Multiple Classification Analysis (MCA) Using WinIDAMS (Widyo Pura Buana)
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Transcript of Multiple Classification Analysis (MCA) Using WinIDAMS (Widyo Pura Buana)
![Page 1: Multiple Classification Analysis (MCA) Using WinIDAMS (Widyo Pura Buana)](https://reader033.fdocuments.net/reader033/viewer/2022051512/54511330af795903098b50af/html5/thumbnails/1.jpg)
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
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 2
Tampilan Awal WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
Create an Application Environment (1)(Membuat/Menentukan Tempat Kerja)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 3
Create an Application Environment (2)(Membuat/Menentukan Tempat Kerja)
Widyo Pura Buana (MCA using WinIDAMS)
Create an Application Environment (3)(Membuat/Menentukan Tempat Kerja)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 4
Kembali ke Windows Semula
Widyo Pura Buana (MCA using WinIDAMS)
File Data dalam Format Excel(Akan di Import ke dalamWinIDAMS)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 5
File XLSX Save ke CSV Format
Widyo Pura Buana (MCA using WinIDAMS)
Import ke WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 6
Pilih File yang akan diimport keWinIDAMS (1)
Widyo Pura Buana (MCA using WinIDAMS)
Pilih File yang akan diimport keWinIDAMS (2)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 7
Pilih File yang akan diimport keWinIDAMS (3)
Widyo Pura Buana (MCA using WinIDAMS)
Hasil Import ke WinIDAMS (1)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 8
Hasil Import ke WinIDAMS (2)
Widyo Pura Buana (MCA using WinIDAMS)
Hasil Import ke WinIDAMS (3)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 9
Variable Definition
Widyo Pura Buana (MCA using WinIDAMS)
Build Dictionary
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 10
Save Dictionary Data
Widyo Pura Buana (MCA using WinIDAMS)
V2, V4, V7 dan V21 sudah masuk
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 11
V2 dirubah menjadi
Widyo Pura Buana (MCA using WinIDAMS)
Pendefinisian Variabel (1)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 12
Pendefinisian Variabel (2)
Widyo Pura Buana (MCA using WinIDAMS)
Pendefinisian Variabel (3)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 13
Pendefinisian Variabel (4)
Widyo Pura Buana (MCA using WinIDAMS)
Membuat File Setup (SYNTAX)
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 14
Window Setup/Syntax
Widyo Pura Buana (MCA using WinIDAMS)
Syntax MCA ‐WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 15
OUTPUT MCA WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
Output MCA WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
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Widyo Pura Buana (MCA using WinIDAMS) 16
WIDYO PURA BUANAMULTIPLE CLASSIFICATION ANALYSIS
USING WinIDAMS
Widyo Pura Buana (MCA using WinIDAMS)
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| 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
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| 2 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
**** 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
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| 3 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 4 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 5 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
Class Label cases weights cents mean grand mean Coefficient Adjusted mean Stand dev. C. var.
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-square(adj)= 0.0000000
Eta(adj)= 0.0000000
Unadjusted deviation SS = 99.622620
Adjusted deviation SS = 9.9135914
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| 6 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
Predictor statistics
Predictor: Variable 3 SCIENTIFIC DEGREE
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 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-square(adj)= 0.24649109
Eta(adj)= 0.49647868
Unadjusted deviation SS = 1016.7496
Adjusted deviation SS = 1319.6876
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| 7 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 8 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 9 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 10 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 11 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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
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| 12 Widyo Pura Buana Pustaka ( MCA Menggunakan WinIDAMS)
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