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Studies in Higher Education
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cshe20
Identifying skills, qualifications, and attributes
expected to do a PhD
Lilia Mantai & Mauricio Marrone
To cite this article: Lilia Mantai & Mauricio Marrone (2022): Identifying skills,
qualifications, and attributes expected to do a PhD, Studies in Higher Education, DOI:
10.1080/03075079.2022.2061444
To link to this article: https://doi.org/10.1080/03075079.2022.2061444
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
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Identifying skills, qualifications, and attributes expected
to do a PhD
Lilia Mantai
a
and Mauricio Marrone
b
a
Business School, The University of Sydney, Sydney, Australia;
b
Macquarie Business School, Macquarie University,
Sydney, Australia
ABSTRACT
Despite an increasingly competitive academic market, more and more
people are seeking a PhD degree. While significant research focuses on
skill attainment during PhD candidature and at PhD exit, we know little
about the skills that might be present at PhD entry. We developed a
data-driven taxonomy and conducted logistic regressions to analyse
selection criteria (listing skills, qualifications, and personal attributes) of
13,562 PhD advertisements posted in 2016–2019 on Euraxess, a
European recruitment platform for researchers. We analysed the most
prevalent attributes sought for PhD admission, country-based and
discipline-specificdifferences, and changes over time. We find that
many of these admission attributes include diverse and transferable
skills. Specifically, cognitive, interpersonal skills and personal attributes
are trending upwards, and PhD requirements vary significantly by
country, discipline and year of posting. We highlight the attributes
requested by top 5 countries and top 5 disciplines, and show changes
over time. The insights provide guidance for practice, specifically to PhD
applicants, early career researchers, and those who support career
development. We discuss PhD programmes’alignment and policy
implications for pre-doctoral education, redesign of PhD assessment,
and improved training provision for students and supervisors.
KEYWORDS
Doctoral education; PhD; job
market; employability;
transferable skills; natural
language processing
Introduction
Globally, a growing number of people undertake a PhD (hereafter PhD), the common purpose of
which is to prepare for academic careers (OECD 2019). However, we do not sufficiently know
what it takes to be admitted into a PhD programme. One group of studies investigates PhD gradu-
ate
1
skills for diverse careers as academia is growing increasingly competitive (for examples see
Hasgall, Saenen, and Borrell-Damian 2019; Mewburn et al. 2018; Germain-Alamartine and Mogha-
dam-Saman 2020), yet they do not consider what skills might be present at PhD admission.
Another body of literature examines PhD students’characteristics and skill development needs
(examples include Sharmini and Spronken-Smith 2020; Succi and Canovi 2020; Sinche et al. 2017),
yet they do not discuss the makeup of PhD applicants, specifically what skills are requested at
PhD entry and what skills might already be present in PhD candidates.
Our paper identifies what type of skills are requested for PhD admission. Specifically, we investi-
gate what skills, qualities or attributes are requested of PhD applicants. Skill demands essentially
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://
creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the
original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Lilia Mantai lilia.mantai@sydney.edu.au The University of Sydney Business School, The Abercrombie Building
H70, The University of Sydney, NSW, 2006
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
STUDIES IN HIGHER EDUCATION
https://doi.org/10.1080/03075079.2022.2061444
reflect what the economy and society need (Burning Glass Technologies 2015; Mewburn et al. 2018).
We present a demand analysis in PhD recruitment which provides a useful baseline for those who are
investigating the skills and attributes developed and gained throughout the PhD. This understand-
ing will help pre-doctoral students tailor their applications and skill development, as well as inform
supervisors and those supporting PhD applicants or candidates on how to better support the devel-
opment of early researchers and research professionals.
This study draws on the data source of PhD role advertisements (henceforth referred to as ‘job
ads’) to identify what skills and/or other requirements doctoral programmes seek before PhD admis-
sion. We analysed the selection criteria of 13,562 PhD ads posted in 2016–2019 on Euraxess, a Euro-
pean recruitment platform listing available opportunities to undertake a PhD. It is common for a
university in Europe to advertise open spots in PhD programmes as job postings on a platform
such as Euraxess, which lists skill requirements as selection criteria. Therefore, we chose Euraxess
as our data collection source. Analysing the ‘skill section’of each ad revealed that PhD programmes
request not only skills but also personal attributes and qualifications, which we collectively label as
‘admission attributes’henceforth. We developed a taxonomy and extracted attributes present in
each advertisement. Analysing big data, we ask what PhD programmes demand of PhD applicants.
We address this question by analysing which attributes are requested and the effect of discipline,
country and year of posting. We discuss the implications for pre-doctoral and doctoral education,
and make several theoretical, methodological and practical contributions. We also discuss how
our research contributes and extends the literature on PhD skills and graduate employability.
In the following, we explain how and why we chose a particular skill taxonomy to analyse PhD job
selection criteria, outline our methodology, and visualise the findings using various forms of data. We
discuss the findings and their implications as well as applicability for practice.
Skill taxonomies
Skills often have multiple synonyms and are interchangeably used with ‘attributes’,‘competencies’
and ‘qualities’or categorised differently, e.g. soft vs hard skills. Hence, any study on skill research
needs to clarify which skills are referred to and the type of categorisation adopted. To use a
common language and increase the practical applicability of our results, we sought a suitable classifi-
cation of skills in the European context. Three European skill frameworks were considered, namely
Vitae’s‘Researcher Development Framework’(RDF) (2020), the mindSET framework (Nikol and Lietz-
mann 2019), and the ‘Transferable Skills for Early-Career Researchers’framework by the European
Council of Doctoral Candidates and Junior Researchers (Eurodoc) (2018), hereafter the Eurodoc fra-
mework. Our criteria for selecting the most suitable framework were:
(a). a framework needs to be known and widely applied OR comprehensive and incorporating other
frameworks that are country- or discipline-specific,
(b). applicable to the European context,
(c). reference broader skills beyond research skills, and preferably
(d). reflect early career researchers’aspirations
Although developed a decade ago, Vitae’s RDF (2020) provides guidance and reference for
researcher development worldwide. It includes categories like professional and career development,
professional conduct, and other skills often listed in transferable skill frameworks. The RDF lists four
domains (Knowledge and intellectual abilities; Personal effectiveness; Research governance and
organisation; and Engagement, influence, and impact), each specifying three skill categories.
However, the skill classification is oriented towards research skills and researcher development
across different stages and misses some skills, e.g. digital literacies, that emerge as important in
the general labour market. Hence, a framework that spoke to any professional context was
required.
2L. MANTAI AND M. MARRONE
The project, ‘Training the mindSET –Improving and Internationalizing Skills Trainings [sic] for Doc-
toral Candidates’aims to develop a European Core Curriculum in Transferable Skills for doctoral can-
didates in Science, Engineering and Technology (SET) Disciplines. The mindSET study (Nikol and
Lietzmann 2019) developed a skill taxonomy for researchers via a survey with PhD candidates and lit-
erature analysis on employer views to understand which transferable skills are needed in the European
labour market and which skills need to be developed to enhance PhD graduates’employability in
diverse professional areas. The mindSET taxonomy lists eight areas of transferable skills and, while
incorporating multiple frameworks, did not reflect the importance of Communication skills in their
own right but rather absorbed them in other categories. Also, generic Work competence and Personal
competence clusters did not sufficiently accommodate what has been discussed in previous literature.
The Eurodoc website states: ‘it is an international federation of 28 national organisations of PhD
candidates, and more generally of young researchers from 26 countries of the European Union and
the Council of Europe’. Like mindSET, the Eurodoc framework also builds on other frameworks,
including the RDF, OECD (2019), UniWiND, the European Commission, etc. It lists nine skill categories
(see Figure 1). In addition, it is a framework informed by several country-specific PhD models, devel-
oped as a reference for broad skill development in PhDs across Europe, and advocates for young
researchers’career aspirations. Given that countries approach and structure their PhDs differently,
we wanted to account for possible country-specificdifferences in role requirements (Durette, Four-
nier, and Lafon 2016; Santos, Horta, and Heitor 2016). Therefore, the Eurodoc framework provided
the most suitable taxonomy (Figure 1).
Text mining and job ads
With the advent of text mining and machine learning, research has sought to use these approaches
to study job ads and create skill taxonomies. For example, Colombo, Mercorio, and Mezzanzanica
(2019) constructed and mapped a skills taxonomy to the ESCO classification taxonomy to identify
soft and hard skills listed in job ads. Other examples include work by De Mauro et al. (2018) who
used Latent Dirichlet Allocation to determine job families for jobs in Big Data, as core competencies
of librarians (Yang et al. 2016). More recently Australian researchers used machine learning to analyse
job ads in the context of doctoral education (see Pitt and Mewburn 2016; Mewburn et al. 2018).
While the last two studies focus on skills expected at PhD exit, we focus on skill requirements at
PhD entry.
While using an existing framework provides a common language and a shared understanding of
the study objectives, taking a data-driven approach to skill demand analysis allows insights to
emerge that would otherwise be hidden (Sibarani et al. 2017). Looking at the data and applying
emergent thematic analysis, greater granularity and variety in skills was possible. The application
of data-driven taxonomies posits two distinct benefits: the ease of updating and the use of employ-
ers’language, rather than academic (Djumalieva and Sleeman 2018). Several existing skill taxo-
nomies rely on expert consultation and can be slow and costly to adapt, whereas a data-driven
taxonomy is easily updated, and the same methodology can be used with a new set of job
adverts. The text analysis methodology presented in this study is a timely way of capturing infor-
mation on skill dynamics in different work sectors. Big data, such as job advertisements, can
inform quickly; online job adverts, and the skills they list, help us to develop a taxonomy that
uses the same ‘skills language’used by those who employ and supervise PhD candidates, rather
than that of external bodies or policymakers.
Methodology
To find out which skills PhD programmes expect at PhD entry we conducted data collection, data
analysis, taxonomy creation using a data-derived dictionary, and machine-learning analysis, as
follows:
STUDIES IN HIGHER EDUCATION 3
Data collection
Our PhD ads data are gathered from Euraxess, a European platform that lists academic jobs at all
levels (from PhD to Professor) from 40 European countries and non-European countries. Jobs ads
show different fields: a general job description, benefits, requirements (split in skills/qualifications
Figure 1. Eurodoc framework (adapted from Eurodoc Skill Report 2018).
4L. MANTAI AND M. MARRONE
and specific requirements), university, research field, location, required languages, job status, and
starting date. Conceiving the PhD ad as a job and skill requirements as selection criteria, we
assume that applicants who already perform at this level are more likely to get the PhD role.
A total of 270,523 ads were downloaded, and three inclusion criteria were applied:
(1) Date published. According to the number of academic job ads available following its launch, Eur-
axess gained popularity in 2016. Hence, we only included four years, 2016–2019, of data. This
criterion reduced our sample to 251,561.
(2) For PhD Students only. Only advertisements that addressed PhD applicants and had the follow-
ing text in their title were included: PhD, Phd, PhD, doctoral candidate, doctoral student, doc-
toral fellow, doctoral programme, or doctoral research. This further narrowed our data to
36,787 ads.
(3) Only ads with Skills/Qualifications and Specific Requirements text. In Euraxess, each PhD ad is
structured in several sections: we focus on the Skills/Qualification and Specific Requirements sec-
tions and merge these to create one unit of analysis per ad. Entries duplicated in both sections
were considered as one entry, and ads must list content in at least one of these sections to be
considered in our analysis. This resulted in a final data set of 13,562 ads which met all three
criteria.
Table 1 provides a breakdown of 13,562 ads by country, discipline, and year of posting. Sections of
the ad used for our analysis are the year of the ad’s posting, research field, location, required
languages, and skill requirement text.
New taxonomy using data-derived dictionary
We took a data-driven approach to taxonomy building. Any existing taxonomy appears flat and void
of any ranking or hierarchy if not informed by data. Fitting our data-derived categories into an exist-
ing taxonomy meant we could expand and elaborate on the insights that have come before us.
First, random assignment was used to select 200 job ads for closer reading by both authors. The
authors tagged each attribute as it emerged in the data. In the same process, we recorded related
concepts or ‘synonym-like’terms for each attribute. We assumed that skills like ‘teamwork’might
likely be referred to as ‘groupwork’elsewhere but belong to the same category, i.e. collaboration
or working with others. Second, our research identified the most suitable skill framework, i.e.
Eurodoc framework (as discussed above), following previous studies that have relied on existing
skill frameworks to construct a suitable taxonomy (e.g. Colombo, Mercorio, and Mezzanzanica
2019; Sibarani et al. 2017). Third, we added attributes that were not listed in the Eurodoc categories.
This modified the Eurodoc’s nine categories and led to a comprehensive taxonomy of PhD attributes
relevant to our sample. Specifically, three new categories were added: Degree and Achievements
(degree names, credentials, transcripts, academic records, etc.), Previous Work Experience, and Per-
sonal attributes (ambition, enthusiasm, motivation, proactiveness, resilience, etc.). These did not
appear in the Eurodoc’s transferable skills categories but were frequently listed in the Skills/Qualifi-
cations and Specific Requirements sections of the job ads. As the new categories include qualifica-
tions (e. g. Degree and Achievements) and personal attributes, not only skills, we adopted the term
‘admission attributes’to label our revised categories, inspired by O’Leary’s(2021) work that uses the
concept of ‘attributes’to include skills, personal attributes, and qualifications.
Any taxonomy inevitably presents some overlap in the way they categorise attributes; for example,
it can be argued whether creativity fits under Cognitive or Enterprise categories. To avoid as much
overlap as possible and keep the categories distinct, the entire taxonomy was verified by three inde-
pendent and multi-disciplinary peer reviewers and both authors. In several discussions the individual
admission attribute categories were sorted until a consensus agreement was reached.
STUDIES IN HIGHER EDUCATION 5
Attributes were excluded from counting if they were used as part of a technical phrase or referred
to something unrelated, e.g. ‘Article’as in ‘Article of Law’,‘motivation’as in ‘motivational letter from
the candidate’. To do this, we applied the ‘keywords-in-context’approach, which allowed us to manu-
ally check the use of the concept in the ad. As a result, we established 274 individual attributes for our
dictionary, 45 of which were instances of attributes (as per examples above) to be disregarded.
Machine-learning analysis
Dictionary-based entity extraction tools extract features in unstructured text into pre-defined cat-
egories such as company names, medical codes, or topics (Cai et al. 2019; Cook and Jensen 2019).
Entity extraction tools identify occurrences of pre-defined entities in text (such as job ads). We
created a dictionary-based entity extraction tool which searches for occurrences of terms (either
as full or partial matches) within ads, returning the categories which are present in the job ad.
Such dictionaries have been previously used in job ad research (Anne Kennan et al. 2006; Sodhi
and Son 2010; Deming 2015).
The results of our dictionary-based entity extraction tool were then tested for reliability. An inde-
pendent coder reviewed the initial attributes determined by the authors and tagged a sample of 100
random PhD ads to determine the agreement between our human coder and our entity linker-based
results. Krippendorff’s alpha, applicable to nominal data by two or more coders, was used to deter-
mine inter-rater reliability (Krippendorff2011). Results of Krippendorff’s alpha ranged from high
agreement (alpha ≥0.8) to moderate agreement (alpha ≥0.67) and poor agreement (alpha < 0.67)
(Krippendorff2004). The overall interrater reliability for all categories was high,
2
except for Enterprise
and Career Development. Both categories appeared infrequently and hence were removed from our
analysis. The final list of ten categories is Research; Digital; Communication; Interpersonal; Cognitive;
Table 1. Sample characteristics.
Countries Discipline Year of posting
Netherlands 3708 Biological sciences 2795 2016 1250
Germany 1675 Physics 1929 2017 3418
France 1116 Chemistry 1493 2018 4283
Spain 1069 Engineering 1464 2019 4611
United Kingdom 960 Medical sciences 1118
Poland 901 Computer science 977
Belgium 598 Technology 809
Ireland 352 Cultural studies 481
Austria 342 Economics 362
Italy 333 Agricultural sciences 294
Romania 326 Environmental science 215
Croatia 322 Mathematics 188
Norway 321 Language sciences 177
Switzerland 212 Neurosciences 153
Sweden 209 Psychological sciences 146
Denmark 181 Juridical sciences 116
Czech Republic 157 Geosciences 98
Luxembourg 138 History 66
Portugal 112 Sociology 57
Greece 82 Astronomy 54
Finland 66 Anthropology 52
Israel 54 Political sciences 46
Slovakia 40 Educational sciences 44
Slovenia 35 Communication sciences 35
Australia 32 Arts 33
Canada 31 Architecture 29
Latvia 30 Geography 27
Estonia 28 Others 304
Iceland 27
Others 105
6L. MANTAI AND M. MARRONE
Teaching and Supervision; Personal attributes; Degrees and Achievements; Previous work experi-
ence; and Mobility.
Data analysis
This research aims to identify desirable admission attributes in PhD student recruitment. In other
words, what skills, attributes, and qualifications do applicants need to demonstrate when applying
for a PhD. To ascertain the likelihood of an attribute category being present in a PhD ad, logistic
regressions (LR) were performed for each category as our outcome variable (i.e. category) is either
present or not in a PhD ad. The reference category is ‘not present’. The predictor variables are: (1)
Year of ad publication, (2) Country of ad and (3) Discipline of ad. A logistic regression analysis was
conducted to understand the likelihood of a category being present in an ad, given the year of pub-
lication, Discipline and Country. We considered Year as a covariate (continuous variable), while Dis-
cipline and Country were considered factors (categorical variables).
Findings
We analyse our data in two parts:
(1) What admission attributes are listed in PhD adverts?
(2) What is the effect of discipline, country, and year of posting?
Admission attributes listed in adverts
When examining the data, the Degree and Achievements category was present in 81% of ads. This
might be expected, as, e.g. in Europe, a PhD commonly requires a higher education degree, at least
at Bachelor but more likely at Master level, although a Bachelor degree in the US and perhaps other
countries would suffice. The top three desired categories after Degree and Achievements are Com-
munication, Research, and Interpersonal skills, recorded in close to half of all the PhD
postings (Figure 2). In contrast, prior teaching and work experience are considered least important
before doing a PhD. The preponderance of categories varies for disciplines and countries.
The top five disciplines in our sample are Biological sciences, Physics, Chemistry, Engineering and
Medical Sciences (see Table 1). Together, they account for more than half of all PhD postings 2016–
2019, reflecting the strong representation of Sciences or STEM disciplines in our data. Figure 3
presents the percentages of ads listing a category per discipline; for instance, the Interpersonal
skill category is mentioned in 62% of Medical Science ads; twice as often as in Biological Science
ads. Digital skills appear in 38% of all Engineering ads; almost three times as frequently as in Biologi-
cal Sciences and Chemistry.
Most of the countries represented are in Europe. The top five countries are the Netherlands,
Germany, France, Spain, and the UK (see Table 1); together, they supplied more than half of all
Figure 2. Attribute category dominance in data.
STUDIES IN HIGHER EDUCATION 7
the PhD posts between 2016 and 2019. Figure 4 presents a breakdown of categories per country. For
instance, Mobility –a category that includes intercultural awareness and foreign language skills –is
almost eight times more frequent as a requirement in France than in the UK and features highest in
Engineering (Figure 3). The Netherlands places greater focus on Communication, Interpersonal, Per-
sonal attributes, and Research, showing up to double the counts or more of these categories than
other countries. Digital and Cognitive categories also score much higher in the Netherlands than
in other countries. Interestingly, across the top five countries Interpersonal and Personal attributes
rank lowest for the UK but highest for the Netherlands.
Table 2 presents a breakdown of categories per year of posting. Further analysis showed an
increase in the number of categories listed per ad. On average, in 2016, a PhD ad would mention
2.4 categories, and this steadily increased year on year (3.2 categories in 2017, 3.2 in 2018),
leading to 3.6 categories being represented per ad on average in 2019. This suggests that
between 2016 and 2019 alone, PhD ads asked for more attributes and more diverse attributes of
their applicants. Table 2 also shows that Communication, Interpersonal, Personal attributes, and
Digital categories, along with Cognitive, are trending; hence, gaining importance. The fastest trend-
ing categories are Cognitive (doubled in four years), Interpersonal (doubled in four years) and Per-
sonal attributes (increase by 90% in four years).
Effect of discipline, country, and year of posting
Overall, we find that the occurrence and frequency of attributes differ by country, discipline, and year
of ad posting. To determine the likelihood of an attribute appearing in an ad or not, we conducted a
logistic regression analysis. Results are presented in Table 3. We have selected the top five countries
and disciplines that posted the most job ads in 2016–2019 (see Table 1) as a sample to conduct an in-
depth analysis. The top five countries are the Netherlands, Germany, France, Spain, and the UK. The
top five disciplines are Biological Sciences, Physics, Chemistry, Engineering, and Medical Sciences.
Countries and disciplines not in the top five are collectively labelled as Others and form the reference
Figure 3. Admission attribute category by discipline.
Figure 4. Attribute category by country.
8L. MANTAI AND M. MARRONE
groups for the logistic regression analysis.
3
The odds ratios help show the likelihood of a skill cat-
egory being present. The higher the odds ratio, the higher the likelihood of the skill category appear-
ing in the ad; conversely, odds ratio between 0 and 1 present lower likelihoods. For example,
Netherlands places a strong value on Degree and Achievements (3.48) when compared with
other countries, and the UK PhD ads are less likely to request Teaching and Supervision skills
(0.12). When comparing disciplines, we find Medical Sciences are 40% more likely to request Inter-
personal skills (1.4). On the other hand, Biological Sciences are much less likely to request skills in the
same category (0.82).
A visual summary of Table 3 is presented in Figure 5, which shows all odds ratios that are signifi-
cant. Lower likelihood, where the odds ratio is between 0 and 1, is marked as dotted, while higher
likelihoods, where odds ratio is above 1, is marked as striped. The symbol ‘/’is shown for non-signifi-
cant values. Germany is less likely to mention most skills when compared to ‘Other’countries. Neth-
erlands, on the other hand, is more likely to request skills such as Degree and Achievements,
Interpersonal skills, and Personal attributes in their PhD ads. When comparing Biological Sciences
with other disciplines, Biology ads are less likely to request all skills except Degree and Achievement.
Comparing Medical Science with other disciplines, the ads are more likely to request Personal Attri-
butes, Interpersonal skills, and Communication.
Discussion
While previous research has examined the various skills and attributes required for post-PhD careers,
our PhD data tells us that a number of these attributes are already sought in PhD student recruit-
ment. As Pitt and Mewburn (2016) found that current academic positions expected successful appli-
cants to be nothing short of academic superheroes, we show evidence that expectations are already
high at PhD entry. The bar is likely to rise even higher in the researcher career trajectory. We find that
during a relatively short period (2016–2019), PhD programmes have increased their expectations for
more attributes and more diverse attributes (from 3.2 categories in 2017 to 3.6 in 2019). Indeed, Pitt
and Mewburn (2016) also found that academic employers (i.e. universities) look for a wide range of
highly developed skills and expertise across academic careers. Further, our data shows that countries
and disciplines request different attributes. Understanding these differences informs candidates
seeking doctoral training in a particular country or discipline. Based on our findings, Communication,
Research, and Interpersonal skills are the top three skills required for PhD admission, following
Degree and Achievements. Hence, PhD applicants would benefit from showcasing these skills in
their PhD applications before embarking on graduate research training.
Our findings show that many attributes requested in PhD student recruitment are what is com-
monly referred to as transferable skills (Germain-Alamartine and Moghadam-Saman 2020; Sinche
et al. 2017), skills that are applied across many different professional tasks. The fact that Communi-
cation, Interpersonal skills, Personal attributes, and Digital skills, along with Cognitive skills, are
Table 2. Attribute category by year of posting.
Year
2016 2017 2018 2019
Degree and Achievements 75% 84% 84% 82%
Communication 41% 54% 58% 64%
Research 33% 43% 46% 49%
Interpersonal 24% 42% 44% 50%
Personal attributes 23% 36% 40% 44%
Digital 17% 25% 30% 32%
Cognitive 11% 18% 18% 23%
Mobility 10% 9% 10% 12%
Teaching and Supervision 5% 3% 5% 4%
Previous work experience 2% 3% 3% 4%
STUDIES IN HIGHER EDUCATION 9
trending in our data (Table 2), indicates that it takes transferable skills to do a PhD. Our research sides
with previous research (OECD 2017;Deming2015; Succi and Canovi 2020) that projected that two
types of skills would be particularly important in the future: soft and digital skills. PhD candidates
Table 3. Logistic regression results per attribute category.
Research Digital Communication Interpersonal Cognitive
Variables B (SE) OR B (SE) OR B (SE) OR B (SE) OR B (SE) OR
Year 0.12 (.02)*** 1.13 0.19 (.02) *** 1.20 0.21 (.02) *** 1.24 0.19 (.02) *** 1.21 0.15 (.03) *** 1.16
Countries
(Others)
France −0.29 (.07)*** .75 0.14 (.08) 1.15 −0.73 (.07)
***
.48 −0.22 (.07) ** .81 −0.8 (.11) *** .45
Germany −0.6 (.06)*** .55 −0.25 (.07) ** .78 −0.63 (.06)
***
.53 −0.38 (.06) *** .69 −0.47 (.09) *** .63
Netherlands 0.41 (.05)*** 1.51 0.1 (.05) 1.10 0.74 (.05) *** 2.10 1 (.05) *** 2.71 0.66 (.06) *** 1.94
Spain −0.07 (.07) .93 0.23 (.08) ** 1.26 −0.36 (.07)
***
.70 −0.1 (.07) .90 −0.5 (0.1) *** .61
United
Kingdom
−0.76 (.08)*** .47 −0.54 (.09) *** .59 −0.54 (.07)
***
.58 −1.16 (.09) *** .31 −1.15 (.14) *** .32
Areas (Others)
Biological
Science
−0.54 (.05)*** .58 −1.23 (.07) *** .29 −0.54 (.05)
***
.58 −0.19 (.05) *** .82 −0.54 (.07) *** .58
Chemistry −0.49 (.06)*** .62 −1.27 (.08) *** .28 −0.11 (.06) .90 −0.04 (.07) .96 −0.25 (.09) ** .78
Engineering −0.25 (.06)*** .78 0.12 (.07) 1.13 0.19 (.06) ** 1.20 −0.05 (.07) .95 −0.14 (.09) .87
Medical
Science
−0.36 (.07)*** .70 −0.47 (.08) *** .62 0.11 (.08) 1.11 0.34 (.07) *** 1.40 −0.18 (.08) * .84
Physics −0.22 (.06)*** .80 −0.07 (.06) .93 −0.12 (.06) * .89 −0.07 (.06) .93 −0.34 (.07) *** .71
Constant −236.75 (.49)*** −374.11 (.75)
***
−427.4 (.05)
***
−384.16 (.84)
***
−307.46 (.65)
***
χ2(11) = 779, p< .001 χ2(11) = 984, p<
.001
χ2(11) = 1358, p<
.001
χ2(11) = 1496, p<
.001
χ2(11) = 784, p<
.001
Nagelkerke R
2
8% 10% 13% 14% 9%
Classification
accuracy
60.40% 71.90% 63.90% 65.70% 81.10%
Teaching and
Supervision
Personal
Attributes
Degree and
Achievement
Previous Work
Experience Mobility
Variable B (SE) OR B (SE) OR B (SE) OR B (SE) OR B (SE) OR
Year 0.03 (.05) 1.03 0.16 (.02) *** 1.18 0.03 (.02) 1.03 0.2 (.05) *** 1.23 0.12 (.03) *** 1.13
Countries
(Others)
France −2.05 (.31) *** .13 −0.05 (.07) .95 −0.39 (.08)
***
.68 −0.48 (.19) * .62 0.55 (.08) *** 1.74
Germany −1.23 (.19) *** .29 −0.16 (.06) * .86 0.12 (.07) 1.12 −1.45 (.25) *** .24 −0.67 (.11) *** .51
Netherlands −0.86 (.11) *** .42 0.86 (.05) *** 2.36 1.25 (.08) *** 3.48 −0.37 (.12) ** .69 −1.64 (0.1) *** .20
Spain −1.81 (.28) *** .17 −0.06 (.07) .94 0.12 (.08) 1.13 −0.52 (.19) ** .60 0.09 (.09) 1.09
United
Kingdom
−2.14 (.34) *** .12 −0.92 (.09) *** .40 0.67 (0.1) *** 1.95 −0.8 (.23) ** .45 −1.91 (.21) *** .15
Areas (Others)
Biological
Science
−1.46 (.16) *** .23 0.03 (.05) 1.03 0.15 (.07) * 1.16 −0.33 (.14) * .72 −0.88 (.09) *** .41
Chemistry −1.28 (.19) *** .28 0.04 (.07) 1.05 −0.12 (.08) .89 −0.68 (0.2) ** .51 −0.67 (0.1) *** .51
Engineering −0.61 (.15) *** .55 −0.05 (.07) .95 −0.14 (.08) .87 0.15 (.15) 1.17 −0.21 (.09) * .81
Medical
Science
−0.32 (.16) * .72 0.4 (.07) *** 1.49 −0.27 (0.1) ** .76 −0.55 (.21) * .58 −0.19 (.13) .83
Physics −0.46 (.13) *** .63 0.15 (.06) * 1.16 0.06 (.08) 1.06 0.1 (.14) 1.11 −0.44 (0.1) *** .65
Constant −64.46 (.03) −327.97 (.02)
***
−55.76 (.91) −413.03 (.53)
***
−239 (.67) ***
χ2(11) = 366, p<.001 χ2(11) = 1022,
p<.001
χ2(11) = 519,
p< .001
χ2(11) = 115,
p< .001
χ2(11) = 736,
p< .001
Nagelkerke R
2
9% 10% 6% 3% 11%
Classification
accuracy
95.70% 61.10% 82.40% 96.40% 89.40%
10 L. MANTAI AND M. MARRONE
are expected to network, collaborate, co-publish, pitch, and communicate their research to diverse
audiences and have advanced technical and digital competencies. The fastest trending categories
of Interpersonal skills (including teamwork, negotiation, networking, conflict resolution, etc.) and Per-
sonal attributes (including resilience, enthusiasm, motivation, etc.)in PhD ads suggest that doing a PhD
requires such qualities. Interpersonal skills and Personal attributes would be particularly useful in doc-
toral programmes that are more collaborative and interdisciplinary (Blessinger 2016;Borrell-Damian,
Morais, and Smith 2015). Overall, PhD programmes would do well in communicating how the attri-
butes required for PhD admission will be applied and further developed during the PhD.
Our data also shows that Research experience is required in 45% of our sample and increasing by
13% per year (Table 2). This points to heightened demands placed on research training. PhD appli-
cants need to arrive already equipped with skills like Communication and Interpersonal skills, as PhD
programmes are tightly regulated and structured to support timely completion and boost research
outputs (Humphrey, Marshall, and Leonardo 2012; Sharmini and Spronken-Smith 2020; Bosanquet,
Mantai, and Fredericks 2020).
Further, our research insights inform the PhD design literature by adding a better understanding of
what is expected pre-PhD. Albeit its primary purpose of preparing for academic careers, the PhD needs
to develop students for diverse careers as the academic employment market is increasingly competi-
tive. Taking a curriculum development perspective, Sharmini and Spronken-Smith (2020) argue that
the current PhD format is not aligned with the need to adequately prepare PhD students for academic
and non-academic careers. Examining the multiple skill frameworks earlier (i.e. Vitae’s RDF, mindSET,
Eurodoc), it is undeniable that PhD students are expected to do more than ‘just’advancing research
skills and generating original knowledge. Increasingly, PhD students need to demonstrate superior
communication, networking and leadership skills (Borrell-Damian, Morais, and Smith 2015). These
skills are not commonly reflected in PhD programme descriptions or outcomes but are visible in
the PhD selection criteria in our data. Our research supports Sharmini and Spronken-Smith’s argument
(2020) that the PhD needs to be revised to reflect and provide evidence for the multifaceted learning
and development that occurs in the PhD, which extends beyond the student’s particular discipline,
including leadership training, outreach activities, and interdisciplinary projects (Sharmini and Spron-
ken-Smith 2020; Blessinger 2016). These expectations explain the diversity of skills found in our data.
Assessment and diverse evidence, other than the thesis, would better reflect the breadth of knowl-
edge and skills possessed pre-PhD and gained during PhD, and potentially be easier for diverse
employers to appreciate. Alternative assessments might include creative literary outputs, multidisci-
plinary collaborative team projects, implemented initiatives and impact, innovations, collaborations
with industry, business plans for start-up companies, strategic plans, policy documents, etc. Assess-
ment could also focus on the skill gains from pre-PhD to post-PhD.
Finally, our research informs PhD graduate employability research as it provides the baseline of
the attributes desired at PhD entry. Our data shows that successful PhD applicants may possess a
breadth of attributes transferable to other careers already. Hence, we should be able to expect
PhD graduates to be able to follow diverse careers (Germain-Alamartine and Moghadam-Saman
2020). However, to further one’s employability and become ‘more well-rounded researchers, prac-
titioners and leaders’(Blessinger 2016) is so far left up to the doctoral candidate’s initiative and
Figure 5. Odds Ratio by top five disciplines and countries.
STUDIES IN HIGHER EDUCATION 11
ability to firstly, locate formal or informal development opportunities and secondly, accommodate
these in already busy PhD schedules. We recommend embedding an explicit career development
focus in PhD programmes and promote candidates’work-readiness building on and furthering
the attributes that we found might already be present at PhD admission.
Limitations
Developing static definitions for skill categories is an ambitious task, and the skill categories are likely
to evolve. Skills are notoriously difficult to distinguish (Djumalieva and Sleeman 2018), which is why
some countries and disciplines do not have an established skill framework. Others before us have
acknowledged there is no single ‘right way’to group skills (Djumalieva and Sleeman 2018). Never-
theless, the dictionary developed and the taxonomy used in this paper provide a shared understand-
ing of the concepts and types of skills examined in this study. We also acknowledge that we cannot
assume that all work, let alone PhD roles, are advertised online or if and how the roles are filled (e.g.
whether requested skills and attributes are found or whether successful candidates accepted the
role). Further, we do not claim that all PhDs require the identified admission attributes to complete
or succeed in a PhD, however, as our data shows PhD programmes increasingly request such attri-
butes. We wish to reiterate that our analysis concerns pre-PhD attributes only; we do not measure or
compare these against post-PhD employability attributes, nor do we measure whether selected can-
didates possess all attributes requested. This warrants further research and a systematic comparison
of attributes at PhD recruitment, admission, and graduation.
Study implications and applicability
Our research makes several contributions. From a theoretical point of view, we place research train-
ing in the context of employability development and use the pre-PhD application stage as the first
point in this development journey where we measure the baseline of desirable attributes. Our paper
revealed that diverse skills and attributes are requested before PhD entry, many of which are com-
monly expected at PhD exit.
On a methodological level, we empirically validated the Eurodoc skill framework and expanded it
by generating our data-derived dictionary. We developed a dictionary of attributes based on a big
data set that can be applied to any academic job ad data in the future and provide useful data com-
parisons and changes over time. Further empirical research might use our framework to assess and
measure the baseline of admission attributes in PhD students to determine individual development
needs that the institutions can provide.
This study has significant relevance for practice. Our insights benefit any student on a pathway to
the PhD, PhD applicants themselves, early career researchers, and those supporting and educating
them. We provide more transparency and understanding of admission attributes expected of PhD
applicants in different disciplines and countries to make informed decisions, adopt the appropriate
language of employment requirements, plan their career development accordingly, and improve
their competitiveness. Our results point to differences and nuances in skill demands across disci-
plines and countries that need to be considered in pre-doctoral education and its policies.
In relation to policy, we recommend that PhD programme design and descriptions emphasise the
need to have diverse and transferable attributes pre-PhD. These are likely to lead to success in doing
PhD research as well as navigating common PhD challenges. PhD pathway programmes and pre-
PhD education more broadly are well-advised to embed skill training and development of the top
attributes we identified. Authentic research experiences (e.g. summer or winter scholarships, intern-
ships, research for coursework, witnessing educators being engaged in research) offer ideal oppor-
tunities to prepare for a PhD (Brew and Mantai 2020; Hajdarpasic, Brew, and Popenici 2015). During
PhD candidature, we can assume that PhD candidates will build on and further enhance their admis-
sion attributes but might need help in developing those that were not present pre-PhD. In this case,
12 L. MANTAI AND M. MARRONE
career development training for students needs to be embedded in PhD programmes, and appro-
priate training for supervisors might be required.
Notes
1. The term ‘graduate’or ‘PhD graduate’in this paper refers to those who completed their PhD, while ‘student’and
‘candidate’both refer to those who have not yet been awarded the PhD.
2. Admission attribute categories and their Kippendorff’s alpha: Research –0.80, Teaching and Supervision: 0.89,
Cognitive: 0.90, Interpersonal: 0.86, Digital: 0.93, Communication: 0.88, Mobility: 0.87, Personal attributes:
0.80, Degree and Achievements: 0.86, Previous work experience: 0.89.
3. Detailed data and data analysis on specific disciplines and countries can be obtained from the authors upon
request.
Acknowledgements
The authors acknowledge the technical assistance of the Senior Consultant in Statistics, Jim Matthews, from the Sydney
Informatics Hub, a Core Research Facility of the University of Sydney. We are very grateful to the reviewers for their
constructive and helpful feedback on the earlier draft of this paper. We also wish to thank EURAXESS - Researchers
in Motion, a pan-European initiative by the European Commission, for the permission to analyse EURAXESS job data.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Lilia Mantai http://orcid.org/0000-0002-4603-3831
Mauricio Marrone http://orcid.org/0000-0003-3896-6049
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