حسام الدین قنبر

Second language (L2) researchers often rely on quantitative methods and measurement instruments like questionnaires and scales to explore latent constructs. They usually borrow sophisticated statistical techniques from disciplines like psychology and education to conduct quantitative analyses in L2 research. However, there are growing concerns about the inappropriate use and reporting practices of such statistical procedures like exploratory factor analysis (EFA). This study aimed to describe and evaluate the methodological issues in EFA research practices, examine the relationship between study features and outcomes, and improve future L2 research practices. This study presented the results of a methodological synthesis study on exploratory factor analysis use published in five universally reputable psychology-related journals since 2000. Specifically, the researchers identified 93 EFA studies and developed a coding scheme of key EFA considerations to analyze how EFA was used and reported in these articles. The article discussed the several results and provides essential recommendations for L2 researchers intending to employ EFA in their works.

This research synthesized the methodological issues of studies published in English for Specific Purposes from its debut in 1980 up to 2020, from both an overall perspective and also periodically in four decades. To this aim, we examined 617 empirical articles with regard to two themes: (a) research methodology, and (b) data sources. More specifically, we probed into the methodological designs, how they were integrated and the points of departure in qualitative research. Our results brought to the fore that the majority of ESP articles were framed as combined methods studies, most frequently concurrent designs, dominated by qualitative data and data collection. As well, about half of qualitative studies did not specify their approach and the majority of quantitative works utilized descriptive statistics and combinations of different tests. Pertaining to data sources, text samples as well as an amalgamation of different data collection techniques were the most prominent ones. Based on these results and discussion, we provided empirically–grounded recommendations and suggestions for promoting methodological transparency and rigor for the prospective studies for the field of ESP and applied linguistics in general.  

Despite being a popular topic in language testing, cognitive load has not received enough attention in vocabulary test items. The purpose of the current study was to scrutinize the cognitive load and vocabulary test items’ differences, examinees’ reaction times, and perceived difficulty. To this end, 150 students were selected using cluster/convenience-sampling, and took the Cambridge Placement Test (CPT) and Vocabulary Size Test (VST; Nation & Beglar, 2007). After uploading the vocabulary-size test’s items in PsychoPy software, there was a behavioral stage to measure students’ reaction times and correct responses. Out of these 150 high school students, a total of 60 (20 from each proficiency level of elementary/intermediate/advanced groups) were selected. In this quantitative study, all 60 students were interviewed to determine their perceived difficulty of the international VST items and their item’s difficulty-index. The data were analyzed quantitatively via simple regression and qualitatively through the examination of the students’ perceived difficulty. The results and interview findings revealed a significant connection between cognitive load/reaction time, difficulty estimate, and perceived difficulty at intermediate level. In contrast, at elementary and advanced levels, these variables could not predict the cognitive load. The findings can help to test, course, and syllabus designers by educating them on the significance of cognitive load theory so that they can base their exam designs on its premises and alleviate students' increased cognitive-workload.

As conceptual models of language learning, use, and processing mature, it is both natural and necessary for the statistical models we apply to follow suit. One statistical approach with great potential in the field Applied Linguistics and L2 studies is structural equation modeling (SEM). SEM is introduced in this paper as a powerful and highly flexible family of analyses. In doing so, the paper outlines (a) the types of variables and possible modeled relationships that SEM is equipped to address and (b) statistical considerations for applying SEM in L2 research, and (c) a number of additional and key considerations for those interested in delving deeper into SEM (e.g., goodness of fit indices, model modification procedures, etc.). This paper also describes the potential of SEM to contribute to construct validation (e.g., convergent and discriminant validity). Throughout the paper, a plethora of examples pertaining to applications of SEM in L2 research are provided.

Structural equation modeling (SEM), as a flexible and versatile multivariate statistical technique, has been growingly used since its introduction in the 1970s. This article presents a methodological synthesis of the characteristics of the use of SEM in L2 research by examining the reporting practices in light of the current SEM literature to eventually provide some empirically grounded recommendations for future research. A total of 722 instances of SEM found in 145 empirical reports published in 16 leading L2 journals across two periods of 1981-2008 and 2009-2020 were systematically reviewed. Each study was coded for a wide range of analytic and reporting practices. The results indicate that despite the growing popularity of SEM in L2 research, there was a wide variation and inconsistency in its uses and reports within and across the two periods in regard to the underlying assumptions, variables and models, model specification and estimation, and fit statistics. Drawing on the current SEM literature, we will discuss the findings and research implications for future use and reporting of SEM in L2 research.