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HOW KNOWLEDGEABLE ARE APPLIED LINGUISTICS AND SLA RESEARCHERS ABOUT BASIC STATISTICS?

DATA FROM NORTH AMERICA AND EUROPE

Published online by Cambridge University Press:  11 October 2019

Shawn Loewen Open the ORCID record for Shawn Loewen [Opens in a new window] ,
Talip Gönülal ,
Daniel R. Isbell Open the ORCID record for Daniel R. Isbell [Opens in a new window] ,
Laura Ballard ,
Dustin Crowther Open the ORCID record for Dustin Crowther [Opens in a new window] ,
Jungmin Lim ,
Jeffrey Maloney  and
Magda Tigchelaar
Shawn Loewen*
Affiliation:
Michigan State University
Talip Gönülal
Affiliation:
Erzincan Binali Yıldırım Üniversitesi
Daniel R. Isbell
Affiliation:
California Polytechnic State University
Laura Ballard
Affiliation:
Educational Testing Services
Dustin Crowther
Affiliation:
University of Hawai’i at Mānoa
Jungmin Lim
Affiliation:
Michigan State University
Jeffrey Maloney
Affiliation:
Northeastern State University
Magda Tigchelaar
Affiliation:
Western Michigan University
*
*Correspondence concerning this article should be addressed to Shawn Loewen, Second Language Studies, B255 Wells Hall, 619 Red Cedar Road, Michigan State University, East Lansing, MI 48824-1027. E-mail: loewens@msu.edu
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Abstract

Despite the prevalence of quantitative approaches in applied linguistics (AL) and second language acquisition (SLA) research (Gass, 2009), evidence indicates a need for improvement in analyzing and reporting SLA data (e.g., Larson-Hall & Plonsky, 2015). However, to improve quantitative research, researchers must possess the statistical knowledge necessary to conduct quality research. This study assesses AL and SLA researchers’ knowledge of key statistical concepts on a statistical knowledge test. One hundred and ninety-eight AL and SLA researchers from North America and Europe responded to 26 discipline-specific questions designed to measure participants’ ability to (a) understand basic statistical concepts and procedures, (b) interpret statistical analyses, and (c) critically evaluate statistical information. Results indicate that participants generally understood basic descriptive statistics, but performance on items requiring more advanced statistical knowledge was lower. Quantitative research orientation, number of statistics courses taken, and frequent use of statistics textbooks had positive influences on researchers’ statistical knowledge.

Information

Type
Research Report
Information
Studies in Second Language Acquisition , Volume 42 , Issue 4 , September 2020 , pp. 871 - 890
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Open Practices
Open materials
Copyright
© Cambridge University Press 2019

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Footnotes

The experiment in this article earned an Open Data badge for transparent practices. The materials are available at https://www.iris-database.org/iris/app/home/detail?id=york%3a932451&ref=search.

We would like to thank Emma Marsden for her help in recruiting European respondents. We would also like to thank the SSLA editors and anonymous reviewers for their insights and input, which made the paper stronger. Any shortcomings are ours alone.

References

REFERENCES

Byrnes, H. (2013). Notes from the editor. The Modern Language Journal, 97, 825–827.CrossRefGoogle Scholar
Dörnyei, Z., & Csizér, K. (2012). How to design and analyze surveys in second language acquisition research. In Mackey, A. & Gass, S. M. (Eds.), Research methods in second language acquisition: A practical guide (pp. 74–94). Malden, MA: Blackwell.CrossRefGoogle Scholar
Finney, S. J., & Schraw, G. (2003). Self-efficacy beliefs in college statistics courses. Contemporary Educational Psychology, 28, 161–186.CrossRefGoogle Scholar
Gass, S. (2009). A survey of SLA research. In Ritchie, W. & Bhatia, T. (Eds.), Handbook of second language acquisition (pp. 3–28). Bingley, UK: Emerald.Google Scholar
Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.Google Scholar
Gonulal, T. (2016). Statistical Literacy among Second Language Acquisition Graduate Students (Unpublished doctoral dissertation). Michigan State University, East Lansing, MI.Google Scholar
Gonulal, T. (2018). An investigation of the predictors of statistical literacy in second language acquisition. Eurasian Journal of Applied Linguistics, 4, 49–70.CrossRefGoogle Scholar
Gonulal, T. (2019). Statistical knowledge and training in second language acquisition: The case of doctoral students. ITL – International Journal of Applied Linguistics. Advanced online publication. doi.org/10.1075/itl.18031.gon.Google Scholar
Gonulal, T., Loewen, S., & Plonsky, L. (2017). The development of statistical literacy in applied linguistics graduate students. ITL – International Journal of Applied Linguistics, 168, 4–32.CrossRefGoogle Scholar
Hall, C. J., Smith, P. H., & Wicaksono, R. (2017). Mapping applied linguistics: A guide for students and practitioners (2nd ed.). New York, NY: Routledge.CrossRefGoogle Scholar
Khany, R., & Tazik, K. (2019). Levels of statistical use in applied linguistics research articles: From 1986 to 2015. Journal of Quantitative Linguistics, 26, 48–65.CrossRefGoogle Scholar
King, K. A., & Mackey, A. (2016). Research methodology in second language studies: Trends, concerns, and new directions. The Modern Language Journal, 100, 209–227.CrossRefGoogle Scholar
Larson-Hall, J. (2010). A guide to doing statistics in second language research using SPSS. New York, NY: Routledge.Google Scholar
Larson-Hall, J., & Plonsky, L. (2015). Reporting and interpreting quantitative research findings: What gets reported and recommendations for the field. Language Learning, 65, 127–159.CrossRefGoogle Scholar
Lazaraton, A. (2000). Current trends in research methodology and statistics in applied linguistics. TESOL Quarterly, 34, 175–181.CrossRefGoogle Scholar
Lazaraton, A., Riggenbach, H., & Ediger, A. (1987). Forming a discipline: Applied linguists’ literacy in research methodology and statistics. TESOL Quarterly, 21, 263–277.CrossRefGoogle Scholar
Linacre, J. M. (2014). Winsteps® Rasch measurement computer program. Beaverton, OR: Winsteps.com.Google Scholar
Loewen, S., & Gass, S. (2009). The use of statistics in L2 acquisition research. Language Teaching, 42, 181–196.CrossRefGoogle Scholar
Loewen, S., Lavolette, E., Spino, L. A., Papi, M., Schmidtke, J., Sterling, S., & Wolff, D. (2014). Statistical literacy among applied linguists and second language acquisition researchers. TESOL Quarterly, 48, 360–388.CrossRefGoogle Scholar
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology and design (2nd ed.). New York, NY: Routledge.CrossRefGoogle Scholar
Norris, J. M. (2015). Statistical significance testing in second language research: Basic problems and suggestions for reform. Language Learning, 65, 97–126.CrossRefGoogle Scholar
Plonsky, L. (2013). Study quality in SLA: An assessment of designs, analyses, and reporting practices in quantitative L2 research. Studies in Second Language Acquisition, 35, 655–687.CrossRefGoogle Scholar
Plonsky, L. (2014). Study quality in quantitative L2 research (1990–2010): A methodological synthesis and call for reform. The Modern Language Journal, 98, 450–470.CrossRefGoogle Scholar
Plonsky, L. (2015). Statistical power, p values, descriptive statistics, and effect sizes: A “back-to-basics” approach to advancing quantitative methods in L2 research. In Plonsky, L. (Ed.), Advancing quantitative methods in second language research (pp. 23–45). New York, NY: Routledge.CrossRefGoogle Scholar
Plonsky, L. (2017). Quantitative research methods in instructed SLA. In Loewen, S. & Sato, M. (Eds.), The Routledge handbook of instructed second language acquisition (pp. 505–521). New York, NY: Routledge.CrossRefGoogle Scholar
Plonsky, L. (Ed.). (2015). Advancing quantitative methods in second language research. New York, NY: Routledge.CrossRefGoogle Scholar
Plonsky, L., & Gass, S. (2011). Quantitative research methods, study quality, and outcomes: The case of interaction research. Language Learning, 61, 325–366.CrossRefGoogle Scholar
Plonsky, L., & Gonulal, T. (2015). Methodological synthesis in quantitative L2 research: A review of reviews and a case study of exploratory factor analysis. Language Learning, 65, 9–36.CrossRefGoogle Scholar
Rasch, G. (1960/1980). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danish Institute for Educational Research.Google Scholar
Ross, S. (1998). Self-assessment in second language testing: A meta-analysis and analysis of experiential factors. Language Testing, 15, 1–20.CrossRefGoogle Scholar
Shrout, P. E., & Rodgers, J. L. (2018). Psychology, science, and knowledge construction: Broadening perspectives from the Replication Crisis. Annual Review of Psychology, 69, 487–510.CrossRefGoogle ScholarPubMed
Trofimovich, P., Isaacs, T., Kennedy, S., Saito, K., & Crowther, D. (2015). Flawed self-assessment: Investigating self- and other-perception of second language speech. Bilingualism: Language and Cognition, 19, 122–140.CrossRefGoogle Scholar
Wright, B. D., & Linacre, J. M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8, 370–371.Google Scholar
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