IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i12d10.1007_s11192-021-04069-9.html
   My bibliography  Save this article

Mapping the field of psychology: Trends in research topics 1995–2015

Author

Listed:
  • Oliver Wieczorek

    (University of Bamberg
    Zeppelin University Friedrichshafen)

  • Saïd Unger

    (University of Stuttgart)

  • Jan Riebling

    (University of Wuppertal)

  • Lukas Erhard

    (University of Wuppertal)

  • Christian Koß

    (University of Stuttgart)

  • Raphael Heiberger

    (University of Stuttgart)

Abstract

We map the topic structure of psychology utilizing a sample of over 500,000 abstracts of research articles and conference proceedings spanning two decades (1995–2015). To do so, we apply structural topic models to examine three research questions: (i) What are the discipline’s most prevalent research topics? (ii) How did the scientific discourse in psychology change over the last decades, especially since the advent of neurosciences? (iii) And was this change carried by high impact (HI) or less prestigious journals? Our results reveal that topics related to natural sciences are trending, while their ’counterparts’ leaning to humanities are declining in popularity. Those trends are even more pronounced in the leading outlets of the field. Furthermore, our findings indicate a continued interest in methodological topics accompanied by the ascent of neurosciences and related methods and technologies (e.g. fMRI’s). At the same time, other established approaches (e.g. psychoanalysis) become less popular and indicate a relative decline of topics related to the social sciences and the humanities.

Suggested Citation

  • Oliver Wieczorek & Saïd Unger & Jan Riebling & Lukas Erhard & Christian Koß & Raphael Heiberger, 2021. "Mapping the field of psychology: Trends in research topics 1995–2015," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9699-9731, December.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:12:d:10.1007_s11192-021-04069-9
    DOI: 10.1007/s11192-021-04069-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04069-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04069-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Holger Billhardt & Daniel Borrajo & Victor Maojo, 2002. "A context vector model for information retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(3), pages 236-249.
    2. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    3. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    4. John G. Benjafield, 2019. "Keyword frequencies in anglophone psychology," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 1051-1064, March.
    5. John G. Benjafield, 2020. "Vocabulary sharing among subjects belonging to the hierarchy of sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1965-1982, December.
    6. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    7. Günter Krampen & Alexander Eye & Gabriel Schui, 2011. "Forecasting trends of development of psychology from a bibliometric perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 687-694, June.
    8. Günter Krampen, 2016. "Scientometric trend analyses of publications on the history of psychology: Is psychology becoming an unhistorical science?," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1217-1238, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Oliver Wieczorek & Melanie Malzahn, 2024. "Exploring an extinct society through the lens of Habitus-Field theory and the Tocharian text corpus," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    2. Manuel Goyanes & Márton Demeter & Zicheng Cheng & Homero Gil Zúñiga, 2022. "Measuring publication diversity among the most productive scholars: how research trajectories differ in communication, psychology, and political science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3661-3682, June.
    3. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
    4. Oliver Wieczorek & Olof Hallonsten & Fredrik Åström, 2024. "Is Management and Organizational Studies divided into (micro-)tribes?," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3871-3995, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2022. "Corporate Social Responsibility Activities Through Twitter: From Topic Model Analysis to Indexes Measuring Communication Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1217-1248, December.
    2. Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Peter Grajzl & Cindy Irby, 2019. "Reflections on study abroad: a computational linguistics approach," Journal of Computational Social Science, Springer, vol. 2(2), pages 151-181, July.
    4. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    5. David Ardia & Keven Bluteau & Mohammad‐Abbas Meghani, 2024. "Thirty years of academic finance," Journal of Economic Surveys, Wiley Blackwell, vol. 38(3), pages 1008-1042, July.
    6. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    7. Bongsug (Kevin) Chae & Eunhye (Olivia) Park, 2018. "Corporate Social Responsibility (CSR): A Survey of Topics and Trends Using Twitter Data and Topic Modeling," Sustainability, MDPI, vol. 10(7), pages 1-20, June.
    8. Peter Grajzl & Peter Murrell, 2021. "Characterizing a legal–intellectual culture: Bacon, Coke, and seventeenth-century England," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 15(1), pages 43-88, January.
    9. Marcel Fratzscher & Tobias Heidland & Lukas Menkhoff & Lucio Sarno & Maik Schmeling, 2023. "Foreign Exchange Intervention: A New Database," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(4), pages 852-884, December.
    10. Arthur Dyevre & Nicolas Lampach, 2021. "Issue attention on international courts: Evidence from the European Court of Justice," The Review of International Organizations, Springer, vol. 16(4), pages 793-815, October.
    11. Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.
    12. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    13. Ferrara, Federico M. & Masciandaro, Donato & Moschella, Manuela & Romelli, Davide, 2022. "Political voice on monetary policy: Evidence from the parliamentary hearings of the European Central Bank," European Journal of Political Economy, Elsevier, vol. 74(C).
    14. Andreas Rehs, 2020. "A structural topic model approach to scientific reorientation of economics and chemistry after German reunification," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1229-1251, November.
    15. Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.
    16. Seraphine F. Maerz & Carsten Q. Schneider, 2020. "Comparing public communication in democracies and autocracies: automated text analyses of speeches by heads of government," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 517-545, April.
    17. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.
    18. Xieling Chen & Juan Chen & Gary Cheng & Tao Gong, 2020. "Topics and trends in artificial intelligence assisted human brain research," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-27, April.
    19. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.
    20. Eunhye (Olivia) Park & Bongsug (Kevin) Chae & Junehee Kwon & Woo-Hyuk Kim, 2020. "The Effects of Green Restaurant Attributes on Customer Satisfaction Using the Structural Topic Model on Online Customer Reviews," Sustainability, MDPI, vol. 12(7), pages 1-20, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:12:d:10.1007_s11192-021-04069-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.