This chapter expands the discussion of memetic quotation to cases, including cases of what we call ‘dialogue labelling’, which do not feature explicit reporting verbs, but rely on depiction of interlocutors, interpretation of embodied behaviour, and sometimes quotation marks to signal the embedded Discourse Spaces, and viewpoints exchanged, in them. We include both one-off dialogue labelling examples and Image Macro memes (such as Anakin and Padmé) in our analysis. We also analyse a range of discourse patterns building on the basic Me/Also Me pattern, and round off with the Repeat after Me meme.

This chapter explores key concepts of inferential statistics, essential for drawing conclusions from data and making inferences. It explains the purpose and significance of inferential statistics in research, covering foundational concepts such as random sampling, probability distributions, and the central limit theorem, which are critical tools for statistical inference. The chapter also guides you through point and interval estimation, with a focus on calculating confidence intervals and understanding the differences between one-tailed and two-tailed intervals. Additionally, the chapter discusses hypothesis testing, explaining the difference between one-tailed and two-tailed tests, along with the concepts of Type I and Type II errors. Practical advice is provided on minimizing these errors to enhance the accuracy of statistical inferences. Examples throughout the chapter illustrate these concepts, making them more accessible and easier to apply.

This chapter discusses the principles of sampling in qualitative research, starting with an overview of various sampling methods and their applications. You will gain a clear understanding of how to determine the appropriate sample size for a qualitative study and the factors that influence this decision. The chapter explores purposive sampling techniques, such as maximum variation sampling, theoretical sampling, extreme case sampling, homogeneous sampling, criterion sampling, and confirming/disconfirming cases. You will learn how to select the most suitable sampling strategy based on your research question and objectives, while considering the strengths and limitations of each technique. The chapter also addresses the important concept of generalizability in qualitative research, explaining how it differs from generalizability in quantitative research. By the end, you will have learned how to make informed decisions about sampling strategies, understand their impact on research findings, and be able to confidently conduct qualitative studies that align with your research goals.

This chapter covers essential inferential statistical tests used in applied linguistics research, focusing on their purpose, assumptions, and interpretation. It classifies the tests into parametric and nonparametric categories, starting with parametric tests. Key tests such as t-tests (independent and paired samples), ANOVAs (one-way, two-way, and repeated measures), and regression analyses (simple linear, multiple, and logistic) are explained in detail, highlighting their importance in comparing group means, analyzing variance, and predicting outcomes. The chapter also covers assumptions such as normality, homogeneity of variance, and linearity, explaining how to assess them and handle violations. Hypothetical scenarios are used to illustrate their application to real-world research questions. Step-by-step instructions for using SPSS to run these tests are provided, along with guidance on interpreting outputs including p-values, effect sizes, and regression coefficients. By the end, you will understand when and how to use parametric tests and analyze data effectively in SPSS.

This chapter provides an overview of different types of research, aiming to provide a comprehensive framework for understanding the various ways research types can be conceptualized. You will learn the key characteristics and features of each research type, and understand the differences between positivist, postpositivist, and interpretivist research, including their advantages and limitations. Additionally, you will learn about the key differences between quantitative, qualitative, and mixed methods research methodologies, as well as their respective strengths and weaknesses. The chapter also defines the main types of research designs – experimental, correlational, and descriptive – highlighting their strengths and weaknesses, and clarifies the distinctions between basic and applied research. It will also explore the differences between cross-sectional, longitudinal, and time-series research designs. By the end of this chapter, you will appreciate the different types of research available to you, which will help you identify the most appropriate research type for your research questions and objectives.

This chapter ties together the various strands of the book, and reflects on the emerging grammar of memes. We revisit some of the questions first asked in the opening chapter, about why linguists should study memes, or how the specific kind of multimodality in the memes we studied differs from other multimodal genres, and we think through the way the space of a meme is used in the types of memes we studied. Finally, we summarize why we think memes are an important object of study.

This chapter expands on traditional parametric and nonparametric methods by introducing generalized linear models (GLMs) and generalized linear mixed models (GLMMs), which broaden statistical analysis in applied linguistics research. GLMMs, for example, enhance traditional methods by incorporating both fixed and random effects, allowing researchers to account for predictors and grouping factors like subjects or items. This makes GLMMs particularly useful for analyzing complex, hierarchical data in linguistics studies. The chapter introduces linear mixed models (LMMs) before diving into GLMMs, highlighting their advantages in handling complex linguistic data. Practical examples and step-by-step instructions for conducting GLM and GLMM analyses using SPSS are provided, ensuring hands-on experience. Additionally, the chapter briefly overviews advanced multivariate tests, such as factor analysis, path analysis, structural equation modeling (SEM), and introduces Bayesian statistics. While not explored in depth, these methods are presented to underscore their significance in applied linguistics research and encourage their use when appropriate.

This chapter turns to labelling memes, where some images may develop into full-blown Image Macros, while others remain non-entrenched. Here, the textual component is different from both when-memes and from the typical Image Macro memes. In typical labelling memes, parts of a depicted scene are labelled with words or phrases which do not describe anything in the image, but instead collectively call up a different frame. Well-known examples discussed include the Is This a Pigeon? meme, and the Distracted Boyfriend meme (DBM), showing a man turning over to admire an attractive passing woman (dressed in red), while the woman (in blue) whose hand he’s holding looks on indignantly. This scene of a change in attention and preference – a choice for a new and attractive opportunity – gets to be applied to unrelated choices and new preferences. Labelling itself can sometimes be visual again. Overall, we stress the constructional properties of DBM – with strong argument structure-like properties – alongside the role of embodied features (emotions and attentions expressed in facial expressions and posture) and the figurative, similative meaning often arrived at compositionally.

This chapter provides a comprehensive overview of the ethical principles and codes of conduct that every researcher in applied linguistics should follow. It emphasizes the importance of ethics in research, ensuring that all studies are conducted with integrity and respect for human rights and welfare. A central focus is placed on informed consent, particularly its role and importance when conducting research with human participants. The chapter discusses various ethical considerations related to data collection, analysis, management, and sharing, while also addressing the responsible reporting of research results. Additionally, unique ethical challenges are explored, especially those that arise when conducting research with children. After reading this chapter, you will be well-prepared to approach your work with a heightened sense of ethical responsibility, ensuring your research is both impactful and respectful of the individuals and communities being studied.

This chapters reflects on the economy of expression required in memes, which encourages Meme Makers to incorporate fictive Discourse Spaces to metonymically call up experiences. It surveys cases of memetic quotation in cases that are close to recognizable existing linguistic constructions involving verbs such as say, tell and be like, but adding further constructional specifications in their memetic applications, thereby yielding very specific meanings. Forms analysed include Said No One Ever, It’ll Be Fun They Said, And Then He Said X, What If I Told You and Be Like; the latter in particular sometimes combines with very complex content being ‘quoted’ or demonstrated, as the chapter illustrates.

This chapter explores the various steps involved in conducting research, including defining the research problem, formulating research questions, selecting an appropriate research design, choosing participants, employing data collection methods, processing and analyzing data, interpreting results, and writing research reports. Understanding these steps is important as it provides a structured framework for approaching research, making the entire process less daunting. Special emphasis is placed on determining the appropriate research design and selecting participants. Additionally, the chapter introduces various data collection methods, techniques, and tools used by applied linguistics researchers. The importance of data processing and analysis will also be highlighted. Moreover, you will explore how to develop purpose statements for both qualitative and quantitative research and learn to identify the strengths of a well-conducted study.

This chapter consider advertising strategies based on, or inspired by, meme genres. Our most interesting examples don’t so much directly borrow a fully-formed, recognizable meme to reuse it in an ad (though this, too, is sometimes done). Instead, really successful memetically inspired ads partly borrow from existing meme codes, such as when-memes or the ‘Sections of’ meme, and adapt these creatively to suit the persuasive goals identified. This suggests that aspects of the grammar of memes are affecting other forms of communication.

This chapter introduces the distinction between entrenched images or Image Macros (IMs) and Non-Entrenched Images (NEIs), and focuses most of its discussion on examples involving IMs that feature the characteristic ‘Top Text’ (TT) and ‘Bottom Text’ (BT), such as the One Does Not Simply and Good Girl Gina memes (ODNS and GGG). It shows how these IM memes allow Meme Makers to categorize experiences very quickly, efficiently and (if successful) humorously, adding further examples to such categories as ‘futile undertakings that are impossible to achieve’ (ODNS) or ‘virtuous behaviour of highly considerate women’ (GGG), thanks in large part to the frames evoked visually. It also discusses aspects of the construction grammar approach to language, as applicable to these meme constructions, including specific constructional properties of GGG memes and the constructional networks they fit into.