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Using bootstrapped quantile regression analysis for small sample research in applied linguistics: Some methodological

Larisa Nikitina1, Rohayati Paidi2, Fumitaka Furuoka3

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This study introduces bootstrapped quantile regression (BQR) for small sample research in applied linguistics. Findings show a significant link between Japanese learners

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Area of Science:

  • Applied Linguistics
  • Quantitative Research Methods
  • Sociolinguistics

Background:

  • Quantitative applied linguistics research frequently uses small sample sizes due to restricted settings.
  • Small sample sizes present methodological challenges in statistical analysis.
  • Bootstrapped quantile regression (BQR) offers a robust statistical method for addressing these challenges.

Purpose of the Study:

  • To provide a detailed explanation of the methodological and practical implications of bootstrapped quantile regression (BQR).
  • To demonstrate the utility of BQR in applied linguistics research with small sample sizes.
  • To examine the relationship between motivation, stereotypes, and attitudes in Japanese language learners.

Main Methods:

  • Employed bootstrapped quantile regression (BQR) analysis.
  • Utilized a small sample size (N = 27) of Japanese language learners.
  • Investigated relationships between integrative orientation, stereotypes about Japan, and attitudes toward Japan.

Main Results:

  • A statistically significant relationship was found between students' attitudes toward Japan and their integrative orientation.
  • Attitudes toward the target language country were the most consistent determinant of integrative orientation.
  • Bootstrapped quantile regression (BQR) proved effective for analyzing small sample data.

Conclusions:

  • Bootstrapped quantile regression (BQR) is a valuable statistical tool for applied linguistics research with small sample sizes.
  • Learner attitudes toward the target country significantly influence integrative motivation.
  • The BQR method is applicable to various human sciences research scenarios involving limited data.