Skip to main content
SpringerLink
Log in

Interpretative structural modeling to social sciences: designing better datasets for mixed method research

  • Published:
Quality & Quantity Aims and scope Submit manuscript

Abstract

The multiplication of complex datasets in empirical social sciences calls for methods that can improve the design of complex datasets before the actual gathering of data. Yet mainstream scholars in related fields have rarely explored such methods. In this study, we introduce Interpretive Structural Modeling (ISM) as such a method. As a mixed method, ISM integrates Boolean algebra, matrix theory, and directed graph theory to impose a formal structure to qualitative understanding of a complex system. ISM’s final output is a directed graph that can be visually and easily interpreted. We show that ISM can structure indicators graphically into a multilayered and multi-blocked model, thus uncovering hidden interactions among indicators. By doing so, ISM can reveal hidden and undesired redundancies and incoherencies among indicators within a complex dataset. Most critically, ISM achieves these goals without relying on statistical analysis and hence before the actual gathering of any data. Deploying ISM when designing complex datasets thus facilitates more rigorous conceptualization and understanding of complex social phenomena, steers us away from badly designed complex datasets, and saves precious resource. We use ISM to probe several complex datasets to demonstrate its potentials.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. At the onset, we like to state explicitly that we are only interested in complex datasets here. For a simple dataset that captures a simple concept with one or two components, there is no need for performing an ISM exercise. For simplicity, we use “dataset(s)” to denote “complex dataset(s)”. By (empirical) social sciences, we mean anthropology, economics, social psychology, sociology, and political sciences.

  2. In the case of Inglehart’s “materialism-postmaterialism value” dataset, “biases” as identified by Clarke et al (1999) are what we mean by “incoherency” here.

  3. Search with ISM in social sciences with google scholar indicates that ISM has not been seriously introduced to social sciences. The only relevant citation we could find is a mentioning of ISM by Dunn (1988) in Policy Studies Review. The utilities of ISM that Dunn has in mind, however, were very conventional.

  4. By multi-layered, we mean that factors can be sorted or arranged into several layers. By multi-blocked, we mean that factors can be sorted or arranged into several blocs. By multi-directional, we mean that a factor can be shown to have many interactions with other factors. See Fig. 5 below for a concrete illustration.

  5. The two software packages will be freely available when the paper is published. Our software programs come with easy to understand and implement instructions. There are other computer programs that have been specifically designed to run ISM (e.g., concept-Star).

  6. See Warfield’s homepage (http://warfield.gmu.edu/exhibits/show/warfield/innovator/ism) for more detailed introduction to ISM. The document “Annotated Mathematical Bibliography for ISM” is especially useful for tracing the technical development and finding the relevant mathematical proofs of ISM.We address the limitation of ISM in the context of our research objectives in the concluding section.

  7. We emphasize this point because if not clearly stated, intentionally designed redundancy poses problem for scholars who use the data but are not the author of the data: data users might be unaware of the redundancy within the dataset and use the dataset as given.

  8. We need only to consider direct interactions when constructing IRM because ISM has the built-in capacity of uncovering indirect interactions: the final reachability matrix (FRM) captures both direct and indirect connections among elements, even though IRM starts with direct connections alone..

  9. Transitivity is roughly equivalent to interactivity. FRM can capture all possible transitivity among elements because through Boolean matrix multiplication, mathematical operations can reveal hidden and indirect transitivity between two elements that may not be connected directly but can be connected indirectly via other elements and pathways. See Sects. 4 and 5 for illustrations and discussion.

  10. In other words, the following mathematical principles only apply to Boolean matrix and Identity Matrix. Note that the Identity Matrix itself is a Boolean matrix.

  11. Of course, the exact value of k depends on the specific SSIM that is derived from the IRM (for illustrations, see Appendixes A and B).

  12. In Appendix B, we subject the dataset constructed by Mainwarning and Pérez-Liñán (2013) to an ISM exercise.

  13. The two experts are two authors of the paper. Both authors are well trained in methodologies and the relevant literature (i.e., democracy/democratization, political culture, and the broader comparative politics literature).

  14. In a broad critique of the broader literature on “political culture” in which WVS has been a recent offshoot, Johnson’s (2003) did question the conceptual problems of the “political culture research”, including WVS.

  15. Due to space constraint, we have moved the tables and figures and the detailed discussion on the “Achievement Motivation” to Appendix A. Here, we summarize our main findings very briefly.

References

  • Abramson, P.R., Ellis, S., Inglehart, R.: Research in context: measuring value change. Polit. Behav. 19(1), 41–59 (1997)

    Article  Google Scholar 

  • Alemán, J., Woods, D.: Value orientations from the world values survey: how comparable are they cross-nationally? Comp. Politics Stud. 49(8), 1039–1067 (2016)

    Article  Google Scholar 

  • Bean, C., Papadakis, E.: Polarized priorities or flexible alternatives? Dimensionality in inglehart’s materialism-postmaterialism scale. Int. J. Public Opin. Res. 6(3), 264–288 (1994)

    Article  Google Scholar 

  • Bollen, K.A.: Structural equations with latent variables. John Wiley, New York (1989)

    Book  Google Scholar 

  • Boese, V.A.: How (Not) to measure democracy. Int. Area Stud. Rev. 22(2), 95–127 (2019)

    Article  Google Scholar 

  • Bowman, K., Lehoucq, F., Mahoney, J.: Measuring political democracy: case expertise, data adequacy, and central America. Comp. Pol. Stud. 38(8), 939–970 (2005)

    Article  Google Scholar 

  • Chandramowli, S., Transue, M., Felder, F.A.: Analysis of barriers to development in landfill communities using interpretive structural modeling. Habitat Int. 35, 246–253 (2011)

    Article  Google Scholar 

  • Clarke, H.D., Kornberg, A., McIntyre, C., Bauer-Kaase, P., Kaase, M.: The effect of economic priorities on the measurement of value change: new experimental evidence. Am. Political Sci. Rev. 93(3), 637–647 (1999)

    Article  Google Scholar 

  • Clarke, H.D., Dutt, N., Rapkin, J.: Conversations in context: the (Mis) measurement of value change in advanced industrial societies. Polit. Behav. 19(1), 19–39 (1997)

    Article  Google Scholar 

  • Coppedge, M., et al.: Conceptualizing and measuring democracy: a new approach. Perspect. Polit. 9(2), 247–267 (2011)

    Article  Google Scholar 

  • Davis, D.W.: Individual level examination of postmaterialism in the U. S.: political tolerance, racial attitudes, environmentalism, and participatory norms. Polit. Res. Q. 53(3), 455–475 (2000)

    ADS  Google Scholar 

  • Davis, D.W., Davenport, C.: Assessing the validity of the postmaterialism index. Am. Political Sci. Rev. 93(3), 649–664 (1999)

    Article  Google Scholar 

  • Davis, D.W., Dowley, K.M., Silver, B.D.: Postmaterialism in world societies: is it really a value dimension? Am. J. Political Sci. 43(3), 935–962 (1999)

    Article  Google Scholar 

  • Dunn, W.N.: Methods of the second type: copying with the wilderness of conventional policy analysis. Policy Stud. Rev. 7(2), 720–737 (1988)

    Article  Google Scholar 

  • Floyd, R.W.: Algorithm 97: shortest path. Commun. Assoc. Comput. Mach. 5(6), 345 (1962)

    Google Scholar 

  • Hadenius, A., Teorell, J.: Cultural and economic prerequisites of democracy: reassessing recent evidence. Stud. Comp. Int. Dev. 39(4), 87–106 (2005)

    Article  Google Scholar 

  • Inglehart, R., Abramson, P.R.: Measuring postmaterialism. Am. Political Sci. Rev. 93(3), 637–647 (1999)

    Article  Google Scholar 

  • Inglehart, R., et al.: World value surveys and European value surveys, 1981–1984, 1990–1993, 1995–1997 (ICPSR Study 2790). Inter-university Consortium for Political and Social Research, Ann Arbor (2000)

    Google Scholar 

  • Inglehart, R.: The renaissance of political culture. Am. Political Sci. Rev. 82(4), 1203–1230 (1988)

    Article  Google Scholar 

  • Inglehart, R.: Culture shift in advanced industrial society. Princeton University Press, Princeton (1990)

    Book  Google Scholar 

  • Inglehart, R.: Polarized priorities of flexible alternatives: a comment. Int. J. Public Opin. Res. 6(3), 289–292 (1994)

    Article  Google Scholar 

  • Inglehart, R.: Modernization and postmodernization: cultural, economic and political change in 43 societies. Princeton University Press, Princeton (1997)

    Book  Google Scholar 

  • Jackman, R.W., Miller, R.A.: A renaissance of political culture? Am. J. Political Sci. 40(3), 632–659 (1996a)

    Article  Google Scholar 

  • Jackman, R.W., Miller, R.A.: The poverty of political culture. Am. J. Political Sci. 40(3), 697–716 (1996b)

    Article  Google Scholar 

  • Johnson, J.: Conceptual problems as obstacles to progress in political sciences: four decades of political research. J. Theor. Polit. 15(1), 87–115 (2003)

    Article  ADS  Google Scholar 

  • Kannan, G., Pokharel, S., Kumar, S.: A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour. Conserv. Recycl. 54(1), 28–36 (2009)

    Article  Google Scholar 

  • Knutsen, C.H.: Measuring effective democracy. Int. Polit. Sci. Rev. 31(2), 109–128 (2010)

    Article  Google Scholar 

  • Kuo, T.C., Ma, H.-Y., Huang, S.H., Hu, A.H., Huang, C.S.: Barrier analysis for product service system using interpretive structural model. Int. J. Adv. Manuf. Technol. 49(1–4), 407–417 (2010)

    Article  Google Scholar 

  • Mainwaring, S., Pérez-Liñán, A.: Democracies and Dictatorships in Latin America: Emergency, Survival, and Fall. Cambridge University Press, Cambridge (2013)

    Google Scholar 

  • Møller, J., Skaaning, S.-E.: Beyond the radial delusion: conceptualizing and measuring democracy and non-democracy. Int. Polit. Sci. Rev. 31(3), 261–283 (2010)

    Article  Google Scholar 

  • Munck, G.L.: Measuring democracy: a bridge between scholarship and politics. John Hopkins University Press, Baltimore (2009)

    Book  Google Scholar 

  • Ragin, C.: Fuzzy-set social science. University of Chicago Press, Chicago (2000)

    Google Scholar 

  • Seawright, J., Collier, D.: Rival strategies of validation: tools for evaluating measures of democracy. Comp. Pol. Stud. 47(1), 111–138 (2014)

    Article  Google Scholar 

  • Seligson, M.: The renaissance of political culture or the renaissance of the ecological fallacy? Comp. Polit. 34(3), 273–292 (2002)

    Article  Google Scholar 

  • Silver, B.D., Dowley, K.M.: Measuring political culture in multiethnic societies: reaggregating the world value survey. Comp. Pol. Stud. 33(4), 517–550 (2000)

    Article  Google Scholar 

  • Thomas, M.A.: What do the worldwide governance indicators measure? Eur. J. Dev. Res. 22(1), 31–54 (2010). https://doi.org/10.1057/ejdr.2009.32

    Article  Google Scholar 

  • Teorell, J., Coppedge, M., Lindberg, S., Skaaning, S.-E.: Measuring polyarchy across the globe, 1900–2017. Stud. Comp. Int. Dev. 54(1), 71–95 (2019)

    Article  Google Scholar 

  • Vaccaro, A.: Comparing measures of democracy: statistical properties, convergence, and interchangeability. Eur. Political Sci. 20(2), 666–684 (2021)

    Article  Google Scholar 

  • Vaccaro, A.: Measures of state capacity: so similar, yet so different. Qual. Quant. (2022). https://doi.org/10.1007/s11135-022-01466-x

    Article  Google Scholar 

  • Wang, G., Wang, Y., Zhao, T.: Analysis of interactions among the barriers to energy saving in China. Energy Policy 36, 1879–1889 (2008)

    Article  Google Scholar 

  • Warfield, J.N.: Structuring complex systems. Battelle, Columbus (1974a)

    Google Scholar 

  • Warfield, J.N.: Developing Interconnection matrices in structural modeling. IEEE Transcr. Syst., Men Cybern. 4(1), 81–87 (1974b)

    Article  MathSciNet  Google Scholar 

  • Warfield, J.N.: A science of generic design: managing complexity through systems design, vol. 1. Intersystems Publications, Salinas (1990)

    Google Scholar 

  • Warshall, S.: A theorem on boolean matrices. J. Assoc. Comput. Mach. 9(1), 11–12 (1962)

    Article  MathSciNet  Google Scholar 

  • Wilson, M.: A discreet critique of discrete regime type data. Comp. Pol. Stud. 47(5), 689–714 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

The first two authors contribute equally to the project. Special thanks go to Prof. Aníbal Pérez-Liñán (University of Notre Dame) for sending us the most updated version of their datasets on political regimes in Latin America and to Prof. Jianhong Yin (Hefei University of Technology, China) for proofreading the mathematics of Boolean matrix operation. The Java-based program for performing ISM operations is developed by Ke Wu. The Python-based program for performing ISM operations is developed by Chen-hui Liu. For critical comments on an earlier draft, we thank Jeff Gill, Dwayne Woods, and an anonymous reviewer of this journal.

Funding

No funding for this project.

Author information

Author notes

  1. Kaiya Wu and Shiping Tang these authors have contribute equally.

Authors and Affiliations

  1. Fudan University, Shanghai, China

    Kaiya Wu & Shiping Tang

  2. Shanghai University of Finance and Economics, Shanghai, China

    Min Tang

Authors
  1. Kaiya Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiping Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shiping Tang or Min Tang.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 58 KB)

Supplementary file2 (DOC 163 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, K., Tang, S. & Tang, M. Interpretative structural modeling to social sciences: designing better datasets for mixed method research. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01838-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11135-024-01838-5

Keywords

Search

Navigation