Valley International Journal Digital Library

VIJ Digital library
Articles

My Data Are Ready, How Do I Analyze Them: Navigating Data Analysis in Social Science Research

PDF
  • Arthur William Fodouop Kouam
Arthur William Fodouop Kouam
School of Management, Hebei University ZIP Code 071000, Baoding City, Hebei Province, China
Published in : International Journal of Scientific Research and Management (IJSRM) , Vol. 12 No. 03 (2024)

Submission to VIJ 2024-03-11

Keywords

  • Data analysis,
  • data quality,
  • research findings,
  • research methods,
  • social science research,
  • statistical techniques
    • ...More
    Less

Copyright (c) 2024 Arthur William Fodouop Kouam

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper offers a practical guide for researchers to effectively analyze their data in social science research, addressing the challenges and pitfalls commonly encountered in data analysis. By exploring various data analysis techniques, highlighting key challenges such as data quality issues and statistical assumptions violations, and providing practical tips and guidelines, this study fills a gap in the existing literature by offering a comprehensive approach to navigating data analysis in social science research. The significance of this study lies in its potential to improve the quality and reliability of research findings in the social sciences, equipping researchers with the necessary knowledge and skills to conduct robust data analysis. This study is a valuable resource for researchers seeking to enhance their analytical skills, avoid common pitfalls, and advance knowledge in their field of study.

PDF

References

  1. Adedoyin-Olowe, M., Gaber, M.M., & Stahl, F.T. (2013). A Survey of Data Mining Techniques for Social Media Analysis. J. Data Min. Digit. Humanit., 2014.
  2. Adeyemi, T.O. (2009). Inferential Statistics for Social and Behavioural Research.
  3. Ajagbe, A.M., Sholanke, A.B., Isiavwe, D.T., & Oke, A.O. (2015). Qualitative Inquiry for Social Sciences.
  4. Andrienko, G.L., & Andrienko, N.V. (1997). Research issues in intelligent data visualisation for exploration and communication. CHI '97 Extended Abstracts on Human Factors in Computing Systems.
  5. Aravindh, R., & Thirupathi, S. (2019). Data Analysis In Social Sciences: Comparison between Quantitative and Qualitative Research. International Journal of Management Research and Social Science.
  6. Babyak, M.A. (2004). What You See May Not Be What You Get: A Brief, Nontechnical Introduction to Overfitting in Regression-Type Models. Psychosomatic Medicine, 66, 411-421.
  7. Ball, G.H. (1965). Data analysis in the social sciences: what about the details? AFIPS '65 (Fall, part I).
  8. Bartholomew, D.J., Moustaki, I., Galbraith, J.I., & Steele, F. (2002). The Analysis and Interpretation of Multivariate Data for Social Scientists.
  9. Bartholomew, T.T., Joy, E.E., Kang, E., & Brown, J. (2021). A choir or cacophony? Sample sizes and quality of conveying participants’ voices in phenomenological research. Methodological Innovations, 14.
  10. Beran, J. (2003). Time series analysis.
  11. Berk, R.A., McCleary, R., & Hay, R.J. (1980). Applied Time Series Analysis for the Social Sciences.
  12. Blank, C., McBurney, M., Morgan, M., & Seetan, R.I. (2021). A Survey of Big Data Techniques for Extracting Information from Social Media Data. Advances in Science, Technology and Engineering Systems Journal, 6, 189-204.
  13. Boruch, R.F., & Rui, N. (2008). From randomized controlled trials to evidence grading schemes: current state of evidence‐based practice in social sciences. Journal of Evidence‐Based Medicine, 1.
  14. Box-Steffensmeier, J. M., Freeman, J. R., Hitt, M. P., & Pevehouse, J. C. (2014). Time series analysis for the social sciences. Cambridge University Press.
  15. Brenner, M. (1981). Problems in collecting social data: a review for the information researcher. Social Science Information Studies, 1, 139-151.
  16. Bryman, A.E., & Cramer, D. (1990). Quantitative data analysis for social scientists.
  17. Cao, K.S. (2019). Data mining for social network data.
  18. Chen, Y., Sherren, K., Smit, M., & Lee, K.Y. (2021). Using social media images as data in social science research. New Media & Society, 25, 849 - 871.
  19. Clarke, A.M. (1999). Qualitative research: data analysis techniques. Professional nurse, 14 8, 531-3.
  20. Cook, L.A., Sachs, J., & Weiskopf, N.G. (2021). The quality of social determinants data in the electronic health record: a systematic review. Journal of the American Medical Informatics Association: JAMIA, 29, 187 - 196.
  21. Cuomo, S., & Maiorano, F. (2018). Social network data analysis and mining applications for the Internet of Data. Concurrency and Computation: Practice and Experience, 30.
  22. Edwards, M. (2005). Social Science Research and Public Policy: Narrowing the Divide1. Australian Journal of Public Administration, 64, 68-74.
  23. Gailmard, S. (2014). Statistical Modeling and Inference for Social Science.
  24. Gainey, M.A., Gunderman, H.C., Slayton, E., & Splenda, R. (2021). Understanding the Practices and Challenges of Teaching with Data in Undergraduate Social Science Courses at Carnegie Mellon University.
  25. Gibbs, B.G., Shafer, K., & Miles, A.R. (2017). Inferential statistics and the use of administrative data in US educational research. International Journal of Research & Method in Education, 40, 214 - 220.
  26. Gygi, J.P., Kleinstein, S.H., & Guan, L. (2023). Predictive overfitting in immunological applications: Pitfalls and solutions. Human Vaccines & Immunotherapeutics, 19.
  27. Hall, O. (2010). Remote Sensing in Social Science Research. The Open Remote Sensing Journal, 3, 1-16.
  28. Healy, K., & Moody, J. (2014). Data Visualization in Sociology. Annual review of sociology, 40, 105-128.
  29. Heineman, M.B. (1981). The Obsolete Scientific Imperative in Social Work Research. Social Service Review, 55, 371 - 397.
  30. Holland, B.S., fang, S., & Basu, S. (2009). Neglect of Multiplicity When Testing Families of Related Hypotheses*. Research Methods & Methodology in Accounting eJournal.
  31. Hou, Q., Han, M., & Cai, Z. (2020). Survey on data analysis in social media: A practical application aspect. Big Data Min. Anal., 3, 259-279.
  32. Jensen, J.B., Singh, L., Davis‐Kean, P.E., Abraham, K., Beatty, P.C., Bode, L., Chau, D., Eliassi-Rad, T., Gonzalez, R., Hamilton, R.W., Kim, I.S., Kuchler, T., Ladd, J.M., Lerman, K., Levenstein, M.C., Mneimneh, Z., Nguyen, Q.C., Pasek, J., Raghunathan, T.M., Ryan, R., Soroka, S., Tadesse, M.G., & Traugott, M.W. (2021). Analysis and Visualization Considerations for Quantitative Social Science Research Using Social Media Data.
  33. Keselman, H.J., Cribbie, R.A., & Holland, B.S. (2002). Controlling the rate of Type I error over a large set of statistical tests. The British journal of mathematical and statistical psychology, 55 Pt 1, 27-39.
  34. King, G. (2011). Ensuring the Data-Rich Future of the Social Sciences. Science, 331, 719 - 721.
  35. Krejčí, J. (2010). Approaching Quality in Survey Research: Towards a Comprehensive Perspective. Sociologicky Casopis-czech Sociological Review, 46.
  36. Lachlan, K.A., & Spence, P.R. (2005). Corrections for Type I Error in Social Science Research: A Disconnect between Theory and Practice. Journal of Modern Applied Statistical Methods, 5, 23.
  37. Lehrer, D., Leschke, J., Lhachimi, S.K., Vasiliu, A.M., & Weiffen, B. (2007). negative results in social science. European Political Science, 6, 51-68.
  38. Mair, M., & Kierans, C. (2007). Descriptions as data: developing techniques to elicit descriptive materials in social research. Visual Studies, 22, 120 - 136.
  39. Manian, V., & Vadivel, P. (2021). Challenges and Applications of Data Analytics in Social Perspectives: Introduction of Data Science. Advanced Deep Learning Applications in Big Data Analytics, 51-67.
  40. Maravelakis, P.E. (2019). The use of statistics in social sciences.
  41. Mohajan, H.K. (2018). Qualitative Research Methodology in Social Sciences and Related Subjects. Journal of Economic Development, Environment and People, 7, 23-48.
  42. Nanjundeswaraswamy, T. S., & Divakar, S. (2021). Determination of sample size and sampling methods in applied research. Proceedings on engineering sciences, 3(1), 25-32.
  43. O’Keefe, D.J. (2003). Colloquy: Should Familywise Alpha Be Adjusted? Against Familywise Alpha Adjustment.
  44. Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2.
  45. Osbourne, J.W., & Waters, E. (2002). Four Assumptions of Multiple Regression That Researchers Should Always Test. Practical Assessment, Research and Evaluation, 8, 2.
  46. Reyes, J. M. M. (2022). Data Visualization for Social and Policy Research: A Step-by-step Approach Using R and Python. Cambridge University Press.
  47. Rice, E., & Yoshioka-Maxwell, A. (2015). Social Network Analysis as a Toolkit for the Science of Social Work. Journal of the Society for Social Work and Research, 6, 369 - 383.
  48. Rogers, B. (1998). Descriptive Analysis of Research Data. Workplace Health & Safety, 46, 266 - 267.
  49. Rose, D.F., & Sullivan, O. (1996). Introducing data analysis for social scientists.
  50. Rupp, A.A., & Sweet, S.J. (2011). Analysis of Multivariate Social Science Data (2nd ed.). Structural Equation Modeling: A Multidisciplinary Journal, 18, 686 - 693.
  51. Shanks, G., & Corbitt, B. (1999, December). Understanding data quality: Social and cultural aspects. In Proceedings of the 10th Australasian conference on information systems (Vol. 785). Victoria University of Wellington, New Zealand.
  52. Shrum, W., & Mullins, N.C. (1988). NETWORK ANALYSIS IN THE STUDY OF SCIENCE AND TECHNOLOGY.
  53. Silver, I.A. (2021). The Violating Assumptions Series: Simulated demonstrations to illustrate how assumptions can affect statistical estimates. arXiv: Methodology.
  54. Srivastava, A.K., & Mishra, R. (2021). Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective. IIM Kozhikode Society & Management Review, 12, 39 - 49.
  55. Stoodley, K.D. (1980). Statistical Inference in the Social Sciences. Educational Research, 23, 51-56.
  56. Streeter, C.L., & Gillespie, D.F. (1993). Social Network Analysis. Journal of Social Service Research, 16, 201-222.
  57. Stukal, D.K., Belenkov, V.E., & Philippov, I. (2021). Data science methods in political science research: analyzing protest activity in social media. Political Science (RU).
  58. Tindall, D.B., McLevey, J., Koop-Monteiro, Y., & Graham, A.V. (2022). Big data, computational social science, and other recent innovations in social network analysis. Canadian review of sociology = Revue canadienne de sociologie.
  59. Toker, A. (2022). A GUIDE FOR QUALITATIVE DATA ANALYSIS IN SOCIAL SCIENCES. Pamukkale University Journal of Social Sciences Institute.
  60. Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. ArXiv, abs/1403.7400.
  61. Vedanayaki, M. (2014). A Study of Data Mining and Social Network Analysis. Indian journal of science and technology, 7, 185-187.
  62. William, F.K.A. (2024). Mastering Validity and Reliability in Academic Research: Meaning and Significance. International Journal of Research Publications, 144(1), 287-292.
  63. William, F.K.A. (2024). Understanding Endogeneity, Exogeneity, Heterogeneity, Homogeneity, Homoskedasticity, Heteroskedasticity in Statistical Analysis: Avoiding Misinterpretations in Social Science Research. International Journal of Research Publications, 143(1).
  64. Yaffee, R.A., & McGee, M. (2000). Introduction to Time Series Analysis and Forecasting: With Applications of SAS and SPSS.
  65. Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series, 1168.
  66. Yu, C. H. (2002). An overview of remedial tools for violations of parametric test assumptions in the SAS system. In Proceedings of 2002 western users of sas software conference (Vol. 172178).
  67. Zhao, K. (2020). Sample representation in the social sciences. Synthese, 1-19.
  68. Zwilling, M. (2023). Big Data Challenges in Social Sciences: An NLP Analysis. Journal of Computer Information Systems, 63, 537 - 554.