Questionnaire survey is among the main research instruments for data collection, where participants are required to respond by selecting from existing options or writing out answers. With the availability of easy-to-use online survey platforms that enhancing research efficiency in terms of time, cost, and access to participants, scholars have brought to bear a large number of questionnaire survey-based approaches to researching English Medium Instruction (EMI), putting stakeholders’ perspectives under the microscope. This chapter discusses how to plan and conduct a questionnaire survey study in EMI. Starting with the definition of questionnaire survey, this chapter centers on some key issues related to its use in EMI research, such as the selection of a sample frame, as well as design and distribution of questionnaire design. These issues are then exemplified in the subsequent case study of the acceptability and usefulness of collaborative writing activities in promoting university students’ online engagement.

This chapter elaborates on ways of carrying out a comprehensive review based on searching the research literature systematically in the context of English Medium Instruction (EMI). Teaching content subjects in English is now a growing phenomenon around the world. Many researchers, teacher educators and teachers want to read and understand the latest findings of studies on EMI. A systematic review, which ‘systematically’ locates all relevant studies, evaluates these studies’ findings and synthesizes the findings that have implications for teaching and learning in EMI, can provide numerous benefits to researchers and writers. First, it draws readers’ attention to different findings about the same issues in the literature, such as the use of native languages (L1) in EMI classrooms, translanguaging pedagogy (i.e. refer to a pedagogical process of utilizing more than one language in a classroom) and learning in EMI. It can also indicate whether a consensus exists on effective ways of teaching and learning in EMI classrooms. A well-structured systematic review in which writers follow existing review protocols reduces the potential bias inherent in synthesizing research. For example, some of the standard procedures that are agreed on in the research community (e.g., PRISMA guidelines) include review teams having diversified research expertise, inter-rater reliability checking, rigorous screening procedures, data extraction, and assessment of the quality of studies. These procedures can largely eliminate bias and offer the EMI research community authoritative information about gaps in the research that need to be filled. By examining the evidence in the research, they can highlight conflicting views on the same teaching issues in the context of EMI. In this chapter, we use a case study that explores the teaching and learning issues encountered by teachers and students in EMI science classrooms, introducing different approaches to carrying out research reviews, particularly reviews that use quantitative approaches, such as systematic quantitative reviews and meta-analyses. We outline the key steps when conducting a systematic review: (1) formulating the topic; (2) locating and screening the literature; (3) evaluating the data; (4) extracting the data and assessing the study quality; (5) analyzing the data; (6) interpreting the results; (7) presenting the results; and (8) writing up the review. The implications and limitations of writing a systematic review in the EMI context are discussed.

This chapter explores the use of one-way between-groups multivariate analysis of variance (MANOVA) to investigate the effects of gender on perceived English language ability challenges in an English Medium Instruction (EMI) university in Hong Kong. Contrasting with ANOVA which evaluates mean differences on one dependent variable, MANOVA evaluates if there are mean differences on two or more dependent variables. The two dependent variables used were perceived writing challenges and perceived speaking challenges. The independent variable was gender. We checked for univariate and multivariate outliers, homogeneity of variance-covariance matrices and normality and found no deviation from the standard parameters. The results show a significant difference between males and females in terms of the combined dependent variables, F (2, 75) = 3.444, p = 0.037, Wilks’ Lambda = 0.913 and partial eta squared = 0.087. When the dependent variable is analysed separately with reference to the Bonferroni adjusted alpha level of 0.025, a significant difference is only found between males and females in terms of the perceived speaking challenges, F (1, 75) = 6.658, p = 0.012, partial eta squared = 0.084. A closer examination of the mean score shows that females (M = 3.042, SD = 0.34) perceived speaking English to be more of a challenge than males did (M = 2.71, SD = 0.55). The findings are discussed in relation to teaching and learning in an EMI university.

Grammatical complexity has been considered as an important research construct closely related to second language (L2) writing development. Although theoretical models were developed to demonstrate what grammatical complexity is, few studies have been conducted to analyze how this construct is represented from an empirical perspective. This chapter presents a data-driven investigation on the representation of grammatical complexity with an exploratory factor analysis (EFA). The investigation is based on (1) a corpus of scientific research reports written by Hong Kong students in an English Medium Instruction (EMI) scientific English course, and (2) an EFA, which is a statistical approach to uncover an underlying structure of a phenomenon, which fits this research purpose well. A corpus has been built with the science writing from EMI undergraduate students in Hong Kong. After corpus cleaning, Second Language Syntactic Complexity Analyzer – a software – was applied to output the values of fourteen effective measures of grammatical complexity for running the EFA in SPSS, and a step-by-step instruction was described in the chapter. The final model includes three latent factors: clausal (subordination) complexity, nominal phrasal complexity, and coordinate phrasal complexity. This EFA model is generally consistent with the argument of investigating grammatical complexity as a multidimensional construct (Biber et al., 2011; Norris & Ortega, 2009). In the end, we highlighted the research and pedagogical implications that readers should pay attention to when the EFA is applied in other EMI contexts in the future.

Covariance-based SEM (CB-SEM) has become one of the most prominent statistical analysis techniques in understanding latent phenomena such as students and teachers’ perceptions, attitudes, or intentions and their influence on learning or teaching outcomes. This chapter introduces an alternative technique for SEM, variance-based partial least squares SEM (PLS-SEM), which has multiple advantages over CB-SEM in several situations commonly encountered in social sciences research. A case study in the English Medium Instruction (EMI) context is also demonstrated as an example to facilitate comprehension of the method. The chapter concludes with a discussion of potential applications for other EMI-related contexts and lines of inquiry.

Despite the enduring popularity of path analysis, there has been limited research in the context of English Medium of Instruction (EMI) to illustrate established theories. Moreover, researchers have yet to incorporate statistical data to refine the theoretical models and better elucidate the causal relationships between various factors that potentially influence students’ academic achievement. To fill this gap, this study aims to develop and analyze a well-fitted model that could account for contingent links between variables that directly and indirectly affect EMI students’ academic achievement in science. Drawing on survey data from eight EMI secondary schools in Hong Kong, the current study identified interplayed roles of students’ English proficiency, language use in science classroom, self-perceived English difficulty in the science classroom, and self-concept on science learning on science achievement by using path analysis – one of the structural equation modeling (SEM) models, which is also illustrated in Chapter 5 of the book.