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In:Research on Second Language Processing and Processing Instruction: Studies in honor of Bill VanPatten
Edited by Michael J. Leeser, Gregory D. Keating and Wynne Wong
[Studies in Bilingualism 62] 2021
► pp. 235–260

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Chapter 8
Trials-to-criterion as a methodological option to measure language processing in processing instruction

 | University of Illinois at Chicago
Published online: 17 March 2021
DOI logo with link
https://doi.org/10.1075/sibil.62.08fer
Abstract
Trials-to-criterion – a method commonly used in applied behavior analysis as an alternative to the pre-/post-test study design – has been used to observe and assess input processing in processing instruction (PI) research. The results from trials-to-criterion studies have made important contributions to our understanding of the effects of PI in input processing, particularly regarding the role of explicit information. In order to promote trials-to-criterion as a method in PI research, this chapter reviews and discusses studies that have used trials-to-criterion data to investigate the effects of PI on second language processing. It also examines crucial design choices in the implementation of trials-to-criterion as a measure of language processing. Finally, it discusses future considerations for trials-to-criterion research.
Article outline
  • Trials-to-criterion: Introduction and rationale
  • Fernández (2008)
  • Overview of studies using trials-to-criterion since Fernández (2008)
    • The effects of EI on criterion rate
      • The interplay between EI and individual differences on criterion rate
      • The effects of EI plus other factors on criterion rate
    • Discussion of findings from trials-to-criterion research
  • Trials-to-criterion: Implementation
    • Types of SI activities
      • Procedure, setting, and corrective feedback
    • Criterion
    • Total number of trials available for reaching criterion
      • Trials-to-criterion and pre-/post-tests
      • Trials-to-criterion and qualitative measures
      • Criteria for participant inclusion in statistical analyses
    • Explicit information
  • Future directions
  • Conclusion
  • Notes
  • References
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