C-test Research Brief
By Şeyma Toker, Todd McKay, and Amy Kim
Do you need a good short-cut measure of global foreign language proficiency? This research brief will inform you about the C-test proficiency measure.
This research brief can also be viewed and downloaded as a pdf here.
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C-tests are cost-effective and efficient assessment instruments that measure global foreign language proficiency in the written modality. They can be used for proficiency measurement in foreign language research (see Norris, 2018 for examples in diverse languages), and placement/screening instruments in foreign language educational settings (e.g. Mozgalina & Ryshina-Pankova, 2015). C-tests are considered ‘short-cut’ – quick estimations of language proficiency – since they are able to tap into a variety of language features, such as vocabulary, syntax, morphology, and spelling. This allows educators to gauge learners’ overall language proficiency. C-tests are easy for researchers and practitioners to administer and score. The development of C-tests and validation of interpretations made on the basis of C-test scores, however, require a rigorous process. Below, we present major findings from several studies on the development of C-tests in various languages.
What does it mean?
C-test: A paragraph-length text in which the second half of every second word is deleted (language-specific variations in deletion procedures may exist). Examinees reconstruct responses by completing the deleted parts of the words.
A C-test example from Norris (2018):
This is an example C-test passage. Starting wi___ the sec_____ word o__ this sent_____, the la___ half fr___ each consec____ word h__ been del_____.
- C-test scores are reliable measures of global language proficiency. (Eckes & Grotjahn, 2006)
- First language users can complete C-tests with very high accuracy rates. (Grothjahn, Klein-Braley, & Raatz, 2002)
- C-tests are more powerful predictors of language proficiency than the other short-cut measures. (Harsch & Hartig, 2016)
- C-tests function well across languages that have distinct writing systems (e.g. Japanese, Korean, Bangla, Turkish). (Norris, 2018)
- Standardized text-selection procedures produce more reliable results. (Norris, 2018)
McKay, T. H., & Son, Y. A. (2017). Analyzing C-test data in Winsteps: A practical how-to guide.
Norris, J. M.(2018). Developing C-tests for estimating proficiency in foreign language research. New York, NY: Peter Lang.
Eckes, T., & Grotjahn, R. (2006). A closer look at the construct validity of C-tests. Language Testing, 23(3), 290-325.
Grothjahn, R., Klein-Braley, C., & Raatz, U.(2002). C-tests: An overview. In J. Coleman, R. Grotjahn, & U. Raatz (Eds.), University language testing and the C-test (pp. 93-114). Bochum, Germany: AKS-Verlag.
Harsch, C., & Hartig, J. (2016). Comparing C-tests and Yes/No vocabulary size tests as predictors of receptive language skills. Language Testing, 33(4), 555-575.
Mozgalina, A., & Ryshina–Pankova, M. (2015). Meeting the challenges of curriculum construction and change: Revision and validity evaluation of a placement test. The Modern Language Journal, 99(2), 346-370.
Norris, J. M. (2018). Developing C-tests for estimating proficiency in foreign language research. New York, NY: Peter Lang.
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