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Gifted and Talented Services for EFL Learners in China: A Step-by-Step Guide to Propensity Score Matching Analysis in R

Author

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  • Shifang Tang

    (Center for Research and Development in Dual Language and Literacy Acquisition (CRDLLA), Department of Educational Psychology, College of Education and Human Development, Texas A&M University, College Station, TX 77843, USA
    Department of Educational Psychology, College of Education and Human Development, Texas A&M University, College Station, TX 77843, USA)

  • Fuhui Tong

    (Center for Research and Development in Dual Language and Literacy Acquisition (CRDLLA), Department of Educational Psychology, College of Education and Human Development, Texas A&M University, College Station, TX 77843, USA
    Department of Educational Psychology, College of Education and Human Development, Texas A&M University, College Station, TX 77843, USA)

  • Xiuhong Lu

    (School of Foreign Languages, Hubei University of Technology, Wuhan 430068, China)

Abstract

We sought to quantify the effectiveness of a gifted and talented (GT) program, as was provided to university students who demonstrated a talent for learning English as a foreign language (EFL) in China. To do so, we used propensity score matching (PSM) techniques to analyze data collected from a tier-1 university where an English talent (ET) program was provided. Specifically, we provided (a) a step-by-step guide of PSM analysis using the R analytical package, (b) the codes for PSM analysis and visualization, and (c) the final analysis of baseline equivalence and treatment effect based on the matching sample. Collectively, the results of descriptive statistics, visualization, and baseline equivalence indicate that PSM is an effective matching technique for generating an unbiased counterfactual analysis. Moreover, the ET program yields a statistically significant, positive effect on ET students’ English language proficiency.

Suggested Citation

  • Shifang Tang & Fuhui Tong & Xiuhong Lu, 2019. "Gifted and Talented Services for EFL Learners in China: A Step-by-Step Guide to Propensity Score Matching Analysis in R," Data, MDPI, vol. 4(3), pages 1-15, August.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:119-:d:254569
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    References listed on IDEAS

    as
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    2. Wei Tao, 2019. "Less Classroom Hours of EFL Instruction to Non-English Majors in Chinese Universities Is It a Reason-Based Policy that Provokes No Response?," English Language Teaching, Canadian Center of Science and Education, vol. 12(5), pages 170-170, May.
    3. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
    4. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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