sample qualitative research about language

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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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Qualitative Research in English Language Teaching and Learning

• Jixian Wang
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• Education, Linguistics
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The implementation of character building in english language teaching activities, identifying and addressing challenges in teaching english at sdn mangunsari, a study on indian esp teachers' classroom practices in fostering social inclusivity, the rhetorical structure of review article abstracts in applied linguistics published in high-impact international journals, exploring first-year efl students’ problems in essay writing, research on english teaching of professional skilled talents training based on artificial intelligence, 'oh,' 'well,' and hedges as negative politeness strategy: the different use of discourse markers in female and male students' utterances, the role of adjacency pairs to create politeness strategies in students’ phatic utterances, security vulnerabilities and encryption technologies of computer information technology data under the background of big data, a joint autoethnography on collecting qualitative data remotely: insights from doctoral students’ experiences.

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Qualitative research in language teaching and learning journals, 1997-2006., the contribution made by qualitative research to tesol (teaching english to speakers of other languages), qualitative research in applied linguistics : a practical introduction, current trends in research methodology and statistics in applied linguistics.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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 goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Published: 10 July 2008

Language and rigour in qualitative research: Problems and principles in analyzing data collected in Mandarin

• Helen J Smith 1 ,
• Jing Chen 2 &
• Xiaoyun Liu 1 , 3  

BMC Medical Research Methodology volume  8 , Article number:  44 ( 2008 ) Cite this article

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In collaborative qualitative research in Asia, data are usually collected in the national language, and this poses challenges for analysis. Translation of transcripts to a language common to the whole research team is time consuming and expensive; meaning can easily be lost in translation; and validity of the data may be compromised in this process. We draw on several published examples from public health research conducted in mainland China, to highlight how language can influence rigour in the qualitative research process; for each problem we suggest potential solutions based on the methods used in one of our research projects in China.

Problems we have encountered include obtaining sufficient depth and detail in qualitative data; deciding on language for data collection; managing data collected in Mandarin; and the influence of language on interpreting meaning.

We have suggested methods for overcoming problems associated with collecting, analysing, and interpreting qualitative data in a local language, that we think help maintain analytical openness in collaborative qualitative research. We developed these methods specifically in research conducted in Mandarin in mainland China; but they need further testing in other countries with data collected in other languages. Examples from other researchers are needed.

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In collaborative qualitative research in Asia, data are usually collected in the national language, and this poses challenges for analysis. [ 1 ] Translation of transcripts to a language common to the whole research team costs time and money; and meaning is easily distorted or lost in translation: in some languages and dialects there are literally no direct translations for some words and for other words several meanings can be assigned [ 2 ]. These problems are accentuated in qualitative studies carried out in mainland China, in collaboration with people for whom English is their first language, to be published in international English language journals. The grammatical structure of Mandarin differs substantially to English language which means the narrative of an interview might not be captured accurately [ 3 , 4 ].

We have encountered several problems to do with language when collecting, analysing and reporting data in Mandarin. In this paper we use examples from several published research papers to illustrate the challenges posed; in particular we focus on the implications of language and interpretation on rigour in qualitative research. We propose possible solutions to these problems, based on sound methodological principles that we have used in our subsequent research to help maintain rigour in the research process.

To illustrate the potential solutions we draw on specific examples from a descriptive study of directly observed therapy for administration of TB drugs to outpatients in the community in Chongqing Municipality; the findings are published elsewhere and ethics approval was granted by Chongqing Medical University[ 5 ]. The study aimed to identify ways that the TB health service delivery could be improved. We employed mixed methods: a survey to measure the level of direct observation by health workers, health facility patient record analysis to estimate treatment completion rates, and qualitative methods to explore patient and provider views on factors influencing adherence. For the qualitative component we conducted in-depth interviews in Mandarin to find out from patients and doctors about reported adherence, their views on direct observation, and factors that might influence adherence. Our research was directly relevant to the TB control programme in China, so we used the principles of Framework analysis [ 6 ], commonly used for applied or policy relevant qualitative research where the questions are clearly defined and objectives are set in advance. Framework is a systematic matrix based approach with distinct stages that allow transparent data management and interpretation. As with any qualitative analysis approach, the traditionally manual processes of data reduction (coding, searching, retrieving and sorting data) can be facilitated by using specialist software. We decided to use MAXqda to manage our data because most team members were experienced in using the software, and it is possible to import transcripts in Chinese characters (on computers with Chinese language support installed). Our research team comprised two social scientists (one with qualitative expertise), a postgraduate public health student, a medically trained tuberculosis expert, a statistician and a medically trained epidemiologist.

In the next section we describe four specific problems we have encountered conducting qualitative health research in mainland China, and for each problem we suggest potential solutions.

Depth and detail in qualitative data

Collecting qualitative data using individual interviews or focus group discussions requires considerable skill; too little direction and the participant will digress creating a high 'dross rate'[ 4 ]; ask too many short prompting questions and the interview turns into a structured questionnaire. Problems with question structure and flow, use of prompts, probes, 'directiveness' and non verbal feedback can usually be addressed through pilot testing and careful interviewing technique [ 7 , 8 ].

But there are special cultural issues to consider when conducting qualitative research in mainland China. For example, certain population groups are more likely to resist a researcher's prompt for detail and personal experiences. In a recent evaluation of a workplace intervention to provide family planning services to migrant workers in Shanghai, we found qualitative interviews yielded short responses that lacked depth and detail and interviewers found it difficult to encourage open dialogue [ 9 ]. In post-research debriefing discussions, the research team agreed that there could be several reasons for this. Most important was an appreciation of the unique circumstances of the participant – in this case young, unmarried female migrants who are considered a marginalised population in host cities – was crucial to understanding the reasons why they chose not to express their views openly. The team also considered that the prevailing culture of courtesy in mainland China may have prevented participants from being openly critical about people or services, particularly those in authority; this has been documented elsewhere in Asia [ 10 ]. This means that participants may conform to socially expected behaviour rather than disclose personal viewpoints [ 11 ].

Suggested solutions

To help ensure our interviews yielded rich and detailed data, in our subsequent TB research we paid particular attention to the development and pilot testing of topic guides. We jointly developed topic guides in English for in depth interviews. This allowed for specialist input to the structure of the interviews, ensured relevant topic areas were covered, and that the types of questions asked were suitable for the target population. Topic guides were translated and pilot tested in Mandarin, Southwest dialect (a variant of standard Mandarin with different pronunciation). The interviewer was a postgraduate student from Chongqing and fluent in Southwest dialect.

An English speaking social scientist observed the conduct of the pilot interviews, with some simultaneous translation. This allowed for useful feedback on the use of probing questions and prompts to facilitate the conversation, observation of body language and active listening. The pilot interview recordings were transcribed in Mandarin, and to ensure transcript quality, the first few were translated and checked by the social scientists in the team. A brief 'eyeballing' of a transcript in any language where questions and responses are clearly marked can usually detect the balance of narrative between interviewer and interviewee, pick up on short responses and lack of probing, and sometimes identify where leading questions have been used.

It is difficult to negate the cultural barriers to open dialogue with interview participants in China. Most participants are naturally hesitant about sharing opinions, even more so when the interviewer probes for details about sensitive issues, such as illness. The patients interviewed as part of our study were at various stages of treatment for active tuberculosis, and some were initially unwilling to talk about their illness experience. We found that a good approach was to take time to build up trust with the participant, and de-personalise the questions so that participants felt they were not necessarily talking about themselves when they responded. As the student interviewer gained confidence in using these and probing and prompting questions, we found patients were more likely to discuss issues important to them in adhering to TB treatment; the resulting transcripts provided a rich and detailed narrative.

Language and data collection

The use of interpreters in international public health research is advocated by some, and different models are proposed for conducting interviews in this way [ 12 ]; but the impact of the interpreter on the research process needs consideration. Having an interpreter translate the researcher's questions directly can interrupt the flow of conversation and be distracting for the respondent and interviewer; while a more active model that allows the interpreter to carry out the interview means the researcher must relinquish control of the interview. In some cross-national research, 'tactical sampling' is employed to actively seek out key informants able to converse in English; researchers claim this avoids problems with interpretation, translation and miscommunication [ 1 ].

Simultaneous translation during interviews or focus group discussions can work where infrastructure allows for real time translation [ 13 ]; but this is dependent on the translator's skill and knowledge of the local dialect of the study population. An alternative is to translate all transcripts into a language common to the whole team after data is collected, but the risk of misinterpretation, misunderstanding and loss of a respondent's intended meaning is high unless the translator is familiar enough with the dialect to convey 'conceptual equivalence'[ 1 ]. This refers to the extent to which a term used in one language has a comparable meaning when translated into another language. Conceptual equivalence is particularly important in qualitative research collected in Mandarin, where some words have no linguistic equivalent in English or have more than one meaning [ 2 ]. Decisions made about translation can directly affect the accuracy of data collected and the validity of the research reported; researchers are therefore increasingly being encouraged to explain how translation was carried out, by whom, and how local meaning and cultural connotations are captured and reported in their data [ 14 ].

One way to avoid problems of interpretation and ensure accurate meaning is captured during data collection is to conduct interviews and focus group discussions in the local language; this is greatly facilitated in a research team comprising bilingual researchers fluent in the local language (and dialect). Original words, phrases and concepts are securely embedded in context and the risk of misinterpretation and loss of participants' intended meaning is minimised. In our study, qualitative interviews were carried out by a postgraduate student fluent in Southwest dialect, and tape recorded with participant's permission. Recordings were transcribed verbatim in Chinese characters. Two Mandarin speaking researchers checked a sub-sample of transcripts against the original tape recordings to ensure local meanings were captured as far as possible.

Managing data in Mandarin

Important decisions during qualitative data analysis include: which sections of text to code, which data to retrieve and how, what search terms to use to explore the dataset, deciding which themes appear most important in understanding and explaining the phenomenon being studied, and how to explore and display relationships between themes. Traditional manual methods of cut and paste, filing and sorting of textual data can be slow to execute and difficult to describe accurately. When working as a team and with data across languages, we have found it is even more crucial to determine who is responsible for each component of the analysis.

In applied qualitative research it is important that the thematic framework used to code or index transcripts is informed by both the original topic guides and concepts emerging directly from the participants themselves. Identifying an initial coding frame requires researcher skill in pinpointing recurring themes and concepts, and developing meaningful labels for the data. When data are collected in a local language, those team members who are not fluent in this language are excluded from this process. On the other hand, collaborative working at this stage can prevent a profusion of inappropriate codes and arbitrary generation of emergent themes [ 15 ].

Teams working together across languages use various procedures to facilitate analysis – sometimes coding is completed in the local language and English summaries are provided for the whole team [ 16 ], others working with translated data may code and analyse all data in English [ 17 ]. In previous research in China we have used combinations of both, but usually where time is short the data coding, categorisation and identification of themes is done manually and in Mandarin, with discussion of important content of themes aided by English summaries [ 18 ]. We have found that in using these approaches it is difficult keep track of decisions made during the analysis, and even more difficult to describe exactly how it was done. Disclosure of qualitative analysis procedures is increasingly important [ 19 ], and 'audit' or 'decision' trails can help other researchers' judge for themselves whether the findings and interpretations are credible [ 20 ]. There is increasing recognition that computer software packages can help document these decisions, and ensure the processes of data reduction are visible, documented, retrievable and accessible [ 21 ].

Based on our experience in the research with TB patients and providers, we recommend the coding framework is developed in the local language by more than one researcher, and is subsequently made available and discussed in a language common to the research team (in this case English). In our study, two bilingual researchers read through the Mandarin transcripts and independently listed recurring viewpoints relevant to the areas of questioning, and identified common themes emerging from the responses. Doing this independently allowed for more possibilities and ideas about relevant and meaningful code words. Consensus on a final thematic framework was reached through discussion.

We established a coding system in MAXqda based on the thematic framework. At the time the version of MAXqda we used (version 2) allowed transcripts to be imported in Mandarin, but did not support direct text input using Chinese characters, so the coding system had to be set up in English. Figure 1 shows a screenshot of MAXqda with a list of imported transcripts, the code system in English, and a transcript with coded segments.

Screenshot of MAXqda.

We quickly realised the advantage of having the coding system in English; any refinement of the thematic framework could easily be discussed by the whole team, and this facilitated joint decision making between bilingual and English speaking researchers on the categorisation of coded data. The thematic framework and codes were modified and added to as other important issues and viewpoints emerged. The end product was a single database containing all coded interviews (using English labels as shown in figure 1 ) that could be viewed and accessed by the whole team.

Making sense of qualitative data requires systematic re-organisation and ordering of data chunks, allocating meaning and detecting patterns. MAXqda offers several tools for browsing and searching coded segments of text, which we found useful when working collaboratively across languages. In our study, a bilingual and an English speaking researcher explored the entire dataset by first using the 'code matrix browser' (see figure 2 ). This shows the frequency of use of codes across selected interviews; the size of the square shows how often the code was applied. Despite the English speaking researcher not being able to familiarise herself with the original data and the actual statements made by participants, this matrix helped both researchers determine and discuss which concepts were common across interviews, and identify codes that were used infrequently. We also browsed the data using the 'code relation browser', a matrix showing the concurrent use of codes. This helped identify where similar concepts were discussed together and where two or more codes could be collapsed into an overarching category.

Code matrix browser.

Language and interpreting meaning

Working in teams to conduct qualitative research can increase rigor in analysis and encourage richer interpretation [ 22 ], but there are few examples of how teams working over geographic, cultural and linguistic distance actually achieve this. Social scientists will often recognise different patterns, meanings and interpretation in qualitative data to disease experts or epidemiologists, and local researchers are more likely to be familiar with the intricacies of the health system and socio-cultural characteristics of participants than those from outside. Bringing these differing perspectives to bear on emerging conceptual frameworks and explanations helps ensure the findings are grounded in and supported by the data and accurate underlying meaning (and conceptual equivalence) is conveyed.

It is our experience from previous research in mainland China that recognising patterns and being able to interpret the data accurately can be the most challenging part of the process and can involve lengthy discussion about the meaning of informant accounts. For example, in a qualitative study exploring women's views of obstetric care in government hospitals in Shanghai we found that traditional beliefs influenced women's decisions to avoid Caesarean section as it would damage their 'yuan qi'. There is no English equivalent of 'yuan qi', which refers to inherited energy of the body. We consulted together as a research team to make sure the meaning of women's statements was clear and not misinterpreted in the English write up [ 23 ]. Similarly, in the study of young female migrants' views of family planning services, a main theme was related to privacy in obtaining services [ 9 ]. Interviewees frequently used the term 'bu hao yi si'; again there is no literal translation but, after much discussion about the appropriate meaning in English, we agreed to refer to it as 'feeling uncomfortable or embarrassed', and quoted the Mandarin phrase in the final publication.

The decision to publish in a local or international journal is dependent on the research question, funding requirements and judgements about the policy and practice implications of the research. Publication in a language other than that which the data were collected and analysed clearly affects the researcher's ability to accurately convey the meaning of the data, particularly through verbatim quotes; and some argue that translation of direct quotations conceals culturally-loaded meanings [ 1 ]. These are decisions an international collaborative research team must consider.

Data interpretation is frequently described as an intuitive and imaginative process which cannot be reduced to simple mechanical steps [ 24 ], but we found this process can be more critical, the interpretations more valid, and the findings more credible, by involving researchers with a) different methodological perspectives and disciplinary interests, b) detailed understanding of the study context including the cultural characteristics of participants and the structure of the health system, and c) the ability to accurately convey meaning of data collected in a local language. The process of seeing patterns in qualitative data and drawing out meaning is made more thorough by involving the entire research team, taking advantage of their differing disciplinary, language and cultural perspectives. In our study of TB treatment in Chongqing, we established a system that enabled this.

After identifying categories of data, we summarised data relevant to each category in a matrix by case, using translated extracts and direct quotes in English. A bilingual postgraduate student, who also conducted the interviews, translated the extracts and quotes, and these were independently translated by a bilingual social scientist; any disagreements were resolved by discussion or involvement of another bilingual researcher. These English summaries formed the basis of discussions about patterns in the data, comparisons of individual accounts and experiences, and alternative plausible explanations of the themes. For each of the categories, English and bilingual researchers looked across the data and explored the range of attitudes and experiences of sub-groups (i.e patients and doctors, male and female, new and re-treated patients, and across counties). One surprising and important outcome of these discussions was being able to clarify that patient reports of expensive treatment costs did not refer to the cost of anti-tuberculosis treatment (which is provided for free), but to the cost of additional traditional Chinese medicines.

The literature relating to rigor in qualitative research suggests researchers should display enough data to allow a judgement on whether the interpretations are supported by the data and the conclusions are justified [ 15 , 25 ]. Our research on TB was written up as a policy brief for circulation in China and in English for an international peer reviewed journal. We used verbatim quotes in Mandarin together with English translations to illustrate the meaning of each main theme so that Mandarin and English speaking readers could judge for themselves the credibility of our interpretations and research findings [ 5 ].

Descriptive qualitative analysis is an iterative process, with the aim of meaningfully re-classifying codes into categories and themes. Published examples of qualitative research conducted in local languages sometimes do not describe the analysis process adequately; it is often difficult to discern in what language data were analysed, how coding frames were developed and codes derived, and how consensus was reached on analysis and interpretation. Some researchers devise their own ways of assuring data quality, analysing data in the local language using translated summaries of relevant text extracts [ 26 ], and we believe these should be made explicit.

We have described methods we used that allowed us to collect detailed qualitative data in Mandarin, manage that data effectively, produce plausible data categories and arrive at a meaningful interpretation of our data. This level of teamwork across languages was made possible by using a matrix based approach that allowed each stage of the analysis to remain visible to all researchers, and software that facilitated browsing and retrieval of relevant data. We are aware that these may not be the only solutions to the problems highlighted in this paper, and obstacles remain even within our proposed methods.

We found qualitative analysis software useful for keeping track of decisions made during analysis and for sharing the project coding and categorisation between the whole team. We are aware that there are limitations with most software packages, for example, the language restrictions in MAXqda (version 2) meant we underutilised the text search functions available. However, we are aware that the most recent version (MAXqda2007) and other software programmes such as NVivo 7 now support coding and searching in any language including Unicode [ 27 ] character languages such as Chinese. We developed data matrices and charts using Microsoft Word, but MAXqda2007 now includes a facility to construct data matrices, tables and other visual models [ 28 ]. In addition, the founders of the Framework approach, the National centre for Social Research, have just launched their own software with matrix capabilities, specifically for use alongside the Framework approach [ 29 ].

We accept that cultural issues may only be partly responsible for the problems we encountered in obtaining depth and detail in qualitative interviews. Another important consideration is capacity in interviewing technique. Although the use of qualitative data collection methods in public health research is growing, medical and public health training in universities in mainland China emphasises epidemiological methods, and qualitative methods receive limited consideration. We believe capacity in qualitative health research will develop as researchers begin to recognise the contribution of this approach. Better integration of basic and social science research is needed in health systems and health services research, and better collaboration between researchers across cultures is important in achieving this, particularly in countries like China. International collaborative programmes present the opportunity to acquire research methods expertise and disease specific knowledge that can be applied to public health priorities in mainland China, and this will encourage greater participation of Chinese researchers [ 30 ]. However, due to the language barrier, cultural differences, and difficulties in applying the research methods across languages, Chinese researchers may face obstacles to participating in global research programmes, and particularly in qualitative health research. We think the methodological principles outlined in this paper contribute to the growing consensus on acceptable methods for conducting qualitative health research across languages and cultures.

We are aware that in our example we relied on English summaries to form the basis of team discussions about patterns in the data, explanations of themes and interpretation. We emphasise that these translated extracts are to aid discussion; often interpreting the data requires the research team returning to individual transcripts to clarify meaning and concepts and make sure that data categories are reasonable and emerging themes are meaningful. In various research projects we have found that this stage in the analysis is the most time consuming.

When qualitative research conducted in one language is written up in another (for example in English for publication in international journals) in our experience it makes sense to provide at least some of the data as illustrative quotes in the local language. Depending on the journal, this may or may not be straightforward. Public health journals whose editors are used to publishing epidemiological research sometimes have restricted word limits. On the other hand, this can be easier in journals operating an open access policy, or who publish primarily online, as they often allow for additional tables or files.

We have described how language can influence rigour in the qualitative research process, using examples specifically from public health research conducted in mainland China. We have suggested methods for overcoming problems associated with collecting, analysing, and interpreting qualitative data in a local language, that we think help maintain analytical openness in collaborative qualitative research. We developed these methods specifically in research conducted in Mandarin in mainland China; they require further testing and evaluation in other countries with data collected in other languages. We agree that analysis and 'seeing meaning' in qualitative data is inherently collaborative [ 31 ], and have demonstrated where computer software can open the possibilities to do this when working in teams and across languages. Examples from other researchers conducting collaborative qualitative research internationally are needed.

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Acknowledgements

The authors would like to thank Lynn Richards for comments on an earlier draft of this paper.

The qualitative research reported in this paper was part of a larger descriptive study funded by the World Bank/DFID loan project and the DFID Effective Health Care Research Programme Consortium. The views expressed are not necessarily those of the funding organizations.

This paper was developed from an oral presentation given at the 12th International Qualitative Health Research Conference: Understanding differing perspectives in health and healthcare. April 2–5, 2006, The Westin Hotel, Edmonton, Alberta, Canada.

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HJS conceived of and wrote the paper in discussion with JC and XL. All authors contributed to the design of data collection tools in the initial research study cited in reference 5. JC collected the data for that study as part of a Masters thesis, and conducted the analysis with HJS and XL. All authors contributed to revising the final manuscript.

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Smith, H.J., Chen, J. & Liu, X. Language and rigour in qualitative research: Problems and principles in analyzing data collected in Mandarin. BMC Med Res Methodol 8 , 44 (2008). https://doi.org/10.1186/1471-2288-8-44

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18 Qualitative Research Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Qualitative research is an approach to scientific research that involves using observation to gather and analyze non-numerical, in-depth, and well-contextualized datasets.

It serves as an integral part of academic, professional, and even daily decision-making processes (Baxter & Jack, 2008).

Methods of qualitative research encompass a wide range of techniques, from in-depth personal encounters, like ethnographies (studying cultures in-depth) and autoethnographies (examining one’s own cultural experiences), to collection of diverse perspectives on topics through methods like interviewing focus groups (gatherings of individuals to discuss specific topics).

Qualitative Research Examples

1. ethnography.

Definition: Ethnography is a qualitative research design aimed at exploring cultural phenomena. Rooted in the discipline of anthropology , this research approach investigates the social interactions, behaviors, and perceptions within groups, communities, or organizations.

Ethnographic research is characterized by extended observation of the group, often through direct participation, in the participants’ environment. An ethnographer typically lives with the study group for extended periods, intricately observing their everyday lives (Khan, 2014).

It aims to present a complete, detailed and accurate picture of the observed social life, rituals, symbols, and values from the perspective of the study group.

Example of Ethnographic Research

Title: “ The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity “

Citation: Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Overview: This study by Evans (2010) provides a rich narrative of young adult male identity as experienced in everyday life. The author immersed himself among a group of young men, participating in their activities and cultivating a deep understanding of their lifestyle, values, and motivations. This research exemplified the ethnographic approach, revealing complexities of the subjects’ identities and societal roles, which could hardly be accessed through other qualitative research designs.

Read my Full Guide on Ethnography Here

2. Autoethnography

Definition: Autoethnography is an approach to qualitative research where the researcher uses their own personal experiences to extend the understanding of a certain group, culture, or setting. Essentially, it allows for the exploration of self within the context of social phenomena.

Unlike traditional ethnography, which focuses on the study of others, autoethnography turns the ethnographic gaze inward, allowing the researcher to use their personal experiences within a culture as rich qualitative data (Durham, 2019).

The objective is to critically appraise one’s personal experiences as they navigate and negotiate cultural, political, and social meanings. The researcher becomes both the observer and the participant, intertwining personal and cultural experiences in the research.

Example of Autoethnographic Research

Title: “ A Day In The Life Of An NHS Nurse “

Citation: Osben, J. (2019). A day in the life of a NHS nurse in 21st Century Britain: An auto-ethnography. The Journal of Autoethnography for Health & Social Care. 1(1).

Overview: This study presents an autoethnography of a day in the life of an NHS nurse (who, of course, is also the researcher). The author uses the research to achieve reflexivity, with the researcher concluding: “Scrutinising my practice and situating it within a wider contextual backdrop has compelled me to significantly increase my level of scrutiny into the driving forces that influence my practice.”

Read my Full Guide on Autoethnography Here

3. Semi-Structured Interviews

Definition: Semi-structured interviews stand as one of the most frequently used methods in qualitative research. These interviews are planned and utilize a set of pre-established questions, but also allow for the interviewer to steer the conversation in other directions based on the responses given by the interviewee.

In semi-structured interviews, the interviewer prepares a guide that outlines the focal points of the discussion. However, the interview is flexible, allowing for more in-depth probing if the interviewer deems it necessary (Qu, & Dumay, 2011). This style of interviewing strikes a balance between structured ones which might limit the discussion, and unstructured ones, which could lack focus.

Example of Semi-Structured Interview Research

Title: “ Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review “

Citation: Puts, M., et al. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Overview: Puts et al. (2014) executed an extensive systematic review in which they conducted semi-structured interviews with older adults suffering from cancer to examine the factors influencing their adherence to cancer treatment. The findings suggested that various factors, including side effects, faith in healthcare professionals, and social support have substantial impacts on treatment adherence. This research demonstrates how semi-structured interviews can provide rich and profound insights into the subjective experiences of patients.

4. Focus Groups

Definition: Focus groups are a qualitative research method that involves organized discussion with a selected group of individuals to gain their perspectives on a specific concept, product, or phenomenon. Typically, these discussions are guided by a moderator.

During a focus group session, the moderator has a list of questions or topics to discuss, and participants are encouraged to interact with each other (Morgan, 2010). This interactivity can stimulate more information and provide a broader understanding of the issue under scrutiny. The open format allows participants to ask questions and respond freely, offering invaluable insights into attitudes, experiences, and group norms.

Example of Focus Group Research

Title: “ Perspectives of Older Adults on Aging Well: A Focus Group Study “

Citation: Halaweh, H., Dahlin-Ivanoff, S., Svantesson, U., & Willén, C. (2018). Perspectives of older adults on aging well: a focus group study. Journal of aging research .

Overview: This study aimed to explore what older adults (aged 60 years and older) perceived to be ‘aging well’. The researchers identified three major themes from their focus group interviews: a sense of well-being, having good physical health, and preserving good mental health. The findings highlight the importance of factors such as positive emotions, social engagement, physical activity, healthy eating habits, and maintaining independence in promoting aging well among older adults.

5. Phenomenology

Definition: Phenomenology, a qualitative research method, involves the examination of lived experiences to gain an in-depth understanding of the essence or underlying meanings of a phenomenon.

The focus of phenomenology lies in meticulously describing participants’ conscious experiences related to the chosen phenomenon (Padilla-Díaz, 2015).

In a phenomenological study, the researcher collects detailed, first-hand perspectives of the participants, typically via in-depth interviews, and then uses various strategies to interpret and structure these experiences, ultimately revealing essential themes (Creswell, 2013). This approach focuses on the perspective of individuals experiencing the phenomenon, seeking to explore, clarify, and understand the meanings they attach to those experiences.

Example of Phenomenology Research

Title: “ A phenomenological approach to experiences with technology: current state, promise, and future directions for research ”

Citation: Cilesiz, S. (2011). A phenomenological approach to experiences with technology: Current state, promise, and future directions for research. Educational Technology Research and Development, 59 , 487-510.

Overview: A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

6. Grounded Theory

Definition: Grounded theory is a systematic methodology in qualitative research that typically applies inductive reasoning . The primary aim is to develop a theoretical explanation or framework for a process, action, or interaction grounded in, and arising from, empirical data (Birks & Mills, 2015).

In grounded theory, data collection and analysis work together in a recursive process. The researcher collects data, analyses it, and then collects more data based on the evolving understanding of the research context. This ongoing process continues until a comprehensive theory that represents the data and the associated phenomenon emerges – a point known as theoretical saturation (Charmaz, 2014).

Example of Grounded Theory Research

Title: “ Student Engagement in High School Classrooms from the Perspective of Flow Theory “

Citation: Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176.

Overview: Shernoff and colleagues (2003) used grounded theory to explore student engagement in high school classrooms. The researchers collected data through student self-reports, interviews, and observations. Key findings revealed that academic challenge, student autonomy, and teacher support emerged as the most significant factors influencing students’ engagement, demonstrating how grounded theory can illuminate complex dynamics within real-world contexts.

7. Narrative Research

Definition: Narrative research is a qualitative research method dedicated to storytelling and understanding how individuals experience the world. It focuses on studying an individual’s life and experiences as narrated by that individual (Polkinghorne, 2013).

In narrative research, the researcher collects data through methods such as interviews, observations , and document analysis. The emphasis is on the stories told by participants – narratives that reflect their experiences, thoughts, and feelings.

These stories are then interpreted by the researcher, who attempts to understand the meaning the participant attributes to these experiences (Josselson, 2011).

Example of Narrative Research

Title: “Narrative Structures and the Language of the Self”

Citation: McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative . American Psychological Association.

Overview: In this innovative study, McAdams et al. (2006) employed narrative research to explore how individuals construct their identities through the stories they tell about themselves. By examining personal narratives, the researchers discerned patterns associated with characters, motivations, conflicts, and resolutions, contributing valuable insights about the relationship between narrative and individual identity.

8. Case Study Research

Definition: Case study research is a qualitative research method that involves an in-depth investigation of a single instance or event: a case. These ‘cases’ can range from individuals, groups, or entities to specific projects, programs, or strategies (Creswell, 2013).

The case study method typically uses multiple sources of information for comprehensive contextual analysis. It aims to explore and understand the complexity and uniqueness of a particular case in a real-world context (Merriam & Tisdell, 2015). This investigation could result in a detailed description of the case, a process for its development, or an exploration of a related issue or problem.

Example of Case Study Research

Title: “ Teacher’s Role in Fostering Preschoolers’ Computational Thinking: An Exploratory Case Study “

Citation: Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development , 32 (1), 26-48.

Overview: This study investigates the role of teachers in promoting computational thinking skills in preschoolers. The study utilized a qualitative case study methodology to examine the computational thinking scaffolding strategies employed by a teacher interacting with three preschoolers in a small group setting. The findings highlight the importance of teachers’ guidance in fostering computational thinking practices such as problem reformulation/decomposition, systematic testing, and debugging.

Read about some Famous Case Studies in Psychology Here

9. Participant Observation

Definition: Participant observation has the researcher immerse themselves in a group or community setting to observe the behavior of its members. It is similar to ethnography, but generally, the researcher isn’t embedded for a long period of time.

The researcher, being a participant, engages in daily activities, interactions, and events as a way of conducting a detailed study of a particular social phenomenon (Kawulich, 2005).

The method involves long-term engagement in the field, maintaining detailed records of observed events, informal interviews, direct participation, and reflexivity. This approach allows for a holistic view of the participants’ lived experiences, behaviours, and interactions within their everyday environment (Dewalt, 2011).

Example of Participant Observation Research

Title: Conflict in the boardroom: a participant observation study of supervisory board dynamics

Citation: Heemskerk, E. M., Heemskerk, K., & Wats, M. M. (2017). Conflict in the boardroom: a participant observation study of supervisory board dynamics. Journal of Management & Governance , 21 , 233-263.

Overview: This study examined how conflicts within corporate boards affect their performance. The researchers used a participant observation method, where they actively engaged with 11 supervisory boards and observed their dynamics. They found that having a shared understanding of the board’s role called a common framework, improved performance by reducing relationship conflicts, encouraging task conflicts, and minimizing conflicts between the board and CEO.

10. Non-Participant Observation

Definition: Non-participant observation is a qualitative research method in which the researcher observes the phenomena of interest without actively participating in the situation, setting, or community being studied.

This method allows the researcher to maintain a position of distance, as they are solely an observer and not a participant in the activities being observed (Kawulich, 2005).

During non-participant observation, the researcher typically records field notes on the actions, interactions, and behaviors observed , focusing on specific aspects of the situation deemed relevant to the research question.

This could include verbal and nonverbal communication , activities, interactions, and environmental contexts (Angrosino, 2007). They could also use video or audio recordings or other methods to collect data.

Example of Non-Participant Observation Research

Title: Mental Health Nurses’ attitudes towards mental illness and recovery-oriented practice in acute inpatient psychiatric units: A non-participant observation study

Citation: Sreeram, A., Cross, W. M., & Townsin, L. (2023). Mental Health Nurses’ attitudes towards mental illness and recovery‐oriented practice in acute inpatient psychiatric units: A non‐participant observation study. International Journal of Mental Health Nursing .

Overview: This study investigated the attitudes of mental health nurses towards mental illness and recovery-oriented practice in acute inpatient psychiatric units. The researchers used a non-participant observation method, meaning they observed the nurses without directly participating in their activities. The findings shed light on the nurses’ perspectives and behaviors, providing valuable insights into their attitudes toward mental health and recovery-focused care in these settings.

11. Content Analysis

Definition: Content Analysis involves scrutinizing textual, visual, or spoken content to categorize and quantify information. The goal is to identify patterns, themes, biases, or other characteristics (Hsieh & Shannon, 2005).

Content Analysis is widely used in various disciplines for a multitude of purposes. Researchers typically use this method to distill large amounts of unstructured data, like interview transcripts, newspaper articles, or social media posts, into manageable and meaningful chunks.

When wielded appropriately, Content Analysis can illuminate the density and frequency of certain themes within a dataset, provide insights into how specific terms or concepts are applied contextually, and offer inferences about the meanings of their content and use (Duriau, Reger, & Pfarrer, 2007).

Example of Content Analysis

Title: Framing European politics: A content analysis of press and television news .

Citation: Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of Communication, 50 (2), 93-109.

Overview: This study analyzed press and television news articles about European politics using a method called content analysis. The researchers examined the prevalence of different “frames” in the news, which are ways of presenting information to shape audience perceptions. They found that the most common frames were attribution of responsibility, conflict, economic consequences, human interest, and morality.

Read my Full Guide on Content Analysis Here

12. Discourse Analysis

Definition: Discourse Analysis, a qualitative research method, interprets the meanings, functions, and coherence of certain languages in context.

Discourse analysis is typically understood through social constructionism, critical theory , and poststructuralism and used for understanding how language constructs social concepts (Cheek, 2004).

Discourse Analysis offers great breadth, providing tools to examine spoken or written language, often beyond the level of the sentence. It enables researchers to scrutinize how text and talk articulate social and political interactions and hierarchies.

Insight can be garnered from different conversations, institutional text, and media coverage to understand how topics are addressed or framed within a specific social context (Jorgensen & Phillips, 2002).

Example of Discourse Analysis

Title: The construction of teacher identities in educational policy documents: A critical discourse analysis

Citation: Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education, 46 (2), 25-44.

Overview: The author examines how an education policy in one state of Australia positions teacher professionalism and teacher identities. While there are competing discourses about professional identity, the policy framework privileges a  narrative that frames the ‘good’ teacher as one that accepts ever-tightening control and regulation over their professional practice.

Read my Full Guide on Discourse Analysis Here

13. Action Research

Definition: Action Research is a qualitative research technique that is employed to bring about change while simultaneously studying the process and results of that change.

This method involves a cyclical process of fact-finding, action, evaluation, and reflection (Greenwood & Levin, 2016).

Typically, Action Research is used in the fields of education, social sciences , and community development. The process isn’t just about resolving an issue but also developing knowledge that can be used in the future to address similar or related problems.

The researcher plays an active role in the research process, which is normally broken down into four steps: 

• developing a plan to improve what is currently being done
• implementing the plan
• observing the effects of the plan, and
• reflecting upon these effects (Smith, 2010).

Example of Action Research

Title: Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing

Citation: Ellison, M., & Drew, C. (2020). Using digital sandbox gaming to improve creativity within boys’ writing. Journal of Research in Childhood Education , 34 (2), 277-287.

Overview: This was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

Read my Full Guide on Action Research Here

14. Semiotic Analysis

Definition: Semiotic Analysis is a qualitative method of research that interprets signs and symbols in communication to understand sociocultural phenomena. It stems from semiotics, the study of signs and symbols and their use or interpretation (Chandler, 2017).

In a Semiotic Analysis, signs (anything that represents something else) are interpreted based on their significance and the role they play in representing ideas.

This type of research often involves the examination of images, sounds, and word choice to uncover the embedded sociocultural meanings. For example, an advertisement for a car might be studied to learn more about societal views on masculinity or success (Berger, 2010).

Example of Semiotic Research

Title: Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia

Citation: Symes, C. (2023). Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia. Semiotica , 2023 (250), 167-190.

Overview: This study examines school badges in New South Wales, Australia, and explores their significance through a semiotic analysis. The badges, which are part of the school’s visual identity, are seen as symbolic representations that convey meanings. The analysis reveals that these badges often draw on heraldic models, incorporating elements like colors, names, motifs, and mottoes that reflect local culture and history, thus connecting students to their national identity. Additionally, the study highlights how some schools have shifted from traditional badges to modern logos and slogans, reflecting a more business-oriented approach.

15. Qualitative Longitudinal Studies

Definition: Qualitative Longitudinal Studies are a research method that involves repeated observation of the same items over an extended period of time.

Unlike a snapshot perspective, this method aims to piece together individual histories and examine the influences and impacts of change (Neale, 2019).

Qualitative Longitudinal Studies provide an in-depth understanding of change as it happens, including changes in people’s lives, their perceptions, and their behaviors.

For instance, this method could be used to follow a group of students through their schooling years to understand the evolution of their learning behaviors and attitudes towards education (Saldaña, 2003).

Example of Qualitative Longitudinal Research

Title: Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study

Citation: Hackett, J., Godfrey, M., & Bennett, M. I. (2016). Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study.  Palliative medicine ,  30 (8), 711-719.

Overview: This article examines how patients and their caregivers manage pain in advanced cancer through a qualitative longitudinal study. The researchers interviewed patients and caregivers at two different time points and collected audio diaries to gain insights into their experiences, making this study longitudinal.

Read my Full Guide on Longitudinal Research Here

16. Open-Ended Surveys

Definition: Open-Ended Surveys are a type of qualitative research method where respondents provide answers in their own words. Unlike closed-ended surveys, which limit responses to predefined options, open-ended surveys allow for expansive and unsolicited explanations (Fink, 2013).

Open-ended surveys are commonly used in a range of fields, from market research to social studies. As they don’t force respondents into predefined response categories, these surveys help to draw out rich, detailed data that might uncover new variables or ideas.

For example, an open-ended survey might be used to understand customer opinions about a new product or service (Lavrakas, 2008).

Contrast this to a quantitative closed-ended survey, like a Likert scale, which could theoretically help us to come up with generalizable data but is restricted by the questions on the questionnaire, meaning new and surprising data and insights can’t emerge from the survey results in the same way.

Example of Open-Ended Survey Research

Title: Advantages and disadvantages of technology in relationships: Findings from an open-ended survey

Citation: Hertlein, K. M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: Findings from an open-ended survey.  The Qualitative Report ,  19 (11), 1-11.

Overview: This article examines the advantages and disadvantages of technology in couple relationships through an open-ended survey method. Researchers analyzed responses from 410 undergraduate students to understand how technology affects relationships. They found that technology can contribute to relationship development, management, and enhancement, but it can also create challenges such as distancing, lack of clarity, and impaired trust.

17. Naturalistic Observation

Definition: Naturalistic Observation is a type of qualitative research method that involves observing individuals in their natural environments without interference or manipulation by the researcher.

Naturalistic observation is often used when conducting research on behaviors that cannot be controlled or manipulated in a laboratory setting (Kawulich, 2005).

It is frequently used in the fields of psychology, sociology, and anthropology. For instance, to understand the social dynamics in a schoolyard, a researcher could spend time observing the children interact during their recess, noting their behaviors, interactions, and conflicts without imposing their presence on the children’s activities (Forsyth, 2010).

Example of Naturalistic Observation Research

Title: Dispositional mindfulness in daily life: A naturalistic observation study

Citation: Kaplan, D. M., Raison, C. L., Milek, A., Tackman, A. M., Pace, T. W., & Mehl, M. R. (2018). Dispositional mindfulness in daily life: A naturalistic observation study. PloS one , 13 (11), e0206029.

Overview: In this study, researchers conducted two studies: one exploring assumptions about mindfulness and behavior, and the other using naturalistic observation to examine actual behavioral manifestations of mindfulness. They found that trait mindfulness is associated with a heightened perceptual focus in conversations, suggesting that being mindful is expressed primarily through sharpened attention rather than observable behavioral or social differences.

Read my Full Guide on Naturalistic Observation Here

18. Photo-Elicitation

Definition: Photo-elicitation utilizes photographs as a means to trigger discussions and evoke responses during interviews. This strategy aids in bringing out topics of discussion that may not emerge through verbal prompting alone (Harper, 2002).

Traditionally, Photo-Elicitation has been useful in various fields such as education, psychology, and sociology. The method involves the researcher or participants taking photographs, which are then used as prompts for discussion.

For instance, a researcher studying urban environmental issues might invite participants to photograph areas in their neighborhood that they perceive as environmentally detrimental, and then discuss each photo in depth (Clark-Ibáñez, 2004).

Example of Photo-Elicitation Research

Title: Early adolescent food routines: A photo-elicitation study

Citation: Green, E. M., Spivak, C., & Dollahite, J. S. (2021). Early adolescent food routines: A photo-elicitation study. Appetite, 158 .

Overview: This study focused on early adolescents (ages 10-14) and their food routines. Researchers conducted in-depth interviews using a photo-elicitation approach, where participants took photos related to their food choices and experiences. Through analysis, the study identified various routines and three main themes: family, settings, and meals/foods consumed, revealing how early adolescents view and are influenced by their eating routines.

Features of Qualitative Research

Qualitative research is a research method focused on understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

Some key features of this method include:

• Naturalistic Inquiry: Qualitative research happens in the natural setting of the phenomena, aiming to understand “real world” situations (Patton, 2015). This immersion in the field or subject allows the researcher to gather a deep understanding of the subject matter.
• Emphasis on Process: It aims to understand how events unfold over time rather than focusing solely on outcomes (Merriam & Tisdell, 2015). The process-oriented nature of qualitative research allows researchers to investigate sequences, timing, and changes.
• Interpretive: It involves interpreting and making sense of phenomena in terms of the meanings people assign to them (Denzin & Lincoln, 2011). This interpretive element allows for rich, nuanced insights into human behavior and experiences.
• Holistic Perspective: Qualitative research seeks to understand the whole phenomenon rather than focusing on individual components (Creswell, 2013). It emphasizes the complex interplay of factors, providing a richer, more nuanced view of the research subject.
• Prioritizes Depth over Breadth: Qualitative research favors depth of understanding over breadth, typically involving a smaller but more focused sample size (Hennink, Hutter, & Bailey, 2020). This enables detailed exploration of the phenomena of interest, often leading to rich and complex data.

Qualitative vs Quantitative Research

Qualitative research centers on exploring and understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

It involves an in-depth approach to the subject matter, aiming to capture the richness and complexity of human experience.

Examples include conducting interviews, observing behaviors, or analyzing text and images.

There are strengths inherent in this approach. In its focus on understanding subjective experiences and interpretations, qualitative research can yield rich and detailed data that quantitative research may overlook (Denzin & Lincoln, 2011).

Additionally, qualitative research is adaptive, allowing the researcher to respond to new directions and insights as they emerge during the research process.

However, there are also limitations. Because of the interpretive nature of this research, findings may not be generalizable to a broader population (Marshall & Rossman, 2014). Well-designed quantitative research, on the other hand, can be generalizable.

Moreover, the reliability and validity of qualitative data can be challenging to establish due to its subjective nature, unlike quantitative research, which is ideally more objective.

Compare Qualitative and Quantitative Research Methodologies in This Guide Here

In conclusion, qualitative research methods provide distinctive ways to explore social phenomena and understand nuances that quantitative approaches might overlook. Each method, from Ethnography to Photo-Elicitation, presents its strengths and weaknesses but they all offer valuable means of investigating complex, real-world situations. The goal for the researcher is not to find a definitive tool, but to employ the method best suited for their research questions and the context at hand (Almalki, 2016). Above all, these methods underscore the richness of human experience and deepen our understanding of the world around us.

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

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Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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Language differences in qualitative research: is meaning lost in translation?

• Open access
• Published: 19 November 2010
• Volume 7 , pages 313–316, ( 2010 )

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• Fenna van Nes 1 , 2 ,
• Tineke Abma 3 ,
• Hans Jonsson 4 &
• Dorly Deeg 1  

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This article discusses challenges of language differences in qualitative research, when participants and the main researcher have the same non-English native language and the non-English data lead to an English publication. Challenges of translation are discussed from the perspective that interpretation of meaning is the core of qualitative research. As translation is also an interpretive act, meaning may get lost in the translation process. Recommendations are suggested, aiming to contribute to the best possible representation and understanding of the interpreted experiences of the participants and thereby to the validity of qualitative research.

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Finding Meaning: A Cross-Language Mixed-Methods Research Strategy

Foundations, Mishaps and Dissemination of Qualitative Approaches

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English is the dominant language in cross-European projects and publications (Kushner 2003 ). With European research collaboration and knowledge circulation being stimulated by the European Union as well as by national governments, language differences play an increasingly important role in research. Language differences may have consequences, because concepts in one language may be understood differently in another language. This is in particular relevant for qualitative research, because it works with words; language is central in all phases ranging from data collection to analysis and representation of the textual data in publications. Language differences may occur in the first phase of a qualitative study, when interview data need to be translated to the researcher’s language, for example in qualitative research with immigrants. Consequences for the validity of moving across languages have gained considerably attention in these cross-cultural studies (Squires 2009 ). However, language differences also play a role, when translation is required in later phases. This is the case in most studies with participants and main researcher having the same non-English native language, because publication is sought mainly in English outlets. In these studies, moving to English has gained little methodological attention, although here validity might be threatened as well. This article discusses challenges of language differences in qualitative research, when participants and the main researcher have the same non-English native language and the non-English data lead to an English publication.

Interpretation of meanings

Qualitative research seeks to study meanings in subjective experiences. The relation between subjective experience and language is a two-way process; language is used to express meaning, but the other way round, language influences how meaning is constructed. Giving words to experiences is a complicated process as the meaning of experiences is often not completely accessible for subjects and difficult to express in language. To capture the richness of experience in language, people commonly use narratives and metaphors (Polkinghorne 2005 ). Metaphors vary from culture to culture and are language-specific (Lakoff and Johnson 1980 ). For example in Dutch it is a common saying to give a proposal ‘hands and feet’ (handen en voeten geven in Dutch) to express the physical work that is needed to make the proposal concrete. This expression is not easily understandable for native English speakers (Otis 2008 ). Language also influences what can be expressed, and some linguists even state that social reality as experienced is unique to one’s own language; those who speak different languages would perceive the world differently (Chapman 2006 ).

Qualitative research is considered valid when the distance between the meanings as experienced by the participants and the meanings as interpreted in the findings is as close as possible (Polkinghorne 2007 ). We would like to go one step further, and hold that the findings should be communicated in such a way that the reader of the publication understands the meaning as it was expressed in the findings, originating from data in the source language.

Translation between languages involves interpretation as well. The message communicated in the source language has to be interpreted by the translator (often the researcher him or herself) and transferred into the target language in such a way that the receiver of the message understands what was meant. Challenges in the interpretation and representation of meaning may be experienced in any communicative action, but are more complicated when cultural contexts differ and interlingual translation is required. Because interpretation and understanding meanings are central in qualitative research and text is the ‘vehicle’ with which meaning is ultimately transferred to the reader, language differences generate additional challenges that might hinder the transfer of meaning and might result in loss of meaning and thus loss of the validity of the qualitative study.

Challenges of language differences

We will now discuss the challenges that may arise when moving to English in qualitative research. We give examples to illustrate these challenges, although it is challenging in itself to formulate in English examples of the problems in translation between non-English to English. Where needed we have kept the original words in the source language.

Translation of findings

With participants and the main researcher speaking the same language, no language differences are present in data gathering, transcription and during the first analyses, because usually the first coding phase stays closely to the data. The first language differences may occur when interpretations are being discussed among members of a multi-national research team. This is a fragile phase with multiple interpretations being under discussion as even in the source language it is not yet clear how to express the meanings as interpreted. For discussion, these first interpretations need to be explained in English and a very good understanding of subtle meaning differences is needed to come to the best English wordings. A first example comes from a study with ageing couples. The multi-national team discussed how to express the particular way in which the couples experienced changes, namely as slow and almost unnoticed ‘movements down a slope’. The words ‘shifting’ and ‘gliding’ were considered, but both words seemed not fully suitable to express the intended meaning.

In this example, the translation challenges occurred in the first interpretation phase. In the following example, we were not aware of translation problems when translating the Dutch wandelen to walking, because according to several dictionaries, ‘walking’ was linguistically correct. However, native English speakers understood walking as the Dutch lopen , as to move from one place to another on feet, only as instrumental transportation. However, the activity wandelen consisted of a complex constellation of different meanings including the intrinsic enjoyment of the activity, enjoying nature and its associations with Sunday afternoons and holidays together. Ultimately ‘going for walk’ seemed more appropriate to represent the meaning expressed by the couple.

Challenges of translation may even occur when support of a professional translator is been used. This occurred in a narrative case study of an older couple after the wife had experienced a stroke (Van Nes et al. 2009 ). The findings had the form of narratives with the main meanings expressed in the titles of the narratives. The common narrative was that they acted as one organism, which was expressed as ‘One body, three hands and two minds’. The title of the narrative of the husband was constructed to express the sudden and complete shift in his valued activities. Before the stroke of his wife, he had his own engaging activities (bee-keeping and having a kitchen garden), which he experienced as a way to be independent and to support himself and the household with honey and vegetables. After the stroke of his wife, he was busy all day long with supporting her and there was no time left for these former valued activities. In the title ‘ From being self - supporting in an engaging occupation Footnote 1 to the absorbing occupation of supporting ’, the word engaging was meant to reflect a positive meaning. Absorbing was meant to contrast with engaging and to indicate that he was fully occupied all day long after the stroke. The reviewers of the submitted paper on this study, however, understood both English words as having the same positive connotation, so the intended meaning of the complete turn was lost.

Translation of quotations

Quotations of participants are commonly being used in qualitative research articles. Translation of quotes poses specific challenges, because it may be difficult to translate concepts for which specific culturally bound words were used by the participants. For example, the Dutch word gezellig was used commonly by late-life couples, expressing the feeling they had when doing things together. The meaning expressed with this typical Dutch word included experiencing togetherness in doing everyday activities together, often at specific times of the day and in the own home. Translating the word gezellig , only as ‘cosy’ would reduce the meaning. Using more words than in the original quote, however, changes the voice of the participant. This is especially problematic as giving voice to people is seen as an important aim of qualitative research (Denzin and Lincoln 2000 ).

Back translation

After publication, a new translation challenge may be faced, when back translation to the original source language is undertaken. This was the case when translating ‘One body, three hands and two minds’ back to Dutch. The literal translation of ‘One body’ would have been ‘ Eén lichaam ’, but this appeared to be more physical than one body, because in English the word body is also used in other ways, e.g. as in a body of literature. The chosen solution was translation as Samen Eén (‘Together One’), but here the meaning of functioning as one organism was lost.

We have shown that with interpretation of meaning being central in qualitative research, language differences may affect the understanding and interpretation of meanings in different phases on the way from participant to reader. If translation issues are not given adequate thought and attention, the meaning-transfer-chain may resemble the whispering game children play. In the game players line up in such a way that they can whisper to their immediate neighbours. The first player whispers a phrase to his neighbour, who then passes on the message until it reaches the end of the line. If the game has been ‘successful’, the final message differs considerably from the first. In qualitative research meaning is also transferred from one phase to the next, until it reaches the reader and in each transfer meaning might get lost. Such loss of meaning reduces the validity of the qualitative study.

Recommendations

In the following, we give some recommendations aimed to potentially reduce the loss of meaning and thereby to enhance the validity of cross-English qualitative research. Our first recommendation focuses on the thinking and reflection processes that are needed in the analyses. We experienced that talking and reading in English leads to thinking in the English language as well. The relationship between thinking and language has been studied from different scientific perspectives, e.g. in psychology and in the philosophy of language (see e.g. Jackendoff 2009 ). One view considers language to be an aid to thinking. It is beyond the scope of this article to examine this relationship further. However, we feel it can be stated that there is some influence when analysing in another language than your own. To avoid potential limitations in the analysis we therefore recommend staying in the original language as long and as much as possible.

In discussions with members of the research team or peers who do not speak the source language, we recommend to delay the use of fixed—one word—translations. Instead, the analyses might even benefit from using fluid descriptions of meanings using various English formulations. In doing so, it is important to check the interpretations by going back to the codes and preliminary findings in the source language. Keeping record of these discussions would be useful to make the development of the interpretations transparent when in later phases the translations need to be adapted.

For translation of the most meaningful language parts in the findings, like the titles in narrative research or the themes in phenomenological research, we recommend that the researcher operates as a translation moderator in cooperation with a professional translator. This would involve explaining to the translator the intended meaning and its context in the source language. We recommend this should be done in a side-by-side procedure, in which the researcher and the translator discuss possible wordings. Often, different linguistically correct translations are possible, but there will be subtle meaning differences, which need to be closely examined in order to decide on the best translation.

Rich descriptions with the use of quotes of participants are considered to contribute to trustworthiness in qualitative research. However, using quotes is not unproblematic, because participants might feel that they are not fairly represented, when they see their spoken words in written form. Translating the quotes to another language enlarges this problem, because in the translation the words are literally not their own anymore (Temple 2008 ). Therefore, we recommend that these translations are also undertaken with support of a professional translator. Special attention is needed when metaphors are translated, either in quotes or in the findings.

Currently, in method sections of English articles reporting research with non-English data, translation issues are seldom discussed. In line with cross-language research literature, we recommend to describe and discuss in the research article how translation has been undertaken. This will provide reviewers and readers alike with a better insight into the way potential meaning losses have been avoided in the procedures used (Squires 2009 ).

We are aware that using the services of a professional translator adds to the costs of a study. However, these costs contribute to improving the validity of the research and of the quality of the transference of the findings to the readers of the publication. Furthermore, we suggest that the use of a translator in earlier phases of the research reduces efforts to refine translations in later phases, and may prove to be enriching as the discussing about the best translation may reveal new layers of meanings.

The recommendations we presented here are formulated for qualitative researchers who present findings in English, while the data were gathered in their native non-English language. For cross-European research in general, we consider some of our recommendations also to be relevant. In particular, the recommendation to use fluid descriptions of meanings in discussions might enhance quality also in quantitative studies, because it contributes to a sound understanding among researchers of the concepts central to their research.

We consider these recommendations to be a first step, since more research is needed on this topic. First, we would recommend to undertake an inventory with a questionnaire among non-English researchers who published their qualitative research in English outlets in order to collect data how they handled language differences in their studies and to get insight in and rise their awareness of the potential threat to validity when meaning gets lost in translation. As a next step, a series of focus groups could result in guidelines for cross-English qualitative research. With these suggestions for further steps, we stress the importance of an ongoing dialogue regarding different aspects of translation as an important methodological issue for qualitative cross-English research.

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van Nes, F., Abma, T., Jonsson, H. et al. Language differences in qualitative research: is meaning lost in translation?. Eur J Ageing 7 , 313–316 (2010). https://doi.org/10.1007/s10433-010-0168-y

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Also note that print and electronic references are largely the same. For example, to cite both print books and ebooks, use the books and reference works category and then choose the appropriate type of work (i.e., book ) and follow the relevant example (e.g., whole authored book ).

Examples on these pages illustrate the details of reference formats. We make every attempt to show examples that are in keeping with APA Style’s guiding principles of inclusivity and bias-free language. These examples are presented out of context only to demonstrate formatting issues (e.g., which elements to italicize, where punctuation is needed, placement of parentheses). References, including these examples, are not inherently endorsements for the ideas or content of the works themselves. An author may cite a work to support a statement or an idea, to critique that work, or for many other reasons. For more examples, see our sample papers .

Reference examples are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 10 and the Concise Guide Chapter 10

Related handouts

• Common Reference Examples Guide (PDF, 147KB)
• Reference Quick Guide (PDF, 225KB)

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Qualitative Research Topics in Language Teacher Education

May 2020 – volume 24, number 1.

In Qualitative Research Topics in Language Teacher Education, Gary Barkhuizen assembles a host of researchers in the field of language teacher education (LTE), aiming not only to highlight current topics in second, foreign, and multilingual LTE but also to provide practical advice from seasoned educators and researchers on developing a research topic in LTE. The book includes guidance in pairing qualitative research methods with suggested research topics for readers who are already teaching, who are training to become language teachers, or who are interested in or already pursuing graduate studies.

The book covers a breadth of research areas relevant to language teachers and LTE researchers. After the introduction (Chapter 1), each chapter focuses on a particular theme or area of research in LTE. Chapter 2 addresses topics related to working with LTE doctoral dissertation writers. Chapter 3 is based on the theme of “going beyond familiarity” in LTE research, suggesting that researchers draw on other disciplines or explore commonly studied topics in less traditional settings. Contributors also focus on areas of research in LTE that include learning to teach languages (Chapter 4) and language ideologies (Chapter 5). Other research areas, such as language teacher learning and professional development (Chapters 6 and 7), language teacher psychology (Chapter 8), emotions in language teaching (Chapters 9, 10, and 11), and language teacher identities (Chapters 12 and 13) are addressed. Some authors offer topics from a sociocultural perspective (Chapters 14 and 15), as well as topics for second language academic writing (Chapters 16, 17, and 18), English for academic purposes (Chapter 19), and race and gender in LTE (Chapters 20 and 21, respectively). In addition, English as an international language (Chapter 22), multilingualism (Chapters 23, 24, and 25), and teacher study abroad (Chapters 26 and 27) are presented as LTE research areas. Action research in LTE is highlighted (Chapters 28, 29, and 30), along with topics related to issues in language and content instruction (Chapter 31), LTE in primary and secondary education (Chapter 32), task-based teaching and assessment (Chapter 33), approaches in language teaching (Chapter 34), and strategy instruction (Chapter 35). Although some chapters address similar research areas, each chapter provides a unique perspective to the research topics. In the few cases of redundant topics, each chapter serves to reinforce the need for said research.

Each of the 34 chapters following the introduction is formatted to include the same five sub-sections. The first sub-section is a biographical statement introducing the author(s). Nearly all of the biographical statements include reference to the author’s experience as both an educator and LTE researcher. For instance, Maria Ruohotie-Lyhty (Chapter 13) shares how her career of 15 years as a language teacher has contributed to her research about language teacher identity in her present role as a language teacher researcher and teacher educator.

The second sub-section of each chapter is devoted to strategies for choosing a research topic. Each author’s presentation of suggestions varies, ranging from numbered lists to narratives about the author’s experiences. A majority of the authors emphasize advice applicable to any researcher. For example, multiple authors advise choosing a topic that the researcher cares about. Several authors suggest reading existing literature deeply to identify research gaps. Some chapter authors provide suggestions particular to their research area. For instance, Chapter 7 (written by Simon Borg) provides research selection considerations related to professional development initiatives, the overarching theme of the chapter. Similarly, in Chapter 12 (written by Bonny Norton and Peter de Costa), the authors center their strategies specifically on steps for arriving at a research topic in the area of language teacher identity in teacher education.

The third sub-section of each chapter is a description of five proposed research topics from a particular focus in LTE research. For instance, Chapter 28 (written by Anne Burns) offers topics related to action research, which include research into educators’ identified classroom issues (e.g., materials or approaches to teaching), the process of teachers becoming researchers, the impact of action research on teacher identity, the support needs of teacher researchers, and the sustainment of action research among teachers. Chapter 3 (written by Tan Bee Tin) presents five suggested research topics on the theme of “going beyond familiarity in LTE research,” with topics including creativity in the use, learning, and teaching of language; the role of interest in teaching and learning environments; the learning and teaching of language in diverse settings; teacher talk; and the language learning experiences of students in varied settings. For each potential research topic, a one- to two-paragraph description is included with a rationale explaining the research need.

The fourth sub-section of each chapter is a list of ten specific research questions that frame the identified gaps from the research topic descriptions. For example, Jim McKinley (Chapter 17), based on the suggested topic of English L2 writing standards and norms in international higher education, offers such questions as “How open are learners and teachers to non-standard uses of English in L2 writing?” and “How do attitudes to non-standard Englishes vary across types of writing?” About two-thirds of the chapter authors provide an additional rationale, ideas for different directions, or potential methodologies for each research question. The remaining chapters present only a list of research questions. Brief explanations and suggestions for next steps and/or potential methodologies in every chapter would improve the usefulness of this sub-section for readers who are familiarizing themselves with choosing topics and constructing research questions. The final sub-section of each chapter is a brief list of key references (maximum of 10) for further reading, making continued exploration of the research area accessible to readers.

Despite representing a fair range of countries and languages, the majority of chapter authors represent English-dominant countries, with many world regions underrepresented in authorship, including most of Africa, Central and South America, the Middle East, and Asia. This reflects many of the chapter authors’ calls for research in LTE across diverse global settings. Notably, the book chapters are grounded in the personal experience of their authors, adding credibility to their suggestions. The inclusion of the trajectories the authors followed from their early careers as educators to their current research interests makes this book suitable for those highly invested in LTE—teachers themselves. The strategies and advice provided by the chapter authors, as well as the research topic descriptions, could introduce and encourage discussion about global topics in LTE in a research course for students training to become language teachers. Most especially, the chapters on teacher action research can inspire action research projects for those just beginning or a few years into their teaching careers, expanding on the collective understanding of LTE.

The short chapters, the simplicity of the design, and the consistent sub-section organization in each chapter allow the reader to easily locate or revisit points of interest in Qualitative Research Topics in Language Teacher Education . While some terminology used throughout the book may be new for readers inexperienced with academic literature, the concepts important to the suggested research topics are briefly defined. Overall, fitting with the editor’s outlined purpose, this book serves as a starting point for additional reading on LTE topics that inspire interest in more detail and depth.