FILE SETUP WinIDAMS)

$COMMENT Setup file with examples

$COMMENT of 1 data management

$COMMENT ANOVA and multiple classification analysis

$RUN MCA

$FILES

DICTIN = 'MCA WinIDAMS.dic'

DATAIN = 'MCA WinIDAMS.dat'

$SETUP

'Analysis of variance and multiple classification analysis'

BADDATA=MD1

DEPVAR=v4 CONVARS=(v1,v2,v3)

DEPVAR=v4 CONVARS=v3

DATA

No V2 V4 V7 V21 No V2 V4 V7 V21 No V2 V4 V7 V21

1 1 1 1 12 36 2 2 3 2 71 3 2 9 0

2 2 2 3 3 37 2 2 4 5 72 3 2 6 99

3 2 2 4 1 38 2 2 3 6 73 1 1 1 11

4 2 1 3 2 39 2 2 4 3 74 2 2 2 22

5 2 2 3 15 40 2 2 4 2 75 2 2 4 2

6 2 2 4 2 41 2 2 4 2 76 2 1 4 0

7 2 2 4 3 42 2 1 2 23 77 1 1 1 12

8 2 2 4 0 43 2 1 4 5 78 2 1 3 4

9 2 1 4 21 44 2 2 4 18 79 2 1 3 4

10 2 2 4 2 45 2 2 4 0 80 2 2 4 4

11 2 2 4 2 46 2 2 4 5 81 2 2 4 1

12 2 2 4 5 47 2 2 3 4 82 2 2 4 1

13 2 2 4 1 48 2 1 4 0 83 3 2 6 99

14 2 5 4 3 49 2 2 3 1 84 3 1 6 99

15 2 2 4 0 50 3 2 6 0 85 1 2 2 6

16 2 2 4 3 51 3 2 6 0 86 2 2 3 2

17 2 2 3 0 52 3 2 6 0 87 2 2 3 2

18 2 2 2 11 53 1 1 1 5 88 2 1 4 1

19 3 2 6 0 54 2 2 3 17 89 3 1 6 0

20 3 4 6 0 55 2 2 3 1 90 1 1 1 11

21 3 2 6 0 56 2 2 3 3 91 2 1 4 1

22 1 2 3 1 57 2 2 4 2 92 2 1 4 1

23 2 2 2 2 58 2 1 4 6 93 2 2 2 14

24 2 2 3 25 59 2 1 4 1 94 2 2 4 0

25 3 2 3 0 60 2 2 4 5 95 2 2 4 0

26 3 2 3 0 61 2 1 4 6 96 3 1 6 0

27 3 2 4 0 62 2 2 4 10 97 3 1 6 0

28 3 2 4 0 63 2 2 3 3 98 3 1 6 99

29 3 2 6 0 64 2 1 3 2 99 3 1 6 99

30 3 2 6 0 65 2 2 3 4 100 3 2 6 99

31 3 1 6 0 66 2 2 3 2 101 3 2 6 99

32 3 2 6 0 67 2 1 3 20

33 1 1 1 15 68 2 1 3 2

34 2 2 4 20 69 2 1 4 1

35 2 2 4 3 70 3 2 9 99

multiple classification analysis (mca) using winidams (widyo pura buana)

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