{"title":"应用语言学研究文章中的统计使用水平:从1986年到2015年","authors":"Reza Khany, Khalil Tazik","doi":"10.1080/09296174.2017.1421498","DOIUrl":null,"url":null,"abstract":"Abstract The main objective of this study is to assess the levels of statistical use (basic, intermediate, and advanced) in Applied Linguistics research articles over the past three decades (from 1986 to 2015). The corpus included 4079 quantitative and mixed-methods studies published in ten prominent journals of Applied Linguistics. The articles were analysed and the statistical techniques used were aggregated by two current writers and four PhD students in TEFL. Results showed that descriptive statistics (40.04%) were by far the most commonly used technique followed by one-way ANOVA (14.91%), t-test (10.15%), and Pearson correlation (8.76%). Regarding the sophistication level of statistical use, about 78.77% (n = 4686) of the techniques were classified as basic, 14.49% (n = 862) as intermediate, and 6.74% (n = 401) as advanced. Clearly, most of the techniques were either basic or intermediate, with a significant higher percentage for the former. So, a person with basic knowledge of statistics could understand 69.03% of the papers published during 1986 to 2015. It is discussed that researchers should be updated on recent statistical knowledge if they wish to statistically comprehend research articles published in Applied Linguistics journals.","PeriodicalId":45514,"journal":{"name":"Journal of Quantitative Linguistics","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09296174.2017.1421498","citationCount":"18","resultStr":"{\"title\":\"Levels of Statistical Use in Applied Linguistics Research Articles: From 1986 to 2015\",\"authors\":\"Reza Khany, Khalil Tazik\",\"doi\":\"10.1080/09296174.2017.1421498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The main objective of this study is to assess the levels of statistical use (basic, intermediate, and advanced) in Applied Linguistics research articles over the past three decades (from 1986 to 2015). The corpus included 4079 quantitative and mixed-methods studies published in ten prominent journals of Applied Linguistics. The articles were analysed and the statistical techniques used were aggregated by two current writers and four PhD students in TEFL. Results showed that descriptive statistics (40.04%) were by far the most commonly used technique followed by one-way ANOVA (14.91%), t-test (10.15%), and Pearson correlation (8.76%). Regarding the sophistication level of statistical use, about 78.77% (n = 4686) of the techniques were classified as basic, 14.49% (n = 862) as intermediate, and 6.74% (n = 401) as advanced. Clearly, most of the techniques were either basic or intermediate, with a significant higher percentage for the former. So, a person with basic knowledge of statistics could understand 69.03% of the papers published during 1986 to 2015. It is discussed that researchers should be updated on recent statistical knowledge if they wish to statistically comprehend research articles published in Applied Linguistics journals.\",\"PeriodicalId\":45514,\"journal\":{\"name\":\"Journal of Quantitative Linguistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2019-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/09296174.2017.1421498\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Quantitative Linguistics\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1080/09296174.2017.1421498\",\"RegionNum\":2,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"LANGUAGE & LINGUISTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quantitative Linguistics","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1080/09296174.2017.1421498","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
Levels of Statistical Use in Applied Linguistics Research Articles: From 1986 to 2015
Abstract The main objective of this study is to assess the levels of statistical use (basic, intermediate, and advanced) in Applied Linguistics research articles over the past three decades (from 1986 to 2015). The corpus included 4079 quantitative and mixed-methods studies published in ten prominent journals of Applied Linguistics. The articles were analysed and the statistical techniques used were aggregated by two current writers and four PhD students in TEFL. Results showed that descriptive statistics (40.04%) were by far the most commonly used technique followed by one-way ANOVA (14.91%), t-test (10.15%), and Pearson correlation (8.76%). Regarding the sophistication level of statistical use, about 78.77% (n = 4686) of the techniques were classified as basic, 14.49% (n = 862) as intermediate, and 6.74% (n = 401) as advanced. Clearly, most of the techniques were either basic or intermediate, with a significant higher percentage for the former. So, a person with basic knowledge of statistics could understand 69.03% of the papers published during 1986 to 2015. It is discussed that researchers should be updated on recent statistical knowledge if they wish to statistically comprehend research articles published in Applied Linguistics journals.
期刊介绍:
The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.