### 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
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
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 = 483.453 For 6 groups , Eta = 0.463952 For 6 groups , Etasq = 0.215252 For 6 groups , Eta(adj) = 0.461035 For 6 groups , Etasq(adj) = 0.212553 Between means sum of squares = 104.064 Within groups sum of squares = 379.389 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
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
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