Technological advancements over the past two decades have resulted in an increasing number of changes, especially involving the ways in which applied linguists conduct their work. Today, a sizable amount of the research done by linguists is conducted digitally or in online settings via the internet. As such, the edited volume Digital and Internet-Based Research Methods in Applied Linguistics is devoted to exploring a range of approaches, methods, tools, and sites that are frequently leveraged by contemporary scholars. This chapter introduces readers to the edited volume. It opens by further describing the increasingly digital, online nature of scientific inquiry and research methods in applied linguistics, commenting on related affordances and challenges. This is followed by an introduction to the aims of the volume, a preview of the chapters, and finally, an overview of the internal organization of the chapters.

Synthesis of research evidence drives an academic field forward through reviewing existing research, suggesting future research directions, and appraising methodological rigour of published research. Research synthesis refers to the use of a structured and principled approach to identifying and re-analysing/interpreting published evidence. In this chapter, I introduce research synthesis and its relationship with online/internet-based methodology in applied linguistics. I provide a list of research questions that can be answered by different types of research synthesis. Focusing on implementation, I introduce the Synthesis Methods and Reporting Tool (SMART) that outlines the key stages of conducting a research synthesis. I illustrate these methodological stages through five published research syntheses in applied linguistics. This is followed by a discussion of relevant ethical and methodological issues, as well as how future research syntheses can contribute to applied linguistics research as a whole.

Digital discourse is a routine part of social interaction and can be used as a source of data to answer a variety of research questions. This chapter illustrates a design framework that can be used to generate and evaluate research studies using digital discourse data. Drawing on Paulus and Wise (2019), I deconstruct several studies to illustrate how key research design decisions can be used to assess methodological alignment. These decisions include selecting an object of research interest, identifying the explicit or implied assumptions about the nature and function of digital discourse, defining the technical and social context characteristics of the platforms, creating research questions, selecting a methodological approach, making ethical data bounding and extraction decisions, and choosing data analysis methods. Ethical considerations, challenges and issues, and future directions for the development of digital discourse approaches are also discussed.

The notion of what constitutes the language classroom has developed over time, to the point where applied linguists have increasingly investigated the impact of out-of-class exposure on the traditional bricks-and-mortar classroom. Of particular interest is the digital classroom, and traditional classroom ethnographies have also made this digital migration. Building on these recent developments, in this chapter we discuss how ethnographic tools have to be modified across digital spaces to better understand the complexities surrounding evolving classroom cultures and other applied linguistics contexts. We draw on insights from one particular brand of ethnography, netnography. Specifically, we describe the netnography research process, provide examples of netnographies, and discuss ethical dilemmas that often emerge in netnographic research. The chapter closes with suggestions for the future, in light of the growing influence of generative artificial intelligence on language education and the need for research on underrepresented populations.

Researching online courses and education platforms is key as course developers confront challenges related to the rapid pace of technological change as well as students’ continually evolving needs and expectations related to learning in online environments. This chapter offers specific examples on how online courses and education platforms have been researched within applied linguistics, including common types of courses or platforms that have been studied, frequently asked research questions, and how studies are conducted. It also explores challenges and ethical considerations for conducting research in these environments, summarizes major findings and implications from past studies, and concludes with possible future research directions.

This chapter explores social media as a site for applied linguistics research and the methods that are used for such studies. To date, the majority of social media studies have examined platforms such as Facebook, YouTube, and Twitter/X. Increasingly, however, researchers are also exploring platforms such as Instagram, WhatsApp, and Tik Tok, among others. Topics for study include how language is used — including translanguaging — and how users create their cultural and linguistic identities via different platforms and how learners can develop and expand their language and literacy. We outline the different ways data are captured and the various methods used for analysis (e.g., meta-analysis, discourse analysis, ethnographic approaches). We exemplify these, and the different theoretical frameworks used by describing five different studies. We also provide commentary on some of the ethical issues and challenges related to social media research. We conclude by providing suggestions for future research.

This chapter provides an overview of research methods commonly employed in studies using digital games played both online and offline as tools to support second language (L2) learning and development. Researchers and practitioners have praised online games as rich sources of meaningful and engaging L2 input and interaction that have been shown to have positive effects on learning gains. This chapter provides a methodological review of the games used, common types of research questions asked, the dependent variables targeted, learner populations studied, and procedures followed within this targeted domain of computer-assisted language learning (CALL) research. The chapter concludes with a summary of the methodological challenges and issues in this domain while also providing recommendations for future research.

Modern mobile applications (apps) have profoundly influenced not only social communication and information dissemination but also language learning. These applications, such as Babbel, Busuu, and Duolingo, are increasingly accepted by language learners and have gained attention from researchers exploring their educational contributions. This chapter provides an overview of research on mobile applications as crucial sites for language acquisition, summarizing current research issues and insights of the research methods. It offers practical advice for researchers entering this field, including reviews of exemplary studies that highlight essential methodologies and findings. Additionally, the chapter addresses ethical considerations and research integrity in studying mobile applications. It concludes by examining challenges in research methodologies and suggests future directions, enriching the broader dialogue on how mobile applications can advance language education.

Recent years have seen a rapid growth in the range of web-based corpora and the functionality of web-based corpus platforms available for linguistic research across a wide number of world languages, such that these tools are a key consideration when discussing internet-based research methods. This chapter discusses the use of such resources in applied linguistics research, adopting a focus on corpora or corpus platforms with web-based interfaces that enable linguistic analysis, rather than resources which are hosted online primarily for offline use. After framing this definition, the authors outline commonly used web-based corpora and platforms, as well as research questions and considerations in corpus selection. Five studies which use prominent tools in innovative ways are reviewed. Ethical considerations and other challenges are discussed, and the chapter concludes by projecting future directions in the use of web-based corpora and web-based corpus platforms for research in applied linguistics.

This chapter explores the use of online surveys and questionnaires as one of the most frequent and versatile methods of data collection in applied linguistics research. It describes their multifaceted applications to study phenomena ranging from language attitudes to the measurement of language proficiency. The chapter emphasizes strategic design considerations and implementation procedures, highlighting best practices and common pitfalls in the construction of surveys and analysis of survey data. Additionally, the chapter examines important challenges associated with surveys, such as participant recruitment, data integrity, and ethics, making recommendations and highlighting important future directions in research on this method. By drawing from contemporary literature and studies with an exemplary use of online surveys in diverse contexts, this chapter will equip researchers and practitioners in applied linguistics with valuable knowledge to craft effective, meaningful, and ethically responsible online surveys and questionnaires.

This chapter explores online interviews as a qualitative data collection method in applied linguistics. It begins by defining online interviews as a method, discussing their increasing adoption due to their flexibility, accessibility, and cost-effectiveness. The chapter then examines research areas that could benefit from the use of online interviews and provides some specific research questions. The implementation section explores different online interview modes and discusses key decisions made before, during, and after conducting online interviews. Next, the chapter presents five exemplary studies, illustrating how online interviews have been used in diverse research contexts. Ethical considerations, such as data security and informed consent, are discussed alongside challenges like financial support and power dynamics between researchers and participants. The chapter concludes by outlining future research directions, emphasizing multimodal approaches, researcher reflexivity, and ethical advancements.

This chapter discusses screen capture (i.e., the recording of users’ on-screen activities) as a data collection method in online-based research in applied linguistics. After providing background information on the research contexts in which both still image and video screen captures have been used, we offer an overview of the types of phenomena previously studied through analyses of screen captures, the implementation of this data collection method in existing research, and the most commonly adopted analytical approaches (e.g., conversation analysis, thematic coding). Afterwards, we showcase exemplary empirical research using screen captures, including studies exploring digital discourse or written/oral interactions in non-educational virtual locales (e.g., social media, messaging apps) as well as within computer-assisted language learning settings. Following a discussion of the ethical issues and challenges associated with this data collection method, we propose future research directions (e.g., human-AI interaction, linguistic practices, and communication in virtual realities).

Eye-tracking allows researchers to record eye movements while processing visual stimuli (text, images, video), providing a direct measure of attention and processing effort. It has gained popularity in applied linguistics and has quickly become a main data collection method in our field. While current eye-tracking research is conducted in laboratory settings with specialized hardware, technological advancements are leading to improvements in web-based eye-trackers. This chapter provides an introduction to the technique and an overview of how eye-tracking has been used in applied linguistics research, identifying commonly researched topics and questions. It also provides a discussion of main steps in the implementation of eye-tracking studies, highlighting key considerations in data collection and data analysis procedures. The chapter also showcases key eye-tracking studies that have explored a range of topics in applied linguistics, including language learning, language assessment, and bilingualism. Finally, it discusses some of the main methodological challenges in the use of this data collection method and identifies directions for future research.

Traditionally the purview of psycholinguistic experiments conducted in controlled laboratory environments, researchers can now deploy response time tasks such as lexical decision tasks or self-paced reading paradigms into experiments run on the internet. Data from such tasks are of interest to researchers in applied linguistics, as these data provide insight into the cognitive representation of language (i.e., the mental lexicon) of first, second, and additional users of different languages. This chapter first explains what response time data are and how they are measured, with a particular focus on lexical decision tasks as a core example. Common research questions associated with response time data are also provided. The chapter then reviews how such tasks are implemented online, and afterwards, five example studies are reviewed. Finally, the chapter discusses ethical and practical challenges and considerations for future work in this area.

Keystroke logging serves as a non-intrusive method for capturing the temporal dynamics of text production, making it a valuable tool in applied linguistics research. In this chapter, we introduce the technical and practical details involved in implementing web-based keystroke logging for data collection using FlexKeyLogger as an example. We also present some commonly used keystroke measures to operationalize writing behaviors and discuss how these measures align with cognitive processes underlying text composition. We then summarize and review recent studies that leverage keystroke logging to investigate diverse topics in applied linguistics, demonstrating its versatility and research potential. Finally, we address key ethical considerations, challenges associated with keystroke data collection and interpretation, and potential future directions.

This chapter introduces (web-based) automated text analyzers (ATAs) in applied linguistics research. It begins by briefly surveying key strands of research involving ATAs and outlining three types of text analysis alongside questions commonly addressed by these tools. The core of this chapter presents a conceptual framework for the typology and implementation of ATAs, structured around four continuum dimensions: (1) pre-built corpus platform vs. custom corpus platform, (2) developer-oriented vs. user-oriented, (3) focused vs. versatile, and (4) descriptive vs. interpretive. The framework is illustrated through five practical studies showcasing the application of various types of ATAs in applied linguistics research, including L2SCA, Coh-Metrix, Sketch Engine, #LancsBox, Voyant Tools, and Wmatrix3. The chapter also discusses ethical considerations and methodological challenges associated with ATA use. It concludes by outlining future directions for ATA development and research, including improving annotation accuracy, enhancing qualitative interpretability, and expanding analytical capacities across languages and modalities.

This chapter discusses how generative artificial intelligence (GenAI) tools, particularly large language models (LLMs) like ChatGPT, are emerging as powerful web-based tools for research purposes, such as data analysis, in applied linguistics research. While much attention has focused on pedagogical applications, we review how GenAI can be leveraged to support various stages of the research processes in empirical studies, such as instrument design, automated coding, text annotation, and qualitative data analysis. We address key concerns around validity and reliability as well as ethical considerations related to transparency, data privacy, and potential bias in AI-generated output. Given that GenAI is in the early stage of research application, we describe its current capacities and limitations based on emerging empirical research and propose promising directions for future studies.

JASP is one of the most user-friendly open-source software programs for data analysis available today, and it has recently also become available online through rollApp. This chapter provides a hands-on introduction to quantitative data analysis using this web-based version of JASP and will guide readers through a few traditional statistical analyses (i.e., a Pearson’s r correlation and a t-test) that are commonly used within applied linguistics. Real studies from the field that have used JASP for their data analyses will also be discussed, after which the challenges that are associated with (online) data analysis and statistics will be considered. We will cover a few points on research ethics and research integrity, as both of these have come under greater scrutiny in the past few years and thus have grown in importance. The chapter will conclude by exploring potential avenues for the future.

Applied linguistics researchers are increasingly working with a wider range of data, including audio-recording data, and video-recording data collected in online and internet-based contexts. Transcription of such multimodal data with digital affordances is an important part of the research process. Moreover, the emergence of transcription tools that make use of generative artificial intelligence technologies has made available a variety of web-based transcription tools and apps to choose from, such as Otter.ai and Happy Scribe. This chapter will introduce readers to different types of transcription and transcription tools, specifically in the context of multimodal transcription. In addition, several example studies which adopt such tools will be described, along with ethical considerations related to the use of transcription tools. Finally, directions for future research will be discussed.

Technological advancements over the past two decades have resulted in an increasing number of changes, especially involving the ways in which applied linguists conduct their work. Today, a sizable amount of the research done by linguists is conducted digitally or in online settings via the internet. As such, the edited volume Digital and Internet-Based Research Methods in Applied Linguistics is devoted to exploring a range of approaches, methods, tools, and sites that are frequently leveraged by contemporary scholars. This chapter introduces readers to the edited volume. It opens by further describing the increasingly digital, online nature of scientific inquiry and research methods in applied linguistics, commenting on related affordances and challenges. This is followed by an introduction to the aims of the volume, a preview of the chapters, and finally, an overview of the internal organization of the chapters.

Synthesis of research evidence drives an academic field forward through reviewing existing research, suggesting future research directions, and appraising methodological rigour of published research. Research synthesis refers to the use of a structured and principled approach to identifying and re-analysing/interpreting published evidence. In this chapter, I introduce research synthesis and its relationship with online/internet-based methodology in applied linguistics. I provide a list of research questions that can be answered by different types of research synthesis. Focusing on implementation, I introduce the Synthesis Methods and Reporting Tool (SMART) that outlines the key stages of conducting a research synthesis. I illustrate these methodological stages through five published research syntheses in applied linguistics. This is followed by a discussion of relevant ethical and methodological issues, as well as how future research syntheses can contribute to applied linguistics research as a whole.

Digital discourse is a routine part of social interaction and can be used as a source of data to answer a variety of research questions. This chapter illustrates a design framework that can be used to generate and evaluate research studies using digital discourse data. Drawing on Paulus and Wise (2019), I deconstruct several studies to illustrate how key research design decisions can be used to assess methodological alignment. These decisions include selecting an object of research interest, identifying the explicit or implied assumptions about the nature and function of digital discourse, defining the technical and social context characteristics of the platforms, creating research questions, selecting a methodological approach, making ethical data bounding and extraction decisions, and choosing data analysis methods. Ethical considerations, challenges and issues, and future directions for the development of digital discourse approaches are also discussed.

The notion of what constitutes the language classroom has developed over time, to the point where applied linguists have increasingly investigated the impact of out-of-class exposure on the traditional bricks-and-mortar classroom. Of particular interest is the digital classroom, and traditional classroom ethnographies have also made this digital migration. Building on these recent developments, in this chapter we discuss how ethnographic tools have to be modified across digital spaces to better understand the complexities surrounding evolving classroom cultures and other applied linguistics contexts. We draw on insights from one particular brand of ethnography, netnography. Specifically, we describe the netnography research process, provide examples of netnographies, and discuss ethical dilemmas that often emerge in netnographic research. The chapter closes with suggestions for the future, in light of the growing influence of generative artificial intelligence on language education and the need for research on underrepresented populations.

Researching online courses and education platforms is key as course developers confront challenges related to the rapid pace of technological change as well as students’ continually evolving needs and expectations related to learning in online environments. This chapter offers specific examples on how online courses and education platforms have been researched within applied linguistics, including common types of courses or platforms that have been studied, frequently asked research questions, and how studies are conducted. It also explores challenges and ethical considerations for conducting research in these environments, summarizes major findings and implications from past studies, and concludes with possible future research directions.

This chapter explores social media as a site for applied linguistics research and the methods that are used for such studies. To date, the majority of social media studies have examined platforms such as Facebook, YouTube, and Twitter/X. Increasingly, however, researchers are also exploring platforms such as Instagram, WhatsApp, and Tik Tok, among others. Topics for study include how language is used — including translanguaging — and how users create their cultural and linguistic identities via different platforms and how learners can develop and expand their language and literacy. We outline the different ways data are captured and the various methods used for analysis (e.g., meta-analysis, discourse analysis, ethnographic approaches). We exemplify these, and the different theoretical frameworks used by describing five different studies. We also provide commentary on some of the ethical issues and challenges related to social media research. We conclude by providing suggestions for future research.

This chapter provides an overview of research methods commonly employed in studies using digital games played both online and offline as tools to support second language (L2) learning and development. Researchers and practitioners have praised online games as rich sources of meaningful and engaging L2 input and interaction that have been shown to have positive effects on learning gains. This chapter provides a methodological review of the games used, common types of research questions asked, the dependent variables targeted, learner populations studied, and procedures followed within this targeted domain of computer-assisted language learning (CALL) research. The chapter concludes with a summary of the methodological challenges and issues in this domain while also providing recommendations for future research.

Modern mobile applications (apps) have profoundly influenced not only social communication and information dissemination but also language learning. These applications, such as Babbel, Busuu, and Duolingo, are increasingly accepted by language learners and have gained attention from researchers exploring their educational contributions. This chapter provides an overview of research on mobile applications as crucial sites for language acquisition, summarizing current research issues and insights of the research methods. It offers practical advice for researchers entering this field, including reviews of exemplary studies that highlight essential methodologies and findings. Additionally, the chapter addresses ethical considerations and research integrity in studying mobile applications. It concludes by examining challenges in research methodologies and suggests future directions, enriching the broader dialogue on how mobile applications can advance language education.

Recent years have seen a rapid growth in the range of web-based corpora and the functionality of web-based corpus platforms available for linguistic research across a wide number of world languages, such that these tools are a key consideration when discussing internet-based research methods. This chapter discusses the use of such resources in applied linguistics research, adopting a focus on corpora or corpus platforms with web-based interfaces that enable linguistic analysis, rather than resources which are hosted online primarily for offline use. After framing this definition, the authors outline commonly used web-based corpora and platforms, as well as research questions and considerations in corpus selection. Five studies which use prominent tools in innovative ways are reviewed. Ethical considerations and other challenges are discussed, and the chapter concludes by projecting future directions in the use of web-based corpora and web-based corpus platforms for research in applied linguistics.

This chapter explores the use of online surveys and questionnaires as one of the most frequent and versatile methods of data collection in applied linguistics research. It describes their multifaceted applications to study phenomena ranging from language attitudes to the measurement of language proficiency. The chapter emphasizes strategic design considerations and implementation procedures, highlighting best practices and common pitfalls in the construction of surveys and analysis of survey data. Additionally, the chapter examines important challenges associated with surveys, such as participant recruitment, data integrity, and ethics, making recommendations and highlighting important future directions in research on this method. By drawing from contemporary literature and studies with an exemplary use of online surveys in diverse contexts, this chapter will equip researchers and practitioners in applied linguistics with valuable knowledge to craft effective, meaningful, and ethically responsible online surveys and questionnaires.

This chapter explores online interviews as a qualitative data collection method in applied linguistics. It begins by defining online interviews as a method, discussing their increasing adoption due to their flexibility, accessibility, and cost-effectiveness. The chapter then examines research areas that could benefit from the use of online interviews and provides some specific research questions. The implementation section explores different online interview modes and discusses key decisions made before, during, and after conducting online interviews. Next, the chapter presents five exemplary studies, illustrating how online interviews have been used in diverse research contexts. Ethical considerations, such as data security and informed consent, are discussed alongside challenges like financial support and power dynamics between researchers and participants. The chapter concludes by outlining future research directions, emphasizing multimodal approaches, researcher reflexivity, and ethical advancements.

This chapter discusses screen capture (i.e., the recording of users’ on-screen activities) as a data collection method in online-based research in applied linguistics. After providing background information on the research contexts in which both still image and video screen captures have been used, we offer an overview of the types of phenomena previously studied through analyses of screen captures, the implementation of this data collection method in existing research, and the most commonly adopted analytical approaches (e.g., conversation analysis, thematic coding). Afterwards, we showcase exemplary empirical research using screen captures, including studies exploring digital discourse or written/oral interactions in non-educational virtual locales (e.g., social media, messaging apps) as well as within computer-assisted language learning settings. Following a discussion of the ethical issues and challenges associated with this data collection method, we propose future research directions (e.g., human-AI interaction, linguistic practices, and communication in virtual realities).

Eye-tracking allows researchers to record eye movements while processing visual stimuli (text, images, video), providing a direct measure of attention and processing effort. It has gained popularity in applied linguistics and has quickly become a main data collection method in our field. While current eye-tracking research is conducted in laboratory settings with specialized hardware, technological advancements are leading to improvements in web-based eye-trackers. This chapter provides an introduction to the technique and an overview of how eye-tracking has been used in applied linguistics research, identifying commonly researched topics and questions. It also provides a discussion of main steps in the implementation of eye-tracking studies, highlighting key considerations in data collection and data analysis procedures. The chapter also showcases key eye-tracking studies that have explored a range of topics in applied linguistics, including language learning, language assessment, and bilingualism. Finally, it discusses some of the main methodological challenges in the use of this data collection method and identifies directions for future research.

Traditionally the purview of psycholinguistic experiments conducted in controlled laboratory environments, researchers can now deploy response time tasks such as lexical decision tasks or self-paced reading paradigms into experiments run on the internet. Data from such tasks are of interest to researchers in applied linguistics, as these data provide insight into the cognitive representation of language (i.e., the mental lexicon) of first, second, and additional users of different languages. This chapter first explains what response time data are and how they are measured, with a particular focus on lexical decision tasks as a core example. Common research questions associated with response time data are also provided. The chapter then reviews how such tasks are implemented online, and afterwards, five example studies are reviewed. Finally, the chapter discusses ethical and practical challenges and considerations for future work in this area.

Keystroke logging serves as a non-intrusive method for capturing the temporal dynamics of text production, making it a valuable tool in applied linguistics research. In this chapter, we introduce the technical and practical details involved in implementing web-based keystroke logging for data collection using FlexKeyLogger as an example. We also present some commonly used keystroke measures to operationalize writing behaviors and discuss how these measures align with cognitive processes underlying text composition. We then summarize and review recent studies that leverage keystroke logging to investigate diverse topics in applied linguistics, demonstrating its versatility and research potential. Finally, we address key ethical considerations, challenges associated with keystroke data collection and interpretation, and potential future directions.

This chapter introduces (web-based) automated text analyzers (ATAs) in applied linguistics research. It begins by briefly surveying key strands of research involving ATAs and outlining three types of text analysis alongside questions commonly addressed by these tools. The core of this chapter presents a conceptual framework for the typology and implementation of ATAs, structured around four continuum dimensions: (1) pre-built corpus platform vs. custom corpus platform, (2) developer-oriented vs. user-oriented, (3) focused vs. versatile, and (4) descriptive vs. interpretive. The framework is illustrated through five practical studies showcasing the application of various types of ATAs in applied linguistics research, including L2SCA, Coh-Metrix, Sketch Engine, #LancsBox, Voyant Tools, and Wmatrix3. The chapter also discusses ethical considerations and methodological challenges associated with ATA use. It concludes by outlining future directions for ATA development and research, including improving annotation accuracy, enhancing qualitative interpretability, and expanding analytical capacities across languages and modalities.

This chapter discusses how generative artificial intelligence (GenAI) tools, particularly large language models (LLMs) like ChatGPT, are emerging as powerful web-based tools for research purposes, such as data analysis, in applied linguistics research. While much attention has focused on pedagogical applications, we review how GenAI can be leveraged to support various stages of the research processes in empirical studies, such as instrument design, automated coding, text annotation, and qualitative data analysis. We address key concerns around validity and reliability as well as ethical considerations related to transparency, data privacy, and potential bias in AI-generated output. Given that GenAI is in the early stage of research application, we describe its current capacities and limitations based on emerging empirical research and propose promising directions for future studies.

JASP is one of the most user-friendly open-source software programs for data analysis available today, and it has recently also become available online through rollApp. This chapter provides a hands-on introduction to quantitative data analysis using this web-based version of JASP and will guide readers through a few traditional statistical analyses (i.e., a Pearson’s r correlation and a t-test) that are commonly used within applied linguistics. Real studies from the field that have used JASP for their data analyses will also be discussed, after which the challenges that are associated with (online) data analysis and statistics will be considered. We will cover a few points on research ethics and research integrity, as both of these have come under greater scrutiny in the past few years and thus have grown in importance. The chapter will conclude by exploring potential avenues for the future.

Applied linguistics researchers are increasingly working with a wider range of data, including audio-recording data, and video-recording data collected in online and internet-based contexts. Transcription of such multimodal data with digital affordances is an important part of the research process. Moreover, the emergence of transcription tools that make use of generative artificial intelligence technologies has made available a variety of web-based transcription tools and apps to choose from, such as Otter.ai and Happy Scribe. This chapter will introduce readers to different types of transcription and transcription tools, specifically in the context of multimodal transcription. In addition, several example studies which adopt such tools will be described, along with ethical considerations related to the use of transcription tools. Finally, directions for future research will be discussed.

Technological advancements over the past two decades have resulted in an increasing number of changes, especially involving the ways in which applied linguists conduct their work. Today, a sizable amount of the research done by linguists is conducted digitally or in online settings via the internet. As such, the edited volume Digital and Internet-Based Research Methods in Applied Linguistics is devoted to exploring a range of approaches, methods, tools, and sites that are frequently leveraged by contemporary scholars. This chapter introduces readers to the edited volume. It opens by further describing the increasingly digital, online nature of scientific inquiry and research methods in applied linguistics, commenting on related affordances and challenges. This is followed by an introduction to the aims of the volume, a preview of the chapters, and finally, an overview of the internal organization of the chapters.

Synthesis of research evidence drives an academic field forward through reviewing existing research, suggesting future research directions, and appraising methodological rigour of published research. Research synthesis refers to the use of a structured and principled approach to identifying and re-analysing/interpreting published evidence. In this chapter, I introduce research synthesis and its relationship with online/internet-based methodology in applied linguistics. I provide a list of research questions that can be answered by different types of research synthesis. Focusing on implementation, I introduce the Synthesis Methods and Reporting Tool (SMART) that outlines the key stages of conducting a research synthesis. I illustrate these methodological stages through five published research syntheses in applied linguistics. This is followed by a discussion of relevant ethical and methodological issues, as well as how future research syntheses can contribute to applied linguistics research as a whole.

Digital discourse is a routine part of social interaction and can be used as a source of data to answer a variety of research questions. This chapter illustrates a design framework that can be used to generate and evaluate research studies using digital discourse data. Drawing on Paulus and Wise (2019), I deconstruct several studies to illustrate how key research design decisions can be used to assess methodological alignment. These decisions include selecting an object of research interest, identifying the explicit or implied assumptions about the nature and function of digital discourse, defining the technical and social context characteristics of the platforms, creating research questions, selecting a methodological approach, making ethical data bounding and extraction decisions, and choosing data analysis methods. Ethical considerations, challenges and issues, and future directions for the development of digital discourse approaches are also discussed.

The notion of what constitutes the language classroom has developed over time, to the point where applied linguists have increasingly investigated the impact of out-of-class exposure on the traditional bricks-and-mortar classroom. Of particular interest is the digital classroom, and traditional classroom ethnographies have also made this digital migration. Building on these recent developments, in this chapter we discuss how ethnographic tools have to be modified across digital spaces to better understand the complexities surrounding evolving classroom cultures and other applied linguistics contexts. We draw on insights from one particular brand of ethnography, netnography. Specifically, we describe the netnography research process, provide examples of netnographies, and discuss ethical dilemmas that often emerge in netnographic research. The chapter closes with suggestions for the future, in light of the growing influence of generative artificial intelligence on language education and the need for research on underrepresented populations.

Researching online courses and education platforms is key as course developers confront challenges related to the rapid pace of technological change as well as students’ continually evolving needs and expectations related to learning in online environments. This chapter offers specific examples on how online courses and education platforms have been researched within applied linguistics, including common types of courses or platforms that have been studied, frequently asked research questions, and how studies are conducted. It also explores challenges and ethical considerations for conducting research in these environments, summarizes major findings and implications from past studies, and concludes with possible future research directions.

This chapter explores social media as a site for applied linguistics research and the methods that are used for such studies. To date, the majority of social media studies have examined platforms such as Facebook, YouTube, and Twitter/X. Increasingly, however, researchers are also exploring platforms such as Instagram, WhatsApp, and Tik Tok, among others. Topics for study include how language is used — including translanguaging — and how users create their cultural and linguistic identities via different platforms and how learners can develop and expand their language and literacy. We outline the different ways data are captured and the various methods used for analysis (e.g., meta-analysis, discourse analysis, ethnographic approaches). We exemplify these, and the different theoretical frameworks used by describing five different studies. We also provide commentary on some of the ethical issues and challenges related to social media research. We conclude by providing suggestions for future research.

This chapter provides an overview of research methods commonly employed in studies using digital games played both online and offline as tools to support second language (L2) learning and development. Researchers and practitioners have praised online games as rich sources of meaningful and engaging L2 input and interaction that have been shown to have positive effects on learning gains. This chapter provides a methodological review of the games used, common types of research questions asked, the dependent variables targeted, learner populations studied, and procedures followed within this targeted domain of computer-assisted language learning (CALL) research. The chapter concludes with a summary of the methodological challenges and issues in this domain while also providing recommendations for future research.

Modern mobile applications (apps) have profoundly influenced not only social communication and information dissemination but also language learning. These applications, such as Babbel, Busuu, and Duolingo, are increasingly accepted by language learners and have gained attention from researchers exploring their educational contributions. This chapter provides an overview of research on mobile applications as crucial sites for language acquisition, summarizing current research issues and insights of the research methods. It offers practical advice for researchers entering this field, including reviews of exemplary studies that highlight essential methodologies and findings. Additionally, the chapter addresses ethical considerations and research integrity in studying mobile applications. It concludes by examining challenges in research methodologies and suggests future directions, enriching the broader dialogue on how mobile applications can advance language education.

Recent years have seen a rapid growth in the range of web-based corpora and the functionality of web-based corpus platforms available for linguistic research across a wide number of world languages, such that these tools are a key consideration when discussing internet-based research methods. This chapter discusses the use of such resources in applied linguistics research, adopting a focus on corpora or corpus platforms with web-based interfaces that enable linguistic analysis, rather than resources which are hosted online primarily for offline use. After framing this definition, the authors outline commonly used web-based corpora and platforms, as well as research questions and considerations in corpus selection. Five studies which use prominent tools in innovative ways are reviewed. Ethical considerations and other challenges are discussed, and the chapter concludes by projecting future directions in the use of web-based corpora and web-based corpus platforms for research in applied linguistics.

This chapter explores the use of online surveys and questionnaires as one of the most frequent and versatile methods of data collection in applied linguistics research. It describes their multifaceted applications to study phenomena ranging from language attitudes to the measurement of language proficiency. The chapter emphasizes strategic design considerations and implementation procedures, highlighting best practices and common pitfalls in the construction of surveys and analysis of survey data. Additionally, the chapter examines important challenges associated with surveys, such as participant recruitment, data integrity, and ethics, making recommendations and highlighting important future directions in research on this method. By drawing from contemporary literature and studies with an exemplary use of online surveys in diverse contexts, this chapter will equip researchers and practitioners in applied linguistics with valuable knowledge to craft effective, meaningful, and ethically responsible online surveys and questionnaires.

This chapter explores online interviews as a qualitative data collection method in applied linguistics. It begins by defining online interviews as a method, discussing their increasing adoption due to their flexibility, accessibility, and cost-effectiveness. The chapter then examines research areas that could benefit from the use of online interviews and provides some specific research questions. The implementation section explores different online interview modes and discusses key decisions made before, during, and after conducting online interviews. Next, the chapter presents five exemplary studies, illustrating how online interviews have been used in diverse research contexts. Ethical considerations, such as data security and informed consent, are discussed alongside challenges like financial support and power dynamics between researchers and participants. The chapter concludes by outlining future research directions, emphasizing multimodal approaches, researcher reflexivity, and ethical advancements.

This chapter discusses screen capture (i.e., the recording of users’ on-screen activities) as a data collection method in online-based research in applied linguistics. After providing background information on the research contexts in which both still image and video screen captures have been used, we offer an overview of the types of phenomena previously studied through analyses of screen captures, the implementation of this data collection method in existing research, and the most commonly adopted analytical approaches (e.g., conversation analysis, thematic coding). Afterwards, we showcase exemplary empirical research using screen captures, including studies exploring digital discourse or written/oral interactions in non-educational virtual locales (e.g., social media, messaging apps) as well as within computer-assisted language learning settings. Following a discussion of the ethical issues and challenges associated with this data collection method, we propose future research directions (e.g., human-AI interaction, linguistic practices, and communication in virtual realities).

Eye-tracking allows researchers to record eye movements while processing visual stimuli (text, images, video), providing a direct measure of attention and processing effort. It has gained popularity in applied linguistics and has quickly become a main data collection method in our field. While current eye-tracking research is conducted in laboratory settings with specialized hardware, technological advancements are leading to improvements in web-based eye-trackers. This chapter provides an introduction to the technique and an overview of how eye-tracking has been used in applied linguistics research, identifying commonly researched topics and questions. It also provides a discussion of main steps in the implementation of eye-tracking studies, highlighting key considerations in data collection and data analysis procedures. The chapter also showcases key eye-tracking studies that have explored a range of topics in applied linguistics, including language learning, language assessment, and bilingualism. Finally, it discusses some of the main methodological challenges in the use of this data collection method and identifies directions for future research.

Traditionally the purview of psycholinguistic experiments conducted in controlled laboratory environments, researchers can now deploy response time tasks such as lexical decision tasks or self-paced reading paradigms into experiments run on the internet. Data from such tasks are of interest to researchers in applied linguistics, as these data provide insight into the cognitive representation of language (i.e., the mental lexicon) of first, second, and additional users of different languages. This chapter first explains what response time data are and how they are measured, with a particular focus on lexical decision tasks as a core example. Common research questions associated with response time data are also provided. The chapter then reviews how such tasks are implemented online, and afterwards, five example studies are reviewed. Finally, the chapter discusses ethical and practical challenges and considerations for future work in this area.

Keystroke logging serves as a non-intrusive method for capturing the temporal dynamics of text production, making it a valuable tool in applied linguistics research. In this chapter, we introduce the technical and practical details involved in implementing web-based keystroke logging for data collection using FlexKeyLogger as an example. We also present some commonly used keystroke measures to operationalize writing behaviors and discuss how these measures align with cognitive processes underlying text composition. We then summarize and review recent studies that leverage keystroke logging to investigate diverse topics in applied linguistics, demonstrating its versatility and research potential. Finally, we address key ethical considerations, challenges associated with keystroke data collection and interpretation, and potential future directions.

This chapter introduces (web-based) automated text analyzers (ATAs) in applied linguistics research. It begins by briefly surveying key strands of research involving ATAs and outlining three types of text analysis alongside questions commonly addressed by these tools. The core of this chapter presents a conceptual framework for the typology and implementation of ATAs, structured around four continuum dimensions: (1) pre-built corpus platform vs. custom corpus platform, (2) developer-oriented vs. user-oriented, (3) focused vs. versatile, and (4) descriptive vs. interpretive. The framework is illustrated through five practical studies showcasing the application of various types of ATAs in applied linguistics research, including L2SCA, Coh-Metrix, Sketch Engine, #LancsBox, Voyant Tools, and Wmatrix3. The chapter also discusses ethical considerations and methodological challenges associated with ATA use. It concludes by outlining future directions for ATA development and research, including improving annotation accuracy, enhancing qualitative interpretability, and expanding analytical capacities across languages and modalities.

This chapter discusses how generative artificial intelligence (GenAI) tools, particularly large language models (LLMs) like ChatGPT, are emerging as powerful web-based tools for research purposes, such as data analysis, in applied linguistics research. While much attention has focused on pedagogical applications, we review how GenAI can be leveraged to support various stages of the research processes in empirical studies, such as instrument design, automated coding, text annotation, and qualitative data analysis. We address key concerns around validity and reliability as well as ethical considerations related to transparency, data privacy, and potential bias in AI-generated output. Given that GenAI is in the early stage of research application, we describe its current capacities and limitations based on emerging empirical research and propose promising directions for future studies.

JASP is one of the most user-friendly open-source software programs for data analysis available today, and it has recently also become available online through rollApp. This chapter provides a hands-on introduction to quantitative data analysis using this web-based version of JASP and will guide readers through a few traditional statistical analyses (i.e., a Pearson’s r correlation and a t-test) that are commonly used within applied linguistics. Real studies from the field that have used JASP for their data analyses will also be discussed, after which the challenges that are associated with (online) data analysis and statistics will be considered. We will cover a few points on research ethics and research integrity, as both of these have come under greater scrutiny in the past few years and thus have grown in importance. The chapter will conclude by exploring potential avenues for the future.

Applied linguistics researchers are increasingly working with a wider range of data, including audio-recording data, and video-recording data collected in online and internet-based contexts. Transcription of such multimodal data with digital affordances is an important part of the research process. Moreover, the emergence of transcription tools that make use of generative artificial intelligence technologies has made available a variety of web-based transcription tools and apps to choose from, such as Otter.ai and Happy Scribe. This chapter will introduce readers to different types of transcription and transcription tools, specifically in the context of multimodal transcription. In addition, several example studies which adopt such tools will be described, along with ethical considerations related to the use of transcription tools. Finally, directions for future research will be discussed.