Significance Factor

 

SIGNIFICANCE FACTOR

–A NEW TOOL FOR THE SCIENTOMETRIC AND INFORMETRIC STUDIES

SAHIL PERSHAD

GEEBEE INTERNATIONAL

NEW DELHI

ABSTRACT

A new impact parameter is discussed in this paper for the analysis, categorization and profiling of the prolific institutions of a country, and the efficacy of this approach is studied in relation to Frame’s (1977) Activity Index (AI). Application of this technique is demonstrated for the scientific output from institutes of India as a special case and comparison is done with already published AI analysis on these institutions (Garg, Dutta and Kumar, 2006).

 

INTRODUCTION

Modern information science and technology is almost is synonymous of the nervous system of physical world, and with the advent of the most sophisticated computer technology may overtake soon though it has not replaced it totally. The sharp and undaunted growth of IT has revolutionalised the potential of information retrieval, access and dissemination. We are now able to use more advanced software and hardware tools for communication in almost every walk of life and of course with the users and fellow professionals.

The face of future libraries will not be the same and will be crossing beyond the walls and may be in thumb drives as the rate of information explosion is also simultaneously increasing. They will have to catch up in order to survive in the emerging digital age.

But the scientometry and the bibliometry have also to be on the look of new and faster methods of assessing the new information which may be in the form of articles, papers, research journals, scientific research output activity, institutional or organizational research activities or such similar kinds of the literature or information. Many workers (Frame, 1977; Schubert and Braun, 1986;Garg and Dutt, 1992) have devised different kinds of impact parameters to scientometrically analyze the profiles of the world of research agencies. In the present effort we have given a new assessment and impact parameter in the form of Significance Factor Ratio (SFR). It has been widely used in the exploration fields of uranium (NURE, 1977).

OBJECTIVES OF THE PRESENT APPROACH:

  1. To analyze country-wise distribution of scientific research output.
  1. To find highly productive Indian Institutions.

METHOD OF STUDY

We have adopted the survey method for investigation of the problem by pooling the scientific research out put from various organizations, which is well published and is amenable for public usage. This will help us to find out the real conditions, which are prevailing in national level institutions and will also be able to represent the reality.

The methods which are current (Garg, Dutta and Kumar,2006) in the study for evaluation of the impact of research are Normalized Impact per paper(NIMP), Publication Effective Index(PEI) , Relative Quality Index(RQI), SCI , Activity Index.

AI is computed taking into consideration the total reseach out put of an institution. It characterises the relative research effort and that too by an individual subject or discipline of that institute. It is defined by Frame (1977) and Schubert and Braun (1986) mathematically as follows:

Let

Nij the total number of publications of ith institute for jth discipline

Nio be the total number of publications of ith institute for all the disciplines

Noj be the total number of publications of all institutions for jth discipline

Noo be the total Indian out put in all the disciplines

then

AI = [ (Nij/Nio)/(Noj/Noo) ]x100

This factor is pure ratio and indicative of the research effort by an institue.

SIGNIFICANCE FACTOR INDEX (SFI):

Exploration scientists for the unbiased comparisons and categorization anomalous regions of interest profusely use this factor. It assumes the samples to be random and does not assume to follow any population distribution like normal ,skewed , lognormal etc. but the categorization is done assuming the normalprobability distribution.

Let us estimate the average and standard deviation of variable Xi as x and SD respectively then the SFI will mathematically is defined as follows:

SFI = (Xi –x)/SD

The SFI can be positive or negative and is pure ratio and does not carry any bias of any discipline when we consider average and standard deviation in above-mentioned form. Positive value of SFI will show how larger it is than average trend value and how below it is in its case of negativity.

The SFI will also speak of the level of confidence you can attach to its value. For example if we get value greater than one SD above average we can say with 69% confidence that value that it has deviated. Similarly 2 SD above or below, 3SD above or below average will make them respectively 95%, 99% confidence limits.

SAMPLE

For this purpose of calculations of Ai and SFI we have adopted Random sampling technique for selecting the sample. The sample consists of various disciplines such as biological, medical, engineering, and physical sciences. This is shown in different tables which shall follow in the data analysis section of this paper.

DATA ANALYSIS

Bibliometric analysis primarily is a quantitative description of literature and helps us in knowing the pattern s of various forms of recorded or acquired information and their producers. It helps us to studying the trends in the different subject and subsequently will help in formulating need based development policy and to take timely decisions.

We have considered the published output of premier institutions in five disciplines for the demonstration purpose of the present SFI approach.

CONCLUSION:

There are many kinds of research and technology development studies where such an analytical approach can be adopted. The obvious differences and enhancement of the information can be seen through the SFI parameter which is depicted in Table1 to Table 5 and from figues 1 through figure 5. Explanations are self evident. Some of the peak values in both parameters are evident. SFI in addition to AI gives more spread of the research effort around the average value while AI gives only absolute information. Also SFI has no bias of any of the population considered.The actual reseach and development on thorium , therefore, cannot be obviously measured by counting the number of publications. We have given a bird’s eye view of the bibliometric and scientometric techniques analysis here and more work is in progress for the study of various quantitative aspects of the scientific endeavors of SFI technique.

ACKNOWLEDGEMENTS: The author is thankful for fruitful discussions with Mr Claude and friends at BARC for successful completion of this work.

REFERENCES

  1. Garg K C , Dutt B and Suresh Kumar ,scientometric profile of Indian science as seen through Science Citation Index, Annals of Library and Information Studies, Vol. 53, September,2006 , pp 114-125.
  1. Garg K C and Dutt B, Bibliometrics of Indian science as reflected through science citation Index, Journal of Scientific and Industrial Research, 51 (1992) 329-340.
  1. Frame J D, Mainstream research in Latin America andCaribbean, Interciencia, 2 (1977) 143-148.
  1. Schubert A and Broun T, Relative Indicators and relational charts for comparative assessment of publication output and citation impact, Scientometrics, 9 (1986) 281-291.
  1. Schubert A and Broun T, ibid.

ABBREVIATIONS OF INSTITUTIONS

  1. IISC :Institute of Science, Bangalore.
  2. BARC:Bhabha Atomic Research Centre
  3. TIFR: Tata Institute of Fundamental Research (DAE), Mumbai.
  4. AIIMS: All India Institute of Medical Sciences, New Delhi.
  5. BHU: Banaras Hindu University, Varanasi.
  6. IITM:Institute of Technology, Chennai.
  7. NCL : National Chemical Laboratory (CSIR), Pune.
  8. IACS: Indian Association for the Cultivation of Science (DST), Kolkatta.
  9. IITK:Institute of Technology , Kanpur.
  10. IITB:Institute of Technology, Mumbai.
  11. IITKH:Institute of Technology, Kharagpur.
  12. DU: UniversityDelhi. Delhi.
  13. IITD:Institute of Technology, New Delhi.
  14. JADU: Jadavpur University, Kolkatta.
  15. HYDU: Hyderabad University, Hyderabad.
  16. PGIMER: Postgraduate Institute of Medical Education and Research, Chandigarh.
  17. MADU: Madras University, Chennai.
  18. IICT: Indian Institute of Chemical Technology (CSIR), Hyderabad.
  19. UCAL: UniversityCalcutta, Kolkatta.
  20. PANU: Panjab University, Chandigarh.
  21. PRL: Physical Research Laboratory (DOS), Ahmedabad.
  22. SINP: Saha Institute of Nuclear Physics (DAE), Kolkatta.
  23. CMCH: Christian Medical College and Hospital, Vellore and other centers.
  24. BOMU: Mumbai University (now Mumbai), Mumbai.
  25. AMU: Aligarh Muslim University, Aligarh.
  26. SGPIMS: Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow.
  27. POONU: Pune University, Pune.
  28. RRLT: Regional Research Laboratory (CSIR), Trivandrum.
  29. ISI: Indian Statistical Institute, New Delhi and Kolkatta.

Table-1

Institution

Biological Science

SFI

AI

Total

IISC

86

3.833333

1.258111

547

BARC

14

-0.16667

0.323787

346

TIFR

33

0.888889

0.933115

283

AIIMS

33

0.888889

1.039652

254

BHU

52

1.944444

1.726609

241

IITM

6

-0.61111

0.218241

220

NCL

24

0.388889

0.893265

215

IACS

5

-0.66667

0.186967

214

IITK

3

-0.77778

0.113775

211

IITB

9

-0.44444

0.361907

199

IITKH

1

-0.88889

0.042792

187

DU

41

1.333333

1.763919

186

IITD

12

-0.27778

0.521881

184

JADU

7

-0.55556

0.378481

148

HYDU

20

0.166667

1.111412

144

PGIMER

21

0.222222

1.2635

133

MADU

25

0.444444

1.587732

126

IICT

4

-0.72222

0.258134

124

UCAL

18

0.055556

1.22067

118

PANU

15

-0.11111

1.043761

115

PRL

0

-0.94444

0

96

SINP

4

-0.72222

0.336933

95

CMCH

8

-0.5

0.673867

95

BOMU

2

-0.83333

0.177826

90

AMU

15

-0.11111

1.364006

88

SGPIMR

14

-0.16667

1.287705

87

POONU

9

-0.44444

0.837436

86

RRLT

4

-0.72222

0.376573

85

ISI

4

-0.72222

0.376573

85

         
         
         
         
         

Table-2

Institution

Chemical Science

SFI

AI

Total

IISC

96

1.818182

1.034233

547

BARC

65

0.878788

1.107061

346

TIFR

10

-0.78788

0.208232

283

AIIMS

0

-1.09091

0

254

BHU

26

-0.30303

0.635756

241

IITM

42

0.181818

1.125022

220

NCL

113

2.333333

3.097236

215

IACS

94

1.757576

2.588501

214

IITK

55

0.575758

1.536083

211

IITB

68

0.969697

2.013679

199

IITKH

23

-0.39394

0.724804

187

DU

37

0.030303

1.172258

186

IITD

31

-0.15152

0.992838

184

JADU

48

0.363636

1.911234

148

HYDU

55

0.575758

2.250788

144

PGIMER

2

-1.0303

0.088616

133

MADU

18

-0.54545

0.841853

126

IICT

94

1.757576

4.467252

124

UCAL

11

-0.75758

0.549345

118

PANU

19

-0.51515

0.973621

115

PRL

0

-1.09091

0

96

SINP

11

-0.75758

0.682344

95

CMCH

0

-1.09091

0

95

BOMU

32

-0.12121

2.095279

90

AMU

24

-0.36364

1.607174

88

SGPIMR

0

-1.09091

0

87

POONU

20

-0.48485

1.370458

86

RRLT

37

0.030303

2.565176

85

ISI

1

-1.06061

0.069329

85

Table-3

Institution

Engineering Science

SFI

AI

Total

IISC

74

2

1.263444

547

BARC

77

2.115385

2.078386

346

TIFR

6

-0.61538

0.198005

283

AIIMS

0

-0.84615

0

254

BHU

29

0.269231

1.123809

241

IITM

74

2

3.141381

220

NCL

23

0.038462

0.999082

215

IACS

4

-0.69231

0.174565

214

IITK

64

1.615385

2.832755

211

IITB

49

1.038462

2.299612

199

IITKH

55

1.269231

2.746835

187

DU

5

-0.65385

0.251055

186

IITD

55

1.269231

2.791621

184

JADU

15

-0.26923

0.946545

148

HYDU

1

-0.80769

0.064856

144

PGIMER

0

-0.84615

0

133

MADU

1

-0.80769

0.074121

126

IICT

6

-0.61538

0.451899

124

UCAL

13

-0.34615

1.028899

118

PANU

5

-0.65385

0.406054

115

PRL

8

-0.53846

0.77827

96

SINP

8

-0.53846

0.786462

95

CMCH

0

-0.84615

0

95

BOMU

26

0.153846

2.698003

90

AMU

12

-0.38462

1.273533

88

SGPIMR

0

-0.84615

0

87

POONU

9

-0.5

0.977362

86

RRLT

10

-0.46154

1.098734

85

ISI

19

-0.11538

2.087595

85

Table-4

Institution

Medical Science

SFI

AI

Total

IISC

10

-0.26087

0.111105

547

BARC

15

-0.15217

0.263472

346

TIFR

4

-0.3913

0.0859

283

AIIMS

216

4.217391

5.168208

254

BHU

38

0.347826

0.958267

241

IITM

0

-0.47826

0

220

NCL

0

-0.47826

0

215

IACS

0

-0.47826

0

214

IITK

0

-0.47826

0

211

IITB

3

-0.41304

0.09162

199

IITKH

1

-0.45652

0.0325

187

DU

11

-0.23913

0.359418

186

IITD

2

-0.43478

0.066059

184

JADU

11

-0.23913

0.451701

148

HYDU

7

-0.32609

0.295431

144

PGIMER

108

1.869565

4.935056

133

MADU

33

0.23913

1.591708

126

IICT

1

-0.45652

0.049012

124

UCAL

15

-0.15217

0.772555

118

PANU

20

-0.04348

1.056944

115

PRL

0

-0.47826

0

96

SINP

0

-0.47826

0

95

CMCH

85

1.369565

5.437701

95

BOMU

0

-0.47826

0

90

AMU

3

-0.41304

0.207185

88

SGPIMR

72

1.086957

5.029597

87

POONU

0

-0.47826

0

86

RRLT

0

-0.47826

0

85

ISI

1

-0.45652

0.071499

85

Table-5

Institution

Physical Science

SFI

AI

Total

IISC

155

2.347826

1.190128

547

BARC

131

1.826087

1.590173

346

TIFR

189

3.086957

2.804947

283

AIIMS

0

-1.02174

0

254

BHU

52

0.108696

0.906224

241

IITM

59

0.26087

1.126364

220

NCL

8

-0.84783

0.156279

215

IACS

86

0.847826

1.68785

214

IITK

62

0.326087

1.234123

211

IITB

33

-0.30435

0.696482

199

IITKH

35

-0.26087

0.786096

187

DU

62

0.326087

1.4

186

IITD

45

-0.04348

1.027174

184

JADU

58

0.23913

1.645946

148

HYDU

46

-0.02174

1.341667

144

PGIMER

0

-1.02174

0

133

MADU

21

-0.56522

0.7

126

IICT

7

-0.86957

0.237097

124

UCAL

35

-0.26087

1.245763

118

PANU

43

-0.08696

1.570435

115

PRL

81

0.73913

3.54375

96

SINP

68

0.456522

3.006316

95

CMCH

0

-1.02174

0

95

BOMU

11

-0.78261

0.513333

90

AMU

11

-0.78261

0.525

88

SGPIMR

0

-1.02174

0

87

POONU

26

-0.45652

1.269767

86

RRLT

15

-0.69565

0.741176

85

ISI

27

-0.43478

1.334118

85