2(1) Examples of Construction of Index of Satisfaction with Physical Resources

Research Question

:

Research groups need financial and physical resources for conduct of research in development activity. What are the differences if any in the satisfaction of research groups with the following types of resources?

1.Budget

Methodology

:

IDAMS modules:

  • TRANS (For constructing the index of satisfaction with resources).
  • Oneway ( For analysis of variance )

Dataset

:

ICSOPRU2.DAT  
SYNTAX
$RUN TRANS
   $FILES
   PRINT = TRANS.LST
   DICTIN = ICSOPRU2.DIC
   DATAIN = ICSOPRU2.DAT
   DICTOUT = TRANS.DIC
   DATAOUT =TRANS.DAT
   $SETUP
   prototype for TRANS program
   BADDATA=MD1 - 
     MAXERR=200 - 
     OUTVARS=(R1, R2) - 
     WIDTH=5 - 
     PRINT=(OUTCDICT,DATA)
   VARS=(R1, R2) WIDTH=5 DEC=2
   $RECODE
     R1=MEAN (V72-V84)
     R2=1*V1
$RUN ONEWAY
   $FILES
   PRINT = ONEWAY.LST
   DICTIN = TRANS.DIC
   DATAIN = TRANS.DAT
   $SETUP
   SATISFACTION WITH RESOURCES
   BADDATA=MD1 - 
     PRINT=CDICT
   DEPVARS=(V1) CONVARS=(R3)
   $RECODE
     R3=RECODE V2 (40)=1, (360)=2, (410)=3, (638)=4, (844)=5, (868)=6
Extract from computer output

Program TRANS

The transfer variables are:

         R1   R2

VARIABLE R1 R2

CASE 1 2.87 40.00
CASE 2 2.42 40.00
CASE 3 2.85 40.00
CASE 4 2.64 40.00
CASE 5 2.45 40.00
CASE 6 3.92 40.00
CASE 7 2.39 40.00
CASE 8 2.81 40.00
CASE 9 2.58 40.00

Program ONEWAY

0After filtering 1460 cases read from the input data file

SATISFACTION WITH RESOURCES Table no. 1


Control variable = var -3 Recoded Variable R3
Depend. variable = var 1 Recoded Variable R1

 

 
Code Label
N
Weight-
sum
%
Mean
S.D.(estim.)
Sum of X
%
Sum of X-square
1
334
334
22.9
3.165
.541
.1057220E+04
24.7
.3443916E+04
2
239
239
16.4
3.067
.508
.7330201E+03
17.1
.2309633E+04
3
200
200
13.7
2.515
.459
.5029800E+03
11.8
.1306858E+04
4
225
225
15.4
2.927
.433
.6584799E+03
15.4
.1969123E+04
5
233
233
16.0
3.181
.452
.7412299E+03
17.3
.2405338E+04
6
229
229
15.7
2.557
.626
.5854899E+03
13.7
.1586170E+04
                 
Total
1460
1460
100.0
2.930
.576
.4278420E+04
100.0
.1302104E+05
 
Total sum of squares = .4834526E+03
For 6 groups , Eta = .4639522E+00
For 6 groups , Etasq = .2152517E+00
For 6 groups , Eta(adj) = .4610348E+00
For 6 groups , Etasq(adj) = .2125531E+00
Between means sum of squares = .1040640E+03
Within groups sum of squares = .3793886E+03
F( 5,1454) = 79.765

2(2) Examples of Construction of Index of Scientific Productivity using Partial

Order Scoring

Research Question

:

Research groups have multiple outputs comprising publications, patents, experimental materials etc. What are the differences if any in the performance of the Research Groups of countries participating in the second round of UNESCO International Comparative Study on the Organisation and Performance of Research Groups?

Use the following measures of output:

  • Articles in country
  • Articles abroad
  • Original research reports patents
  • Aalgorithms and designs
  • Experimental material

Methodology

:

IDAMS modules

  • Poscor (Partial order scoring for constructing the index of research output)
  • Oneway ( Analysis of variance).

Since different outputs cannot be combined by constructing the score based on the addition of different types of outputs, the index of research output was computed using Poscor..

Dataset

:

R2RU.DAT
SYNTAX
$RUN POSCOR
   $FILES
   PRINT = POSCOR.LST
   DICTIN = R2R3RU.DIC
   DATAIN = R2RU.DAT
   DICTOUT =POSCOR.DIC
   DATAOUT =POSCOR.DAT
   $SETUP
   POSCOR SCORES OF RU OUTPUTS
   BADDATA=MD1 -
     IDVAR=V2 -
     TRANSVARS=(V1)
   POSCOR
   ORDER=DESR -
     ANAME='RU OUTPUT' -
     VARS=(V116,V118,V122,V126,V128,V130)
$RUN ONEWAY
   $FILES
   PRINT = ONEWAY1.LST
   DICTIN = POSCOR.DIC
   DATAIN = POSCOR.DAT
   $SETUP
   ANALYSIS OF VARIANCE OF LEADERSHIP QUALITY
   BADDATA=MD1 -
     PRINT=CDICT
   DEPVARS=(V17) CONVARS=(R1)
   $RECODE
   R1=RECODE V15 (40)=1, (360)=2, (410)=3, (638)=4, (844)=5, (868)=6

 

EXTRACT FROM COMPUTER OUTPUT

1Analysis specifications:

Analysis 1

Program will count in both directions

In the relations the equality will be taken into account

In computing statistics the cases in relation will be taken into account

New variable name 1 =

New variable name 2 = RU OUTPUT

Analysis variables:

V116 V118 V122 V126 V128 V130

Levels:

  1   1   1   1   1   1

0After filtering       1460 cases read from the input data file

Number of processed cases= 1460

1

After filtering 1460 cases read from the input data file

Control variable = var -1 Recoded Variable R1

Depend. variable = var 17 SATISFACTION

 
Code Label
N
Weight-
sum
%
Mean
S.D.(estim.)
Sum of X
%
Sum of X-square
1
334
334
22.9
37.731
35.794
.1260200E+05
16.8
.9021200E+06
2
239
239
16.4
45.213
35.778
.1080600E+05
14.4
.7932280E+06
3
200
200
13.7
77.585
27.336
.1551700E+05
20.7
.1352587E+07
4
225
225
15.4
52.547
35.430
.1182300E+05
15.7
.9024470E+06
5
233
233
16.0
36.700
33.266
.8551000E+04
11.4
.5705570E+06
6
229
229
15.7
69.074
36.255
.1581800E+05
21.1
.1392298E+07
                 
Total
1460
1460
100.0
51.450
37.470
.7511700E+05
100.0
.5913237E+07
 
Total sum of squares = .2048467E+07
For 6 groups , Eta = .4018943E+00
For 6 groups , Etasq = .1615190E+00
For 6 groups , Eta(adj) = .3982909E+00
For 6 groups , Etasq(adj) = .1586357E+00
Between means sum of squares = .3308665E+06
Within groups sum of squares = .1717601E+07
F( 5,1454) =56.018