Given the various differences between learners, teachers, and instructional methods in English Medium Instruction (EMI), a common purpose of EMI research involves investigating the potential variation between groups. The analysis of variance (ANOVA) test is a common technique used to address such a research aim, as it tests whether there are significant differences between the means of different groups. This chapter introduces the ANOVA test to readers by highlighting how it has been used in research within the field of EMI. To illustrate how different forms of the ANOVA test can be employed, the chapter then provides two case studies: (1) the use of a one-way between subjects ANOVA to examine the differences between three groups of students with respect to their perceptions of the role of English in their academic and career goals and comprehension level of EMI courses; and (2) the use of a mixed ANOVA in a quasi-experimental study that examined the differences in pre- and posttest writing performance and academic motivation of two groups provided with different types of feedback. Each of the case studies summarizes the assumptions required for the use of ANOVA, discusses potential problems that may face EMI researchers, and introduces alternative procedures.

This chapter examines the conceptualization and measurement of contact phenomena in the context of bilingualism across various languages. The goal of the chapter is to account for various phonetic contact phenomena in sociolinguistic analysis, as well as providing context for elaborating on quantitative methodologies in sociophonetic contact linguistics. More specifically, the chapter provides a detailed account of global phenomena in modern natural speech contexts, as well as an up-to-date examination of quantitative methods in the field of sociolinguistics. The first section provides a background of theoretical concepts important to the understanding of sociophonetic contact in the formation of sound systems. The following sections focus on several key social factors that play a major part in the sociolinguistic approach to bilingual phonetics and phonology, including language dominance and age of acquisition at the segmental and the suprasegmental levels, as well as topics of language attitudes and perception, and typical quantitative methods used in sociolinguistics.

Analysis of Variance (ANOVA) is a commonly used test in public administration research when the dependent variable is measured at the interval level and the independent variable is measured at the nominal level with more than two categories.The chapter covers when and how to use ANOVA along with the assumptions of the ANOVA test.Conducting ANOVA in the R Commander and interpreting the output and statistical significance of the test are the main foci of the chapter.

Chapter 4 presents methods of analysis of variance (ANOVA). Based on the GLM, it is shown that ANOVA differs from regression only in the nature of the independent variables. Whereas in regression, the independent variables are usually metric, in ANOVA, they are categorical. In univariate ANOVA, there is one factor (that is, independent variable). In factorial ANOVA, there are multiple factors. In multivariate ANOVA (MANOVA), there are multiple dependent variables. In addition, this chapter discusses ANOVA for repeated observations and ANOVA with metric covariates.

The correlation test is a standard procedure for deciding if two variables are linearly related. This chapter discusses a test for independence that avoids the linearity assumption. The basic idea is the following. If two variables are dependent, then changing the value of one them, say c, changes the distribution of the other. Therefore, if samples are collected for fixed value of c, and additional samples are collected for a different value of c, and so on for different values of c, then a dependence implies that the distributions for different c’s should differ. It follows that deciding that some aspect of the distributions depend on c is equivalent to deciding that the variables are dependent. A special case of this approach is the t-test, which tests if two populations have identical means. Generalizing this test to more than two populations leads to Analysis of Variance (ANOVA), which is the topic of this chapter. ANOVA is a method for testing if two or more populations have the same means. In weather and climate studies, ANOVA is used most often to quantify the predictability of an ensemble forecast, hence this framing is discussed extensively in this chapter.