Reporting and Interpreting Quantitative Research Findings: What Gets Reported and Recommendations for the Field

2015/05/21 by Jenifer Larson‐Hall, Luke Plonsky · 202 citations
Arts and Humanities · Computer Science · Mathematics · Psychology · #Applied psychology #Artificial intelligence #Computer science #Context (archaeology) #Data science #Descriptive statistics #EFL/ESL Teaching and Learning #Field (mathematics) #Mathematics #Mindset #Natural Language Processing Techniques #Psychology #Raw data #Reliability (semiconductor) #Second Language Acquisition and Learning #Set (abstract data type) #Statistics

paper · doi:10.1111/lang.12115

published in Language Learning 65(S1), 127-159 (Wiley)

openalex publication_date 2015/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

This paper presents a set of guidelines for reporting on five types of quantitative data issues: (1) Descriptive statistics, (2) Effect sizes and confidence intervals, (3) Instrument reliability, (4) Visual displays of data, and (5) Raw data. Our recommendations are derived mainly from various professional sources related to L2 research but motivated by results from investigations into how well the field as a whole is following these guidelines for best methodological practices, and illustrated by L2 examples. Although recent surveys of L2 reporting practices have found that more researchers are including important data such as effect sizes, confidence intervals, reliability coefficients, research questions, a priori alpha levels, graphics, and so forth in their research reports, we call for further improvement so that research findings may build upon each other and lend themselves to meta‐analyses and a mindset that sees each research project in the context of a coherent whole.

Citations

• The Effectiveness of Corrective Feedback in SLA: A Meta‐Analysis
• How Big Is “Big”? Interpreting Effect Sizes in L2 Research

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