Sampling, Generalizability, and Reliability

Sampling, Generalizability, and Reliability

ISBN13: 9798369326039|ISBN13 Softcover: 9798369350485|EISBN13: 9798369326046
DOI: 10.4018/979-8-3693-2603-9.ch008
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MLA

Tin, Nghi Tran, and Hung Phu Bui. "Sampling, Generalizability, and Reliability." Applied Linguistics and Language Education Research Methods: Fundamentals and Innovations, edited by Hung Phu Bui, IGI Global, 2024, pp. 103-121. https://doi.org/10.4018/979-8-3693-2603-9.ch008

APA

Tin, N. T. & Bui, H. P. (2024). Sampling, Generalizability, and Reliability. In H. Bui (Ed.), Applied Linguistics and Language Education Research Methods: Fundamentals and Innovations (pp. 103-121). IGI Global. https://doi.org/10.4018/979-8-3693-2603-9.ch008

Chicago

Tin, Nghi Tran, and Hung Phu Bui. "Sampling, Generalizability, and Reliability." In Applied Linguistics and Language Education Research Methods: Fundamentals and Innovations, edited by Hung Phu Bui, 103-121. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-2603-9.ch008

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Abstract

This chapter presents the intricate process of sampling within the context of applied linguistics and language education research, providing a comprehensive overview of various sampling techniques and their critical roles in ensuring the validity and reliability of research outcomes. It thoroughly examines both probability and nonprobability sampling methods, outlining their respective advantages, limitations, and suitable applications. Through detailed discussion, the chapter highlights the importance of careful sample selection to accurately reflect the population of interest, thus ensuring the generalizability of the research findings. Key considerations such as sample size determination, minimizing sampling bias, and ethical issues related to sampling are explored, offering valuable insights for researchers. By integrating these considerations with practical examples and guidelines, this chapter aims to equip researchers in applied linguistics and language education with the knowledge to design robust studies that can contribute significantly to the field.

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