Updated on 2021/06/22

写真b

 
NICKLIN, C. M.
 
*Items subject to periodic update by Rikkyo University (The rest are reprinted from information registered on researchmap.)
Affiliation*
Center for Foreign Language Education and Research English
Title*
Adjunct Lecturer
Campus Career*
  • 4 2020 - Present 
    Center for Foreign Language Education and Research   English   Adjunct Lecturer
  • 4 2016 - 3 2020 
    Center For English Discussion Class   Lecturer in English Education(English Discussion Lecturer until March 2020)
 

Research Areas

  • Humanities & Social Sciences / Cognitive science  / Psycholinguistics

  • Humanities & Social Sciences / Foreign language education  / Research Methodology

  • Humanities & Social Sciences / Foreign language education  / Vocabulary

Research History

  • 4 2020 - Present 
    RIKKYO UNIVERSITY   Center for Foreign Language Education and Research English   Adjunct Lecturer

    More details

  • 4 2016 - Present 
    RIKKYO UNIVERSITY   Center For English Discussion Class   English Discussion Lecturer

    More details

  • 4 2016 - Present 
    RIKKYO UNIVERSITY   Center For English Discussion Class   Lecturer in English Education

    More details

  • 4 2016 - 3 2020 
    RIKKYO UNIVERSITY   Center For English Discussion Class   Lecturer in English Education(English Discussion Lecturer until March 2020)

    More details

Papers

  • Effect‐Driven Sample Sizes in Second Language Instructed Vocabulary Acquisition Research Peer-reviewed

    Christopher Nicklin, Joseph P. Vitta

    The Modern Language Journal105 ( 1 ) 218 - 236   3 3 2021

    More details

    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    DOI: 10.1111/modl.12692

    researchmap

  • Outliers in L2 Research in Applied Linguistics: A Synthesis and Data Re-Analysis Peer-reviewed

    Christopher Nicklin, Luke Plonsky

    Annual Review of Applied Linguistics40   26 - 55   3 2020

    More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    <jats:title>Abstract</jats:title><jats:p>Data from self-paced reading (SPR) tasks are routinely checked for statistical outliers (Marsden, Thompson, &amp; Plonsky, 2018). Such data points can be handled in a variety of ways (e.g., trimming, data transformation), each of which may influence study results in a different manner. This two-phase study sought, first, to systematically review outlier handling techniques found in studies that involve SPR and, second, to re-analyze raw data from SPR tasks to understand the impact of those techniques. Toward these ends, in Phase I, a sample of 104 studies that employed SPR tasks was collected and coded for different outlier treatments. As found in Marsden et al. (2018), wide variability was observed across the sample in terms of selection of time and standard deviation (SD)-based boundaries for determining what constitutes a legitimate reading time (RT). In Phase II, the raw data from the SPR studies in Phase I were requested from the authors. Nineteen usable datasets were obtained and re-analyzed using data transformations, SD boundaries, trimming, and winsorizing, in order to test their relative effectiveness for normalizing SPR reaction time data. The results suggested that, in the vast majority of cases, logarithmic transformation circumvented the need for SD boundaries, which blindly eliminate or alter potentially legitimate data. The results also indicated that choice of SD boundary had little influence on the data and revealed no meaningful difference between trimming and winsorizing, implying that blindly removing data from SPR analyses might be unnecessary. Suggestions are provided for future research involving SPR data and the handling of outliers in second language (L2) research more generally.</jats:p>

    DOI: 10.1017/s0267190520000057

    researchmap