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Cluster 02

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Presentation from STI2014 on individual bibliometrics

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Cluster 02

  1. 1. (Scaling analysis) of author-level bibliometric indicators. Lorna Wildgaard Royal School of Library and Information Science Birger Larsen Department of Communication, AAU-CPH
  2. 2. CONTRIBUTE TO THE DISCUSSION: TODAY’S MEET https://todaysmeet.com/STI2014 sign in with your email, create a password, confirm and log in OR Log in with Gmail 15/09/2014 Dias 2
  3. 3. PURPOSE OF THE INVESTIGATION Quantifiable and objective alternative to other metrics when evaluating faculty members for academic advancement. 15/09/2014 Dias 3
  4. 4. WHAT IS A RESEARCHER? 15/09/2014 Dias 4
  5. 5. MOTIVATION 15/09/2014 Dias 5
  6. 6. DATA 2154 scholars identified in online questionnaire. 793 working links to online CVs identified in sampling strategy across 4 disciplines and 5 seniorities Astronomy n203: PhD n15 Post Doc n49 Assis Prof n27 Assoc Prof n72 Prof n40 Environment n203: PhD n3 Post Doc n18 Assis Prof n42 Assoc Prof n85 Prof n55 Philosophy n250: PhD n9 Post Doc n23 Assis Prof n49 Assoc Prof n82 Prof n87 Public Health n137: PhD n9 Post Doc n14 Assis Prof n31 Assoc Prof n53 Prof n30 Data collection start date: 13th June 2013. Publication data of 750 researchers included. Data collection completed: July 10th 2013 Astronomy n203: PhD n15 Post Doc n48 Assis Prof n26 Assoc Prof n67 Prof n37 Environment n203: PhD n3 Post Doc n17 Assis Prof n39 Assoc Prof n85 Prof n51 Philosophy n250: PhD n9 Post Doc n22 Assis Prof n45 Assoc Prof n75 Prof n78 Public Health n137: PhD n9 Post Doc n14 Assis Prof n30 Assoc Prof n50 Prof n29 CVs and publication data of 793 scholars collected from Google Scholar, via Publish or Perish. Excluded n43: Dead links n12 Not included discipline: n15 Duplicates: n1 No publication list: n13 No identifiable seniority: n1 Impossible to find in POP: n1
  7. 7. IDENTIFICATION OF INDICATORS 15/09/2014 Dias 7
  8. 8. SUBJECTIVE GROUPING OF 54 INDICATORS 15/09/2014 Dias 8 "A review of the characteristics of 108 author-level bibliometric indicators", Scientometrics, DOI: 10.1007/s11192-014-1423-3
  9. 9. METHOD 1: IDENTIFICATION OF CENTRAL INDICATORS Discipline Index Calculation nCorr. Astronomy Hg The square root of (h multiplied by g). 25 Environ. Sci. H, H2 Publications are ranked in descending order after number of citations. H is where number of citations and rank is the same. H2 is where the square of the number of papers is equal to the number of citations. 26 Philosophy IQP IQP= expected average performance of scholar in the field, amount of papers that are cited more frequently than average and how much more than average they are cited (Tc>a) 28 Pub. Health G Publications are ranked in descending order after number of citations. G is where the the square root of the cumulative sum of citations is equal to the rank 23
  10. 10. MDS SCALING: SIMILARITY/DISSIMILARITY OF INDICES ASTRO Enviro. Sci 25% 24% ENVIRO 47% 38 % PHIL PUB. HEALTH
  11. 11. EXPLORATIVE FACTOR ANALYSIS Discipline Publication & 15/09/2014 Dias 11 recognition Normalized for field or time Miscellaneas Astronomy 57.3 % (0.78) 11.8 (0.49) 8.3 (-0.028) Environ. Sci 57.2% (0.77) 6.2 (0.04) 10.4 (0.89) Philosophy 53.6 (0.82) 7.0 (0.50) 10.4 (0.03) Public Health 56.2 (0.77) 6.6 (0.00) 12.1 (0.59) 24-32 indicators in dimension 1 4-9 indicators in dimension 2 3-15 in dimension 3
  12. 12. REASSESSING THE METHOD Purpose: Quantifiable and objective alternative to other metrics when evaluating faculty members for academic advancement. What we have learnt so far: 1. Publication and citation data is highly skewed 2. Transforming the variables with log, inverse, sqrt did not improve the normality assumption of the data or improve the MDS or the Factor Analysis, 3. Recoding the variables into categorical groups resulted in lack of detail and still not significant results (a lot of work, inconclusive results So we returned to non-parametric and descriptive analyses of the data – simple seems to be more informative when we have skewed data that builds on publications and citations. 15/09/2014 Dias 12
  13. 13. DIFFERENCE IN MEDIAN PUBLICATIONS BETWEEN SENIORITIES Publications Median Post Doc- PhD Assis Prof – Post Doc Assoc. Prof – Assis Prof Prof.-Assoc Prof Mean difference Astronomy 12.5 20 22 28.5 20.7 Environment 5 9 11 22.5 11.8 Philosophy 3 2.5 0.5 11 4.25 Public Health 5 11 21 33 17.5 DIFFERENCE IN MEDIAN CITATIONS BETWEEN SENIORITIES Citations Median 15/09/2014 Dias 13 Post Doc- PhD Assis Prof – Post Doc Assoc. Prof – Assis Prof Prof.-Assoc Prof Mean difference Astronomy 51.1 500.5 512 675 434.7 Environment 7 107 178 109 100.2 Philosophy 7.5 -1.5 1.5 21 7.1 Public Health 20.5 86.5 351 436 223.5
  14. 14. P & C INCREASE WITH SENIORITY. DO OTHER INDICATORS? DISCIPLINE OUTPUT EFFECT OF 15/09/2014 Dias 14 OUTPUT IMPACT OVER TIME QUALIFY IMPACT TO FIELD RANK PORTFOLIO Astronomy P C, sc, nnc, Sig, Csc, Fc, Cage, AWCR, AWCRpa, AW, AR Sum pp top ncits, IQP, NprodP Millers H, h, A, R, g, hg, e, Q2, POPh Enviro. Sci. P, Fp C, CPP, Sc, FracCPP, nnc, Sig, Csc, Fc Cage, AWCR, AWCRpa, AW, AR Mcs, sum pp top ncits, mean mjs mcs, max mjs mcs, IQP, NprodP Millers h, h, m, A, R, g, hg, e, Q2, H2, POPh Philosophy P, Fp C, Sc, nnc, Sig, Csc, Fc Cage, AR NprodP m,A,R,g,e, H2 Pub. Health P, Fp C, Sc, nnc, Sig, Csc, Fc AWCR, AWCRpa, AW, AR Mcs, Sum pp rop ncits, Sum pp top prop, NprodP Millers h, m, A, R, g, hg, e, Q2, H2, PopH
  15. 15. ARE PUBLICATION & CITATION COUNT EFFECTED BY GENDER? nMales nFemales Md P, male Md P, female Md C, male Md C, female Astronomy 162 30 48 39 881 518 Environ. Sci 160 35 29 18 321 135 Philosophy 179 43 9 8 12 8 Pub. Health 79 53 31 29 311 353 Environmental Science: Significant difference in the amount of publications produced by male and female researchers, U=2036, z=-2.525, p=0.012, r=0.18. Significant difference in the amount of citations male and female researchers receive, U=2056, z=-2.460, p=0.014, r=0.176 15/09/2014 Dias 15
  16. 16. ARE PUBLICATION & CITATION COUNT EFFECTED BY ORIGIN? 15/09/2014 Dias 16
  17. 17. ARE PUBLICATION & CITATION COUNT EFFECTED BY ACADEMIC AGE OR SENIORITY? Purpose: How well do seniority and academic age predict number of publications? How much of the variance in publication scores can be explained by scores on these two scales? Method: Multiple Regression Results (ALL FIELDS): The model which controls for seniority and academic age explains between 22-36.2% of the variance in publications (A=36%, E=36%, P=30%, PH=22%) and 1-22% of the variance in citations, (A=18%, E= 19%, P=0,9%, PH=22%. Conclusions: Academic Age makes the largest unique contribution as a predictor of publications or citations, Seniority makes very little contribution . 15/09/2014 Dias 17
  18. 18. ARE PUBLICATION & CITATION COUNT EFFECTED BY ACADEMIC AGE OR SENIORITY WHEN CONTROLLING FOR GENDER AND ORIGIN? Purpose: Controlling for the effect of gender and origin, is our set of variables (academic age and seniority) still able to predict a significant amount of the variance in publication count? Method: Hierarchical Regression (ATT: high correlated data, assumptions of normality violated) Results (All Disciplines): Only Academic age and seniority made a statistically significant contribution to the model. With academic age recording a higher beta value (.30-.46) than seniority (.18-.25) in each discipline. 15/09/2014 Dias 18
  19. 19. FINDINGS: RECOMENDING PUBLICATION AND CITATION INDICATORS
  20. 20. CONCLUSIONS (SO FAR) 1. Indicator values are effected to varying degrees, dependent on discipline, by gender, origin, seniority and academic age (database & version). 2. Academic age is dependent on how it is calculated. Here is highly dependent on database coverage. Seniority is more understandable. But does it make sense? 3. Don’t have to wrap data in algorithims. More informative to summarize patterns between indicators. 4. It is important to report the database and which version of the database was used to collect the data.
  21. 21. CONCLUSIONS (SO FAR) 5. Variance in amount of publications between scholars differs from discipline to discipline. Clear difference in amount of publications and citations. 6. The indicators are estimates. Report confidence intervals and range to contextualize the values. 7. Strong correlation between indicators. Central and isolated indicators need further investigation allowing for confounders. 8. No one indicator can stand alone. Work continues to identify indicators suitable for discipline and seniority.
  22. 22. THANK YOU FOR YOUR ATTENTION! Q. When does adjusting the data to fit the model become cherry picking?

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