View in EDS HTML Full Text PDF Full Text

Then and Now: Twenty Years of Education Research Methods Use in the United Kingdom

Saved in:
Bibliographic Details
Title: Then and Now: Twenty Years of Education Research Methods Use in the United Kingdom
Language: English
Authors: Emma Smith, Stephen Gorard, Rebecca Morris, Thomas Perry (ORCID 0000-0002-6124-467X), Jess Pilgrim-Brown (ORCID 0000-0001-8953-6626)
Source: British Educational Research Journal. 2025 51(5):2426-2449.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 24
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Educational Research, Research Methodology, Educational History, Educational Researchers, Higher Education, Capacity Building, Educational Trends, Data Analysis
Geographic Terms: United Kingdom
DOI: 10.1002/berj.4179
ISSN: 0141-1926
1469-3518
Abstract: There have been debates about the quality and usefulness of education research for a long time, with opinion often dividing along methodological lines. Those on different sides of an apparent methodological schism often bemoan the lack of recognition and resources afforded to their chosen approach. Whatever one's position on the existence, or persistence, of education research's own version of the 'paradigm wars', it is nevertheless the case that research design and methods are central to its identity, its usefulness and the impact it makes upon society. This paper contributes to wider debates around the status of education research as a field, or discipline, by exploring the extent to which the research methods used by education researchers working in UK higher education, and beyond, have varied over the last 20 years. It reports the findings from a comparative analysis of two large-scale surveys--the ESRC-funded Research Capacity Building Network survey of 2002 and the BERA-funded Higher Education Research Census 2022. Both surveys explored the methods used by education researchers, mainly based in higher education, and took place against a backdrop of concern, from within and outside the field, about the quality and reach of its research. The findings show that education researchers draw from a variety of different methods and approaches but that the range of tools that they use has narrowed a lot over the period considered. Furthermore, there appears to be an increase in methodological polarisation, particularly between a minority who only use numbers in their research and a majority who never do. This is despite the considerable resources devoted to building research capacity to undertake numeric and combined research.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1486310
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGxMUNZzC4_T1riGBNkOeirAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDIKpP7MtN_XNh0EMswIBEICBm_jhkQML1hR-OX2KLuD-HGkuW9BjzG1YHort_bKWLf2FOXpyVNHmejTz70LTFiiS01n6XO1A8A9cdSD8kqaqEQSIXLovX0TnOrqXoLtI4_zSipC1sObmcFpTPcrdcK_TatQodxlmqEw7gCFrTR81FWkVSwtVsH76YQtQaxSEO0xGPTWiGCwDDdOkF3tC7SF2j60zsUJpwFESf5b_
Text:
  Availability: 1
  Value: <anid>AN0188606561;bed01oct.25;2025Oct14.06:25;v2.2.500</anid> <title id="AN0188606561-1">Then and now: Twenty years of education research methods use in the United Kingdom </title> <p>There have been debates about the quality and usefulness of education research for a long time, with opinion often dividing along methodological lines. Those on different sides of an apparent methodological schism often bemoan the lack of recognition and resources afforded to their chosen approach. Whatever one's position on the existence, or persistence, of education research's own version of the 'paradigm wars', it is nevertheless the case that research design and methods are central to its identity, its usefulness and the impact it makes upon society. This paper contributes to wider debates around the status of education research as a field, or discipline, by exploring the extent to which the research methods used by education researchers working in UK higher education, and beyond, have varied over the last 20 years. It reports the findings from a comparative analysis of two large‐scale surveys—the ESRC‐funded Research Capacity Building Network survey of 2002 and the BERA‐funded Higher Education Research Census 2022. Both surveys explored the methods used by education researchers, mainly based in higher education, and took place against a backdrop of concern, from within and outside the field, about the quality and reach of its research. The findings show that education researchers draw from a variety of different methods and approaches but that the range of tools that they use has narrowed a lot over the period considered. Furthermore, there appears to be an increase in methodological polarisation, particularly between a minority who only use numbers in their research and a majority who never do. This is despite the considerable resources devoted to building research capacity to undertake numeric and combined research.</p> <p>Keywords: research approaches; research capacity building; research methods</p> <p>Key insights What is the main issue that the paper addresses?The paper considers the extent to which the type of research methods used by education researchers working in UK higher education have changed over a 20‐year period. It contributes to wider debates about issues of purpose, quality and methodology within the discipline. What are the main insights that the paper provides?Two key findings emerge from the study. The first is that current education researchers report using fewer methods than those surveyed 20 years ago. The second suggests an increased polarisation of methods, notably between those who mainly use numeric and those who use non‐numeric approaches to data analysis.</p> <hd id="AN0188606561-2">INTRODUCTION</hd> <p>Education researchers are no strangers to accusations that their research is less robust and less impactful than that which takes place in the medical, physical and even in other social science fields. Arguments posed in defence of education research have tended to focus on its diversity: taking in its conceptual and methodological roots in multiple disciplines as well as its accountability to multiple stakeholders—from pupils and parents to teachers, politicians and funding bodies—whose motivations and expectations shape, explicitly and implicitly, the type of education research we undertake. For recent examples, see Whitty and Furlong ([<reflink idref="bib60" id="ref1">60</reflink>]) and Royal Society/British Academy ([<reflink idref="bib48" id="ref2">48</reflink>]). Such diversity can mean that progress is slow, incremental and often fragile, lacking the standout moments of success that occur in many STEM fields, for instance. Indeed, it is arguable that the complexity of context—coupled with the complexity of interactions that characterise much empirical research in education—make it the 'hardest‐to‐do' science of all (Berliner, [<reflink idref="bib3" id="ref3">3</reflink>]: 18).</p> <p>Given this context, it is perhaps unsurprising that there is a long tradition of debate within the education research community that has focused at various times on issues of purpose, quality and methodology (e.g., Wyse et al., [<reflink idref="bib63" id="ref4">63</reflink>]). This paper contributes to such discussions by presenting an account of the research methods currently reported being used by UK education researchers. Placing these findings alongside those from an earlier and similar study conducted just over 20 years ago enables us to compare these two time points and understand the extent to which methods choices and use have changed and developed across this period. The study addresses the following research questions:</p> <p></p> <ulist> <item> What research methods are used (and valued) by education researchers working in UK higher education?</item> <p></p> <item> To what extent have these methods changed over the last 20 years?</item> <p></p> <item> If we cluster education researchers according to the methods they use, what are the characteristics of the clusters, and how have these changed over time?</item> </ulist> <p>We begin with a brief overview of some of the debates surrounding the purpose and nature of education research in the United Kingdom.</p> <hd id="AN0188606561-3">THE PURPOSE AND NATURE OF EDUCATION RESEARCH</hd> <p>The purpose of education research and its relevance for policy and practice have long been debated. In the United Kingdom, where the present study is located, the focus of these discussions has been wide‐ranging, taking in concerns about its quality (e.g., Hargreaves, [<reflink idref="bib26" id="ref5">26</reflink>]), the most useful (or desirable) methodologies (e.g., Connolly et al., [<reflink idref="bib8" id="ref6">8</reflink>]; Wrigley, [<reflink idref="bib61" id="ref7">61</reflink>]), how research is (or should be) funded (e.g., Rasmussen, [<reflink idref="bib46" id="ref8">46</reflink>]), as well as who should undertake education research and under what circumstances (e.g., Furlong, [<reflink idref="bib17" id="ref9">17</reflink>]; Power, [<reflink idref="bib45" id="ref10">45</reflink>]). It is not the aim of this paper to adjudicate on these concerns, but perhaps we can say that the ultimate purpose of education research is to improve education (Tymms, [<reflink idref="bib57" id="ref11">57</reflink>]), and that education research is driven by three interconnected and potentially overlapping functions (Menter, [<reflink idref="bib34" id="ref12">34</reflink>]). The first is to ensure that the large sums of taxpayer‐generated funds which are spent on the social good that is education, are used effectively. The second is to ensure that the processes of education give the greatest benefit to the learner. The third is intellectual curiosity or 'a simple passion for better understanding' (Menter, [<reflink idref="bib34" id="ref13">34</reflink>]: 40). Thus, there are arguably three reasons to undertake education research—to shape policy and practice and to explore fundamental issues within the broad field of education (see also Royal Society/British Academy, [<reflink idref="bib48" id="ref14">48</reflink>]).</p> <p>Recent discussions about the nature of education research in the United Kingdom have coalesced as questions around its status as a discipline or field. According to Wyse ([<reflink idref="bib62" id="ref15">62</reflink>]), education has many of the features that characterise a discipline. It has a distinct status as a university subject, an accumulated body of specialist knowledge and a set of specific terminologies and research methodologies. While there may be some disagreement about the extent to which education and education research fit these criteria (e.g., Hammersley, [<reflink idref="bib25" id="ref16">25</reflink>]), according to Wyse ([<reflink idref="bib62" id="ref17">62</reflink>]), such precision is required to defend against criticisms of poor quality which, he argues, stem from the perception that education is not a 'proper' academic discipline. Part of this defence is rooted in the different knowledge traditions that underpin research in education and the extent to which they influence policy and practice. For example, in the United Kingdom there has, arguably until relatively recently, been a close connection between teacher preparation and the academic study of education—with both mainly taking place within the university system (Wyse, [<reflink idref="bib62" id="ref18">62</reflink>]). This has meant that much education research has tended to be grounded in academic disciplines such as sociology and psychology rather than having a more overt focus on practice. This has led to criticisms from parts of the teaching profession and some politicians that university‐based knowledge was largely irrelevant to the day‐to‐day concerns of the education system (e.g., Blunkett, [<reflink idref="bib5" id="ref19">5</reflink>]; Gove, [<reflink idref="bib23" id="ref20">23</reflink>]). In contrast, education research traditions in countries such as France and Germany have generally separated theoretical academic research from its practical applications. For example, while in the United Kingdom education research is considered to be inherently interdisciplinary, in Germany the dominant model of research depends less on other disciplines but is instead embedded in approaches that are distinctly 'educational' (Whitty and Furlong, [<reflink idref="bib60" id="ref21">60</reflink>]). Similar distinctions are made in France, where the <emph>Ecoles Normales</emph>—the primary institutions for training teachers—are still only partly integrated into the university system.</p> <p>Thus, claims for the distinctive and disciplinary nature of education research in the United Kingdom, as well as in countries with similar research traditions, such as the United States and Australia, offer a riposte to critics who argue that education research can be of poor quality and lack relevance. Such claims, as we explore next, tend to be rooted in different perspectives about methodological rigour and practice.</p> <hd id="AN0188606561-4">QUALITY AND EDUCATION RESEARCH</hd> <p>For decades, academics, policymakers and commentators have debated the most appropriate approaches, or blend of approaches, for researching educational issues. Recent discussions have been prompted by political interventions that have sought to define both the quality of education research and its methodological parameters. A good example of this is the US Department of Education's prioritisation of 'scientifically based' research and its push towards experimental approaches with the creation of the Institute of Education Sciences (Feuer et al., [<reflink idref="bib14" id="ref22">14</reflink>]). This was later echoed in England with the establishment of the Education Endowment Foundation (EEF) in 2011, of which more later.</p> <p>These attempts by policymakers to legislate for research quality are seen by some to have exacerbated divisions within the education research community, which has itself historically been divided upon methodological (or quasi‐philosophical) lines, with some researchers stating their strong adherence to either what have been termed 'qualitative' or 'quantitative' approaches (e.g., Lather, [<reflink idref="bib32" id="ref23">32</reflink>]). Thus, the 'paradigm wars' remain a live issue despite concerted efforts to promote the value of mixed methods and interdisciplinary approaches (Gorard, [<reflink idref="bib20" id="ref24">20</reflink>]; Erckian & Roth, [<reflink idref="bib13" id="ref25">13</reflink>]; Hammersley, [<reflink idref="bib24" id="ref26">24</reflink>]; Wyse et al., [<reflink idref="bib63" id="ref27">63</reflink>]).</p> <p>Thus, debates and perspectives around the 'quality' of education research are central to any consideration of its purpose and value. For those interested in the possibilities of education research for improving practice and the outcomes of young people, quality has often been positioned as a significant barrier to these aims. A 1975 BERA conference on improving training for educational researchers (nearly 50 years ago) agreed</p> <p>... on the need for harder definitions of research that would enable better and tighter evaluations to be made ... if educational researchers were to avoid the criticism of merely producing self‐validating gloss. (Beard and Bishop, [<reflink idref="bib2" id="ref28">2</reflink>]: 72)</p> <p>Moreover, a BERA‐funded survey of education researchers conducted later that decade found that there is 'considerable dissatisfaction with the quality of research done in the field' and 'unanimity ... that education research is not valued by the public at large or by the teaching profession' (Dooley et al., [<reflink idref="bib11" id="ref29">11</reflink>]: 55). Perspectives in later decades also pointed to education research having an 'awful' reputation (Kaestle, [<reflink idref="bib31" id="ref30">31</reflink>]), being 'not very influential, useful or well‐funded' (Burkhardt & Schoenfeld, [<reflink idref="bib7" id="ref31">7</reflink>]: 3), following fads (Slavin, [<reflink idref="bib52" id="ref32">52</reflink>]) and being of indifferent quality (Hargreaves, [<reflink idref="bib26" id="ref33">26</reflink>]). In 2002, the Chief Executive of the Higher Education Funding Council for England, and former Chair of the Economic and Social Research Council (ESRC), reported to the House of Commons Select Committee on Education and Skills that:</p> <p>Education in this country on the whole has a problem with the <emph>quality</emph> of research, not with the amount of it. (The UK Parliament, [<reflink idref="bib54" id="ref34">54</reflink>]: para. 447)</p> <p>These concerns have continued through to the present, with the recently outgoing editors of the <emph>British Educational Research Journal</emph> beginning their term aware of, and indeed galvanised by, a perception that educational research was in a 'parlous state' (Scott, [<reflink idref="bib50" id="ref35">50</reflink>]). These editors concluded, in 2023, that the research published during their tenure demonstrated that the situation has improved (Biesta et al., [<reflink idref="bib4" id="ref36">4</reflink>]: 2), citing increased 'diversity in terms of topics, methods and methodologies'.</p> <p>Ostensibly, the most comprehensive and objective indicators of quality come from the Research Excellence Framework (REF), and before that the Research Assessment Exercise (RAE). In the 2001 RAE there was a considerable increase in the judged quality of research across all fields, but the progress in education was regarded as being much less significant (The UK Parliament, [<reflink idref="bib54" id="ref37">54</reflink>]). More recently, the quality of outputs submitted to the Education Unit of Assessment in the 2021 REF were judged to have 'risen markedly' (p. 169) compared to REF 2014, although the proportion of outputs deemed world‐leading (i.e., awarded 4*) was lower in education than in other social science fields such as sociology, politics and economics (REF, [<reflink idref="bib47" id="ref38">47</reflink>]). Of course, education research has varying aims and audiences, and so it is conceivable—and perhaps likely—that there will be different views on its quality and value (Oancea, [<reflink idref="bib39" id="ref39">39</reflink>]). Regardless of whether concerns about the quality of education research are justified, there have been increasing demands from funders and governments for publicly funded research to be of higher quality and to have real‐world 'impact' (e.g., Hillage et al., [<reflink idref="bib27" id="ref40">27</reflink>]; OECD, [<reflink idref="bib41" id="ref41">41</reflink>]). Linked to these calls have been discussions of skill needs and usage, particularly where there are skills gaps, or where new developments (e.g., use of new technologies) necessitate capacity building. Ultimately though, issues of quality in education research rest largely on questions of the methods researchers use and the extent to which research methods training equips researchers to undertake robust research with impact.</p> <hd id="AN0188606561-5">METHODS AND EDUCATION RESEARCH</hd> <p>Issues of research design and methods are integral to a discussion of education research, and its aims, quality and impact. Conversations about which methods are employed then connect to ongoing critiques of researchers' methodological knowledge, rigour and concerns about the quality and purpose of education research (Perry and Morris, [<reflink idref="bib43" id="ref42">43</reflink>]; Thomas & Gorard, [<reflink idref="bib55" id="ref43">55</reflink>]).</p> <p>Thus, from <emph>why</emph> we do education research it is a small step to thinking about <emph>how</emph> and <emph>how well</emph> we do education research, and so to the role of methods. Here again we come across an established, although contested, body of work, especially in the United Kingdom. Indeed, to appreciate the extent and longevity of debates around methods use in education research, one need only look at the presidential addresses of past presidents of BERA to gain a sense of how deeply embedded these discussions are within the academic education community, at least in the United Kingdom. Over five decades the challenges of education method(ologies) have been well rehearsed through statements about, for example, interdisciplinary methods (Simon, [<reflink idref="bib51" id="ref44">51</reflink>]), samples and cases (Stenhouse, [<reflink idref="bib53" id="ref45">53</reflink>]), feminist methodologies (Delamont, [<reflink idref="bib10" id="ref46">10</reflink>]), researcher identities (Edwards, [<reflink idref="bib12" id="ref47">12</reflink>]) and close‐to‐practice methods (Wyse, [<reflink idref="bib62" id="ref48">62</reflink>]). Other presidential addresses have focused, to a lesser or greater extent, on research quality (e.g., Baumfield, [<reflink idref="bib1" id="ref49">1</reflink>]; Furlong, [<reflink idref="bib16" id="ref50">16</reflink>]; Mortimore, [<reflink idref="bib36" id="ref51">36</reflink>]; Munn, [<reflink idref="bib37" id="ref52">37</reflink>]; Whitty, [<reflink idref="bib58" id="ref53">58</reflink>]). Indeed, it is arguable that research quality is one theme that, explicitly or implicitly, runs through most of these discussions, with concerns that certain method(ologies) are not used enough, or well enough, to achieve the purposes of educational research.</p> <p>Perhaps, echoing the above, there is an appetite among some education researchers to develop their expertise in research design and/or methods. This was a point largely supported by respondents to the BERA State of the Discipline survey—87% of whom agreed that knowledge and skills about method were at least as important as topic expertise (Morris et al., [<reflink idref="bib35" id="ref54">35</reflink>]). In addition, just 6% of those completing the BERA survey reported not wanting to develop their skills in this area. However, and perhaps concernedly, when asked to reflect on the level of their own formal training in research design and methods, half of the respondents rated their levels as 'limited or none' or 'basic', with only 12% considering their training to be of 'excellent' quality.</p> <p>While reports such as this point to the possibility of important methodological skills gaps among education researchers, they highlight both an awareness of this skills deficit as well as a motivation to remedy it. There have, of course, been initiatives to build the capacity of education researchers to undertake research in the past and the ESRC‐funded Research Capacity Building Network, which we discuss below, is a good example of this type of work. Perhaps the key recent initiative aimed at developing the research skills of education researchers has been the ESRC Quantitative Methods Initiative. Supported by the Nuffield Foundation and the British Academy, the Q‐Step programme aimed to promote and widen the use of 'quantitative' methods in social science research (Nuffield Foundation, [<reflink idref="bib38" id="ref55">38</reflink>]). This is based on the idea that there was/is a shortage of 'quantitative' skills and poor‐quality methods training in this area:</p> <p>... by far the most important barrier to change is the very low proportion of staff in university social science departments who themselves have quantitative skills, and the inertia in the system that makes raising this proportion difficult. (MacInnes et al., [<reflink idref="bib33" id="ref56">33</reflink>]: 15)</p> <p>The extent to which the apparent shortage of researchers who use quantitative approaches in their research persists in education is an important question, especially in the context of initiatives such as the Q‐Step programme, and is one that we will consider further in this study. So, in summary, there are several possible reasons why education research has the reputation of being poor quality in relation to other disciplines. As noted above, some commentators claim that education research is intrinsically harder than research in other areas (Berliner, [<reflink idref="bib3" id="ref57">3</reflink>]; Fischman et al., [<reflink idref="bib15" id="ref58">15</reflink>]). Perhaps it is different to disciplines like economics or psychology in having less well‐defined sets of disciplinary tools or methods, or because, unlike sociology, its close link to practice means that many practitioners enter research later and have useful practical experience but less formal methods training (e.g., Morris et al., [<reflink idref="bib35" id="ref59">35</reflink>]; Oancea et al., [<reflink idref="bib40" id="ref60">40</reflink>]). Or it might be because of strong methods identities adopted by education researchers, leading to real or perceived schisms. The methods researchers choose, and the training they receive, shed light on questions of quality. Our aim in the present study is to better understand how the research methods used by education researchers have changed over the last two decades. We do this through considering two surveys conducted approximately 20 years apart.</p> <hd id="AN0188606561-6">THE TWO STUDIES</hd> <p>We report on the findings from a comparative analysis of two large‐scale surveys—the ESRC‐funded Research Capacity Building Network (RCBN) survey of 2002 and the BERA‐funded Higher Education Research Census which took place in 2022. Both surveys, taken 20 years apart, explored the methods used by education researchers, largely based in UK higher education. Throughout the paper we use the term 'methods' as an umbrella term for the range of approaches that researchers use when they design their studies and collect (or source) as well as analyse their data. We briefly summarise the aims of both surveys before describing how the data were prepared for analysis.</p> <hd id="AN0188606561-7">The RCBN survey</hd> <p>At the time, the Teaching and Learning Research Programme (TLRP) was the ESRC's largest ever educational research programme. It ran between 2000 and 2011, involving around 700 researchers across more than 50 projects and with a budget in the region of £43m (JISC, [<reflink idref="bib30" id="ref61">30</reflink>]; TLRP, [<reflink idref="bib56" id="ref62">56</reflink>]). The origins of the TLRP date to the mid‐1990s when, as discussed above, education research received widespread criticism for being 'small scale, irrelevant, inaccessible and of low quality' (Pollard, [<reflink idref="bib44" id="ref63">44</reflink>]). Thus, the overarching idea of the TLRP was to 'support research which is of both high quality in social scientific terms and of high relevance in terms of policy and practice', and among its six aims was a focus on developing expertise and specifically to 'enhance capacity for all forms of research on teaching and learning, and for research‐informed policy and practice' (Pollard, [<reflink idref="bib44" id="ref64">44</reflink>]). This was the particular focus of a separate strand of work undertaken by the programme's Research Capacity Building Network.</p> <p>One of the initial objectives of the RCBN was to undertake an extensive consultation exercise to identify the priorities for research capacity building and to generate a database of expertise from across the UK educational research community. This exercise involved three elements. The first was to interview key stakeholders, from policymakers and practitioners to funders and researchers. The second was to survey, as widely as possible, educational researchers to identify current expertise in research and future training needs. The third was a simple review of the 'best' education research literature, as determined by publications returned to the 2002 RAE (for further details, see Gorard et al., [<reflink idref="bib18" id="ref65">18</reflink>], [<reflink idref="bib19" id="ref66">19</reflink>]). It is the second element—the survey of education research skills and training needs—that is the focus of this paper.</p> <p>The survey asked respondents to summarise their knowledge and use of a range of methods for design, data collection and analysis. Questions were grouped across four themes—research design, skills for data collection, accessing other sources of data and skills for data analysis. A list of methods was specified for each theme (around 300 in the whole survey) with respondents invited to add further techniques as needed. For each method, respondents were asked to indicate whether they <emph>consume</emph>, <emph>use</emph> or <emph>have expertise</emph> in it, and were invited to tick all that applied. For our new analysis we were interested in responses to the latter two categories of whether a researcher has used or claims expertise in each approach.</p> <p>All researchers within the TLRP, as well as all members of BERA, and associates of the Learning and Skills Network, were sent a self‐completion questionnaire during 2002, which was open for responses for 6 weeks. Responses were received from 521 researchers, including around 80% of the researchers involved in the TLRP at the time.</p> <hd id="AN0188606561-8">The BERA Higher Education Research Census</hd> <p>BERA launched its 'State of the Discipline' initiative in 2019, with the aim of offering 'a clear, comprehensive account of the state of education as an academic discipline—as a field of practice and as a pivotal element of social and political policy across the four nations of the UK' (British Educational Research Association, [<reflink idref="bib6" id="ref67">6</reflink>]). As part of this initiative, BERA commissioned five research projects whose findings were intended to 'equip stakeholders in every part of the sector with the most objective and powerful information on which to base their advocacy for, and their efforts to grow the size, influence and impact of, education'. One of those studies, whose findings we consider here, was a large‐scale census of education researchers' work, experiences and identities (Morris et al., [<reflink idref="bib35" id="ref68">35</reflink>]). In addition to inviting respondents to share their views about current issues and debates relating to education research, the census explored the following themes:</p> <p></p> <ulist> <item> identity and background</item> <p></p> <item> employment, career and institutions</item> <p></p> <item> research motivations, activities, experiences and expertise.</item> </ulist> <p>The research theme, which we focus on in this paper, included questions about research foci, motivations, experiences and activities, methodologies and methods, engagement and dissemination, as well as research support and conditions. Here we focus on respondents' reports about the methods they use in their research. Responses were gathered across four areas: research approaches, methods of data collection, sources of data and methods of data analysis. Each set of questions began with the following stem: <emph>In my own research I have used/done ...</emph> and was followed by a list of items with an 'other' option. Respondents were invited to tick all options that applied. The list of items was partly modelled on those used in the RCBN survey, to enhance comparability.</p> <p>The survey was available to all education researchers working in UK university departments of education (and associated research centres) for 8 weeks during spring and summer 2022. Education researchers were invited to take part in the survey if they met both the following criteria:</p> <p></p> <ulist> <item> Engaged in <emph>any form</emph> of education research and/or scholarship.</item> <p></p> <item> A paid employee of a higher education institution in the United Kingdom (on <emph>any</emph> contractual basis, including part‐time, fixed‐term and teaching‐only contracts).</item> </ulist> <p>Reporting an exact response rate for either survey is not straightforward as estimates of the number of research staff working in higher education departments vary widely. Nevertheless, the study estimates a response rate of around 20% (achieving 1559 useable responses). Missing data across both surveys was minimal, for example for the BERA survey it was less than 4% for all items. Further detail about the survey's methodology and findings are available in Morris et al. ([<reflink idref="bib35" id="ref69">35</reflink>]).</p> <hd id="AN0188606561-9">DESIGN OF THE COMPARATIVE STUDY IN THIS PAPER</hd> <p>This paper re‐analyses the data from both surveys based on common items that would allow comparative study of the extent to which the methods used in academic education research have changed over the last 20 years. Although the scope of the BERA survey was more general than that of the RCBN, which focused entirely on research methods skills and training needs, the presence of common questions in both surveys affords a unique opportunity to examine the extent to which education researchers' use of different approaches to conducting their research has varied over the last two decades.</p> <p>We were able to directly compare 26 different categories of methods across both surveys. The full list, plus four additional approaches included in the BERA survey but not the RCBN study, and relevant for this paper, are provided in Table 1. There are several limitations to this approach. For example, there is some degree of overlap between categories (e.g., a case study probably involves interviews), and some could represent an entire approach to research rather than just a method. Furthermore, approaches such as using 'big data' or machine learning are excluded from the comparison because they were not relevant to education researchers in 2002. We group research approaches into distinct categories: research design, methods of data collection, sources of existing (secondary) data and finally approaches to analysis. As far as possible we have avoided grouping approaches as qualitative or quantitative (except for the description of sources of existing data) because we recognise that such categories can be arbitrary. For example, many approaches to data analysis can have quantitative and qualitative components.</p> <p>1 TABLE List of methods used in the comparison.</p> <p> <ephtml> <table><tbody valign="top"><tr><td align="left">Research designs</td></tr><tr><td align="left">Case studies</td></tr><tr><td align="left">Experimental/quasi‐experimental design</td></tr><tr><td align="left">Programme evaluation</td></tr><tr><td align="left">Action research</td></tr><tr><td align="left">Systematic reviews</td></tr><tr><td align="left">Meta‐analysis</td></tr><tr><td align="left">Longitudinal studies</td></tr><tr><td align="left">Historical design</td></tr><tr><td align="left">Arts‐based methods (e.g., visual or drama‐based approaches)</td></tr><tr><td align="left">Qualitative methodologies (e.g., ethno‐methodology, phenomenology)</td></tr><tr><td align="left">Data collection</td></tr><tr><td align="left">Interviews</td></tr><tr><td align="left">Observations</td></tr><tr><td align="left">Surveys</td></tr><tr><td align="left">Diaries</td></tr><tr><td align="left">Behaviour/performance tests</td></tr><tr><td align="left">Sources of data</td></tr><tr><td align="left">Numeric secondary data (e.g., administrative and survey‐based secondary data)</td></tr><tr><td align="left">Non‐numeric secondary data [including textual sources (e.g., letters, diaries), sound data sources and visual sources (e.g., films, videos, paintings)]</td></tr><tr><td align="left">Approaches to data analysis</td></tr><tr><td align="left">Descriptive univariate analysis (e.g., frequencies, percentages, averages)</td></tr><tr><td align="left">Descriptive bivariate analysis (e.g., cross‐tabulations, comparing means, correlations)</td></tr><tr><td align="left">Inferential statistical testing (e.g., chi‐squared, t‐tests, interpreting p‐values, confidence intervals)</td></tr><tr><td align="left">Multivariate statistical modelling (multiple regression, factor analysis, structural equation modelling, multi‐level modelling)</td></tr><tr><td align="left">Content analysis</td></tr><tr><td align="left">Discourse analysis</td></tr><tr><td align="left">Narrative analysis</td></tr><tr><td align="left">Conversation analysis</td></tr><tr><td align="left">Grounded theory</td></tr><tr><td align="left">Additional approaches (BERA survey only)</td></tr><tr><td align="left">Mixed‐methods approaches</td></tr><tr><td align="left">Philosophical and/or theoretical study</td></tr><tr><td align="left">Thematic analysis</td></tr><tr><td align="left">Participatory approaches</td></tr></tbody></table> </ephtml> </p> <p>As explained above, in each survey, respondents were encouraged to tick as many options as were relevant to them. In addition to the categories listed, both surveys contained an 'other' option, which invited respondents to include methods that were not listed. This option was not widely used, suggesting that the majority of approaches adopted by researchers were captured in the surveys, with 98% of respondents indicating that they used at least one of the methods listed.</p> <p>Several adjustments needed to be made to the data in order to prepare them for analysis. For example, because of its explicit focus on methods, the RCBN questionnaire listed multiple approaches within one method. It included eight different approaches to doing interviews (e.g., individual, group or telephone). In this example, as long as the respondent indicated that they had used at least one approach to doing an interview, they were coded as using interviews in their research. We were not interested in how often these techniques were used, but whether or not the respondent had used them in their research at any time. Similarly, the RCBN questionnaire asked respondents whether they were able to consume, use or were expert in each method. As our interest in this paper lies in the range of methods education researchers use, only responses indicating use or expertise in a method were coded as 'use' in this study.</p> <hd id="AN0188606561-10">Further variables used in the analysis</hd> <p>In addition to exploring how use of methods has varied between the two surveys, we were also interested in the extent to which it differs according to researcher characteristics. As with any study of this nature, the extent to which we can compare key variables depends on the data originally collected. Here the data only allowed comparison according to three types of researcher characteristics—gender, age and length of time they had been a higher education researcher. We note the limitation of only exploring these three variables and the lack of comparison of key characteristics such as ethnicity and disability. The following adjustments were made to the available variables to prepare them for analysis.</p> <hd id="AN0188606561-11">Gender</hd> <p>While the BERA survey collected data on three categories (with an 'other' option), the RCBN survey included two (male or female). For purposes of comparison, only the male and female gender categories from the BERA survey (corresponding to 98% of the sample) were retained in this analysis.</p> <hd id="AN0188606561-12">Age</hd> <p>Both surveys gathered data on the age of the respondent (RCBN asked for year of birth, BERA for age group). The mean age of respondents to the RCBN survey was 50 years old and in the BERA survey the two largest age groups were 50–54 and 55–59. For analysis, two age groups were created: those aged below 50 years and those aged 50 and above.</p> <hd id="AN0188606561-13">Length of time as an education researcher</hd> <p>Both surveys asked respondents to estimate how many years they had worked as an education researcher. Here, the data was collapsed into three categories that correspond to the different career stages of a researcher as used by UK Research and Innovation (UKRI—the body that oversees government funding for research and innovation in the United Kingdom). So, education researchers working in higher education for 5 years or less were designated as 'early career', those working between 6 and 15 years as 'mid‐career' and those working 16 years and over as 'senior researchers'.</p> <hd id="AN0188606561-14">APPROACH TO ANALYSIS</hd> <p>The data from both surveys represent a sample of education researchers who agreed to take part in the study, rather than any kind of random sample. No cases were randomised. Therefore, inferential statistical tests were neither appropriate nor necessary (Gorard, [<reflink idref="bib21" id="ref70">21</reflink>]). Patterns in the data were first explored using bivariate techniques before proceeding to a cluster analysis to identify whether education researchers could be grouped, or clustered, according to the types of methods they report using (further details about approaches to analysis can be found in Morris et al., [<reflink idref="bib35" id="ref71">35</reflink>]). In brief, however, to develop the cluster analysis, exploratory analysis was first undertaken with both datasets separately using hierarchical cluster modelling followed by a <emph>k</emph>‐mean cluster analysis in order to identify the optimum number of clusters (Scitovski et al., [<reflink idref="bib49" id="ref72">49</reflink>]). Finally, a two‐step cluster analysis was carried out to explore the relative importance of different methods in grouping the education researchers into the clusters. For reasons of space, it is this final stage of the analysis that is reported in this paper.</p> <p>It is not unusual for comparative studies of this nature to have some limitations. Where research background characteristics enable comparison between the RCBN and BERA surveys, the two samples appear similar in age, gender and career experience. The BERA sample is larger, and the two surveys had slightly different purposes and contexts. Each asked additional questions that the other did not, and which are not covered here. Similarly, the RCBN sample, by virtue of including researchers employed on large‐scale ESRC‐funded studies, may have been skewed towards those who were more disposed to thinking about methods, whereas the BERA survey drew its sample from a more general population of education researchers. With this in mind, we cannot be certain that any difference in response is not at least partly due to differences in the sampling. Nevertheless, by focusing on the strongest patterns, we can use the samples to give some insight into the work and characteristics of researchers employed to conduct academic education research in each era. The study is also limited by only using two time points (2002 and 2022) in a study that explores changes over time, and we acknowledge the challenges posed by this research design.</p> <hd id="AN0188606561-15">CHARACTERISTICS OF THE SAMPLE</hd> <p>We begin by describing the characteristics of the sample, and the frequency and type of methods they use, before exploring the extent to which these characteristics vary according to methods used across both surveys. We then present the findings from the cluster analysis that assigns researchers to different groups based on their reporting of the methods they use in their research.</p> <p>The two surveys have respondents with very similar background characteristics in terms of age and career stage (Table 2). The BERA respondents were fractionally younger on average, with slightly more RCBN respondents reporting themselves as senior in terms of career stage. The largest group of respondents (around 40%) in both surveys were mid‐career researchers. Around half were above and below the age of 50, respectively. Both surveys have slightly more females than males. These findings confirm those of other studies into the characteristics of education researchers who tend to be mostly female, older and more experienced, reflecting the occupational trajectory of many education researchers who enter academia following careers in teaching. For example, the recent BERA census identified that nearly half of education researchers had a school‐based teacher training qualification (Morris et al., [<reflink idref="bib35" id="ref73">35</reflink>]; see also Oancea et al., [<reflink idref="bib40" id="ref74">40</reflink>].</p> <p>2 TABLE The percentage of respondents with each background characteristic.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">RCBN</th><th align="left">BERA</th></tr></thead><tbody valign="top"><tr><td align="left">Gender*</td></tr><tr><td align="left">Female</td><td align="char" char=".">59</td><td align="char" char=".">65</td></tr><tr><td align="left">Male</td><td align="char" char=".">41</td><td align="char" char=".">33</td></tr><tr><td align="left">Age group</td></tr><tr><td align="left">Below 50</td><td align="char" char=".">46</td><td align="char" char=".">48</td></tr><tr><td align="left">50+</td><td align="char" char=".">54</td><td align="char" char=".">52</td></tr><tr><td align="left">Career stage</td></tr><tr><td align="left">Early (5 years or less)</td><td align="char" char=".">25</td><td align="char" char=".">24</td></tr><tr><td align="left">Mid (6–15 years)</td><td align="char" char=".">40</td><td align="char" char=".">44</td></tr><tr><td align="left">Senior (16+ years)</td><td align="char" char=".">35</td><td align="char" char=".">32</td></tr><tr><td align="left">Number of cases (N)</td><td align="char" char=".">498</td><td align="char" char=".">1559</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>: *Terms are those used in the surveys. For BERA, gender is less than 100% to account for the additional categories included in this survey but not in the RCBN (see methods section).</p> <hd id="AN0188606561-16">METHODS USED BY EDUCATION RESEARCHERS</hd> <p>In the approximately 20‐year period since the RCBN study was conducted, it appears that the number of distinct methods used by researchers has decreased quite substantially, from around 13 of the 26 methods listed in the survey to only 9 (Table 3). The question structure was deliberately similar in the two instruments. Both instruments offered an option of 'other', which was rarely used, so it is unlikely that the range of options available was a constraint in either survey. As might be expected, the average number of distinct methods used is larger for the more senior and older researchers, especially in the RCBN group. It also appears to be the case that researchers in 2022 reported using fewer methods than researchers in 2002, with respondents reporting using an average of 13 different methods in the RCBN survey compared to 9 in the BERA survey.</p> <p>3 TABLE Median number of distinct methods used by respondents, in terms of background.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Median RCBN<xref ref-type="fn" rid="tfn2" /></th><th align="left">Median BERA<xref ref-type="fn" rid="tfn3" /></th><th align="left">Difference</th></tr></thead><tbody valign="top"><tr><td align="left">All respondents</td><td align="char" char=".">13</td><td align="char" char=".">9</td><td align="char" char=".">−4</td></tr><tr><td align="left">Gender</td></tr><tr><td align="left">Female</td><td align="char" char=".">13</td><td align="char" char=".">9</td><td align="char" char=".">−4</td></tr><tr><td align="left">Male</td><td align="char" char=".">14</td><td align="char" char=".">10</td><td align="char" char=".">−4</td></tr><tr><td align="left">Age group</td></tr><tr><td align="left">Below 50</td><td align="char" char=".">13</td><td align="char" char=".">9</td><td align="char" char=".">−4</td></tr><tr><td align="left">50+</td><td align="char" char=".">15</td><td align="char" char=".">9</td><td align="char" char=".">−6</td></tr><tr><td align="left">Career stage</td></tr><tr><td align="left">Early (5 years or less)</td><td align="char" char=".">10</td><td align="char" char=".">8</td><td align="char" char=".">−2</td></tr><tr><td align="left">Mid (6–15 years)</td><td align="char" char=".">13</td><td align="char" char=".">9</td><td align="char" char=".">−4</td></tr><tr><td align="left">Senior (16+ years)</td><td align="char" char=".">16</td><td align="char" char=".">10</td><td align="char" char=".">−6</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 a Respondents reporting that they use or have expertise in a given approach.</item> <item>3 b Respondents reporting that they have used a given approach.</item> </ulist> <p>When the RCBN was set up, a major concern about education research quality was the apparent narrow range of research designs being adopted (Gorard, [<reflink idref="bib20" id="ref75">20</reflink>]). An important aim for the ESRC was especially to increase the number and range of experimental designs that were being used at the time—not because these are innately superior but because they were seen as lacking in the field. It is interesting to note therefore that experimental and quasi‐experimental designs are apparently even less used by academic researchers now than they were in 2002 (Table 4). This is not because they have been replaced by other designs. Instead, the use of most of the other common research designs has also fallen. The one research 'design' that has seen some increase in use—'qualitative' methodologies—is not actually a study design (Gorard, [<reflink idref="bib20" id="ref76">20</reflink>]), and it also includes phenomenology and ethno‐methods.</p> <p>4 TABLE Percentage of researchers who report using different research designs across both surveys.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">RCBN</th><th align="left">BERA</th><th align="left">Percentage point difference</th></tr></thead><tbody valign="top"><tr><td align="left">Case studies</td><td align="char" char=".">81</td><td align="char" char=".">58</td><td align="char" char=".">−23</td></tr><tr><td align="left">Evaluations</td><td align="char" char=".">56</td><td align="char" char=".">25</td><td align="char" char=".">−31</td></tr><tr><td align="left">Action research</td><td align="char" char=".">52</td><td align="char" char=".">39</td><td align="char" char=".">−13</td></tr><tr><td align="left">Experiments and quasi‐experiments</td><td align="char" char=".">41</td><td align="char" char=".">17</td><td align="char" char=".">−24</td></tr><tr><td align="left">Longitudinal research</td><td align="char" char=".">40</td><td align="char" char=".">16</td><td align="char" char=".">−24</td></tr><tr><td align="left">Qualitative methodologies</td><td align="char" char=".">58</td><td align="char" char=".">65</td><td align="char" char=".">+7</td></tr></tbody></table> </ephtml> </p> <p>The EEF was established in 2011 with the dual aims of increasing the number of experimental designs and the amount of robustly evaluated education research. It was not universally popular with academic researchers (Whitty, [<reflink idref="bib59" id="ref77">59</reflink>]), but it did create a ready source of funding for those who wanted to use experimental designs. The brief supplementary analysis we present below was derived from a desk‐based study that characterised the evaluators for every EEF‐funded evaluation from 2011 to the present. The aim of the analysis was to identify which evaluators were led by academic teams (i.e., those whose principal investigators were based at a university) and which were led by not‐for‐profit or other organisations.</p> <p>In its first year, 80% of the EEF's appointed evaluators were academic teams (Table 5). However, the proportion of evaluations funded by the EEF and led by academics then dropped sharply over time until 2020 when, due to the COVID‐19 pandemic, the number of evaluations being funded also dropped. Beyond 2020 the trend has so far continued, with the funding for this type of research going largely to not‐for‐profit and similar organisations, using teams set up specifically to service such grants (including a small number of repeat higher education institutions organised along the same lines). Therefore, while funding is available to conduct randomised control trials and so on, education academics still appear unwilling or unable to use that funding. A similar phenomenon was observed by the Institute of Education Sciences in the United States.</p> <p>5 TABLE EEF evaluations by year, and percentage led by academic teams.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Year</th><th align="left">Number of evaluations started</th><th align="left">Percentage led by academic teams</th></tr></thead><tbody valign="top"><tr><td align="left">2011</td><td align="char" char=".">5</td><td align="char" char=".">80</td></tr><tr><td align="left">2012</td><td align="char" char=".">35</td><td align="char" char=".">60</td></tr><tr><td align="left">2013</td><td align="char" char=".">28</td><td align="char" char=".">79</td></tr><tr><td align="left">2014</td><td align="char" char=".">13</td><td align="char" char=".">62</td></tr><tr><td align="left">2015</td><td align="char" char=".">21</td><td align="char" char=".">48</td></tr><tr><td align="left">2016</td><td align="char" char=".">18</td><td align="char" char=".">44</td></tr><tr><td align="left">2017</td><td align="char" char=".">21</td><td align="char" char=".">38</td></tr><tr><td align="left">2018</td><td align="char" char=".">26</td><td align="char" char=".">39</td></tr><tr><td align="left">2019</td><td align="char" char=".">20</td><td align="char" char=".">40</td></tr><tr><td align="left">2020</td><td align="char" char=".">9</td><td align="char" char=".">0</td></tr><tr><td align="left">2021</td><td align="char" char=".">8</td><td align="char" char=".">50</td></tr><tr><td align="left">2022</td><td align="char" char=".">11</td><td align="char" char=".">64</td></tr><tr><td align="left">2023</td><td align="char" char=".">19</td><td align="char" char=".">21</td></tr><tr><td align="left">2024</td><td align="char" char=".">5</td><td align="char" char=".">20</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note</emph>: <emph>N</emph> = 239 evaluations with confirmed evaluators taken from EEF website.</p> <p>There has also been a noticeable drop in the range of data collection methods used by UK researchers over time (Table 6). The standard interview and survey approaches remain similar in frequency. Formal testing remains at a low level. Observation, diaries and art‐based methods have declined in use considerably.</p> <p>6 TABLE Percentage of researchers who report using different methods of data collection across both surveys.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">RCBN</th><th align="left">BERA</th><th align="left">Percentage point difference</th></tr></thead><tbody valign="top"><tr><td align="left">Interviews</td><td align="char" char=".">94</td><td align="char" char=".">89</td><td align="char" char=".">−5</td></tr><tr><td align="left">Observations</td><td align="char" char=".">80</td><td align="char" char=".">57</td><td align="char" char=".">−24</td></tr><tr><td align="left">Surveys/questionnaires</td><td align="char" char=".">75</td><td align="char" char=".">71</td><td align="char" char=".">−4</td></tr><tr><td align="left">Art‐based methods</td><td align="char" char=".">53</td><td align="char" char=".">19</td><td align="char" char=".">−34</td></tr><tr><td align="left">Diaries</td><td align="char" char=".">46</td><td align="char" char=".">21</td><td align="char" char=".">−25</td></tr><tr><td align="left">Behavioural/performance tests</td><td align="char" char=".">17</td><td align="char" char=".">16</td><td align="char" char=".">−1</td></tr><tr><td align="left">Historical research</td><td align="char" char=".">14</td><td align="char" char=".">8</td><td align="char" char=".">−6</td></tr></tbody></table> </ephtml> </p> <p>All methods of data analysis listed in the survey have also apparently declined in use (Table 7). The staple approach of grounded theory has gone down a lot, as have all forms of numeric data analysis—from simple univariate calculations like frequencies to multivariate models like multiple regression.</p> <p>7 TABLE Percentage of researchers who report using different methods of data analysis across both surveys.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">RCBN</th><th align="left">BERA</th><th align="left">Percentage point difference</th></tr></thead><tbody valign="top"><tr><td align="left">Univariate descriptive</td><td align="char" char=".">67</td><td align="char" char=".">36</td><td align="char" char=".">−31</td></tr><tr><td align="left">Content analysis</td><td align="char" char=".">64</td><td align="char" char=".">34</td><td align="char" char=".">−30</td></tr><tr><td align="left">Bivariate (non‐inferential)</td><td align="char" char=".">61</td><td align="char" char=".">31</td><td align="char" char=".">−30</td></tr><tr><td align="left">Grounded theory</td><td align="char" char=".">53</td><td align="char" char=".">23</td><td align="char" char=".">−30</td></tr><tr><td align="left">Bivariate (inferential)</td><td align="char" char=".">50</td><td align="char" char=".">31</td><td align="char" char=".">−19</td></tr><tr><td align="left">Discourse analysis</td><td align="char" char=".">44</td><td align="char" char=".">32</td><td align="char" char=".">−12</td></tr><tr><td align="left">Multivariate</td><td align="char" char=".">43</td><td align="char" char=".">21</td><td align="char" char=".">−21</td></tr><tr><td align="left">Narrative analysis</td><td align="char" char=".">37</td><td align="char" char=".">34</td><td align="char" char=".">−3</td></tr><tr><td align="left">Conversation analysis</td><td align="char" char=".">36</td><td align="char" char=".">11</td><td align="char" char=".">−25</td></tr></tbody></table> </ephtml> </p> <p>Table 8 summarises the use of secondary data sources of all kinds. Non‐numeric secondary data use has increased—one reason for this may be the use of sources such as audio‐visual data, which 31% of respondents reported using in the BERA survey, compared with 18% in the RCBN. Given the considerable growth in the range and availability of all forms of secondary data over 20 years, the reduction in the number of researchers using numeric secondary data is surprising. This could be an outcome of increased sensitivity to personal data, and the protection of data provided by the Office of National Statistics and the UK Data Service.</p> <p>8 TABLE Percentage of researchers who report using syntheses and secondary data approaches across both surveys.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">RCBN</th><th align="left">BERA</th><th align="left">Percentage point difference</th></tr></thead><tbody valign="top"><tr><td align="left">All numeric secondary data</td><td align="char" char=".">66</td><td align="char" char=".">43</td><td align="char" char=".">−23</td></tr><tr><td align="left">All non‐numeric secondary data</td><td align="char" char=".">32</td><td align="char" char=".">71</td><td align="char" char=".">+39</td></tr><tr><td align="left">Text‐based secondary data</td><td align="char" char=".">27</td><td align="char" char=".">62</td><td align="char" char=".">+35</td></tr><tr><td align="left">Audio/visual secondary data</td><td align="char" char=".">18</td><td align="char" char=".">31</td><td align="char" char=".">+13</td></tr><tr><td align="left">Meta‐analysis</td><td align="char" char=".">24</td><td align="char" char=".">9</td><td align="char" char=".">−15</td></tr><tr><td align="left">Systematic reviews</td><td align="char" char=".">44</td><td align="char" char=".">37</td><td align="char" char=".">−7</td></tr></tbody></table> </ephtml> </p> <p>In addition, the BERA survey listed several options that did not feature in the RCBN survey, including philosophy/theory (used by 31%), corpus linguistics (4%), international comparisons (23%), thematic analysis (80%) and mixed methods (54%). It is not possible to compare these items over time. The option of thematic analysis, included in the BERA study but not the RCBN, may explain some of the discrepancy in Table 7 in the use of approaches to non‐numeric data analysis.</p> <p>In summary, the main conclusion from this phase of analysis is that researchers in 2022 report being less methodologically eclectic and using a narrower range of approaches than in 2002. In addition, the trend is away from statistical analysis (the exact opposite of that intended by the many initiatives mentioned in the introduction) and towards a narrower range of non‐numeric analytical approaches, but not including many of the standard ways in which such data is analysed (e.g., grounded theory).</p> <p>In terms of how methods use varies according to researcher characteristics, the breakdown of methods used by male and female researchers is reasonably balanced across both surveys. Female researchers were more likely to use 'qualitative' methodologies (e.g., 61% compared with 54% for men in the RCBN survey), with male researchers slightly favouring evaluations (25% compared with 24% for women in the BERA survey) and numeric approaches to analysis (e.g., 35% of men and 29% of women reported using bivariate inferential analysis). As noted above, older researchers reported using a wider range of methods in both surveys. In the RCBN survey this was often using a wider range of research designs (e.g., 56% used experiments compared to 45% of younger researchers). In the BERA survey, older researchers also used more designs (e.g., 65% used case studies compared to 51% in the younger group). Finally, and also as noted above, more experienced researchers reported using more methods in both surveys, but slightly less so in the BERA survey. As with age, more senior researchers in the RCBN survey were more likely to use different methods of numeric analysis, whereas this is not apparent in the BERA survey.</p> <hd id="AN0188606561-17">THE CLUSTERS OF EDUCATION RESEARCHERS</hd> <p>The aim of this part of the study was to explore whether education researchers could be clustered according to the methods that they use and, if so, to examine the characteristics of these clusters in terms of the background variables we are using in this study. We were interested in exploring, for example, whether some or all researchers tended to cluster around particular methods, or whether they were more eclectic in their approaches.</p> <hd id="AN0188606561-18">RCBN survey</hd> <p>Four clusters were identified from the RCBN survey (Table 9), confirming an earlier analysis using muti‐dimensional scaling with the same data (Gorard et al., [<reflink idref="bib18" id="ref78">18</reflink>], [<reflink idref="bib19" id="ref79">19</reflink>]). The clusters are labelled as numeric‐focused, non‐numeric, mixed and mono‐method, based on the general approaches that respondents clustered in each category reported using. For example, researchers who tended to mainly use statistical methods of data analysis were clustered in the group labelled as numeric‐focused. We appreciate that this description of clusters can be arbitrary and that researchers within each cluster may use a range of different approaches. Here the cluster labels are intended to distinguish where the main focus of methods and approaches lies. The largest cluster of researchers was the numeric‐focused group (comprising 38% of the sample), while the smallest two clusters were the mixed‐method and non‐numeric groups (19% and 20% of the sample, respectively).</p> <p>9 TABLE Cluster membership according to method used, selected methods only (RCBN survey, data in %).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Numeric‐focused</th><th align="left">Mixed method</th><th align="left">Non‐numeric</th><th align="left">Mono‐method</th></tr></thead><tbody valign="top"><tr><td align="left">% of sample allocated to each cluster</td><td align="char" char=".">38</td><td align="char" char=".">19</td><td align="char" char=".">20</td><td align="char" char=".">24</td></tr><tr><td align="left">10 key predictors of cluster membership in order of importance</td></tr><tr><td align="left">Bivariate (inferential)</td><td align="char" char=".">84</td><td align="char" char=".">87</td><td align="char" char=".">1</td><td align="char" char=".">8</td></tr><tr><td align="left">Bivariate (non‐inferential)</td><td align="char" char=".">94</td><td align="char" char=".">98</td><td align="char" char=".">17</td><td align="char" char=".">18</td></tr><tr><td align="left">Narrative analysis</td><td align="char" char=".">6</td><td align="char" char=".">84</td><td align="char" char=".">83</td><td align="char" char=".">11</td></tr><tr><td align="left">Univariate analysis</td><td align="char" char=".">97</td><td align="char" char=".">100</td><td align="char" char=".">27</td><td align="char" char=".">28</td></tr><tr><td align="left">Multivariate analysis</td><td align="char" char=".">69</td><td align="char" char=".">83</td><td align="char" char=".">4</td><td align="char" char=".">2</td></tr><tr><td align="left">Conversational analysis</td><td align="char" char=".">12</td><td align="char" char=".">83</td><td align="char" char=".">71</td><td align="char" char=".">8</td></tr><tr><td align="left">Qualitative methodologies</td><td align="char" char=".">40</td><td align="char" char=".">95</td><td align="char" char=".">98</td><td align="char" char=".">25</td></tr><tr><td align="left">Discourse analysis</td><td align="char" char=".">24</td><td align="char" char=".">80</td><td align="char" char=".">79</td><td align="char" char=".">16</td></tr><tr><td align="left">Diaries</td><td align="char" char=".">29</td><td align="char" char=".">78</td><td align="char" char=".">79</td><td align="char" char=".">20</td></tr><tr><td align="left">Content analysis</td><td align="char" char=".">57</td><td align="char" char=".">98</td><td align="char" char=".">84</td><td align="char" char=".">29</td></tr><tr><td align="left">Other commonly used methods</td></tr><tr><td align="left">Surveys</td><td align="char" char=".">87</td><td align="char" char=".">96</td><td align="char" char=".">71</td><td align="char" char=".">44</td></tr><tr><td align="left">Case studies</td><td align="char" char=".">77</td><td align="char" char=".">99</td><td align="char" char=".">96</td><td align="char" char=".">61</td></tr><tr><td align="left">Action research</td><td align="char" char=".">43</td><td align="char" char=".">77</td><td align="char" char=".">76</td><td align="char" char=".">26</td></tr><tr><td align="left">Interviews</td><td align="char" char=".">95</td><td align="char" char=".">100</td><td align="char" char=".">100</td><td align="char" char=".">85</td></tr></tbody></table> </ephtml> </p> <p>Figure 1 lists the ten methods that best predict cluster membership. The most important predictors were those that involved the use of statistical methods of analysis—particularly bivariate analysis—as well as the extent to which researchers used, or did not use, narrative analytical approaches.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/BED/01oct25/berj4179-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="berj4179-fig-0001.jpg" title="1 Most important methods in predicting cluster membership, RCBN survey. [Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>Table 9 provides more detail on the types of methods used by members of the different research clusters. The top part of the table shows the percentage of respondents in each cluster who report using each of the ten variables that best predict cluster membership (as shown in Figure 1). The bottom part of the table shows the percentage of respondents who use the most widely used methods—these methods are not good predictors of cluster membership (and so are not listed in Figure 1) because they are so ubiquitous among the sample.</p> <p>As noted above, the key characteristic of researchers clustered in the 'numeric‐focused' group (38% of the sample) was, unsurprisingly, their extensive use of statistical methods of data analysis. That is not to say that members of this cluster did not use other, non‐numeric, approaches. Indeed, along with most of the sample, many in this cluster also reported using interviews. But what these researchers have in common is that they were more likely to report using mainly 'quantitative' approaches to research. Only a relatively small proportion of researchers clustered in the 'non‐numeric' cluster (20% of the sample) reported using any form of statistical analysis, fewer than use non‐numeric approaches in the numeric‐focused cluster. They more commonly reported using solely 'qualitative' approaches to research.</p> <p>The smallest cluster of researchers (19% of the sample) are labelled as 'mixed‐method' researchers because they report using both numeric and non‐numeric approaches to data collection and analysis. They are strong users of the methods in all rows and differ from the numeric‐focused group in their greater focus on textual data—in other words, this group are more likely to use the full range of social science methods.</p> <p>The final cluster (24% of the sample) can be described as 'mono‐method' researchers. In terms of the key methods that predict cluster membership, this group can be characterised by the approaches that they do not use rather than the ones that they do. For example, like the non‐numeric cluster, relatively few report using statistics in their research but unlike them, they do not particularly use a range of non‐numeric methods either. Instead, they were more likely to report predominantly using methods such as interviews (85%) and case studies (61%) that were also widely used among the rest of the sample.</p> <p>The extent to which cluster membership varied according to researchers' background characteristics is shown in Table 10. Senior researchers were more likely to report using numeric or mixed approaches. Newer researchers were more likely to report being mono‐method users. These differences are not clearly linked to age. The pattern, noted above, that males are more common in the numeric‐focused group and females in the non‐numeric group, is repeated.</p> <p>10 TABLE Percentage of researchers with each characteristic, by cluster (RCBN survey).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Numeric‐focused</th><th align="left">Mixed method</th><th align="left">Non‐numeric</th><th align="left">Mono‐method</th></tr></thead><tbody valign="top"><tr><td align="left">All respondents</td><td align="char" char=".">38</td><td align="char" char=".">19</td><td align="char" char=".">20</td><td align="char" char=".">24</td></tr><tr><td align="left">Female</td><td align="char" char=".">33</td><td align="char" char=".">18</td><td align="char" char=".">24</td><td align="char" char=".">25</td></tr><tr><td align="left">Male</td><td align="char" char=".">45</td><td align="char" char=".">19</td><td align="char" char=".">15</td><td align="char" char=".">21</td></tr><tr><td align="left">Below 50</td><td align="char" char=".">39</td><td align="char" char=".">15</td><td align="char" char=".">19</td><td align="char" char=".">27</td></tr><tr><td align="left">50+</td><td align="char" char=".">37</td><td align="char" char=".">22</td><td align="char" char=".">21</td><td align="char" char=".">20</td></tr><tr><td align="left">Early (5 years or less)</td><td align="char" char=".">32</td><td align="char" char=".">8</td><td align="char" char=".">15</td><td align="char" char=".">46</td></tr><tr><td align="left">Mid (6–15 years)</td><td align="char" char=".">35</td><td align="char" char=".">15</td><td align="char" char=".">29</td><td align="char" char=".">22</td></tr><tr><td align="left">Senior (16+ years)</td><td align="char" char=".">46</td><td align="char" char=".">30</td><td align="char" char=".">15</td><td align="char" char=".">10</td></tr></tbody></table> </ephtml> </p> <hd id="AN0188606561-20">BERA survey</hd> <p>The same analysis was run for the BERA survey, and this time only two clear clusters were identified. Again, the most important variables that predicted cluster membership were the use of numeric methods of analysis (Figure 2). Researchers in the first cluster, which comprised two‐thirds of respondents, shared methods that suggested they mostly used non‐numeric approaches when carrying out research. The second, comprising the remaining one‐third of the sample, included researchers who tended to focus on more 'quantitative' work. Using (quasi)experiments or behavioural/performance tests was also reasonably important in terms of predicting cluster membership, but their use was not widely reported among respondents to the BERA survey.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/BED/01oct25/berj4179-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="berj4179-fig-0002.jpg" title="2 Most important methods in predicting cluster membership, BERA dataset. [Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <p>The extent to which there is a 'schism' between the two clusters of researchers in the BERA survey is apparent in Table 11. The data is presented in the same way as for the RCBN clusters above—again, the top part of the table shows the proportion of respondents who report using each method that was a key predictor of cluster membership. Although the smaller of the two clusters (35%), the 'numeric‐focused' group, can be characterised largely by their use of statistical methods of analysis and surveys, they also actually use a wider range of approaches, including non‐numeric ones. This group perhaps subsumes the mixed‐methods group identified in the RCBN survey.</p> <p>11 TABLE Cluster membership according to method used, selected methods only (BERA survey data in %).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Non‐numeric</th><th align="left">Numeric‐focused</th></tr></thead><tbody valign="top"><tr><td align="left">% of sample allocated to each cluster</td><td align="char" char=".">65</td><td align="char" char=".">35</td></tr><tr><td align="left">10 key predictors of cluster membership in order of importance</td></tr><tr><td align="left">Bivariate analysis (non‐inferential)</td><td align="char" char=".">1</td><td align="char" char=".">86</td></tr><tr><td align="left">Bivariate analysis (inferential)</td><td align="char" char=".">3</td><td align="char" char=".">81</td></tr><tr><td align="left">Univariate analysis</td><td align="char" char=".">7</td><td align="char" char=".">89</td></tr><tr><td align="left">Multivariate analysis</td><td align="char" char=".">1</td><td align="char" char=".">57</td></tr><tr><td align="left">Experiments</td><td align="char" char=".">2</td><td align="char" char=".">43</td></tr><tr><td align="left">Behavioural tests</td><td align="char" char=".">2</td><td align="char" char=".">41</td></tr><tr><td align="left">Evaluation methods</td><td align="char" char=".">13</td><td align="char" char=".">48</td></tr><tr><td align="left">Surveys</td><td align="char" char=".">59</td><td align="char" char=".">93</td></tr><tr><td align="left">Longitudinal approaches</td><td align="char" char=".">7</td><td align="char" char=".">33</td></tr><tr><td align="left">Numeric secondary data</td><td align="char" char=".">33</td><td align="char" char=".">61</td></tr><tr><td align="left">Other commonly used methods</td></tr><tr><td align="left">Interviews</td><td align="char" char=".">88</td><td align="char" char=".">89</td></tr><tr><td align="left">Case studies</td><td align="char" char=".">58</td><td align="char" char=".">58</td></tr><tr><td align="left">Observations</td><td align="char" char=".">54</td><td align="char" char=".">61</td></tr></tbody></table> </ephtml> </p> <p>On the other hand, the non‐numeric group (65%) appeared to be clustered more according to what they did not do. Compared with the RCBN dataset, even smaller proportions of non‐numeric researchers report using any statistical methods—even the most basic—to analyse their data. In addition, only very small proportions of researchers in this cluster report using experimental approaches or behavioural/performance tests. Researchers in this cluster were also likely to report using some of the most widely used methods, as reported across the whole sample, such as interviews.</p> <p>The next stage of the analysis considered how different groups of researchers were distributed among the two clusters (Table 12). Perhaps the most obvious and important difference is that despite two decades of government 'quantitative' methods initiatives, the early career researchers are predominantly working with non‐numeric approaches.</p> <p>12 TABLE Percentage of researchers with each characteristic, by cluster (BERA survey).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Non‐numeric</th><th align="left">Numeric‐focused</th></tr></thead><tbody valign="top"><tr><td align="left">All respondents</td><td align="char" char=".">65</td><td align="char" char=".">35</td></tr><tr><td align="left">Female</td><td align="char" char=".">66</td><td align="char" char=".">34</td></tr><tr><td align="left">Male</td><td align="char" char=".">60</td><td align="char" char=".">40</td></tr><tr><td align="left">Below 50</td><td align="char" char=".">59</td><td align="char" char=".">41</td></tr><tr><td align="left">50+</td><td align="char" char=".">70</td><td align="char" char=".">30</td></tr><tr><td align="left">Early (5 years or less)</td><td align="char" char=".">70</td><td align="char" char=".">30</td></tr><tr><td align="left">Mid (6–15 years)</td><td align="char" char=".">63</td><td align="char" char=".">37</td></tr><tr><td align="left">Senior (16+ years)</td><td align="char" char=".">63</td><td align="char" char=".">37</td></tr></tbody></table> </ephtml> </p> <hd id="AN0188606561-22">SUMMARY</hd> <p>In UK academic education research there is a range of methodological specialisms and expertise. The majority of respondents across both of the surveys explored here use traditional social science techniques (e.g., interviews, questionnaires and case studies), while a substantial minority have engaged with practice‐focused approaches (e.g., action research and arts‐based methods).</p> <p>There have apparently been some important changes in how research is carried out over the past 20 years. Insofar as the samples from the two surveys are comparable, it seems that both the number and variety of designs and methods used have decreased over time. Experimental research designs are less common now than in 2002, and despite government and funder interventions, the number of researchers using numeric forms of data analysis has also declined. One likely reason for the reduction in the number of experimental approaches in higher education research is that, as noted above, there has been a gradual shift in where this type of work takes place, with our analysis suggesting that fewer higher education‐based researchers are receiving funding to carry out these evaluations than when these initiatives first began.</p> <p>In 2002, our findings show that there were four 'types' of researcher in relation to methods use. The most common used largely numeric data, but there were also those using mainly non‐numeric data, those using both almost equally and those working largely with just one method, such as interviews. By 2022, only two types of researchers were identified. The larger cluster used mainly or exclusively non‐numeric data, with a particular inclination for this type of work among newer researchers. There was a second, smaller group of predominantly numeric researchers who, as in 2002, tended to use a wider range of methods than the non‐numeric group. The mixed and single‐method researchers that were apparent in the RCBN analysis were absent from the BERA data, suggesting increased polarisation between those that do, or do not, use numbers in their research.</p> <hd id="AN0188606561-23">DISCUSSION</hd> <p>There are two key findings to emerge from this study. The first is that education researchers currently working in UK higher education report using fewer methods and approaches than a reasonably comparable group of researchers surveyed approximately 20 years ago. Although present‐day education research is characterised by an eclectic and diverse range of approaches for some, these appear to be currently less widely spread among the research community than previously. One explanation for why some education researchers report using a less broad range of approaches may reflect an increased depth of expertise in using a smaller range of methods. Another explanation could be that large, funded research projects typically require teams of people with a breadth of complementary skills and where specific methodological expertise is valued. However, another explanation is that many education researchers are simply less prepared than previously to use a range of approaches. It is intriguing to consider how these researchers would be able to review and assess the full range of work conducted on their topic of interest, or whether their syntheses are limited to work of the kind they are familiar with (Gorard, [<reflink idref="bib22" id="ref80">22</reflink>]).</p> <p>The second main finding relates to the apparent increased polarisation in the types of methods used, notably between those who mainly use numeric and those who use non‐numeric approaches to data analysis. While around one‐third of education researchers report using numbers in their research, there were a larger group of researchers who never do. This apparent methodological schism is concerning, especially considering the efforts of funding councils, for example through the Q‐Step programme, to increase expertise in 'quantitative' skills in our community. It is perhaps the case that rather than building skills capacity among those new to such research these initiatives may instead, at least in education, have served to enhance and develop the statistical skills of the already committed.</p> <p>This second finding is also surprising when set against recent research that has explored submissions to the Education Unit of Assessment for REF 2021 in the United Kingdom, and which suggests that education research has become 'less qualitative and more quantitative' (e.g., Inglis et al., [<reflink idref="bib29" id="ref81">29</reflink>]: 1). While interesting and important, analyses of REF submissions naturally only focus on selected outputs submitted to REF. The REF presumably over‐represents work that was pre‐decided to be among the best available to each higher education institution, and this seems to include a higher proportion of work using large‐scale data. In the BERA survey, less than half of respondents were submitted to REF 2021, suggesting that among the education research community more broadly there may be less 'quantitative' work taking place than there might seem from looking at submissions to REF.</p> <p>This is not to suggest that there is a dearth of 'quantitative' skills within education research more generally. On the contrary, where these skills exist, they appear to be relatively well embedded and integrated in projects with work of other kinds. Nor does this downplay the need for support in all areas of research methods. Indeed, as noted above, most respondents to the BERA State of the Discipline survey reported wanting to develop their methodological expertise, with only a minority reporting that their own research methods training was of high quality (Morris et al., [<reflink idref="bib35" id="ref82">35</reflink>]).</p> <p>To what extent are these findings an issue for education and its identity as a discipline or field of inquiry? One argument is that the apparent narrowing and polarisation of methods, if not addressed, limits the kinds of questions and the scope and quality of work that can be carried out, although we acknowledge alternative explanations for this finding above. Most professionals working in public policy and practice would agree that evidence derived from research is important in contributing to improvements in understanding, fairness and efficiency (Davies, [<reflink idref="bib9" id="ref83">9</reflink>]; Oancea et al., [<reflink idref="bib40" id="ref84">40</reflink>]). And education is no exception. Robust high‐quality evidence, regardless of method, can be used to help understand education better, and so help create better and fairer systems leading to important gains for children, the public and society (Hollands et al., [<reflink idref="bib28" id="ref85">28</reflink>]; Palmer, [<reflink idref="bib42" id="ref86">42</reflink>]). However, the findings to emerge from this study suggest that in education, we may be making only partial use of our collective methodological toolkit, with implications for the quality and type of evidence we produce and the impact that it has.</p> <hd id="AN0188606561-24">ACKNOWLEDGEMENTS</hd> <p>The work described here was partly funded by BERA and the ESRC. The authors would like to thank Wenquing Chen for assistance.</p> <hd id="AN0188606561-25">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors report no conflict of interest.</p> <hd id="AN0188606561-26">DATA AVAILABILITY STATEMENT</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p> <hd id="AN0188606561-27">ETHICS STATEMENT</hd> <p>Ethical approval was not required for this study.</p> <ref id="AN0188606561-28"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref49" type="bt">1</bibl> <bibtext> Baumfield, V. (2023). BERA. Who are we? How did we get here? Where are we going? British Educational Research Journal, 49, 427 – 438.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref28" type="bt">2</bibl> <bibtext> Beard, R., & Bishop, A. (1975). Training education research workers, BERA conference 1975. Research Intelligence, 1 (1), 72 – 74.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Berliner, D. C. (2002). Comment: Educational research: The hardest science of all. Educational Researcher, 31 (8), 18 – 20.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref36" type="bt">4</bibl> <bibtext> Biesta, G., Wainwright, E., & Aldridge, D. (2023). The future of educational research: Observations from the outgoing editors of the British Educational Research Journal. British Educational Research Journal, 49 (6), 1133 – 1141. https://doi.org/10.1002/berj.3941</bibtext> </blist> <blist> <bibl id="bib5" idref="ref19" type="bt">5</bibl> <bibtext> Blunkett, D. (2000). Influence or irrelevance: Can social science improve government? Department for Education and Employment.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref67" type="bt">6</bibl> <bibtext> British Educational Research Association. (2021). Education: The state of the discipline. https://<ulink href="http://www.bera.ac.uk/project/education‐the‐state‐of‐the‐discipline">www.bera.ac.uk/project/education‐the‐state‐of‐the‐discipline</ulink></bibtext> </blist> <blist> <bibl id="bib7" idref="ref31" type="bt">7</bibl> <bibtext> Burkhardt, H., & Schoenfeld, A. H. (2003). Improving educational research: Toward a more useful, more influential and better‐funded enterprise. Educational Researcher, 32 (9), 3 – 14. https://doi.org/10.3102/0013189X032009003</bibtext> </blist> <blist> <bibl id="bib8" idref="ref6" type="bt">8</bibl> <bibtext> Connolly, P., Keenan, C., & Urbanska, K. (2018). The trials of evidence‐based practice in education: A systematic review of randomised controlled trials in education research 1980–2016. Educational Research, 60 (3), 276 – 291.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref83" type="bt">9</bibl> <bibtext> Davies, P. (1999). What is evidence‐based education? British Journal of Educational Studies, 47 (2), 108 – 121.</bibtext> </blist> <blist> <bibtext> Delamont, S. (1983). A women's place in education: Myths, monsters and misapprehensions. https://<ulink href="http://www.bera.ac.uk/publication/1983‐sara‐delamont‐bera‐presidential‐address">www.bera.ac.uk/publication/1983‐sara‐delamont‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Dooley, P., Graham, N., Whitfield, R., Comber, L., Coxhead, P., & Goodyear, P. (1981). Survey of educational researchers in Britain. Aston Educational Enquiry (Occasional Paper No. 4). University of Aston.</bibtext> </blist> <blist> <bibtext> Edwards, A. (2001). Responsible research: Ways of being a researcher. https://<ulink href="http://www.bera.ac.uk/publication/2001‐anne‐edwards‐bera‐presidential‐address">www.bera.ac.uk/publication/2001‐anne‐edwards‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Erckian, K., & Roth, W. M. (2006). What good is polarizing research into qualitative and quantitative? Educational Researcher, 35 (5), 14 – 23. https://doi.org/10.3102/0013189X035005014</bibtext> </blist> <blist> <bibtext> Feuer, M. J., Towne, L., & Shavelson, R. J. (2002). Scientific culture and educational research. Educational Researcher, 31 (8), 4 – 14.</bibtext> </blist> <blist> <bibtext> Fischman, G., Amrein‐Beardsley, A., & McBride‐Schreiner, S. (2022). Education research is still the hardest science: A proposal for improving its trustworthiness and usability. F1000Research, 11, 230. https://doi.org/10.12688/f1000research.109700.1</bibtext> </blist> <blist> <bibtext> Furlong, J. (2003). BERA at 30: Have we come of age? https://<ulink href="http://www.bera.ac.uk/publication/2003‐john‐furlong‐bera‐presidential‐address">www.bera.ac.uk/publication/2003‐john‐furlong‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Furlong, J. (2013). Education—an anatomy of the discipline. Rescuing the university project? Routledge.</bibtext> </blist> <blist> <bibtext> Gorard, S., & Taylor, C. (2004a). Combining methods in educational and social research. Open University Press.</bibtext> </blist> <blist> <bibtext> Gorard, S., Rushforth, K., & Taylor, C. (2004b). Is there a shortage of quantitative work in education research? Oxford Review of Education, 30 (3), 371 – 395.</bibtext> </blist> <blist> <bibtext> Gorard, S. (2013). Research design: Robust approaches for the social sciences. SAGE.</bibtext> </blist> <blist> <bibtext> Gorard, S. (2021). How to make sense of statistics: Everything you need to know about using numbers in social science. SAGE.</bibtext> </blist> <blist> <bibtext> Gorard, S. (2024). Judging the relative trustworthiness of research results: How to do it and why it matters. Review of Education, 12 (1), e3448. https://doi.org/10.1002/rev3.3448</bibtext> </blist> <blist> <bibtext> Gove, M. (2013). Michael Gove speech about the importance of teaching, September 5th. <ulink href="http://www.gov.uk/government/speeches/michael‐gove‐speaks‐about‐the‐importance‐of‐teaching">www.gov.uk/government/speeches/michael‐gove‐speaks‐about‐the‐importance‐of‐teaching</ulink></bibtext> </blist> <blist> <bibtext> Hammersley, M. (2008). Paradigm war revived? On the diagnosis of resistance to randomized controlled trials and systematic review in education. International Journal of Research & Method in Education, 31 (1), 3 – 10.</bibtext> </blist> <blist> <bibtext> Hammersley, M. (2021). Is education a discipline? https://<ulink href="http://www.bera.ac.uk/blog/is‐education‐a‐discipline">www.bera.ac.uk/blog/is‐education‐a‐discipline</ulink></bibtext> </blist> <blist> <bibtext> Hargreaves, D. (1996). Teaching as a research‐based profession: Possibilities and prospects. The Teacher Training Agency Annual Lecture, April. https://eppi.ioe.ac.uk/cms/Portals/0/</bibtext> </blist> <blist> <bibtext> Hillage, J., Pearson, R., Anderson, A., & Tamkin, P. (1998). Excellence on research in schools. Department for Education and Employment.</bibtext> </blist> <blist> <bibtext> Hollands, F., Pan, Y., & Escueta, M. (2019). What is the potential for applying cost–utility analysis to facilitate evidence‐based decision making in schools? Educational Researcher, 48 (5), 287 – 295.</bibtext> </blist> <blist> <bibtext> Inglis, M., Foster, C., Lortie‐Forgues, H., & Stokoe, E. (2024). British education research and its quality: An analysis of Research Excellence Framework submissions. British Educational Research Journal, 50, 2495 – 2518. https://doi.org/10.1002/berj.4040</bibtext> </blist> <blist> <bibtext> JISC. (n.d.). Records of the Teaching, Learning and Research Programme (TLRP). https://archiveshub.jisc.ac.uk/search/archives/02bdb74d‐a05c‐3a40‐b775‐a6dde23ee4ef</bibtext> </blist> <blist> <bibtext> Kaestle, C. F. (1993). The awful reputation of education research. Educational Researcher, 22 (1), 23, 26–31.</bibtext> </blist> <blist> <bibtext> Lather, P. (2004). Scientific research in education: A critical perspective. British Educational Research Journal, 30 (6), 759 – 772.</bibtext> </blist> <blist> <bibtext> MacInnes, J., Breeze, M., de Haro, M., Kandlik, M., & Karels, M. (2016). Measuring up: International case studies on the teaching of quantitative methods in the social sciences. The British Academy.</bibtext> </blist> <blist> <bibtext> Menter, I. (2017). The reasons for education research, in Wyse, D., Selwyn, N., Smith, E., Suter, L.E. (eds), The BERA/SAGE handbook of educational research, pp. 37–52. SAGE.</bibtext> </blist> <blist> <bibtext> Morris, R., Perry, T., Smith, E., & Pilgrim‐Brown, J. (2023). Education: The state of the discipline. British Educational Research Association. https://<ulink href="http://www.bera.ac.uk/project/education‐the‐state‐of‐the‐discipline">www.bera.ac.uk/project/education‐the‐state‐of‐the‐discipline</ulink></bibtext> </blist> <blist> <bibtext> Mortimore, P. (1999). Does educational research matter? https://<ulink href="http://www.bera.ac.uk/publication/1999‐peter‐mortimore‐bera‐presidential‐address">www.bera.ac.uk/publication/1999‐peter‐mortimore‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Munn, P. (2007). Building research capacity collaboratively: Can we take ownership of our future? https://<ulink href="http://www.bera.ac.uk/publication/2007‐pamela‐munn‐bera‐presidential‐address">www.bera.ac.uk/publication/2007‐pamela‐munn‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Nuffield Foundation. (2022). Evaluation of the Q‐Step programme. https://<ulink href="http://www.nuffieldfoundation.org/publications/q‐step‐evaluation">www.nuffieldfoundation.org/publications/q‐step‐evaluation</ulink></bibtext> </blist> <blist> <bibtext> Oancea, A. (2019). Research governance and the future(s) of research assessment. Palgrave Communications, 5, 27. https://doi.org/10.1057/s41599‐018‐0213‐6</bibtext> </blist> <blist> <bibtext> Oancea, A., McDermott, T., Robson, J., Scutt, C., Xu, X., Mun, O., et al. 2021. The landscape of educational research in the UK. Report to the Royal Society and British Academy Joint Enquiry on Educational Research. Royal Society/British Academy.</bibtext> </blist> <blist> <bibtext> OECD. (2022). Who cares about using education research in policy and practice? Strengthening research engagement, educational research and innovation. OECD Publishing. https://doi.org/10.1787/d7ff793d‐en</bibtext> </blist> <blist> <bibtext> Palmer, J. (1999). Research matters: A call for the application of empirical evidence to the task of improving the quality and impact of environmental education. Cambridge Journal of Education, 29 (3), 379 – 395. https://doi.org/10.1080/0305764990290308</bibtext> </blist> <blist> <bibtext> Perry, T., & Morris, R. (2023). A critical guide to evidence‐informed education. McGraw‐Hill Education.</bibtext> </blist> <blist> <bibtext> Pollard, A. (n.d.). Case study of TLRP: Its origins and purposes, a brief description of activities, and key issues relating to impact. https://web.archive.org/web/20120207101400/http://www.tlrp‐archive.org/tlrp/upload/assets/1176821531_OECD_EbPR_case_study_of_TLRP.pdf</bibtext> </blist> <blist> <bibtext> Power, S. (2023). Confronting the 'coming crisis' in education research. British Journal of Educational Studies, 71 (5), 477 – 491.</bibtext> </blist> <blist> <bibtext> Rasmussen, P. (2021). Educational research—public responsibility, private funding? Nordic Journal of Studies in Educational Policy, 8 (1), 65 – 74. https://doi.org/10.1080/20020317.2021.2018786</bibtext> </blist> <blist> <bibtext> REF. (2022). Overview report by Main Panel D and Sub‐panels 25 to 34, Research Excellence Framework. https://2021.ref.ac.uk/media/1913/mp‐d‐overview‐report‐final‐updated‐september‐2022.pdf</bibtext> </blist> <blist> <bibtext> Royal Society/British Academy. (2018). Harnessing educational research. https://royalsociety.org/‐/media/policy/projects/rs‐ba‐educational‐research/educational‐research‐report.pdf</bibtext> </blist> <blist> <bibtext> Scitovski, R., Sabo, K., Martinez‐Alvarez, F., & Ungar, S. (2021). Cluster analysis and applications. Springer.</bibtext> </blist> <blist> <bibtext> Scott, D. (2017). The parlous state of educational research. https://<ulink href="http://www.bera.ac.uk/blog/the‐parlous‐state‐of‐educational‐research">www.bera.ac.uk/blog/the‐parlous‐state‐of‐educational‐research</ulink>.</bibtext> </blist> <blist> <bibtext> Simon, B. (1977). Education research: Which way? https://<ulink href="http://www.bera.ac.uk/publication/1977‐brian‐simon‐bera‐presidential‐address">www.bera.ac.uk/publication/1977‐brian‐simon‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> Slavin, R. (1989). PET and the pendulum: Faddism in education and how to stop it. Phi Delta Kappan, 70, 752 – 775.</bibtext> </blist> <blist> <bibtext> Stenhouse, L. (1979). The study of samples and the study of cases. https://<ulink href="http://www.bera.ac.uk/publication/1979‐lawrence‐stenhouse‐bera‐presidential‐address">www.bera.ac.uk/publication/1979‐lawrence‐stenhouse‐bera‐presidential‐address</ulink></bibtext> </blist> <blist> <bibtext> The UK Parliament. (2003). Select Committee on Education and Skills. https://publications.parliament.uk/pa/cm200203/cmselect/cmeduski/425/3030504.htm</bibtext> </blist> <blist> <bibtext> Thomas, G., & Gorard, S. (2007). Quality in education research. International Journal of Research & Method in Education, 30 (3), 239 – 242.</bibtext> </blist> <blist> <bibtext> TLRP. (n.d.). Teaching and Learning Research Programme. https://web.archive.org/web/20070622062127/<ulink href="http://www.tlrp.org/proj/expand.html">http://www.tlrp.org/proj/expand.html</ulink></bibtext> </blist> <blist> <bibtext> Tymms, P. (2017). Purposes for education research, in Wyse, D., Selwyn, N., Smith, E., Suter, L. (eds), The BERA/SAGE handbook of educational research, pp. 165–179. SAGE.</bibtext> </blist> <blist> <bibtext> Whitty, G. (2006). Education(al) research and education policy making: Is conflict inevitable? British Educational Research Journal, 32 (2), 159 – 176.</bibtext> </blist> <blist> <bibtext> Whitty, G. (2013). Evidence‐informed policy and practice—we should welcome it, but also be realistic. https://cerp.aqa.org.uk/perspectives/evidence‐informed‐policy‐practice</bibtext> </blist> <blist> <bibtext> Whitty, G., & Furlong, J. (Eds.). (2017). Knowledge and the study of education: An international exploration. Symposium Books.</bibtext> </blist> <blist> <bibtext> Wrigley, T. (2018). The power of 'evidence': Reliable science or a set of blunt tools? British Educational Research Journal, 44, 359 – 376.</bibtext> </blist> <blist> <bibtext> Wyse, D. (2020). Presidential address: The academic discipline of education. Reciprocal relationships between practical knowledge and academic knowledge. British Educational Research Journal, 46 (1), 6 – 25.</bibtext> </blist> <blist> <bibtext> Wyse, D., Selwyn, N., Smith, E., & Suter, L. E. (2017). The BERA/SAGE handbook of educational research. SAGE.</bibtext> </blist> </ref> <aug> <p>By Emma Smith; Stephen Gorard; Rebecca Morris; Thomas Perry and Jess Pilgrim‐Brown</p> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib60" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib48" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib63" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib61" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib46" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib17" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib45" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib57" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib34" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib62" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib25" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib23" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib14" firstref="ref22"></nolink> <nolink nlid="nl15" bibid="bib32" firstref="ref23"></nolink> <nolink nlid="nl16" bibid="bib20" firstref="ref24"></nolink> <nolink nlid="nl17" bibid="bib13" firstref="ref25"></nolink> <nolink nlid="nl18" bibid="bib24" firstref="ref26"></nolink> <nolink nlid="nl19" bibid="bib11" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib31" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib52" firstref="ref32"></nolink> <nolink nlid="nl22" bibid="bib54" firstref="ref34"></nolink> <nolink nlid="nl23" bibid="bib50" firstref="ref35"></nolink> <nolink nlid="nl24" bibid="bib47" firstref="ref38"></nolink> <nolink nlid="nl25" bibid="bib39" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib27" firstref="ref40"></nolink> <nolink nlid="nl27" bibid="bib41" firstref="ref41"></nolink> <nolink nlid="nl28" bibid="bib43" firstref="ref42"></nolink> <nolink nlid="nl29" bibid="bib55" firstref="ref43"></nolink> <nolink nlid="nl30" bibid="bib51" firstref="ref44"></nolink> <nolink nlid="nl31" bibid="bib53" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib10" firstref="ref46"></nolink> <nolink nlid="nl33" bibid="bib12" firstref="ref47"></nolink> <nolink nlid="nl34" bibid="bib16" firstref="ref50"></nolink> <nolink nlid="nl35" bibid="bib36" firstref="ref51"></nolink> <nolink nlid="nl36" bibid="bib37" firstref="ref52"></nolink> <nolink nlid="nl37" bibid="bib58" firstref="ref53"></nolink> <nolink nlid="nl38" bibid="bib35" firstref="ref54"></nolink> <nolink nlid="nl39" bibid="bib38" firstref="ref55"></nolink> <nolink nlid="nl40" bibid="bib33" firstref="ref56"></nolink> <nolink nlid="nl41" bibid="bib15" firstref="ref58"></nolink> <nolink nlid="nl42" bibid="bib40" firstref="ref60"></nolink> <nolink nlid="nl43" bibid="bib30" firstref="ref61"></nolink> <nolink nlid="nl44" bibid="bib56" firstref="ref62"></nolink> <nolink nlid="nl45" bibid="bib44" firstref="ref63"></nolink> <nolink nlid="nl46" bibid="bib18" firstref="ref65"></nolink> <nolink nlid="nl47" bibid="bib19" firstref="ref66"></nolink> <nolink nlid="nl48" bibid="bib21" firstref="ref70"></nolink> <nolink nlid="nl49" bibid="bib49" firstref="ref72"></nolink> <nolink nlid="nl50" bibid="bib59" firstref="ref77"></nolink> <nolink nlid="nl51" bibid="bib22" firstref="ref80"></nolink> <nolink nlid="nl52" bibid="bib29" firstref="ref81"></nolink> <nolink nlid="nl53" bibid="bib28" firstref="ref85"></nolink> <nolink nlid="nl54" bibid="bib42" firstref="ref86"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1486310
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Then and Now: Twenty Years of Education Research Methods Use in the United Kingdom
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Emma+Smith%22">Emma Smith</searchLink><br /><searchLink fieldCode="AR" term="%22Stephen+Gorard%22">Stephen Gorard</searchLink><br /><searchLink fieldCode="AR" term="%22Rebecca+Morris%22">Rebecca Morris</searchLink><br /><searchLink fieldCode="AR" term="%22Thomas+Perry%22">Thomas Perry</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6124-467X">0000-0002-6124-467X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jess+Pilgrim-Brown%22">Jess Pilgrim-Brown</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8953-6626">0000-0001-8953-6626</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Educational+Research+Journal%22"><i>British Educational Research Journal</i></searchLink>. 2025 51(5):2426-2449.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 24
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Methodology%22">Research Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+History%22">Educational History</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Researchers%22">Educational Researchers</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Capacity+Building%22">Capacity Building</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Trends%22">Educational Trends</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22United+Kingdom%22">United Kingdom</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/berj.4179
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-1926<br />1469-3518
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: There have been debates about the quality and usefulness of education research for a long time, with opinion often dividing along methodological lines. Those on different sides of an apparent methodological schism often bemoan the lack of recognition and resources afforded to their chosen approach. Whatever one's position on the existence, or persistence, of education research's own version of the 'paradigm wars', it is nevertheless the case that research design and methods are central to its identity, its usefulness and the impact it makes upon society. This paper contributes to wider debates around the status of education research as a field, or discipline, by exploring the extent to which the research methods used by education researchers working in UK higher education, and beyond, have varied over the last 20 years. It reports the findings from a comparative analysis of two large-scale surveys--the ESRC-funded Research Capacity Building Network survey of 2002 and the BERA-funded Higher Education Research Census 2022. Both surveys explored the methods used by education researchers, mainly based in higher education, and took place against a backdrop of concern, from within and outside the field, about the quality and reach of its research. The findings show that education researchers draw from a variety of different methods and approaches but that the range of tools that they use has narrowed a lot over the period considered. Furthermore, there appears to be an increase in methodological polarisation, particularly between a minority who only use numbers in their research and a majority who never do. This is despite the considerable resources devoted to building research capacity to undertake numeric and combined research.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1486310
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1486310
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/berj.4179
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2426
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Educational Research
        Type: general
      – SubjectFull: Research Methodology
        Type: general
      – SubjectFull: Educational History
        Type: general
      – SubjectFull: Educational Researchers
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Capacity Building
        Type: general
      – SubjectFull: Educational Trends
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: United Kingdom
        Type: general
    Titles:
      – TitleFull: Then and Now: Twenty Years of Education Research Methods Use in the United Kingdom
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Emma Smith
      – PersonEntity:
          Name:
            NameFull: Stephen Gorard
      – PersonEntity:
          Name:
            NameFull: Rebecca Morris
      – PersonEntity:
          Name:
            NameFull: Thomas Perry
      – PersonEntity:
          Name:
            NameFull: Jess Pilgrim-Brown
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0141-1926
            – Type: issn-electronic
              Value: 1469-3518
          Numbering:
            – Type: volume
              Value: 51
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: British Educational Research Journal
              Type: main
ResultId 1