A Methodological Protocol for Analyzing Dyadic Phenomena: The Cross-Network Informational Analysis (CNIA)
Abstract views: 0
/ 
PDF downloads: 0
DOI:
https://doi.org/10.59455/jomes.51Keywords:
dyadic phenomena, categorical variables, mixed methods research paradigm, informational network analysis, research synthesisAbstract
Bu makale, nitel ve nicel yöntemleri entegre ederek iki (veya daha fazla) kategorik değişkene dayalı ikili olguları analiz etmek için metodolojik bir protokol önermektedir. Bulguların sağlamlığını ve derinlemesine incelenmesini artıran metodolojik bir entegrasyonla ikili olguları yeterince araştırma ihtiyacını karşılamak için MMR Paradigmasına dayalı yeni bir metodolojik protokol önerilmiştir. Bu protokol, Araştırma Sentezi, Sınıflandırma, Bilgi Ağı Analizi ve Keşifsel Veri Analizi'nden oluşmakta ve meta-çıkarım ile tamamlanmaktadır. Uygulaması ampirik araştırmalar, literatür incelemeleri ve diğer teorik çalışmalar için uygundur. CNIA protokolünün MMR'ye ana katkısı, MMR Paradigması altında ikili olgularla ilgili teorik araştırmalar için uygulanabilirliğidir. Pratik bir uygulama ve metodolojik öneriler sunulmuştur.
References
Aggarwal, C. C. (2017). Outlier Analysis (second edition). Cham, Switzerland: Springer Nature. DOI: https://doi.org/10.1007/978-3-319-47578-3
Agresti, A. (2002). Categorical Data Analysis. Hoboken: John Wiley & Sons. DOI: https://doi.org/10.1002/0471249688
Åkerblad, L., Seppänen-Järvelä, R., & Haapakoski, K. (2021). Integrative strategies in mixed methods research. Journal of Mixed Methods Research, 15(2), 152-170. https://doi.org/10.1177/1558689820957125 DOI: https://doi.org/10.1177/1558689820957125
Altman, D. G. (1991). Practical Statistics for Medical Research. London, England: Chapman and Hall/CRC.
Atkinson, K. M., Koenka, A. C., Sanchez, C. E., Moshontz, H., & Cooper, H. (2015). Reporting standards for literature searches and report inclusion criteria: making research syntheses more transparent and easy to replicate. Research Synthesis Methods, 6(1), 87-95. https://doi.org/10.1002/jrsm.1127 DOI: https://doi.org/10.1002/jrsm.1127
Barbi, A. Q., & Prataviera, G. A. (2019). Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees. Physica A: Statistical Mechanics and its Applications, 523, 876-885. https://doi.org/10.1016/j.physa.2019.04.147 DOI: https://doi.org/10.1016/j.physa.2019.04.147
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361-362. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/13937 DOI: https://doi.org/10.1609/icwsm.v3i1.13937
Beck, D., & Ferasso, M. (in press). How can Stakeholder Capitalism contribute to achieving the Sustainable Development Goals? A Cross-network Literature Analysis. Ecological Economics.
Beck, D., & Storopoli, J. (2021). Cities through the lens of Stakeholder Theory: A literature review. Cities, 118, 103377. https://doi.org/10.1016/j.cities.2021.103377 DOI: https://doi.org/10.1016/j.cities.2021.103377
Beck, D., & Ferasso, M. (2022). Image of Cities as Tool for Urban Governance in Mercosur: Contributions from Urban and City Branding. Brazilian Journal of Marketing, 21(1), 9-28. https://doi.org/10.5585/remark.v21i1.19354 DOI: https://doi.org/10.5585/remark.v21i1.19354
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892-895. https://doi.org/10.1126/science.1165821 DOI: https://doi.org/10.1126/science.1165821
Canché, M. S. G. (2022). Network Analysis of Qualitative Data: An Integrative Software Application to Visualize and Assess Similarities in Participants’ Qualitative Contributions. Journal of Mixed Methods Research, 16(3), 373-377. https://doi.org/10.1177/15586898211051584 DOI: https://doi.org/10.1177/15586898211051584
Chalmers, I., Hedges, L. V., & Cooper, H. (2002). A brief history of research synthesis. Evaluation & the Health Professions, 25(1), 12-37. https://doi.org/10.1177/0163278702025001003 DOI: https://doi.org/10.1177/0163278702025001003
Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2019). The handbook of research synthesis and meta-analysis. New York: Russell Sage Foundation. DOI: https://doi.org/10.7758/9781610448864
Cooper, H. (2015). Research synthesis and meta-analysis: A step-by-step approach (Vol. 2). Thousand Oaks: SAGE.
Cordray, D. S., Harris, T. R., & Klein, S. (2009). A research synthesis of the effectiveness, replicability, and generality of the VaNTH challenge‐based instructional modules in bioengineering. Journal of Engineering Education, 98(4), 335-348. https://doi.org/10.1002/j.2168-9830.2009.tb01031.x DOI: https://doi.org/10.1002/j.2168-9830.2009.tb01031.x
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102 DOI: https://doi.org/10.1002/spe.4380211102
Goldsmith, B. E., Semenovich, D., Sowmya, A., & Grgic, G. (2017). Political Competition and the Initiation of International Conflict: A New Perspective on the Institutional Foundations of Democratic Peace. World Politics, 69(3), 493-531. https://doi.org/10.1017/S0043887116000307 DOI: https://doi.org/10.1017/S0043887116000307
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (eighth edition). Andover: Cengage Learning, EMEA.
Hedges, L. V., & Cooper, H. (2009). Research synthesis as a scientific process. In H. Cooper, L. V. Hedges, & Valentine J. C. (Eds.). The handbook of research synthesis and meta-analysis, New York: Russel Sage Foundation, 3-18.
Heyvaert, M., Maes, B., & Onghena, P. (2013). Mixed methods research synthesis: definition, framework, and potential. Quality & Quantity, 47(2), 659-676. https://doi.org/10.1007/s11135-011-9538-6 DOI: https://doi.org/10.1007/s11135-011-9538-6
Hu, Y. (2005). Efficient, High-Quality Force-Directed Graph Drawing. The Mathematica Journal, 10(1), 37–71.
Huang, L., Wu, J., & Yan, L. (2015). Defining and measuring urban sustainability: A review of indicators. Landscape Ecology, 30(7), 1175-1193. https://doi.org/10.1007/s10980-015-0208-2 DOI: https://doi.org/10.1007/s10980-015-0208-2
Jacobs, D. B., & Cramer, L. A. (2017). Applying information network analysis to fire-prone landscapes: implications for community resilience. Ecology and Society, 22(1), 52. https://doi.org/10.5751/ES-09119-220152 DOI: https://doi.org/10.5751/ES-09119-220152
Kim, J., Yammarino, F. J., Dionne, S. D., Eckardt, R., Cheong, M., Tsai, C. Y., ... & Park, J. W. (2020). State-of-the-science review of leader-follower dyads research. The Leadership Quarterly, 31(1), 101306. https://doi.org/10.1016/j.leaqua.2019.101306 DOI: https://doi.org/10.1016/j.leaqua.2019.101306
Korsgaard, M. A., Brower, H. H., & Lester, S. W. (2015). It isn’t always mutual: A critical review of dyadic trust. Journal of Management, 41(1), 47-70. https://doi.org/10.1177/0149206314547521 DOI: https://doi.org/10.1177/0149206314547521
Krasikova, D. V., & LeBreton, J. M. (2012). Just the two of us: Misalignment of theory and methods in examining dyadic phenomena. Journal of Applied Psychology, 97(4), 739. https://doi.org/10.1037/a0027962 DOI: https://doi.org/10.1037/a0027962
Laudan, L. (1981). Science and hypothesis: Historical essays on scientific methodology. Netherlands: Springer Science. https://doi.org/10.1007/978-94-015-7288-0 DOI: https://doi.org/10.1007/978-94-015-7288-0
Lyons, K. S., & Lee, C. S. (2018). The theory of dyadic illness management. Journal of Family Nursing, 24(1), 8-28. https://doi.org/10.1177/1074840717745669 DOI: https://doi.org/10.1177/1074840717745669
Minner, D. D., Levy, A. J., & Century, J. (2010). Inquiry‐based science instruction—what is it and does it matter? Results from a research synthesis years 1984 to 2002. Journal of Research in Science Teaching, 47(4), 474-496. https://doi.org/10.1002/tea.20347 DOI: https://doi.org/10.1002/tea.20347
Newman, M. (2018). Networks (second edition). Oxford: Oxford University Press.
Nooraie, R. Y., Sale, J. E., Marin, A., & Ross, L. E. (2020). Social network analysis: An example of fusion between quantitative and qualitative methods. Journal of Mixed Methods Research, 14(1), 110-124. https://doi.org/10.1177/1558689818804060 DOI: https://doi.org/10.1177/1558689818804060
Norris, J. M., & Ortega, L. (2000). Effectiveness of L2 instruction: A research synthesis and quantitative meta‐analysis. Language Learning, 50(3), 417-528. https://doi.org/10.1111/0023-8333.00136 DOI: https://doi.org/10.1111/0023-8333.00136
Nuzzo, R. L. (2016). The box plots alternative for visualizing quantitative data. PM&R, 8(3), 268-272. http://dx.doi.org/10.1016/j.pmrj.2016.02.001 DOI: https://doi.org/10.1016/j.pmrj.2016.02.001
Orwin, R. G. (1994). Evaluating coding decisions. In H. Cooper & L. V. Hedges (Eds.). The Handbook of Research Synthesis. New York: Russell Sage Foundation, 139-162.
Sandelowski, M., Voils, C. I., Leeman, J., & Crandell, J. L. (2012). Mapping the mixed methods–mixed research synthesis terrain. Journal of Mixed Methods Research, 6(4), 317-331. https://doi.org/10.1177/1558689811427913 DOI: https://doi.org/10.1177/1558689811427913
Schmitt, J. B., Rieger, D., Rutkowski, O., & Ernst, J. (2018). Counter-messages as prevention or promotion of extremism?! the potential role of youtube: Recommendation algorithms. Journal of Communication, 68(4), 758-779. https://doi.org/10.1093/joc/jqy029 DOI: https://doi.org/10.1093/joc/jqy029
Shi, C., Li, Y., Yu, P. S., & Wu, B. (2016). Constrained-meta-path-based ranking in heterogeneous information network. Knowledge and Information Systems, 49(2), 719-747. https://doi.org/10.1007/s10115-016-0916-1 DOI: https://doi.org/10.1007/s10115-016-0916-1
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017). A Survey of Heterogeneous Information Network Analysis. IEEE Transactions on Knowledge and Data Engineering, 29(1), 17-37. https://doi.org/10.1109/TKDE.2016.2598561 DOI: https://doi.org/10.1109/TKDE.2016.2598561
Spitzer, M., Wildenhain, J., Rappsilber, J., & Tyers, M. (2014). BoxPlotR: a web tool for generation of box plots. Nature Methods, 11(2), 121-122.https://doi.org/10.1038/nmeth.2811 DOI: https://doi.org/10.1038/nmeth.2811
Stock, W. A. (1994). Systematic coding for research synthesis. In H. Cooper & L. V. Hedges (Eds.). The Handbook of Research Synthesis. New York: Russell Sage Foundation, 125-138.
Sun, Y., Han, J., Yan, X., & Yu, P. S. (2012). Mining knowledge from interconnected data: A heterogeneous information network analysis approach Proceedings of the VLDB Endowment, 5(12), 2022-2023. https://doi.org/10.14778/2367502.2367566 DOI: https://doi.org/10.14778/2367502.2367566
Suri, H. (2011). Purposeful sampling in qualitative research synthesis. Qualitative Research Journal, 11(2), 63-75. https://doi.org/10.3316/QRJ1102063 DOI: https://doi.org/10.3316/QRJ1102063
Tashakkori, A., Johnson, R. B., & Teddlie, C. (2020). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences (2nd edition). Thousand Oaks: Sage Publications.
Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Thousand Oaks: Sage.
Tukey, J. W. (1977). Exploratory Data Analysis. Reading, Massachusetts: Addison-Wesley.
United Nations (2022). The 17 Goals | Sustainable Development. Retrieved from: https://sdgs.un.org/goals
Voils, C. I., Sandelowski, M., Barroso, J., & Hasselblad, V. (2008). Making sense of qualitative and quantitative findings in mixed research synthesis studies. Field Methods, 20(1), 3-25. https://doi.org/10.1177/1525822X07307463 DOI: https://doi.org/10.1177/1525822X07307463
Wang, J., Chen, C., Li, H. F., Jiang, X. L., & Zhang, L. (2016). Investigating key genes associated with ovarian cancer by integrating affinity propagation clustering and mutual information network analysis. European Review for Medical and Pharmacological Sciences, 20(12), 2532-40.
Yu, P. S., Han, J., & Faloutsos, C. (2010). Link mining: Models, Algorithms, and Applications. New York: Springer Nature. DOI: https://doi.org/10.1007/978-1-4419-6515-8
Xie, Y., Yu, B., Lv, S., Zhang, C., Wang, G., & Gong, M. (2021). A survey on heterogeneous network representation learning. Pattern Recognition, 116, 107936. https://doi.org/10.1016/j.patcog.2021.107936 DOI: https://doi.org/10.1016/j.patcog.2021.107936
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Mixed Methods Studies
This work is licensed under a Creative Commons Attribution 4.0 International License.
