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A mathematical approach to assess research diversity: operationalization and applicability in communication sciences, political science, and beyond

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Abstract

With today’s research production and global dissemination, there is growing pressure to assess how academic fields foster diversity. Based on a mathematical problem/solve scheme, the aim of this study is twofold. First, the paper elaborates on how research diversity in scientific fields can be empirically gauged, proposing six working definitions. Second, drawing on these theoretical explanations, we introduce an original methodological protocol for research diversity evaluation. Third, the study puts this mathematical model to an empirical test by comparatively evaluating (1) communication research diversity in 2017, with respect to field’s diversity in 1997, and (2) communication research and political science diversity in 2017. Our results indicate that, contrasted to pattern diversity, communication research in 2017 is not a diverse field. However, throughout the years (1997–2017), there is a statistically significant improvement. Finally, the cross-comparison examination between political and communication sciences reveals the latter to be significantly more diverse.

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Notes

  1. \(X_{1} =\) First author affiliation; \(X_{2} =\) First author gender; \(X_{3} =\) First author ethnicity; \(X_{4} =\) First author affiliation type; \(X_{5} =\) Type of authorship; \(X_{6} =\) Form of collaboration; \(X_{7} =\) Interdisciplinarity; \(X_{8} =\) Area of data collection; \(X_{9} =\) Methodologies; \(X_{10} =\) Research approach; \(X_{11} =\) Type of samples; \(X_{12} =\) Paradigms; \(X_{13} =\) Content area; \(X_{14} =\) Analytical focus; \(X_{15} =\) Theoretical framework.

  2. Remind that all bootstrap procedures are done case-wise in order to preserve the multivariate structure of the data, which may be of importance if variables are not independent.

  3. Note that all expected cell values are greater than 5, hence no Yates correction is needed. For example, if we compute expected cell values in the worst case, which are those corresponding to variable “area of data collection” with k = 13 categories, we have that for a sample size of n = 283, they are n·1/k = 21.77.

  4. Note that, all expected cell values are greater than 5, hence no Yates correction is needed. For example, if we compute expected cell values in the worst case, which are those corresponding to variable “area of data collection” with k = 13 categories, we have that for a sample size of n = 263, they are n·1/k = 20.23.

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Authors and Affiliations

  1. Department of Communication, Carlos III University, C/Madrid 133, Madrid, Spain

    Manuel Goyanes

  2. Democracy Research Unit (DRU), Political Science, University of Salamanca, Salamanca, Spain

    Manuel Goyanes & Homero Gil de Zúñiga

  3. Department of Social Communication, National University of Public Service, Budapest, Hungary

    Márton Demeter

  4. Department of Statistics, Universidad Carlos III de Madrid, Madrid, Spain

    Aurea Grané & Irene Albarrán-Lozano

  5. Department of Film Production and Media Studies, Pennsylvania State University, State College, USA

    Homero Gil de Zúñiga

  6. Facultad de Comunicación y Letras, Universidad Diego Portales, Santiago, Chile

    Homero Gil de Zúñiga

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  1. Manuel Goyanes
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  2. Márton Demeter
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  3. Aurea Grané
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  4. Irene Albarrán-Lozano
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  5. Homero Gil de Zúñiga
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Correspondence to Manuel Goyanes.

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Goyanes, M., Demeter, M., Grané, A. et al. A mathematical approach to assess research diversity: operationalization and applicability in communication sciences, political science, and beyond. Scientometrics 125, 2299–2322 (2020). https://doi.org/10.1007/s11192-020-03680-6

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