Skip to main content
SpringerLink
Log in

Food for Thought: Managing Secondary Data for Research

  • Chapter
  • First Online:
Navigating the Education Research Maze

Part of the book series: Palgrave Studies in Education Research Methods ((PSERM))

  • 427 Accesses

Abstract

Social science researchers are strongly motivated to understand the world of business and its associated phenomena. In contrast to pure science research, social science studies combine strong narratives with empirical or analytical investigation. Such research is enticing, invigorating, and essential in current academic and practitioner domains, but each aspect is challenging and resembles a maze. Researchers must navigate diverse paths to identify appropriate theory, concepts, data sources, and knowledge for analysing and understanding social science phenomena. In this chapter, the authors explore new sources of data and the challenges of using them. Drawing upon personal experiences, as empirical researchers, the authors make recommendations about the use of secondary data.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abel, F., Gao, Q., Houben, G.-J., & Tao, K. (2011). Analyzing user modeling on Twitter for personalized news recommendations. In Proceedings of the 19th international conference on user modeling, adaption, and personalization, UMAP’11, pp. 1–12. Berlin/Heidelberg: Springer-Verlag.

    Google Scholar 

  • Analytics, P. (2009). Twitter study. http://www.pearanalytics.com/wpcontent/uploads/2009/08/Twitter-Study-August-2009.pdf

  • Atkinson, A. B., & Brandolini, A. (2001). Promise and pitfalls in the use of “secondary” data-sets: Income inequality in OECD countries as a case study. Journal of Economic Literature, 39(3), 771–799.

    Article  Google Scholar 

  • Bamberger, M., Rugh, J., Church, M., & Fort, L. (2004). Shoestring evaluation: Designing impact evaluations under budget, time and data constraints. American Journal of Evaluation, 25(1), 5–37.

    Article  Google Scholar 

  • Beaton, A., & Johnson, E. (1992). Overview of the scaling methodology used in the national assessment. Journal of Educational Measurement, 29, 163–175.

    Article  Google Scholar 

  • Benevenuto, F., Magno, G., Rodrigues, T., & Almeida, V. (2010). Detecting spammers on Twitter. Paper presented at the 7th annual Collaboration, electronic messaging, anti-abuse and spam conference (CEAS). Vol. 6. Retrieved from http://www.decom.ufop.br/fabricio/download/ceas10.pdf

  • Boyd, D., Golder, S., & Gilad L. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In System Sciences (HICSS), 2010 43rd Hawaii International Conference on, pp. 1–10. IEEE.

    Google Scholar 

  • Bruns, A., & Liang, Y. E. L. (2012). Tools and methods for capturing Twitter data during natural disasters. First Monday, 17(4), 1–8.

    Article  Google Scholar 

  • Bryman, A. (2012). Social research methods. Oxford: Oxford University Press.

    Google Scholar 

  • Byron, K., & Thatcher, S. M. (2016). Editors’ comments: “what I know now that I wish I knew then”—Teaching theory and theory building. Academy of Management Review, 41(1), 1–8.

    Article  Google Scholar 

  • Forbes. (2014). Twitter’s growth will continue to slow, says new forecast http://www.forbes.com/sites/jeffbercovici/2014/05/27/twitters-growth-will-continue-to-slow-says-new-forecast/. Accessed 12 Sept.

  • Gerlitz, C., & Rieder, B. (2013). Mining one percent of Twitter: Collections, baselines, sampling. M/C Journal, 16(2), 620.

    Google Scholar 

  • Ha, S., & Ahn, J. (2011). Why are you sharing others’ tweets?: The impact of argument quality and source credibility on information sharing behavior. In Proceedings of the 32th International Conference on Information Systems (ICIS 2011). Paper 4. Retrieved from http://aisel.aisnet.org/icis2011/proceedings/humanbehavior/4

  • Hong, L., Ovidiu, D., & Davison, B. D. (2011). Predicting popular messages in Twitter. In Proceedings of the 20th international conference companion on World wide web, pp. 57–58. ACM.

    Google Scholar 

  • Hughes, A., & Palen, L. (2009). Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management, 6(3), 248–260.

    Article  Google Scholar 

  • Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188.

    Article  Google Scholar 

  • Katsikeas, C. S., Leonidou, L. C., & Morgan, N. A. (2000). Firm-level export performance assessment: Review, evaluation, and development. Journal of the Academy of Marketing Science, 28(4), 493–511.

    Article  Google Scholar 

  • Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217.

    Article  Google Scholar 

  • Liu, X., & Koirala, H. (2013). Fitting proportional odds models to educational data with complex sampling designs in ordinal logistic regression. Journal of Modern Applied Statistical Methods, 12(1), 26.

    Google Scholar 

  • Marti, E., & Scherer, A. (2015). Financial regulation and social welfare: The critical contribution of management theory. Academy of Management Review, Published online before print May 27, 2015, doi:10.5465/amr.2013.0469 ACAD MANAGE REV May 27, 2015 amr.2013.0469.

  • Mccord, M., & Chuah, M. (2011). Spam detection on twitter using traditional classifiers. In Autonomic and trusted computing (pp. 175–186). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Morstatter, F., et al. (2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. arXiv preprint arXiv:1306.5204.

    Google Scholar 

  • Pervin, N., Fang, F., Datta, A., Dutta, K., & Vandermeer, D. (2013). Fast, scalable, and context-sensitive detection of trending topics in microblog post streams. ACM Transactions on Management Information Systems (TMIS), 3(4), 19.

    Google Scholar 

  • Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (Eds.) (2013). Qualitative research practice: A guide for social science students and researchers. London: Sage.

    Google Scholar 

  • Shi, Z., Rui, H., & Whinston, A. B. (2014). Content sharing in a social broadcasting environment: Evidence from Twitter. MIS Quarterly, 38(1), 123–142.

    Google Scholar 

  • Shuai, X. Ding, Y., & Busemeyer, J. (2012). Multiple spreaders affect the indirect influence on twitter. In Proceedings of the 21st international conference companion on World Wide Web, pp. 597–598. ACM.

    Google Scholar 

  • Smith, E. (2008). Pitfalls and promises: The use of secondary data analysis in educational research. British Journal of Educational Studies, 56(3), 323–339.

    Article  Google Scholar 

  • Spurious Correlations. (2016). http://www.tylervigen.com/spurious-correlations

  • Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In Social Computing (socialcom), IEEE second international conference, pp. 177–184.

    Google Scholar 

  • Thomas, S., & Heck, R. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42, 517–540.

    Article  Google Scholar 

  • Tinati, R., Carr, L., Hall, W., & Bentwood, J. (2012). Identifying communicator roles in Twitter. In Proceedings of the 21st international conference companion on World Wide Web, pp. 1161–1168. ACM.

    Google Scholar 

  • Twitter, Twitter API Terms and Conditions. 2010. https://dev.twitter.com/overview/terms/agreement-and-policy. Accessed 4 Jan 2016.

  • Vieweg, S., Hughes, A. L., Starbird, K., & Palen L. (2010). Microblogging during two natural hazards events: What twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1079–1088. ACM.

    Google Scholar 

  • Wang, Alex Hai. “Don’t follow me: Spam detection in twitter.” Security and Cryptography (SECRYPT). Proceedings of the 2010 International Conference on. IEEE, 2010.

    Google Scholar 

  • Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441–458.

    Article  Google Scholar 

  • Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.

    Article  Google Scholar 

  • Yardi, S., Romero, D., & Schoenebeck, G. (2009). Detecting spam in a Twitter network. First Monday, 15, 1.

    Article  Google Scholar 

  • Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: A case study of the Haitian earthquake. World Medical & Health Policy, 2(2), 7–33.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Design and Manufacturing Kancheepuram, Chennai, TN, India

    Nargis Pervin

  2. Supply Chain/ESC Rennes School of Business, Rennes, Brittany, France

    Rohit Nishant

  3. Department of Marketing, Salford University and ESC Rennes School of Business, Salford, UK

    Philip J. Kitchen

  4. Department of Marketing, Salford University and ESC Rennes School of Business, Rennes, France

    Philip J. Kitchen

Authors
  1. Nargis Pervin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohit Nishant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip J. Kitchen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Central Queensland University, North Rockhampton, Queensland, Australia

    Dolene Rossi

  2. University of Southern Queensland, Toowoomba, Queensland, Australia

    Francis Gacenga

  3. University of Southern Queensland, Toowoomba, Queensland, Australia

    Patrick Alan Danaher

Rights and permissions

Reprints and permissions

Copyright information

© 2016 The Author(s)

About this chapter

Cite this chapter

Pervin, N., Nishant, R., Kitchen, P.J. (2016). Food for Thought: Managing Secondary Data for Research. In: Rossi, D., Gacenga, F., Danaher, P. (eds) Navigating the Education Research Maze. Palgrave Studies in Education Research Methods. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-39853-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-39853-2_12

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-319-39852-5

  • Online ISBN: 978-3-319-39853-2

  • eBook Packages: Social SciencesSocial Sciences (R0)

Publish with us

Policies and ethics

Search

Navigation