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Research Methods in Language Teaching and Learning: A Practical Guide

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Kenan Dikilitas , Kate Mastruserio Reynolds , Li Wei

A practical guide to the methodologies used in language teaching and learning research, providing expert advice and real-life examples from leading TESOL researchers

Research Methods in Language Teaching and Learning provides practical guidance on the primary research methods used in second language teaching, learning, and education. Designed to support researchers and students in language education and learning, this highly accessible book covers a wide range of research methodologies in the context of actual practice to help readers fully understand the process of conducting research.

Organized into three parts, the book covers qualitative studies, quantitative studies, and systematic reviews. Contributions by an international team of distinguished researchers and practitioners explain and demonstrate narrative inquiry, discourse analysis, ethnography, heuristic inquiry, mixed methods, experimental and quasi-experimental studies, and more. Each chapter presents an overview of a method of research, an in-depth description of the research framework or data analysis process, and a meta-analysis of choices made and challenges encountered. Offering invaluable insights and hands-on research knowledge to students and early-career practitioners alike, this book:

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Part of the Guides to Research Methods in Language and Linguistics series, Research Methods in Language Teaching and Learning is essential reading for students, educators, and researchers in all related fields, including TESOL, second language acquisition, English language teaching, and applied linguistics.

Kate Mastruserio Reynolds is Professor of TESOL and Literacy at Central Washington University, USA. She has authored and edited many works in the field of TESOL, including Introduction to TESOL: Becoming a Language Teaching Professional with Kenan Dikilits and Steve Close (Wiley Blackwell, 2021). She was Associate Editor of the vocabulary volume of The TESOL Encyclopedia of English Language Teaching (Wiley Blackwell, 2018).

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Article contents

• Introduction
• Discussion and conclusions

Competing interests

How do language education researchers attend to quality in qualitative studies.

Published online by Cambridge University Press:  09 April 2024

The steady expansion in qualitative research in the area of language education over the last two decades indicates the growing recognition of its importance to investigating issues of language teaching and learning. Along with this recognition, understanding and assessing the quality of qualitative studies in this area has gained increasing significance. Addressing this concern, in this research synthesis, we qualitatively explore how 236 qualitative language education studies published in seven leading journals explicitly foreground the issue of ‘research quality’. We conducted a qualitative content analysis of how authors of these studies addressed the main quality concepts proposed by well-known frameworks of qualitative research quality. Our findings, presented as ten major themes, show that qualitative researchers' overt treatment of research quality is realised based on three distinct orientations: no explicit quality criteria, positivist views of quality, and interpretive quality conceptions. We discuss aspects of these orientations and their implications for qualitative research in language education.

1. Introduction

In the 25 years since the publishing of Edge and Richards’ ( Reference Edge and Richards 1998 ) landmark discussion of epistemological claims in qualitative applied linguistics research, the volume of published qualitative studies in language education has notably increased (Benson et al., Reference Benson, Chik, Gao, Huang and Wang 2009 ; Lazaraton, Reference Lazaraton 2000 ; McKinley, Reference Mckinley 2019 ; Richards, Reference Richards 2006 , Reference Richards 2009 ; Zhang, Reference Zhang 2019 ). Within the context of the methodological turn in language education (Plonsky, Reference Plonsky 2013 ), it has become more important than ever for authors, readers, and stakeholders of such research to be able to judge its quality. Assessments of study quality determine whether manuscripts submitted to academic journals are put forward for review (Mahboob et al., Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ) and are central to the peer review process itself (Marsden, Reference Marsden and Chapelle 2019 ). They also play an important role in institutional decision-making concerning what is researched as well as the allocation of funding (Chowdhury et al., Reference Chowdhury, Koya and Philipson 2016 ; Pinar & Unlu, Reference Pinar and Unlu 2020 ). Furthermore, there is a continuing need for qualitative researchers to foreground evidence and claims of legitimacy to counter misconceptions about the concept of good/bad or strong/weak qualitative research and questions about clearly defined criteria for judging its quality that can render the value of such research uncertain (Hammersley, Reference Hammersley 2007 ; Mirhosseini, Reference Mirhosseini 2020 ; Morse, Reference Morse, Denzin and Lincoln 2018 ; Shohamy, Reference Shohamy 2004 ).

Like any sensible process of inquiry, ‘good’ qualitative research should be ‘timely, original, rigorous, and relevant’ (Stenfors et al., Reference Stenfors, Kajamaa and Bennett 2020 , p. 596), make explicit the grounds on which authors claim justification for their findings (Edge & Richards, Reference Edge and Richards 1998 ), and offer sensitive and careful exploration (Richards, Reference Richards 2009 ). It should be shaped by taking the relevant and appropriate research path, making principled and defensible decisions about aspects of data collection and analysis, generating logical inferences and interpretations, and gaining meaningful insight and knowledge (Mirhosseini, Reference Mirhosseini 2020 ). How qualitative research is communicated necessitates equivalent flexibility, demanding that the research report be engaging, significant, and convincing (Skinner et al., Reference Skinner, Edwards and Smith 2021 ; Tracy, Reference Tracy 2010 , Reference Tracy 2020 ). Yet, how this is executed in empirical language education research has not yet been examined, unlike in other disciplines (e.g., Raskind et al., Reference Raskind, Shelton, Comeau, Cooper, Griffith and Kegler 2019 ). Therefore, in this meta-research study, we adopt a qualitative approach to examine conceptions of research quality embedded in a sample of qualitative language education articles published in major journals of the field over the past two and a half decades. Specifically, we address the following research question: How do authors of these qualitative studies explicitly attend to quality concerns in their research reports? By gaining greater understandings of how language education scholars foreground quality considerations in qualitative research, we hope to identify opportunities for enhancing how such research is perceived across the field through strengthening our collective methodological toolkit.

1.1 Conceptions of quality in qualitative research

Qualitative research is characterised by its social constructivist epistemological essence (Kress, Reference Kress 2011 ; Pascale, Reference Pascale 2011 ) realised through various methodological traditions and approaches (Creswell, Reference Creswell 2007 ; Flick, Reference Flick 2007 ). The issue of quality in qualitative inquiry has, therefore, been interconnected with diverse understandings of such epistemological and methodological perspectives. Developments in the concern for qualitative research quality can, by some accounts, be divided into different phases across the wider general literature (Morse, Reference Morse, Denzin and Lincoln 2018 ; Ravenek & Rudman, Reference Ravenek and Rudman 2013 ; Seale, Reference Seale 1999 ). Initially (and ironically continuing to this day as a significant stream), authors of social research within language education and beyond have employed terms developed within the positivist scientific and quantitative tradition, notably validity and reliability (Kirk & Miller, Reference Kirk and Miller 1986 ; Long & Johnson, Reference Long and Johnson 2000 ; Mirhosseini, Reference Mirhosseini 2020 ). Under criticisms from interpretive epistemological orientations, new terms specific to qualitative research (e.g., dependability, transferability ) were spawned in the 1980s and 1990s (Lincoln & Guba, Reference Lincoln and Guba 1985 ; Seale, Reference Seale 1999 ), sometimes framed as fixed and direct equivalences of quantitative concepts (e.g., Noble & Smith, Reference Noble and Smith 2015 ) or tied to particular paradigms/methodologies (Creswell, Reference Creswell 2007 ).

Consequently, the literature is now replete with concepts posited for judging qualitative study quality (e.g., Edge & Richards, Reference Edge and Richards 1998 ; Lincoln, Reference Lincoln, Atkinson and Delamont 2011 ; Seale, Reference Seale 1999 , Reference Seale, Atkinson and Delamont 2011 ; Tracy, Reference Tracy 2010 ), ranging from widely used terms such as credibility, dependability , and reflexivity (Lincoln & Guba, Reference Lincoln and Guba 1985 ; Miles & Huberman, Reference Miles and Huberman 1994 ), to ideas that seem to have garnered less traction, including, perspicacity (Stewart, Reference Stewart 1998 ), emotional vulnerability (Bochner, Reference Bochner 2000 ), and resonance (Tracy, Reference Tracy 2010 ). This variety poses difficulties for novice qualitative researchers (Roulston, Reference Roulston 2010 ) and for reviewers of studies submitted to academic journals as well as the audience of qualitative research reports in general. Since no paper, to the best of our knowledge, has explored which particular quality concepts have cascaded down to language education, or the extent to which they vary in prevalence across empirical qualitative studies, there is likely uncertainty (particularly among early career qualitative researchers), over which concepts to foreground in research papers and how.

Inevitably, quality conceptions in qualitative research have amalgamated into frameworks for making claims of and evaluating study worth (e.g., Denzin & Lincoln, Reference Denzin and Lincoln 2005 ; Lincoln & Guba, Reference Lincoln and Guba 1985 ; Morse et al., Reference Morse, Barrett, Mayan, Olson and Spiers 2002 ; Patton, Reference Patton 2002 ; Richardson, Reference Richardson 2000 ; Skinner et al., Reference Skinner, Edwards and Smith 2021 ; Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ; Stewart, Reference Stewart 1998 ; Tracy, Reference Tracy 2010 , Reference Tracy 2020 ). Perhaps the most famous of these is Lincoln and Guba's ( Reference Lincoln and Guba 1985 ) notion of trustworthiness , composed of four discernible components: credibility, dependability, transferability , and confirmability . To these, the concept of reflexivity is often added (e.g., Richardson, Reference Richardson 2000 ; Stenfors et al., Reference Stenfors, Kajamaa and Bennett 2020 ), although some see reflexivity as a means of ensuring research findings are credible and confirmable. Such criteria reflect features of how research is conducted (e.g., prolonged engagement, member checking), reported (thick description), or a combination of both (reflexivity, methodological transparency). Consequently, quality in qualitative research can be discerned both from authors' descriptions of how the study was designed and conducted as well as their explicit accounts of how quality was attended to, typically in the methodology section of research articles (Marsden, Reference Marsden and Chapelle 2019 ; Stenfors et al., Reference Stenfors, Kajamaa and Bennett 2020 ). This complexity is, for instance, reflected in the eight ‘big tent’ quality criteria proposed by Tracy ( Reference Tracy 2010 ), which encompass design features that authors may need to present explicitly (e.g., time in the field, triangulation), along with more nebulous characteristics inherent to the framing and reporting of the study (research relevance/significance).

Evaluative criteria, which may illuminate how ‘good’ qualitative research is conducted and reported, are being increasingly adopted by academic publications (Korstjens & Moser, Reference Korstjens and Moser 2018 ; Richards, Reference Richards 2009 ; Rose & Johnson, Reference Rose and Johnson 2020 ). Yet, the prevailing view held by qualitative scholars from a social constructivist standpoint (Kress, Reference Kress 2011 ), that we would align ourselves with, is that the idea of foundational criteria (i.e., fixed and universal) against which the quality of qualitative research can be judged is flawed (Barbour, Reference Barbour 2001 ; Hammersley, Reference Hammersley 2007 ; Lazaraton, Reference Lazaraton 2003 ; Richards, Reference Richards 2009 ; Shohamy, Reference Shohamy 2004 ; Tracy, Reference Tracy 2020 ). Evaluative criteria for any form of research are underscored by value judgements that shape different research approaches and methods, how and where the results are reported, and who ‘good researchers’ are (Lazaraton, Reference Lazaraton 2003 ). Calcifying good practice into immovable criteria is considered fundamentally at odds with the guiding philosophy of qualitative research (Bochner, Reference Bochner 2000 ; Pascale, Reference Pascale 2011 ; Shohamy, Reference Shohamy 2004 ), which stresses creativity, exploration, conceptual flexibility, and freedom of spirit (Seale, Reference Seale 1999 ). Furthermore, as Tracy ( Reference Tracy 2010 ) highlights, notions such as quality, like any social knowledge, are not temporally or contextually fixed.

As such, dialogues of what constitutes effective qualitative research should be considered as unresolved. Indeed, the prevailing postmodern turn has ripped apart the notion of agreed criteria for good qualitative research (Seale, Reference Seale 1999 ), leading some authors to adopt anti-foundational perspectives (e.g., Bochner, Reference Bochner 2000 ; Shohamy, Reference Shohamy 2004 ), albeit rejecting shared notions of ‘good’ research risks undermining the credibility and relevance of qualitative research (Flick, Reference Flick 2009 ). Within this complex climate, a moderate practical position is sometimes adopted in different fields including language education (Chapelle & Duff, Reference Chapelle and Duff 2003 ; Mahboob et al., Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ). It tends to balance the need for agreement over what constitutes ‘goodness’ in qualitative research and respecting authors’ desire for interesting, innovative, and evocative research based on their sociopolitical agendas (Bochner, Reference Bochner 2000 ; Lazaraton, Reference Lazaraton 2003 ; Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ). On this basis, in research reporting, there is an expectation that authors both implicitly and explicitly attend to study quality, albeit research articles in language education, as elsewhere, are subject to (often restrictive) publication word limits (Marsden, Reference Marsden and Chapelle 2019 ).

1.2 Quality in qualitative language education research

Increasing scholarly attention is being paid to matters of quality in qualitative language education research, usually through chapters in books dedicated to qualitative research in the field more broadly (see Mirhosseini, Reference Mirhosseini 2020 ; Richards, Reference Richards 2006 ) and theoretical reviews/perspective pieces (Davis, Reference Davis 1992 , Reference Davis 1995 ; Duff & Bachman, Reference Duff and Bachman 2004 ; Holliday, Reference Holliday 2004 ; Johnson & Saville-Troike, Reference Johnson and Saville-Troike 1992 ; Mirhosseini, Reference Mirhosseini 2018 ; Shohamy, Reference Shohamy 2004 ), as well as occasional journal guidelines for authors of prospective manuscripts (Chapelle & Duff, Reference Chapelle and Duff 2003 ; Mahboob et al., Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ). The latter are particularly salient for the present study since they constitute both a set of evaluative criteria on conducting and reporting qualitative research in language education, as well as addressing how and why it is important for researchers to attend to quality. In several instances, such features overlap, as in this instance from TESOL Quarterly : ‘Practice reflexivity, a process of self-examination and self-disclosure about aspects of your own background, identities or subjectivities, and assumptions that influence data collection and interpretation’ (Chapelle & Duff, Reference Chapelle and Duff 2003 , p. 175).

For the purposes of author and reviewer clarity, Chapelle and Duff's ( Reference Chapelle and Duff 2003 ) guidelines are wedded to discrete, fixed depictions of research methodologies. Their original iteration outlined quality considerations for case studies and ethnographic research, with a clear epistemological distinction between qualitative and quantitative research, highlighting (perhaps unnecessarily) paradigmatic divisions (Bochner, Reference Bochner 2000 ; Shohamy, Reference Shohamy 2004 ). More recently (Mahboob et al., Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ), this distinction has been revisited, likely out of recognition that, for the purpose of comprehensiveness, researchers increasingly gather data drawing on both paradigmatic traditions (Hashemi, Reference Hashemi 2012 , Reference Hashemi, McKinley and Rose 2020 ; Hashemi & Babaii, Reference Hashemi and Babaii 2013 ; Mirhosseini, Reference Mirhosseini 2018 ; Riazi & Candlin, Reference Riazi and Candlin 2014 ). In terms of quality concepts, Mahboob et al.'s ( Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ) guidelines are orientated towards rich rigor and credibility. Regarding the former, they advise ethnographers to show evidence of ‘residing or spending considerable lengths of time interacting with people in the study setting’ and ‘triangulation’, and to ‘practice reflexivity’ (p. 175). It also informs qualitative researchers to triangulate ‘multiple perspectives, methods, and sources of information’ and employ illustrative quotations that highlight emic (i.e., participant) ‘attitudes, beliefs, behaviors, and practices’ (p. 175). They illustrate such principles through sample studies, albeit it might be argued that this practice further serves to narrow research into fixed acceptable forms followed prescriptively by would-be scholars in a bid for successful publication.

2.1 Data retrieval

Although this study is qualitative in nature, our first step was similar to other reviews of research in the field (e.g., Lei & Liu, Reference Lei and Liu 2019 ; Liu & Brown, Reference Liu and Brown 2015 ), that is, determining a principled, representative, and accessible domain of empirical research for the investigation of study quality. We decided to include only academic journal papers in the dataset, based partly on the assumption, also held elsewhere (e.g., Plonsky & Gass, Reference Plonsky and Gass 2011 ), that journals constitute the primary means of disseminating high-quality empirical language education research. We also thought that comparisons of study quality would be more meaningful within a single publication format, instead of bringing in longer formats (e.g., books, theses), where authors have significantly greater scope for ruminating on matters of study quality. Furthermore, empirical studies presented in book chapters were excluded on the grounds that the final set of included studies would have been unbalanced, being heavily skewed towards journal articles (Plonsky, Reference Plonsky 2013 ). We then consulted several meta-research studies in the field as sources of reference for selecting high-quality language education research journals while we were conscious that the nature of our analysis would be different from those mostly quantitative studies (e.g., Lei & Liu, Reference Lei and Liu 2019 ; Plonsky, Reference Plonsky 2013 ; Zhang, Reference Zhang 2019 ). We decided not to include specialist journals (such as Applied Psycholinguistics , Computer Assisted Language Learning , Studies in Second Language Acquisition ), as well as journals that do not typically publish primary research ( ELT Journal ). Our final selection included seven venues renowned for publishing robust primary research in language education: Applied Linguistics, Foreign Language Annals , Language Learning , Language Teaching Research , Modern Language Journal , System , and TESOL Quarterly .

Informed by prior literature (e.g., Thelwall & Nevill, Reference Thelwall and Nevill 2021 ), and with reference to various theoretical discussions of quality in qualitative research (discussed earlier in this article), we devised a series of inclusionary search terms (such as qualitative, narrative, ethnography, phenomenology, and grounded theory; with possible additional lemmas) to generate a representative sample of empirical qualitative research. The terms were applied to the title, keyword, and abstract fields in Scopus to determine and retrieve a more consistent body of results (rather than querying the different journal publishers directly). Such an approach naturally hinges on authors explicitly positioning their study as, say, ‘qualitative’ or ‘ethnographic’, which in light of the diversity and complexity of ways to communicate research, we acknowledge as a limitation. Moreover, we did not include approaches that cut across paradigmatic traditions (such as feminist approaches and participatory action research).

Exclusionary terms (like quantitative, experiment, statistic, mixed method, and control group; with possible additional lemmas) were applied to reduce the size of the sample and the amount of manual checking required to eliminate irrelevant studies. To obtain an indicative body of recent and older qualitative research, we decided the publication period of the included studies to be between 1999 and 2021 and chose the year 1999 as a cut-off owing to the publication of Edge and Richards’ ( Reference Edge and Richards 1998 ) seminal work on knowledge claim warrants in applied linguistics research. We should also add that this meant that more recently established journals that publish qualitative research (such as Critical Inquiry in Language Studies and Journal of Language, Identity and Education ) could not be included. Additional filters in Scopus were applied to the results, removing works not written in English and those not labelled by Scopus as ‘articles’, and limiting article versions to ‘final’ rather than ‘in press’.

Search processes generated records for 741 studies, the bibliometric records for which were downloaded and imported into Excel. With the articles arranged chronologically and then alphabetically by publication, every alternate record was excluded for the purposes of further reducing the sample. Article abstracts were then manually checked by the researchers to ensure each entry constituted qualitative research. Uncertainty was addressed through retrieval and verification of the article's full text involving, when necessary, discussion between the researchers. At this stage, 134 studies were eliminated primarily because they encompassed mixed methods, were not in fact qualitative, or did not constitute an empirical study. As shown in Table 1 , the studies were not evenly distributed across journals or timeframe. The majority of research studies (56%) constituted single-author works, followed by 35% for two-author, and 8% for three-author works. Full texts for the final sample of 236 studies were retrieved directly from the journal publishers as PDFs and imported into NVivo 12 for data analysis by both researchers.

Table 1. Distribution of the included studies

2.2 Data analysis

In order to explore how the authors of the retrieved reports addressed matters of study quality, we chose to conduct qualitative content analysis of texts’ manifest content (Mayring, Reference Mayring 2019 ; Schreier, Reference Schreier and Flick 2014 ), that is, statements rather than implied between-the-lines meanings (see, Graneheim & Lundman, Reference Graneheim and Lundman 2004 ). Although this approach may in a sense be a limitation of our analysis, it was adopted because we were interested in how the authors themselves explicitly addressed and positioned quality in their research. Additionally, we found it difficult to analyse texts latently without (a) encountering notable uncertainty whether information was being included for the purposes of quality claims (or not), and (b) taking an explicitly evaluative position towards research (including on ‘under the hood’ design issues that we were clearly not equipped to comment on).

We limited the scope of manifest content to a discrete array of quality concepts synthesised across various frameworks developed for qualitative research (mainly including Denzin & Lincoln, Reference Denzin and Lincoln 2005 ; Edge & Richards, Reference Edge and Richards 1998 ; Mirhosseini, Reference Mirhosseini 2020 ; Richardson, Reference Richardson 2000 ; Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ; Stewart, Reference Stewart 1998 ; Tracy, Reference Tracy 2010 ), outlined in Table 2 . The two terms validity and reliability , while not concepts interpretive researchers would recognise as epistemologically appropriate to apply to qualitative research, were included since much qualitative research still makes references to these notions (as indicated in our sample in Table 2 ). It was not feasible to incorporate qualitative concepts that could be linguistically operationalised in very diverse ways, such as prolonged engagement, evocative representation , and contextual detail , which we acknowledge somewhat reduces the nuance of the analysis. We should also mention that the terms member reflection and multivocality were included in the search process but generated no hits and are thus excluded from the search results.

Table 2. Quality concepts investigated in the retrieved literature

Article full texts were searched in NVivo for lemma forms of the identified concepts (provided in Table 2 ). We captured concepts within their immediate textual surroundings, which we delineated as our meaning units for the present study (Graneheim & Lundman, Reference Graneheim and Lundman 2004 ), using NVivo's ‘broad context’ setting to ensure we could analyse authors’ sometimes lengthy descriptions and explanations. The textual extracts that included the 801 mentions of the different variants of the search terms were exported into Word files for manifest content analysis (Graneheim & Lundman, Reference Graneheim and Lundman 2004 ). Initially, the extracts were subject to cleaning to (a) attend to instances where the contextual information either preceding or following the quality concept was missing and to manually amend them, (b) remove the relatively large number of occurrences that did not refer to research quality but were used in a general sense of the word (prevalent with the terms, validity, reliability, credibility, reflexivity, transparency, rigor , and corroboration ), and (c) exclude examples where lemma constituted a heading or cited source title. We then coded the clean data (overall 380 instances of the search terms, as seen in Table 2 ) for each keyword in turn with short labels that aligned with the express surface-level meaning conveyed by the authors. Together, we looked for meaningful patterns across codes (within discrete keywords) that could be constellated into themes, paying attention to the need for both internal homogeneity and external heterogeneity (Patton, Reference Patton 2002 ). The final ten themes emerging from our data are presented in the next section along with quoted extracts from the articles that are provided to illustrate claims, although we refer to authors and articles using a numbered system to establish a degree of anonymity.

As for our own study, we attended closely to its quality based on an interpretive epistemological view. Given the nature of the data and the approach to the analysis of manifest article content, reflexivity was a particularly significant consideration of ours. We were cognizant of our subjectivities and our own positions as researchers in the field with regard to the work of fellow researchers and the possibilities and limitations of expressing issues of quality within the limits of journal articles, hence our decision (and, in a sense, struggle) to stay focused on the manifest content of explicit propositions about research quality in the examined articles rather than reading between-the-line latent content (Graneheim & Lundman, Reference Graneheim and Lundman 2004 ). Moreover, through our constant exchange of ideas and discussion of disagreements and differences of interpretation about realisations of quality concepts in these articles, we practiced our customised conceptions of researcher triangulation and corroboration. We also consciously provided as much detailed description of our data retrieval and analytical procedures as possible and tried to transparently reflect them in our writing, hoping that this will all add up to a rigorous inquiry process to the highest extent meaningful in a qualitative study.

3. Findings

3.1 triangulation.

Triangulation (as the most frequent term related to qualitative research quality) in the examined texts primarily referred to the mere collection of multiple types of data. Applying it as a well-known set of procedures, most researchers appear not to find it necessary to specify how triangulation was undertaken or how it enhanced research quality. They often only describe how they used ‘varied sources’ [671], ‘multiple collection methods’ [657], or ‘thick data’ [137] to ‘ensure triangulation of the data’ [337] or ‘achieve an element of triangulation’ [717]. A diversity of data sources and data collection procedures are mentioned in the overall bulk of the examined articles, with no clear patterns in the types of data being triangulated: ‘participant observation, in-depth interviews with key informants, and the analysis of textual documents including field notes, teaching materials, and student assignments’ [369]; ‘questionnaires, think-aloud protocols, gaming journals, and debriefing interviews’ [137]; ‘policy documents, media reports, and academic papers’ [199]. The quotes below are examples of how this general conception of triangulation is reflected in our data:

• We triangulated the results from data obtained in various ways: the pre-departure questionnaires, semi-structured pre-departure interviews, field notes from naturalistic observations, reflective journals, and the semi-structured post-sojourn interviews …  [455].

• The use of the various data-gathering techniques, including the self-assessment inventory, the think-aloud task, and the open-ended interviews, allowed for triangulation of the data collected [673].

There are also many articles that do hint at researchers’ views of how triangulation contributed to the quality of their research. A few of these articles referred to triangulation as a way ‘to ensure the trustworthiness’ [1 & 619], ‘to strengthen the rigour and trustworthiness’ [119], or to strive ‘for enhanced credibility’ [543] of their studies. However, the authors of most of this category of published works turned to traditional positivist terminology in this regard. They relied on triangulation, in their own words, ‘to address reliability’ [81], ‘to increase the validity of evaluation and research findings’ [321], or ‘to ensure the validity and reliability of the data and its analysis’ [337]. Helping with more profound insights into the data is a further specified role of triangulation, as reflected below:

• In order to ensure reliability and validity of the study, I employed several forms of triangulation …  [553].

• The triangulation of inferences from session transcripts, interview transcripts, and field notes shed light on complex connections between learning and practice and on the rationale behind teachers’ instructional choices …  [67].

Other perceptions of the role of triangulation are also found to underlie these published studies. By some accounts, it is a mechanism of testing and confirming researchers’ interpretations, as they use data from different sources for the purpose of ‘confirming, disconfirming, and altering initial themes’ [715] and in order to ‘confirm emergent categories’ [589] or ‘reveal and confirm or refute patterns and trends’ [227]. It is also relied on as a strategy ‘to minimize drawbacks’ [621] in data collection and analysis and ‘to avoid the pitfall of relying solely on one data source’ [119]. Accordingly, ‘the lack of triangulation of the findings through other instruments’ [569] is seen by some researchers a shortcoming or limitation of their research. Finally, a particularly intriguing image of the versatile notion of triangulation and its connection with researcher reflexivity is presented through the introduction of novel concepts in one of the studied articles: ‘our collaborative analytical process allowed us to triangulate inferences across our different stories, enhancing both believability and possibility’ [31].

3.2 Reliability

Reliability is the second most frequent term related to research quality in the body of data that we investigated. Unlike triangulation, that projects an understanding of research quality congruent with qualitative research epistemology and methodology, the frequency and nature of referring to reliability can raise questions about prevailing conceptions of qualitative research quality. Uses of this term depict either a rather broad and vague image of rigor and robustness of research or refer to the idea of consistency close to its traditional positivist conception, in some cases even indicated by numerical measures. Moreover, sometimes, the absence of any measures undertaken to ensure this kind of consistency is mentioned as a limitation and weakness of the reported study.

Relatively frequent mentions of reliability (in some cases along with the word ‘validity’) seem to be in a general sense as an equivalent to high quality and rigorous research: ‘By augmenting our perspectives … , we think we have increased the reliability of our diagnosis’ [729]; ‘all attempts have been made to minimize the effects of the limitations of the study to increase the validity, reliability … of the study’ [549]. In addition, authors of a few of the studied articles appear to subsume strategies like triangulation and member checking under an attempt to take care of reliability (and validity) in their purportedly qualitative studies. In one of these cases, member checking is said to have been done in follow-up interviews, but it is described as a ‘technique’ employed ‘to ensure the validity and reliability of the data and its analysis’ [337]. The quote below is a similar example:

• In order to ensure reliability and validity of the study, I employed … triangulation by collecting multiple sources of data … [and] a member check …  [553].

Apart from such instances, reliability is most obviously used in relation to coding, featuring overtly positivist terminology. Authors of many of the examined articles describe how they conducted their qualitative data analysis in more than one round or by more than one person to ensure or strengthen ‘inter-coder reliability’ [155, 157, 173, 549] or ‘inter-rater reliability’ [21, 81]. Some of these articles link these attempts to broader research quality concepts like trustworthiness and rigor: ‘To increase trustworthiness, first, intercoder reliability was negotiated via researchers independently coding the data … ’ [51]; ‘Two researchers independently analyzed the data to enhance reliability and rigor … ’ [35]. More specifically, there are instances of providing numerical figures of ‘interrater agreement’ [153] or ‘inter-coder/interrater reliability’ [57, 415] in percentages. But what we find particularly significant and ironic is that in several articles, statistical measures like ‘Cronbach's Alpha Test for Reliability’ [721] and ‘Cohen's kappa’ [173, 391] are used to compute reliability in an explicitly quantitative sense. The following are two examples in a blunt statistical language:

• Cohen's Kappa coefficients were calculated to assess inter-coder reliability on each of the four categories: 0.84 (assessment context), 0.84 (assessment training experience) … , suggesting satisfactory intercoder reliability. [203].

• Reliability was computed by submitting the independent ratings of the two researchers to a measure of internal consistency. Cronbach's alpha coefficient was computed …  [693].

Other than agreement on data coding, a few researchers consider increased reliability through other procedures and approaches like collecting multiple data sources and data triangulation: ‘drawing on data collected from multiple sources, allows useful comparisons across the multiple cases and increases reliability’ [449]; ‘For enhanced reliability, data were collected through multiple written sources’ [543]. With these conceptions of reliability and measures undertaken to calculate and ensure them, the absence of such measures is expectedly acknowledged by some researchers as shortcomings and limitations of their research. As an instance, one article suggests that findings ‘should be taken with a grain of salt’ [467] because ‘inter-rater reliability’ was not calculated. In other cases, ‘the reliability of the recall procedure’ is considered a ‘limiting factor’ [173], and ‘a larger sample size and integration of qualitative and quantitative methods’ is suggested as a way to provide findings ‘in a more valid and reliable way’ [569].

3.3 Validity

The term validity is used in a variety of senses in the examined body of studies. Among these generally vague conceptions, perhaps the broadest and most difficult to interpret is the use of the term to indicate a kind of internal validity roughly meaning robust research. Here is a typical example also quoted in the section on reliability: ‘ … all attempts have been made to minimize the effects of the limitations of the study to increase the validity, reliability, authenticity, as well as the ethics of the study’ [549]. Other than such general statements about attempts ‘to improve the reliability and validity’ [721] of research and to attain ‘greater validity’ [7], there are indications of different measures purportedly undertaken to enhance validity. A frequently mentioned procedure used for this purpose is triangulation, which is said to have been used to ‘increase’ [321], ‘ensure’ [337], ‘address’ [81], or ‘demonstrate’ [83] validity, or to ‘to retain a strong level of validity’ [563]. ‘Enhance’ is another word to be added to this repertoire, as seen in the following example:

• To enhance the internal validity of the study, a number of strategies and techniques were employed by the teacher-researcher. One such technique was the triangulation of multiple data sources …  [609].

Little idea is provided in these cases about what the authors mean by validity and how they employed triangulation to boost it. The same is true about the employment of member checking for the purpose of validity. It is named in several articles as an adopted procedure for this purpose but it usually remains rather broad and vague: ‘member checks … helped increase the validity of my interpretations’ [369]; ‘various member checks were performed … as a means of further validating the data’ [321]; ‘member checking … strengthened the validity of the final analysis’ [251]. There are also a few even more difficult-to-interpret cases in which conducting multiple rounds of coding is stated as a mechanism of ensuring validity. Below are two examples, the first of which oddly refers to the notion ‘inter-rating process’ in relation with validity:

• Regarding the interview data, we were both responsible for analysis to ensure the validity of the inter-rating process [125].

• To increase validity, however, a research assistant and the researcher separately read a portion of one interview transcript to develop codes [175].

Moreover, in two unique instances, the authors report that ‘the survey was piloted’ [35] with a small number of participants, and ‘the constant comparison technique’ [331] was employed in order to take care of validity. Along with this conceptual mix of the term validity, in a few articles, concerns and gaps related to a still fuzzy idea of this notion are stated as limitations of the reported research. In two cases, ‘self-reported data’ [227] and ‘overreliance on self-report verbal expressions’ [57] are seen as sources of potential limitations and threats in terms of validity, and in one case, long time research engagement, focusing on real-life contexts, and collaborative work are mentioned as important issues concerning ‘the ecological validity’ [263] of research. Finally, in one instance that may raise further epistemological questions about positivist/constructivist foundations of quality in qualitative inquiry, ‘larger sample size and integration of qualitative and quantitative methods’ are mentioned as requirements for ‘a more valid and reliable’ study [569].

3.4 Trustworthiness (credibility, dependability, confirmability, transferability)

Trustworthiness, as conceptualised by Lincoln and Guba ( Reference Lincoln and Guba 1985 ) and discussed in the context of applied linguistics by Edge and Richards ( Reference Edge and Richards 1998 ), is the next theme in our findings. As a broad concept, used almost synonymous with quality, this term is predominantly employed to explain authors’ efforts and adopted strategies for the enhancement of the quality and strength of the reported research. Different measures undertaken for increasing trustworthiness are explained, including ‘triangulation’ [e.g., 1, 119], ‘member checking’ [e.g., 175, 619], ‘thick description’ [e.g., 257, 473], and ‘corroboration’ [e.g., 7, 51, 525]. It is also more specifically used to refer to an outcome of the four components of Lincoln and Guba's ( Reference Lincoln and Guba 1985 ) model (credibility, dependability, confirmability, and transferability): ‘This study followed the set of alternative quality criteria – credibility, transferability, dependability, confirmability … to maintain the trustworthiness of this study … ’ [45].

These four components also feature as separate aspects of research quality in the examined body of articles. Credibility, as the most frequent one, is primarily used in relation with ‘prolonged engagement with the participants’ [295] and establishing trust and in-depth familiarity and understanding: ‘four years of “prolonged engagement” with the participants ensured the credibility of the findings’ [155]. Member checking is the second strategy applied ‘to establish credibility of the research method’ [173] and ‘to enhance credibility of findings’ [467]. In one case, member checking is even equated to credibility: ‘ … for purposes of member-checking (credibility)  …  ’ [329]. Moreover, several authors mention procedures like thick description and triangulation used for the specific purpose of establishing credibility rather than general trustworthiness:

• Credibility was established through the use of anonymized multiple data sources … with an emphasis on thick descriptions to develop conceptual themes …  [21].

• This situation was addressed through a process of triangulation, which is central to achieving credibility …  [385].

The term transferability is also observed in several texts. As a way of contributing to the quality (trustworthiness) of qualitative research, it is said to be strengthened through increasing the number and the diversity of participants [333, 653], ‘member checking’ [373], and ‘thick description’ [473]. In one case, the researcher specifically highlights the interface of transferability and generalisability: ‘The study results are specific to this context and are not generalizable to other contexts, although they may be transferable … ’ [87]. Moreover, there are a few instances of the word dependability, again in connection with triangulation, member checking, and general trustworthiness. As an example, one author's conception of the term can be placed somewhere between member checking and triangulation: ‘ … the dependability of my findings was verified by my inclusion of a group interview, where I asked the students to comment on and discuss some of the themes that had emerged in their individual interviews’ [523]. Finally, ‘confirmability’ appears only once in our data (in the quote at the end of the first paragraph of this section above [45]) along with the other three components as an aspect of the broader conception of trustworthiness.

3.5 Member checking

Authors describe how they carried out member checking and how it can serve research quality in their studies. Sending researchers’ draft analyses and interpretations to be verified and authenticated by participants [121, 125, 619]; conducting group interviews for member checking [683]; additional interviews for this purpose [435, 467]; ‘interviews and subsequent email exchanges’ [251]; ‘having respondents review their interview transcripts’ [609]; and asking the participants ‘to respond to a draft of the paper’ [553] are some of the adopted procedures. As for purposes, member checking is said to be undertaken ‘to enhance credibility of findings’ [467], to strengthen ‘the validity of the final analysis’ [251], to make sure that ‘analysis and interpretation is accurate and plausible’ [553], and ‘to ensure that the views, actions, perceptions, and voices of the participants are accurately portrayed’ [609]. There is also an individual case that considers member checking as a data collection source in its own right [7], and one that mentions the absence of member checking as a limitation of the study [465].

3.6 Corroboration

The conception of corroboration reflected in the examined research articles hardly resembles the way Edge and Richards ( Reference Edge and Richards 1998 ) conceptualise it. In our data, it is used in almost the same sense as data triangulation. The small number of instances of this term in these texts mostly refer to how certain bodies of data are used to corroborate ideas gained from other data sources. For example, ‘questionnaires, interviews, and field notes were used to corroborate the analysis’ [227]; ‘observation notes and video-recordings were used as complementary data, mainly in order to corroborate interview comments’ [431]. There are only two cases in which the meaning of corroboration is similar to that of Edge and Richards ( Reference Edge and Richards 1998 ). In one of them member checking is said to have been used ‘to corroborate the accuracy, credibility, validity, and transferability of the study’ [373]. In the other one, corroboration is mentioned in relation with how the research is reported: ‘Verbatim quotes were used frequently in presenting the study's findings in order to corroborate the researcher's interpretations’ [525].

Instances of the term rigor in the studied articles show perhaps the least coherence in terms of its different intended meanings and ideas. There are some cases of broad emphasis on the importance of ‘a systematic and rigorous process’ [375] in qualitative inquiry, and the application of the term rigorous as a general positive adjective, for instance in claiming the implementation of ‘rigorous analytical procedures’ [471] or more specifically, ‘rigorous thematic analysis’ [449]. In addition, various procedures are said to have been adopted to strengthen rigor, like independent analysis of data by two researchers, ‘to enhance reliability and rigor’ [35] or the application of both deductive and inductive approaches to thematic analysis, ‘to ensure further rigor’ [7]. The triangulation of different data sources and the combination of various data types are also described in a few articles [97, 119] as the adopted practical procedures of taking care of rigor in these qualitative studies. Finally, one of the studied articles refers to a complex mechanism adopted in favour of transparency and rigor: ‘In order to be as transparent and as rigorous as possible in testing the analyses and interpretations, eight tactics were adopted … ’ [711].

3.8 Thick description

Thick description did not frequently appear in the body of examined research articles. The small number of instances, however, depict the potentially important role that it can play in enhancing the quality of qualitative research through not just telling the reader what was done, but how. The importance of looking at a phenomenon in-depth and holistically, going beyond surface-level appearances, featured several times as an explicit strategy to position research as complementing existing knowledge, including:

• This study has obtained and provided thick descriptions of the participant's perceptions, behaviors and surrounding environment. These thick descriptions could create a transparency and assist the reader in judging the transferability of the findings …  [473].

• Through a thick description of various classroom tasks used by the teachers, the study has provided a useful reference for EFL teachers in Vietnam and similar settings … [47].

Moreover, researchers reportedly employed thick description ‘to add lifelike elements’ [31] to the interpretive narrative, and ‘to develop theoretical arguments’ [377] based on contextualised details. In other cases, the scope of the matters typically associated with thick description – history, context, and physical setting (Mills et al., Reference Mills, Durepos, Wiebe, Mills, Durepos and Wiebe 2010 ) – were not explicated, with the concept foregrounded as a means of facilitating a higher-order feature of qualitative research quality such as ‘credibility’ [21] and ‘reflexivity’ [257]. Our codes also include clues as to researchers’ awareness of the tension between ‘in-depth thick description’ [301] and the size of the recruited sample. In such cases, illustrating contextual peculiarities through ‘the creation of a “thick description” … of the situation’ was framed as the goal of research, ‘instead of generalizability’ [475].

3.9 Transparency

The notion of transparency does feature in our data as an aspect of quality in qualitative studies but is not frequent. Apart from some general reflections of researcher concern about a broadly perceived, ‘more transparent and accountable...research process’ [39], transparency in the articles that we examined is mostly linked to authors’ perspectives towards the processes and procedures of data analysis. Researchers state that they attempted ‘to be as transparent and as rigorous as possible in testing the analyses and interpretations ’ [711] and explain how their adopted approach ‘makes transparent to readers the inductive processes of data analysis’ [719]. The need for transparency is explained by one researcher because ‘I see myself as a major research instrument’ [539], while for another, the ‘off the record’ nature of the qualitative data collection methods employed necessitated ‘render[ing] more transparent and accountable the research process’ [133]. There is also a single case indicating that thick description was used to ‘create transparency and assist the reader in judging the transferability of the findings’ [473]. Moreover, a particular conception of transparency is reflected in viewing overall trustworthiness, ‘as well as reflexivity’ of research as a way ‘to ensure the transparency of the possible researchers’ bias’ [45]. In one instance that reflects this conception, the transparency of researcher positioning appears to be a central concern: ‘As an ethnographer, I see myself as a major research instrument, and believe it is essential to be as transparent as possible in my positions and approaches so that readers can make their own interpretations’ [539].

3.10 Reflexivity

As noted above, conceptions of transparency can be closely connected with reflexivity. The understanding of the researcher bias and research transparency in relation with reflexivity is explicitly projected in the very small number of examined articles that specifically mentioned reflexivity as a significant consideration in their research process. However, there was rarely an overt indication of if/how reflexivity was understood in connection with qualitative research quality. One article referred to the ‘powerfully reflexive’ [39] nature of the methodological frameworks and approaches that they adopted, while another stated that data analysis was viewed as a ‘reflective activity’ but did not specify the purpose, nature, or features of this reflexivity: ‘We viewed analysis as an ongoing, cyclical, and reflexive activity’ [11]. Instead, connections with study quality could only be discerned on the two occasions when authors stressed that heightened reflexive awareness, stemming from the processes of conducting action research, allowed them to be more attuned to the needs of their learner-participants.

4. Discussion and conclusions

4.1 no explicit quality criteria.

The notion of quality in qualitative research is complex, requiring authors to address certain fundamental epistemological and methodological issues that compare with quantitative research as well as producing a creative, engaging, and convincing report (Bridges, Reference Bridges 2017 ; Flick, Reference Flick 2007 ; Mahboob et al., Reference Mahboob, Paltridge, Phakiti, Wagner, Starfield, Burns, Jones and De Costa 2016 ). Among the 236 studies that we investigated, three broad orientations were apparent in authors’ approaches to attending to research quality in language education research. The first (and most ambiguous) was where little explicit consideration of research quality could be uncovered. We would obviously not interpret this necessarily as an indication of weak or weaker research. It could be that authors did indeed imbue their articles with the qualities examined in the respective study but took quality for granted or understood it as an aspect of research embedded in how it is conducted without feeling the need to explicitly describe and explain their approach(es). It could also be that they employed more linguistically varied concepts that were missed in our analysis (e.g., ‘collecting different types of data’ instead of ‘data triangulation’).

While we acknowledge the necessary fluidity and creativity of qualitative research and do not seek to constrain authors’ approaches, we feel there are good reasons why researchers ought to explicitly foreground attention to quality. The first argument stems from a concept familiar to many researchers, that of methodological transparency (Marsden, Reference Marsden and Chapelle 2019 ; Mirhosseini, Reference Mirhosseini 2020 ; Tracy, Reference Tracy 2020 ). This encompasses not a call for greater procedural objectivity (Hammersley, Reference Hammersley 2013 ), but rather, the provision of a fuller description of the nuances and complexities of the processes that feature implications for study quality (Mills et al., Reference Mills, Durepos, Wiebe, Mills, Durepos and Wiebe 2010 ), allowing greater retrospective monitoring and assessment of research (Tracy, Reference Tracy 2010 ), enhancing trustworthiness (Hammersley, Reference Hammersley 2013 ), and facilitating ‘thick interpretations’ of the nature and value of the research at the broadest level (Mills et al., Reference Mills, Durepos, Wiebe, Mills, Durepos and Wiebe 2010 ). For the second reason, we invoke the centrality of argumentation to the ‘truth’ of qualitative research (Shohamy, Reference Shohamy 2004 ). Greater explicit attention to quality may bring to the foreground additional relevant research evidence, providing a more secure foundation for scholars’ warrants (Edge & Richards, Reference Edge and Richards 1998 ; Usher, Reference Usher, Scott and Usher 1996 ). This could particularly help a manuscript's prospects at peer review, given that journals are increasingly utilising criterion-referenced checklists for reporting qualitative research (Korstjens & Moser, Reference Korstjens and Moser 2018 ), and because inexperienced reviewers may struggle to assess the rigor of qualitative research (Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ), or owing to pressure on their time, be positively disposed towards concise, explicit explanations of research quality (Cho & Trent, Reference Cho, Trent and Leavy 2014 ).

4.2 Positivist views of quality

The study found that the terms validity and reliability predominantly associated with positivist traditions were prevalent across qualitative language education research, even among recent studies. In a number of instances, it was evident that authors were adopting reliability in alignment with the positivistic sense of ‘the measurement method's ability to produce the same results’ (Stenbacka, Reference Stenbacka 2001 , p. 552). This was apparent in the attention paid to the reliability of coding data by multiple researchers, indicative of an underlying belief in the identification and mitigation of researcher bias (Barbour, Reference Barbour 2001 ; Kvale, Reference Kvale 1996 ) and a singular meaning or truth being embedded within a given transcript (Terry et al., Reference Terry, Hayfield, Clarke, Braun, Willig and Rogers 2017 ). Indeed, the provision of a figure for the proportion of the dataset that had been re-coded at random along with an interrater reliability statistic, such as Cohen's kappa, indicated arithmetic intersubjectivity (Kvale, Reference Kvale 1996 ), evidence that some authors had – consciously or otherwise – adopted a stronger positivistic philosophy.

While analysts do need to pay careful attention to faithfully representing the meanings conveyed by their participants (Krumer-Nevo, Reference Krumer-Nevo 2002 ), there is hardly any sympathy in qualitative research literature for the position that analysts need to disavow their own perspectives in the search for objective truth (Kress, Reference Kress 2011 ). Instead, qualitative analysis must always be meaningful to the researcher, who endeavours to capture their own interpretations of the data, as opposed to ‘right’ or ‘wrong’ (Terry et al., Reference Terry, Hayfield, Clarke, Braun, Willig and Rogers 2017 ), and to account for the unique perspectives they bring to the analysis through reflexive insights into the process. We agree with the position of Barbour ( Reference Barbour 2001 ) in that double coding of transcripts offers value more in how the content of coding disagreements and the discussions that follow alert coders to alternative interpretations and shape the evolution of coding categories, rather than to indicate the exact degree of intersubjectivity. Nonetheless, such insights were absent across our data, possibly resonating limitations imposed upon authors by some journals, but also indicating still lingering positivist mentalities among qualitative researchers in applied linguistics and language education.

In other instances, it was evident that validity and reliability were being used more qualitatively, such as providing participants with the final say through member checking to enhance a conception of study reliability (and validity ) equated to consistency (Noble & Smith, Reference Noble and Smith 2015 ). However, owing to the lack of thorough description and explanation, a point we remark on later, in several examples it was entirely unclear what precise conception of validity or reliability language education researchers were drawing upon. In such cases, a burden was varyingly placed upon the reader to interpret the mechanism(s) through which the stated quality measures enhanced the study (and according to what epistemological perspective). This was particularly the case with validity , which was usually conceived of from a holistic, whole-study perspective, rather than addressing discrete forms of data collection and analytical techniques.

Where usages of the terms validity and reliability were not explained, it appeared that language education researchers seem content using the language of positivist inquiry, perhaps as part of a conscious effort to improve the credibility and legitimacy of the study for a more successful peer review (Patton, Reference Patton 2002 ), since not all reviewers are adept at judging qualitative research using interpretive concepts (Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ), or some still hold the perspective that ‘anything goes’ in qualitative research (Mirhosseini, Reference Mirhosseini 2020 ). However, as it was beyond the scope of the study to query authors directly on their use of such terms, we cannot judge if such representations indicate qualitative language education scholars adhered to positivist or critical realist traditions, or even that they are explicitly aware that validity and reliability carry significant epistemological baggage (since explicit discussion of epistemological views underlying research methods is not always a component of research methodology courses and texts). Indeed, the use of such terms could constitute an effort to bring the interpretive and positivist traditions together by emphasising the universality of research quality conceptions (Patton, Reference Patton 2002 ).

4.3 Interpretive quality conceptions

It appeared that many authors opted for diverse interpretivist concepts and strategies in which to foreground claims of qualitative research quality. However, it was exceedingly rare for authors to explicitly situate consideration of research quality fully within any one theoretical framework. Just one study made complete use of Lincoln and Guba's ( Reference Lincoln and Guba 1985 ) componential model of trustworthiness, with several other authors drawing upon one or more strands in order to enhance rigor. It would appear, consciously or not, that the vast majority of authors adopted a flexible position on quality, engaging the reader with an overarching argument for research relevance, originality, and rigor (Shohamy, Reference Shohamy 2004 ), rather than elaborating a more foundationalist exercise in quality control. It also reflects the subjective, interpretive nature of the provision of adequate warrants for knowledge claims, a comprehensive undertaking likely beyond the modest length limitations afforded to authors in journals that publish qualitative manuscripts (Tupas, Reference Tupas and Mirhosseini 2017 ). No studies were found to formally adhere to other well-known quality frameworks (e.g., Richardson, Reference Richardson 2000 ; Spencer et al., Reference Spencer, Ritchie, Lewis and Dillon 2003 ; Stewart, Reference Stewart 1998 ; Tracy, Reference Tracy 2010 ), suggesting a failure of such models to permeate the language education literature for reasons beyond the scope of the present study.

The preference for author argumentation over criterion-referenced explication was visible in authors’ selection of eclectic quality assurance measures and rationales. Researchers adopted a range of strategies, albeit triangulation and member checking prevailed. No clear patterns emerged in the selection of procedures to enhance study rigor specific to particular methodological approaches, (e.g., Creswell's ‘Five approaches’), in spite of much ‘research handbook’ guidance that aligns particular strategies to methodologies (see Creswell, Reference Creswell 2007 ). This would further indicate that qualitative language education researchers value creativity and flexibility in constructing an argument of study quality, and that quality assurance strategies are judged on their conceptual and theoretical ‘soundness’, rather than as a requirement to align to a given methodology.

It is also important to note that attention to quality using either positivist or interpretivist concepts was usually presented descriptively and procedurally, with authors seldom engaging in deeper philosophical discussions of research quality. This may be as a result of the many well-meaning guidelines on qualitative research, where issues of quality are often presented procedurally for easy comprehension and implementation (Flick, Reference Flick 2007 ; Maxwell, Reference Maxwell 2013 ). Procedural information helpfully verified that certain techniques had been adopted or processes followed, albeit questions or uncertainties that (we felt) warranted further discussion were often apparent (we again acknowledge the constraints imposed upon authors in this regard). Facileness in attention to quality particularly encompassed the triangulation of sources and methods, the possible role of disconfirming evidence (across sources, methods, and participants), and the outcomes of respondent validation. While attention to quality certainly encompasses a procedural dimension, we also feel that notions of research quality constitute part of the broader epistemological argument running through the reporting of a research study. In this way, researchers need to present a convincing case explaining the basis for selecting the various approaches and how they enhanced study rigor.

We recognise that existing publishing constraints (like word limits) are hardly helpful in elaborately addressing issues of research quality. Indeed, in reporting our own findings here, we were limited in the level of nuance and detail that we were able to convey, for example, concerning our positionality and epistemological stance. However, we believe it can benefit qualitative research in the area of language education if authors reflexively address the complexities involved in attending to various dimensions of research quality, as reflected in the ten thematic notions illustrated in this paper. Although these diverse dimensions were not strongly projected in all the research articles that we examined, visible in our data were reflections upon how study quality could be enhanced in future research, which could offer alternatives to how authors in our field address matters of research quality.

The author(s) declare none.

Seyyed-Abdolhamid Mirhosseini is an Associate Professor at The University of Hong Kong. His research areas include the sociopolitics of language education, qualitative research methodology, and critical studies of discourse in society. His writing has appeared in journals including Applied Linguistics ; Language, Identity and Education ; Critical Inquiry in Language Studies ; and TESOL Quarterly . His most recent book is Doing qualitative research in language education (Palgrave Macmillan, 2020).

William S. Pearson is a lecturer in language education at the School of Education, University of Exeter. His research interests include candidate preparation for high-stakes language tests, written feedback on second language writing, and meta-research in language education. His works have appeared in Assessing Writing, the Journal of English for Academic Purposes , Lingua , and ELT Journal .

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• Seyyed-Abdolhamid Mirhosseini (a1) and William S. Pearson (a2)
• DOI: https://doi.org/10.1017/S0261444824000053

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The Relevance of Language for Scientific Research

• First Online: 28 April 2021

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• Wenceslao J. Gonzalez 2  

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The historical framework of the origin of the relevance of language for scientific research is the previous step for its philosophical analysis, which considers a number of aspects of special importance. (1) Language is one of the constitutive elements of science. It accompanies the other elements that configure science: the structure in which scientific theories are articulated, scientific knowledge, research methods, scientific activity, scientific aims and the values of science. (2) Language has two main roles in the configuration of science. (a) It contributes to establishing scientific thinking (either in natural language or in a formal language, such as mathematics). Thus, language shapes how scientists conceive scientific activity (problems, models and contrasts). (b) Language has a heuristic function, insofar as it allows us to explore new possibilities, create new forms of expression for possible phenomena (in the short, middle and long run). (3) Language allows differences to be shaped between basic science, applied science and application of science. Thus, we have three main options: (i) explanatory and predictive statements; (ii) predictive and prescriptive statements; and (iii) statements oriented to differentiated contexts of use. (4) Scientific language cannot be reduced to structural components (macro-theoretical frameworks, theories, models, hypotheses, etc.), because it encompasses dynamic components of science as well. These dynamic components cannot be condensed merely in terms of processes and evolution, since at least the social sciences and the science of the artificial perform research on phenomena characterized by historicity. (5) In addition to the dimension of language in scientific activity, we should consider the language of this activity as connected with other human activities in social life. This leads to differences with the language of technology and the language of ordinary social life.

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This preeminent role is now best appreciated in the sciences of the Internet, within the framework of the sciences of the artificial, where the role of language in Web science is central, as semantic web research has highlighted. Cf. Tiropanis et al. ( 2015 ); and Hendler and Hall ( 2016 ).

According to James Hendler, “the social nature of the Web 2.0 sites primarily allows linking between people, not content, thus creating large, and valuable, social networks, but with impoverished semantic value among the tagget content,” Hendler and Golbeck ( 2008 , p. 15).

This relevance accentuates the semantic or the pragmatic trait according to the approach on the theory of meaning, which is the initial focus for characterizing scientific language. This leads to choices that give priority to the sense and reference of the words or to the meaning conceived as use. In addition, this in turn can lead to giving primacy to the significance of the words rather than to the context or to holistic options, when the meaning is seen to depend on a certain set or whole.

A philosophical characterization of the theory of meaning that has been very influential is found in Dummett ( 1975 ) and Dummett ( 1976 ).

Cf. Frege ( 1892 ). See also Frege ( 1918 ).

Frege “starts from meaning by taking the theory of meaning as the only part of philosophy whose results do not depend upon those of any other part, but which underlies all the rest. By doing this, he effected a revolution in philosophy as great as the similar revolution previously effected by Descartes; and he was able to this even though there was only one other part of philosophy to which Frege applied the results he obtained in the theory of meaning. We can, therefore, date a whole epoch in philosophy as beginning with the work of Frege, just as we can do with Descartes.” Dummett ([ 1973 ] 1981), p. 669.

Cf. Gadamer ( 1960 ). See also Gadamer ( 1975 ).

Besides the relationship between Frege and Wittgenstein in the initial phase of the Tractatus Logico-Philosophicus , there are also common points with the later period of Philosophiche Untersuchungen , cf. Dummett ( 1981b ).

The “received view” is an expression used by H. Putnam the same year as the publication of Kuhn’s main book. Cf. Putnam ( 1962 ). With it, the historical conjuncture lived at that time by the methodology of science of verificationist inspiration is adequately reflected.

On Kuhn’s stages of philosophical-methodological evolution and the turns in the role of language, see Gonzalez ( 2004b ).

Cf. Popper ( 1935 , 1945a , 1945b , 1957 ). See Gonzalez ( 2004a ).

Other authors paid attention to Frege over the years, which led to the publication of Dummett ( 1981a ). In turn, within the philosophy of language, in general, and in the sphere of reference theory, in particular, there was an abundant number of publications and in various philosophical directions. See, in this regard, Gonzalez ( 1986b ).

Cf. Gonzalez ( 2006a ), especially, pp. 1–19.

Cf. Salmon ( 1992 ), especially, pp. 408–410.

“These contemporary versions of scientific realisms include the following philosophical versions, among others: structural realism, critical realism, referential realism, entity realism, instrumental realism, socially embedded realism, constructive realism, some versions of scientific perspectivism (or perspectivalism), dispositional realism, convergent realism, pragmatic realism, selective realism, minimal realism, and the so-called ‘preservative realism’.” Gonzalez ( 2020a ), p. 4. The main features of these various orientations can be found in Gonzalez ( 2020a ), pp. 6–16.

The role of language, structure, knowledge, methods, activity, ends and values as constitutive elements of science is set out in Gonzalez ( 2013b ), especially, pp. 15–17.

All of them are particularly important for having a proper analysis of central scientific issues, such as scientific prediction. See Gonzalez ( 2015a ), pp. 11–13

On interdisciplinarity, see Niiniluoto ( 2020 ).

This issue is discussed in detail in Gonzalez ( 2021 ).

Ian Hacking has insisted that not everything is constructed. See Hacking ( 1999 ).

The referent is then part of what we consider to be a “fact,” so without a referent we can hardly have anything that we can call “fact.” Regarding what is a fact , Peter F. Strawson wrote: “facts are what statements (when true) state.” Strawson ( 1950 ), p. 136.

On collective morality, see Rescher ( 2003 ).

As science has a role in shaping technology, its values also have a role for values in technology, cf. Gonzalez ( 2015b ).

The use of mathematics for the heuristic function through predictions is what focused attention during the pandemic generated by the Covid-19 virus. Various mathematical models have been used to anticipate the possible future course of the disease at a general level and in each country. They have been used by the World Health Organization to make recommendations and by health authorities in each nation to make decisions.

The philosophical status of mathematical language has also been discussed. In this regard, there are also various philosophical orientations. See, in this regard, the text presented in the 2008 Biennial meeting of the Philosophy of Science Association: Psillos ( 2010 ). A different approach can be found in Azzouni and Bueno ( 2016 ).

It seems to me important to consider that mathematics is also a human activity, which has philosophical consequences. See Gonzalez ( 1991 ).

The idea of diversity in scientific explanations is already present in the influential book of Nagel ( 1961 ). Although, over the years, Salmon made various proposals for scientific explanations, they usually revolved around three elements: preference for the causal explanation over other types of explanations, emphasis on the role of probability, and recognition of the presence of pragmatic elements that modulate explanations. His final proposals can be found in Salmon ( 2002a ); and Salmon ( 2002b )

Cf. Gonzalez ( 2015a ), pp. 66, 192, 219, and 251.

A detailed analysis of the distinction between foresight, prediction, forecast and planning is in Gonzalez ( 2015a ), pp. 68–72.

Imre Lakatos insisted on this point. Cf. Lakatos ( 1970 ). See, in this regard, Gonzalez ( 2001 ).

See Sen ( 1986 ); especially, p. 3; and Gonzalez ( 1998a ).

On the various options for observation and experimentation, see Gonzalez ( 2010 ).

“It is a testament to the machinery of science that so much has been learned about covid-19 so rapidly. Since January the number of publications has been doubling every 14 days, reaching 1363 in the past week alone. They have covered everything from the genetics of the virus that causes the disease to computer models of its spread and the scope for vaccines and treatments.” The Economist ( 2020 ).

Current positions on this issue can be found in Gonzalez ( 2020c ).

That science investigates according to a methodological diversity and according to scales of reality, with epistemological differences according to levels is increasingly assumed. Cf. Gonzalez ( 2020d ).

To date, treatment of Covid-19 has often been completely individualized, if not purely ad hoc through trial and error, due to the absence of previous well-founded studies offering well-contrasted solutions to the disease.

de Regt ( 2017 ), p. 12; see also ( 2017 ), pp. 45 and 88.

There has been a very intense debate on the existence and characteristics of the scientific revolutions. After the very influential book by Thomas Kuhn on The Structure of Scientific Revolutions , an important contribution was made in Thagard ( 1992 ). See, in this regard, Gonzalez ( 2011a ).

Cf. Gonzalez ( 2011b , 2013c ).

In the case of sciences of Internet the novelty is clear, cf. Hall et al. ( 2016 ). The design of the network itself is clearly new, cf. Clark ( 2018 ). Overall, it can be said that we are in a new historical stage, which Luciano Floridi calls “hyperhistory,” cf. Floridi ( 2014 ).

“As the deluge of work on covid-19 has shown, fast, free-flowing scientific information is vital for progress. The virus has changed the way scientists do their work and talk to each other, we hope for good.” The Economist ( 2020 ).

At least three philosophical-methodological stages can be distinguished in Kuhn’s publications, cf. Gonzalez ( 2004b ), especially, pp. 48–66.

As a result of the criticism received in the first stage, Kuhn introduced a series of relevant philosophical-methodological changes in the second period. Among them was the prominent role of exemplars, as characteristic solutions to problems posed and accepted as such by the scientific community. In this regard, Kuhn’s second stage — with the exemplars as a route to learn scientific theories — has been associated with concept characterizations within the framework of cognitive psychology. It is clear that the first stage was under the influence of the Gestalt psychological school he knew. Later, that school moved towards a characterization of concepts more in tune with classical positions, where concepts represent features that are typical of a defined class of objects. Cf. Andersen et al. ( 2006 ). A critical analysis of the book can be found at Thagard ( 2009 ).

Cf. Kuhn ([1962] 1970 ), p. 127.

“Paradigm changes do cause scientists to see the world of their research-engagement differently,” Kuhn ([1962] 1970 ), p. 111.

Analytical philosophers who have dealt with perception include Gottlob Frege and Peter F. Strawson. For the former, see the chapter “Frege on Perception,” in Dummett ( 1993 [reprinted 1998]), pp. 84–98. For the second, see Strawson ( 1961 ) and Strawson ( 1979 ).

The chemical revolution receives special attention in Thagard’s perspective on conceptual change. Cf. Thagard ( 1992 ), pp. 34–61; especially, pp. 39–47. An analysis of Thagard’s conceptual revolutions and the need for new aspects can be found in Gonzalez ( 2011a ), pp. 15–21. A review of Thagard’s book is available in Gonzalez ( 1996 ).

For processes from an ontological viewpoint, see Rescher ( 1996 ).

This is the origin of the friendly controversy with Peter Strawson on the characteristics of the concepts. It started with Gonzalez ( 1998b ). The matter went on with his answer: Strawson ( 1998 ). It was then completed in another subsequent paper: Gonzalez ( 2003 ).

The central role of objectivity in the search for truth in science is emphasized in Gonzalez ( 2020b ).

That we do things with words is something that initially discussed by several analytical philosophers: Austin ( 1962 ); Strawson ( 1970 ); and Searle ( 1969 ).

It is interesting that John L. Austin translated into English (with reproduction of the German text) a very important Frege book: Frege ( 1884 ). It is also worth remembering the volume that Strawson edited related to thought and action: Strawson ( 1968 ).

On the relations between science and technology, with their consequent philosophical-methodological differences, see Gonzalez ( 2005 ).

This book is added to the books coming from previous congresses, which are grouped in the Gallaecia Series : Studies in Contemporary Philosophy and Methodology of Science : Progreso científico e innovación tecnológica , 1997; El Pensamiento de L. Laudan. Relaciones entre Historia de la Ciencia y Filosofía de la Ciencia 1998; Ciencia y valores éticos , 1999; Problemas filosóficos y metodológicos de la Economía en la Sociedad tecnológica actual , 2000; La Filosofía de Imre Lakatos : Evaluación de sus propuestas , 2001; Diversidad de la explicación científica , 2002; Análisis de Thomas Kuhn : Las revoluciones científicas , 2004; Karl Popper : Revisión de su legado , 2004; Science, Technology and Society : A Philosophical Perspective , 2005; Evolutionism : Present Approaches , 2008; Evolucionismo : Darwin y enfoques actuales , 2009; New Methodological Perspectives on Observation and Experimentation in Science , 2010; Conceptual Revolutions : From Cognitive Science to Medicine , 2011; Scientific Realism and Democratic Society : The Philosophy of Philip Kitcher , 2011; Las Ciencias de la Complejidad : Vertiente dinámica de las Ciencias de Diseño y sobriedad de factores , 2012, Creativity, Innovation, and Complexity in Science , 2013; Bas van Fraassen’s Approach to Representation and Models in Science , 2014; New Perspectives on Technology, Values, and Ethics : Theoretical and Practical, 2015; The Limits of Science : An Analysis from “Barriers” to “Confines” , 2016; Artificial Intelligence and Contemporary Society : The Role of Information , 2017; Philosophy of Psychology : Causality and Psychological Subject. New Reflections on James Woodward’s Contribution , 2018; and Methodological Prospects for Scientific Research : From Pragmatism to Pluralism , 2020.

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Gonzalez, W.J. (2021). The Relevance of Language for Scientific Research. In: Gonzalez, W.J. (eds) Language and Scientific Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-60537-7_1

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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The Language of Research: an Explanation and Examples

Learning the language of research can help you understand research  answers to important problems. It can also help you read academic texts (and tests) more easily.   

This page explains key words for understanding the process of research. It talks about what can go wrong, leading to false or misleading results. 

At the bottom is a link to a crossword puzzle . Use it to check your understanding of many important words used in research reports.

The Research Process

Next they analyze the data (information) they have collected. Then they publish their procedures, data, and conclusions. This allows other scientists to repeat the experiments and double-check the conclusions.

The treatment being tested should give significantly better results than the placebo. If not, any apparent difference it makes may be due to people’s hopes and expectations. So a double-blind trial is a way to check the effectiveness of a treatment.

The Language of Research: Describing Problems (Bias, Errors, and Distortion)

The language of research: describing errors.

The TED talk gives some clues to recognize when some research results may be biased or missing. (The speaker presents some very useful information. However, he talks quickly and uses a lot of British slang. If you find listening difficult, try reading the written transcript of his talk.) 

Examples of the Language of Research  

Practice the language of research with a crossword .

Right-click here for the crossword , and here for the answers .

You can practice more research vocabulary with Scientific Method Vocabulary , as well as Science Vocabulary , or  a gap-fill practice on climate change at  Conservation Terminology .

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A Comprehensive Guide to Different Types of Research

Updated: June 18, 2024

Published: June 15, 2024

When embarking on a research project, selecting the right methodology can be the difference between success and failure. With various methods available, each suited to different types of research, it’s essential you make an informed choice. This blog post will provide tips on how to choose a research methodology that best fits your research goals .

We’ll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. It involves defining questions, collecting data, analyzing results, and drawing conclusions.

Meanwhile, a research methodology is a structured plan that outlines how your research is to be conducted. A complete methodology should detail the strategies, processes, and techniques you plan to use for your data collection and analysis.

Research Methods

The first step of a research methodology is to identify a focused research topic, which is the question you seek to answer. By setting clear boundaries on the scope of your research, you can concentrate on specific aspects of a problem without being overwhelmed by information. This will produce more accurate findings. 

Along with clarifying your research topic, your methodology should also address your research methods. Let’s look at the four main types of research: descriptive, correlational, experimental, and diagnostic.

Descriptive Research

Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering “what” questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies , and case studies to gather qualitative or quantitative data. 

A real-world example of descriptive research is a survey investigating consumer behavior toward a competitor’s product. By analyzing the survey results, the company can gather detailed insights into how consumers perceive a competitor’s product, which can inform their marketing strategies and product development.

Correlational Research

Correlational research examines the statistical relationship between two or more variables to determine whether a relationship exists. Correlational research is particularly useful when ethical or practical constraints prevent experimental manipulation. It is often employed in fields such as psychology, education, and health sciences to provide insights into complex real-world interactions, helping to develop theories and inform further experimental research.

An example of correlational research is the study of the relationship between smoking and lung cancer. Researchers observe and collect data on individuals’ smoking habits and the incidence of lung cancer to determine if there is a correlation between the two variables. This type of research helps identify patterns and relationships, indicating whether increased smoking is associated with higher rates of lung cancer.

Experimental Research

Experimental research is a scientific approach where researchers manipulate one or more independent variables to observe their effect on a dependent variable. This method is designed to establish cause-and-effect relationships. Fields like psychology , medicine, and social sciences frequently employ experimental research to test hypotheses and theories under controlled conditions. 

A real-world example of experimental research is Pavlov’s Dog experiment. In this experiment, Ivan Pavlov demonstrated classical conditioning by ringing a bell each time he fed his dogs. After repeating this process multiple times, the dogs began to salivate just by hearing the bell, even when no food was presented. This experiment helped to illustrate how certain stimuli can elicit specific responses through associative learning.

Diagnostic Research

Diagnostic research tries to accurately diagnose a problem by identifying its underlying causes. This type of research is crucial for understanding complex situations where a precise diagnosis is necessary for formulating effective solutions. It involves methods such as case studies and data analysis and often integrates both qualitative and quantitative data to provide a comprehensive view of the issue at hand. 

An example of diagnostic research is studying the causes of a specific illness outbreak. During an outbreak of a respiratory virus, researchers might conduct diagnostic research to determine the factors contributing to the spread of the virus. This could involve analyzing patient data, testing environmental samples, and evaluating potential sources of infection. The goal is to identify the root causes and contributing factors to develop effective containment and prevention strategies.

Using an established research method is imperative, no matter if you are researching for marketing , technology , healthcare , engineering, or social science. A methodology lends legitimacy to your research by ensuring your data is both consistent and credible. A well-defined methodology also enhances the reliability and validity of the research findings, which is crucial for drawing accurate and meaningful conclusions. 

Additionally, methodologies help researchers stay focused and on track, limiting the scope of the study to relevant questions and objectives. This not only improves the quality of the research but also ensures that the study can be replicated and verified by other researchers, further solidifying its scientific value.

How to Choose a Research Methodology

Choosing the best research methodology for your project involves several key steps to ensure that your approach aligns with your research goals and questions. Here’s a simplified guide to help you make the best choice.

Understand Your Goals

Clearly define the objectives of your research. What do you aim to discover, prove, or understand? Understanding your goals helps in selecting a methodology that aligns with your research purpose.

Consider the Nature of Your Data

Determine whether your research will involve numerical data, textual data, or both. Quantitative methods are best for numerical data, while qualitative methods are suitable for textual or thematic data.

Understand the Purpose of Each Methodology

Becoming familiar with the four types of research – descriptive, correlational, experimental, and diagnostic – will enable you to select the most appropriate method for your research. Many times, you will want to use a combination of methods to gather meaningful data. 

Evaluate Resources and Constraints

Consider the resources available to you, including time, budget, and access to data. Some methodologies may require more resources or longer timeframes to implement effectively.

Review Similar Studies

Look at previous research in your field to see which methodologies were successful. This can provide insights and help you choose a proven approach.

By following these steps, you can select a research methodology that best fits your project’s requirements and ensures robust, credible results.

Completing Your Research Project

Upon completing your research, the next critical step is to analyze and interpret the data you’ve collected. This involves summarizing the key findings, identifying patterns, and determining how these results address your initial research questions. By thoroughly examining the data, you can draw meaningful conclusions that contribute to the body of knowledge in your field. 

It’s essential that you present these findings clearly and concisely, using charts, graphs, and tables to enhance comprehension. Furthermore, discuss the implications of your results, any limitations encountered during the study, and how your findings align with or challenge existing theories.

Your research project should conclude with a strong statement that encapsulates the essence of your research and its broader impact. This final section should leave readers with a clear understanding of the value of your work and inspire continued exploration and discussion in the field.

Now that you know how to perform quality research , it’s time to get started! Applying the right research methodologies can make a significant difference in the accuracy and reliability of your findings. Remember, the key to successful research is not just in collecting data, but in analyzing it thoughtfully and systematically to draw meaningful conclusions. So, dive in, explore, and contribute to the ever-growing body of knowledge with confidence. Happy researching!

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Organizing Your Social Sciences Research Paper

• Choosing a Title
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
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• Bibliography

The title summarizes the main idea or ideas of your study. A good title contains the fewest possible words needed to adequately describe the content and/or purpose of your research paper.

Importance of Choosing a Good Title

The title is the part of a paper that is read the most, and it is usually read first . It is, therefore, the most important element that defines the research study. With this in mind, avoid the following when creating a title:

• If the title is too long, this usually indicates there are too many unnecessary words. Avoid language, such as, "A Study to Investigate the...," or "An Examination of the...." These phrases are obvious and generally superfluous unless they are necessary to covey the scope, intent, or type of a study.
• On the other hand, a title which is too short often uses words which are too broad and, thus, does not tell the reader what is being studied. For example, a paper with the title, "African Politics" is so non-specific the title could be the title of a book and so ambiguous that it could refer to anything associated with politics in Africa. A good title should provide information about the focus and/or scope of your research study.
• In academic writing, catchy phrases or non-specific language may be used, but only if it's within the context of the study [e.g., "Fair and Impartial Jury--Catch as Catch Can"]. However, in most cases, you should avoid including words or phrases that do not help the reader understand the purpose of your paper.
• Academic writing is a serious and deliberate endeavor. Avoid using humorous or clever journalistic styles of phrasing when creating the title to your paper. Journalistic headlines often use emotional adjectives [e.g., incredible, amazing, effortless] to highlight a problem experienced by the reader or use "trigger words" or interrogative words like how, what, when, or why to persuade people to read the article or click on a link. These approaches are viewed as counter-productive in academic writing. A reader does not need clever or humorous titles to catch their attention because the act of reading research is assumed to be deliberate based on a desire to learn and improve understanding of the problem. In addition, a humorous title can merely detract from the seriousness and authority of your research. 
• Unlike everywhere else in a college-level social sciences research paper [except when using direct quotes in the text], titles do not have to adhere to rigid grammatical or stylistic standards. For example, it could be appropriate to begin a title with a coordinating conjunction [i.e., and, but, or, nor, for, so, yet] if it makes sense to do so and does not detract from the purpose of the study [e.g., "Yet Another Look at Mutual Fund Tournaments"] or beginning the title with an inflected form of a verb such as those ending in -ing [e.g., "Assessing the Political Landscape: Structure, Cognition, and Power in Organizations"].

Appiah, Kingsley Richard et al. “Structural Organisation of Research Article Titles: A Comparative Study of Titles of Business, Gynaecology and Law.” Advances in Language and Literary Studies 10 (2019); Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; Jaakkola, Maarit. “Journalistic Writing and Style.” In Oxford Research Encyclopedia of Communication . Jon F. Nussbaum, editor. (New York: Oxford University Press, 2018): https://oxfordre.com/communication.

Structure and Writing Style

The following parameters can be used to help you formulate a suitable research paper title:

• The purpose of the research
• The scope of the research
• The narrative tone of the paper [typically defined by the type of the research]
• The methods used to study the problem

The initial aim of a title is to capture the reader’s attention and to highlight the research problem under investigation.

Create a Working Title Typically, the final title you submit to your professor is created after the research is complete so that the title accurately captures what has been done . The working title should be developed early in the research process because it can help anchor the focus of the study in much the same way the research problem does. Referring back to the working title can help you reorient yourself back to the main purpose of the study if you find yourself drifting off on a tangent while writing. The Final Title Effective titles in research papers have several characteristics that reflect general principles of academic writing.

• Indicate accurately the subject and scope of the study,
• Rarely use abbreviations or acronyms unless they are commonly known,
• Use words that create a positive impression and stimulate reader interest,
• Use current nomenclature from the field of study,
• Identify key variables, both dependent and independent,
• Reveal how the paper will be organized,
• Suggest a relationship between variables which supports the major hypothesis,
• Is limited to 5 to 15 substantive words,
• Does not include redundant phrasing, such as, "A Study of," "An Analysis of" or similar constructions,
• Takes the form of a question or declarative statement,
• If you use a quote as part of the title, the source of the quote is cited [usually using an asterisk and footnote],
• Use correct grammar and capitalization with all first words and last words capitalized, including the first word of a subtitle. All nouns, pronouns, verbs, adjectives, and adverbs that appear between the first and last words of the title are also capitalized, and
• Rarely uses an exclamation mark at the end of the title.

The Subtitle Subtitles are frequently used in social sciences research papers because it helps the reader understand the scope of the study in relation to how it was designed to address the research problem. Think about what type of subtitle listed below reflects the overall approach to your study and whether you believe a subtitle is needed to emphasize the investigative parameters of your research.

1.  Explains or provides additional context , e.g., "Linguistic Ethnography and the Study of Welfare Institutions as a Flow of Social Practices: The Case of Residential Child Care Institutions as Paradoxical Institutions." [Palomares, Manuel and David Poveda.  Text & Talk: An Interdisciplinary Journal of Language, Discourse and Communication Studies 30 (January 2010): 193-212]

2.  Adds substance to a literary, provocative, or imaginative title or quote , e.g., "Listen to What I Say, Not How I Vote": Congressional Support for the President in Washington and at Home." [Grose, Christian R. and Keesha M. Middlemass. Social Science Quarterly 91 (March 2010): 143-167]

3.  Qualifies the geographic scope of the research , e.g., "The Geopolitics of the Eastern Border of the European Union: The Case of Romania-Moldova-Ukraine." [Marcu, Silvia. Geopolitics 14 (August 2009): 409-432]

4.  Qualifies the temporal scope of the research , e.g., "A Comparison of the Progressive Era and the Depression Years: Societal Influences on Predictions of the Future of the Library, 1895-1940." [Grossman, Hal B. Libraries & the Cultural Record 46 (2011): 102-128]

5.  Focuses on investigating the ideas, theories, or work of a particular individual , e.g., "A Deliberative Conception of Politics: How Francesco Saverio Merlino Related Anarchy and Democracy." [La Torre, Massimo. Sociologia del Diritto 28 (January 2001): 75 - 98]

6.  Identifies the methodology used , e.g. "Student Activism of the 1960s Revisited: A Multivariate Analysis Research Note." [Aron, William S. Social Forces 52 (March 1974): 408-414]

7.  Defines the overarching technique for analyzing the research problem , e.g., "Explaining Territorial Change in Federal Democracies: A Comparative Historical Institutionalist Approach." [ Tillin, Louise. Political Studies 63 (August 2015): 626-641.

With these examples in mind, think about what type of subtitle reflects the overall approach to your study. This will help the reader understand the scope of the study in relation to how it was designed to address the research problem.

Anstey, A. “Writing Style: What's in a Title?” British Journal of Dermatology 170 (May 2014): 1003-1004; Balch, Tucker. How to Compose a Title for Your Research Paper. Augmented Trader blog. School of Interactive Computing, Georgia Tech University; Bavdekar, Sandeep B. “Formulating the Right Title for a Research Article.” Journal of Association of Physicians of India 64 (February 2016); Choosing the Proper Research Paper Titles. AplusReports.com, 2007-2012; Eva, Kevin W. “Titles, Abstracts, and Authors.” In How to Write a Paper . George M. Hall, editor. 5th edition. (Oxford: John Wiley and Sons, 2013), pp. 33-41; Hartley James. “To Attract or to Inform: What are Titles for?” Journal of Technical Writing and Communication 35 (2005): 203-213; General Format. The Writing Lab and The OWL. Purdue University; Kerkut G.A. “Choosing a Title for a Paper.” Comparative Biochemistry and Physiology Part A: Physiology 74 (1983): 1; “Tempting Titles.” In Stylish Academic Writing . Helen Sword, editor. (Cambridge, MA: Harvard University Press, 2012), pp. 63-75; Nundy, Samiran, et al. “How to Choose a Title?” In How to Practice Academic Medicine and Publish from Developing Countries? A Practical Guide . Edited by Samiran Nundy, Atul Kakar, and Zulfiqar A. Bhutta. (Springer Singapore, 2022), pp. 185-192.

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