Vocabulary Research Trends in Applied Linguistics through Factorial Analysis and Thematic Analysis

Main Article Content

Hamisu Hamisu Haruna
https://orcid.org/0000-0002-6318-2652
Azza Jauhar Ahmad Tajuddin
Sulaiman Muhammad Isa
https://orcid.org/0009-0004-5275-7993

Abstract

The study examines vocabulary research trends in applied linguistics from 2004 to 2023, providing a fine-grained understanding of the emerging contours of vocabulary research in the field. This study utilised a mixed methods design, which combined bibliometric analysis, thematic analysis and factorial analysis to capture the evolution of vocabulary research. A dataset of 136 journal articles was composed from the Clarivate Analytics Web of Science journal database. It was found that, while the “learners” cluster showed a central influence, the clusters of “acquisition,” “children,” “model,” and “adjustment” showed varying degree of centrality and interconnectedness, through inductive thematic analysis of the articles, by categorising them into research themes. The triangulation of these qualitatively-developed themes and the statistically-configured themes offered a more comprehensive account of vocabulary research. Thus, the resulting timeline showed the transitions in the themes from 2004 to 2023, and the trends indicated by these transitions: from “acquisition” to “knowledge”, “children” to “frequency” and from “learners” to “size”. The authors concluded that the study makes a substantive contribution to the field in that it presents a multidimensional view of recent trends in vocabulary research. Although this review may not have included all pertinent literature in the subject as it is restricted to the papers indexed in the Clarivate Analytics Web of Science journal database, it offers a multifaceted perspective on current trends in vocabulary research, which significantly advances the subject.

Article Details

How to Cite
Haruna, H. H., Tajuddin, A. J. A., & Isa, S. M. (2024). Vocabulary Research Trends in Applied Linguistics through Factorial Analysis and Thematic Analysis. Zhongguo Kuangye Daxue Xuebao, 29(3), 91-105. https://zkdx.ch/journal/zkdx/article/view/60
Section
Articles

How to Cite

Haruna, H. H., Tajuddin, A. J. A., & Isa, S. M. (2024). Vocabulary Research Trends in Applied Linguistics through Factorial Analysis and Thematic Analysis. Zhongguo Kuangye Daxue Xuebao, 29(3), 91-105. https://zkdx.ch/journal/zkdx/article/view/60

References

Afjal, M. (2023). Bridging the financial divide: a bibliometric analysis on the role of digital financial services within FinTech in enhancing financial inclusion and economic development. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02086-y

Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Barrera, D., Kayacik, H. G., Van Oorschot, P. C., & Somayaji, A. (2010). A methodology for empirical analysis of permission-based security models and its application to android. In Proceedings of the 17th ACM conference on Computer and communications security (pp. 73-84). https://doi.org/10.1145/1866307.1866317

Beck, I. L., McKeown, M. G., & Kucan, L. (2013). Bringing words to life: Robust vocabulary instruction. Guilford Press.

Benzie, H. J. (2010). Graduating as a ‘native speaker’: international students and English language proficiency in higher education. Higher Education Research & Development, 29(4), 447–459. https://doi.org/10.1080/07294361003598824

Berahmand, K., Bouyer, A., & Samadi, N. (2018). A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos Solitons & Fractals, 110, 41–54. https://doi.org/10.1016/j.chaos.2018.03.014

Braun, V., & Clarke, V. (2020). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238

Brunfaut, T., & McCray, G. (2015). Looking into test-takers’ cognitive processes whilst completing reading tasks: A mixed-method eye-tracking and stimulated recall study. ARAGs Research Reports Online, AR/2015/001.

Casadei, P., Bloom, M., Camerani, R., Masucci, M., Siepel, J., & Ospina, J. V. (2023). Mapping the state of the art of creative cluster research: a bibliometric and thematic analysis. European Planning Studies, 31(12), 2531–2551. https://doi.org/10.1080/09654313.2022.2158722

Clarivate Analytics. (2024). Web of Science Fact Sheet. https://clarivate.com/webofsciencegroup/solutions/web-of-science-would-you-like-to-know-more/

Dang, T. N. Y., & Webb, S. (2014). The lexical profile of academic spoken English. English for Specific Purposes, 33, 66–76. https://doi.org/10.1016/j.esp.2013.08.001

Ding, M., Zhang, J., Shen, G., Zheng, Q., & Yuan, H. (2023). From photographic images to hierarchical networks―Color associations of a traditional Chinese garden. Color Research & Application/Color Research and Application, 48(6), 735–747. https://doi.org/10.1002/col.22886

Droogan, J., & Peattie, S. (2017). Mapping the thematic landscape of Dabiq magazine. Australian Journal of International Affairs, 71(6), 591–620. https://doi.org/10.1080/10357718.2017.1303443

Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z

Fàbregues, S., & Fetters, M. D. (2019). Fundamentals of case study research in family medicine and community health. Family Medicine and Community Health, 7(2). 10.1136/fmch-2018-000074

Farsani, M. A., & Jamali, H. R. (2023). Collaboration network of applied linguistics research articles with different methodological orientations. Studies in Second Language Learning and Teaching, 13(4), 727-754.

Fergnani, A. (2019). Mapping futures studies scholarship from 1968 to present: A bibliometric review of thematic clusters, research trends, and research gaps. Futures, 105, 104–123. https://doi.org/10.1016/j.futures.2018.09.007

Foroudi, P., Akarsu, T. N., Marvi, R., & Balakrishnan, J. (2021). Intellectual evolution of social innovation: A bibliometric analysis and avenues for future research trends. Industrial Marketing Management, 93, 446–465. https://doi.org/10.1016/j.indmarman.2020.03.026

Gibson, C. B. (2016). Elaboration, generalization, triangulation, and interpretation. Organizational Research Methods, 20(2), 193–223. https://doi.org/10.1177/1094428116639133

Greenacre, M. (2018). Compositional data analysis in practice. Chapman and Hall/CRC. https://doi.org/10.1201/9780429455537

Guest, G., MacQueen, K. M., & Namey, E. E. (2011). Applied thematic analysis. Sage Publications.

Kuckartz, U. (2014). Qualitative text analysis: A guide to methods, practice and using software. Sage Publications.

Lestari, M., & Wahyudin, A. Y. (2020). Language learning strategies of undergraduate efl students. Journal of English Language Teaching and Learning, 1(1), 25–30. https://doi.org/10.33365/jeltl.v1i1.242

Liu, Z. (2022). Mapping the research trends of third language acquisition: A bibliometric analysis based on Scopus. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1021517

Martínez-López, F. J., Merigó, J. M., Valenzuela-Fernández, L., & Nicolás, C. (2018). Fifty years of the European Journal of Marketing: a bibliometric analysis. European Journal of Marketing, 52(1/2), 439-468.

McLean, S., & Kramer, B. (2015). The creation of a new vocabulary levels test. Shiken, 19(2), 1-11.

Milton, J. (2013). Measuring the contribution of vocabulary knowledge to proficiency in the four skills. C. Bardel, C. Lindqvist, & B. Laufer (Eds.) L, 2, 57-78.

Nagy, W., & Townsend, D. (2012b). Words as tools: Learning academic vocabulary as language acquisition. Reading Research Quarterly, 47(1), 91–108. https://doi.org/10.1002/rrq.011

Nakata, T., & Webb, S. (2015). Does studying vocabulary in smaller sets increase learning? Studies in Second Language Acquisition, 38(3), 523–552. https://doi.org/10.1017/s0272263115000236

Nenadic, O. ., & Greenacre, M. (2007). Correspondence Analysis in R, with Two- and Three-dimensional Graphics: The ca Package. Journal of Statistical Software, 20(3), 1–13. https://doi.org/10.18637/jss.v020.i03

O’Dea, R. E., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W., Parker, T. H., Gurevitch, J., Page, M. J., Stewart, G., Moher, D., & Nakagawa, S. (2021). Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: a PRISMA extension. Biological Reviews/Biological Reviews of the Cambridge Philosophical Society, 96(5), 1695–1722. https://doi.org/10.1111/brv.12721

Pellicer-Sánchez, A. (2015). Learning L2 collocations incidentally from reading. Language Teaching Research, 21(3), 381–402. https://doi.org/10.1177/1362168815618428

Peters, E. (2013). The effects of repetition and time of post-test administration on EFL learners’ form recall of single words and collocations. Language Teaching Research, 18(1), 75–94. https://doi.org/10.1177/1362168813505384

Rosas, S. R., & Kane, M. (2012). Quality and rigor of the concept mapping methodology: A pooled study analysis. Evaluation and Program Planning, 35(2), 236–245. https://doi.org/10.1016/j.evalprogplan.2011.10.003

Rymes, B. (2015). Classroom discourse analysis: A tool for critical reflection. Routledge.

Shernoff, D. J., Sinha, S., Bressler, D. M., & Ginsburg, L. (2017). Assessing teacher education and professional development needs for the implementation of integrated approaches to STEM education. International Journal of STEM Education, 4(1). https://doi.org/10.1186/s40594-017-0068-1

Tan S., Harji M. B., & Hu X. (2023). A Bibliometric Analysis of English for Specific Purposes from 2011 to 2023 Using Citespace: Visualizing Status, Themes, Evolution, and Emerging Trends. Journal of Language and Education, 9(3), 159-175. https://doi.org/10.17323/jle.2023.17632

TENG, M. F., HUANG, Y., & MIZUMOTO, A. (2023). Incidental Vocabulary Learning Through Word-Focused Exercises: The Association with Vocabulary Learning Strategies. Asian Journal of English Language Teaching, 32.

Tseng, J. J. (2018). Exploring TPACK-SLA interface: Insights from the computer-enhanced classroom. Computer Assisted Language Learning, 31(4), 390-412.

Vidal, K. (2011). A comparison of the effects of reading and listening on incidental vocabulary acquisition. Language Learning, 61(1), 219–258. https://doi.org/10.1111/j.1467-9922.2010.00593.x

Watts-Taffe, S., Fisher, P., & Blachowicz, C. (2017). Vocabulary instruction: Research and practice. Handbook of research on teaching the English language arts, 130-161.

Webb, S., & Chang, A. C.-S. (2015). How does prior word knowledge affect vocabulary learning progress in an extensive reading program? Studies in Second Language Acquisition, 37(4), 651-675. https://doi.org/10.1017/S0272263114000606

Williams, J., & Rebuschat, P. (2016). Implicit learning and second language acquisition. Evanston, IL, USA: Routledge.

Xodabande, I., Torabzadeh, S., Ghafouri, M., & Emadi, A. (2022). Academic vocabulary in applied linguistics research articles: A corpus-based study. Journal of Language and Education, 8(2), 154-164. https://doi.org/10.17323/jle.2022.13420

Yan, E., & Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63(7), 1313–1326. https://doi.org/10.1002/asi.22680

Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79-94.

Similar Articles

You may also start an advanced similarity search for this article.