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General digital competences of beginning trainees in commercial vocational education and training

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Against the background of digital transformation processes that are currently changing the world of work, this paper examines general digital competences of beginning trainees in commercial vocational education and training (VET) programs. We are particularly interested in factors influencing digital competence profiles. From survey data including N = 480 trainees in one federal state in Germany, we were able to identify three different competence profiles (based on the trainees’ self-assessment of their general digital competence). Initial descriptive analysis reveals differences between competence profiles of different training professions (industrial clerks and retail salespersons reach higher competence levels than salespersons). However, regression results indicate that these differences can be explained by differences in school leaving certificates. Contrary to prior empirical evidence, we find no significant effect of trainees’ gender. Finally, the frequency of certain private digital activities (e.g. using office programs, conducting internet searches) affects digital competence profiles. Implications for both VET programs and further research are discussed.
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General digital competences ofbeginning
trainees incommercial vocational education
andtraining
Stefanie Findeisen1* and Steffen Wild2
Introduction
Importance ofdigital competences ofbeginning trainees inVET
Digital transformation processes are currently changing the world of work (Arnold etal.
2016; Autor 2015; Frey and Osborne 2017). e increasing use of technology affects
organizational structures as well as communication and collaboration processes and fos-
ters a trend towards knowledge-based work activities (Baethge etal. 2003; van Laar etal.
2017). e growing importance of technology poses new requirements with respect to
skills and competences of the workforce. ere is an increasing need for interdiscipli-
nary skills (e.g. problem solving, creativity, critical thinking, learning skills)—typically
referred to as 21st-century skills (see e.g. Voogt and Roblin 2012)—and for digital com-
petences (Arnold etal. 2016; Autor 2015; Brolpito 2018; van Laar etal. 2017).
VET programs are supposed to foster the competences required to successfully engage
in professional situations (Avis 2018; Seeber 2016). However, to successfully shape
Abstract
Against the background of digital transformation processes that are currently chang-
ing the world of work, this paper examines general digital competences of beginning
trainees in commercial vocational education and training (VET ) programs. We are
particularly interested in factors influencing digital competence profiles. From survey
data including N = 480 trainees in one federal state in Germany, we were able to iden-
tify three different competence profiles (based on the trainees’ self-assessment of their
general digital competence). Initial descriptive analysis reveals differences between
competence profiles of different training professions (industrial clerks and retail
salespersons reach higher competence levels than salespersons). However, regression
results indicate that these differences can be explained by differences in school leaving
certificates. Contrary to prior empirical evidence, we find no significant effect of train-
ees’ gender. Finally, the frequency of certain private digital activities (e.g. using office
programs, conducting internet searches) affects digital competence profiles. Implica-
tions for both VET programs and further research are discussed.
Keywords: Vocational education and training, Trainees, Digital competences,
DigComp
Open Access
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RESEARCH
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
https://doi.org/10.1186/s40461-022-00130-w
*Correspondence: stefanie.
findeisen@uni-konstanz.de
1 Department of Economics,
University of Konstanz,
Universitätsstrasse 10,
78464 Konstanz, Germany
Full list of author information
is available at the end of the
article
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
digital transformation processes training companies are in need of trainees who start
their training program with solid digital competences (Härtel etal. 2018). e on-going
COVID pandemic additionally increases the need for digital competences, as health reg-
ulations require the use of digital tools in both the workplace (e.g. communication and
collaboration) and vocational schools (e.g. distance learning formats) during the training
program.
While it is often assumed that these days adolescents naturally possess certain digital
competences, Kirschner and De Bruyckere (2017) illustrate that the idea of information-
skilled digital natives, who readily apply technology because they grew up in a digital
world, is a myth. In fact, studies among young people during general education in Ger-
many reveal deficits with regard to digital competences (Bos et al. 2014; Eickelmann
etal. 2019; Härtel etal. 2018). However, there is still a lack of empirical evidence on
digital competences of adolescents at the start of VET programs (Härtel etal. 2018). Our
study aims to address this research gap by examining the level of general digital compe-
tences of trainees when they enter their training program. In addition, we are interested
in factors that explain different competence levels. We specifically examine the extent
to which competence differences can be explained by individual characteristics as well
as prior learning processes of adolescents. e study focuses on the most popular field
of VET in Germany (in terms of yearly numbers of beginning trainees) and examines
trainees in three commercial VET programs: industrial clerks, retail salespersons, and
salespersons.
Conceptualizing digital competence
A wide range of terms are used by different authors to conceptualize individuals’ abilities
to use information and communication technology (ICT) (Ilomäki etal. 2016). In the
following, we will refer to the concept of digital competence. Digital competence can be
defined as ‘confident, critical and creative use of ICT to achieve goals related to work,
employability, learning, leisure, inclusion and/or participation in society’ (Ferrari 2013).
In a slightly broader approach, Ilomäki etal. (2016) define digital competence as consist-
ing of ‘(1) technical competence, (2) the ability to use digital technologies in a mean-
ingful way for working, studying and in everyday life, (3) the ability to evaluate digital
technologies critically, and (4) motivation to participate and commit in the digital cul-
ture’. A widely used conceptualization of digital competence is provided by the Euro-
pean Digital Competence Framework (DigComp) (Ferrari 2013). Based on the claim that
every citizen needs digital competences to participate in an increasingly digitalized soci-
ety, the framework distinguishes between five areas of digital competence (Ferrari 2013):
1. Information (browsing, searching and filtering information; evaluating information;
storing and retrieving information).
2. Communication (interacting through technologies; sharing information and content;
engaging in online citizenship; collaborating through digital channels; netiquette;
managing digital identity).
3. Content creation (developing content; integrating and re-elaborating; copyright and
licenses; programming).
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
4. Safety (protecting devices; protecting data and digital identity; protecting health;
protecting the environment).
5. Problem solving (solving technical problems; identifying needs and technological
responses; innovating and creatively using technology; identifying digital compe-
tence gaps).
is conceptualization has to be regarded as a generic framework (Ferrari 2013). In
line with the general differentiation between domain-general and domain-specific com-
petences (see e.g. Löfgren et al. 2019; Seeber 2016; Winther and Achtenhagen 2009),
Wilbers (2019) distinguishes between four different types of digital competences: (1)
general digital competences, (2) professional digital competences, (3) digital compe-
tences that are specific to the field of work and (4) digital competences that are pro-
fession-specific. e general digital competences are regarded as generic and span all
educational sectors, like competences described in the DigComp framework.
e focus of our study is the commercial VET sector, where digital transformation
leads to changes regarding both supplier and customer relations as well as internal
processes (e.g. storage and inventory management systems) and also work equipment
(Kupfer and Jaich 2019; Seeber etal. 2019). Routine tasks become less important for
professions in this field (e.g. salespersons, retail salespersons or industrial clerks) (Berg-
mann 2019; Utecht 2019). At the same time, computer-assisted operations as well as the
use and interpretation of digital data gain importance (Jordanski 2019). Specific digital
competences for these professions contain, for instance, the application of (new) digital
information and communication technologies, the use of hardware and software (e.g.
office packages) and dealing with data protection issues (Bergmann 2019; Jordanski
2019; Utecht 2019).
Profession-specific digital competences (industrial clerks: e.g. handling big data,
using Enterprise Resource Planning (ERP) systems (Bergmann 2019; Traub and Leppert
2019; Wilbers 2019); retail salespersons: e.g. understanding digital networks, conduct-
ing information searches (Holz and Leppert 2019; Kupfer and Jaich 2019); salespersons:
e.g. communication with customers, general willingness to learn (Kupfer and Jaich 2019;
Schmelter 2019)) are expected to be fostered during the course of the respective VET
program. However, in order to successfully manage the digital transformation processes
in the commercial field, training companies need beginning trainees who already pos-
sess a solid level of general digital competence (Härtel etal. 2018). To assess general
digital competences of beginning trainees, the DigComp framework seems to be a suit-
able approach, especially since the framework includes several aspects that are also rel-
evant for commercial training programs (e.g. content creation via office packages, safety
aspects). As such, we believe that the competences measured using the DigComp frame-
work are a solid base for a successful start in commercial training programs and the
acquisition of profession-specific digital competences.
Determinants ofdigital competence: theoretical model
e development of digital competence can be theoretically described as depicted in
Fig.1. is model builds onthe framework for the International Computer and Infor-
mation Literacy Study (ICILS) and depicts the acquisition of digital competence (here:
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
“Computer and information literacy”) based on a classic input-process-outcome model
(see e.g. Fraillon etal. 2020; Heldt etal. 2020). e model assumes that input factors
(antecedents) directly affect learning processes (process), learning processes, in turn,
are expected to correlate with digital competence (outcome)—hence, they affect digital
competences and are also influenced by competences (Heldt etal. 2020).
e model also distinguishes between four levels that are relevant for the acquisi-
tion of digital competence: (1) the wider community (characteristics of the educational
system, policies and curricula), (2) the school/classroom (characteristics of the school,
classroom instruction), (3) the home environment (family background, e.g. migration
background, and ICT access at home), and (4) the individual student (student charac-
teristics, learning process and level of performance). e ICILS framework deliberately
includes individuals’ learning processes outside of school as digital competence is not
only acquired in the school context. e model also accounts for the fact that anteced-
ents and processes might be determined by factors on higher levels (e.g. ICT education
policies determine schools’ ICT resources) (Fraillon etal. 2020).
In our study, we are interested in the individual level, hence, in the role of character-
istics and learning processes of adolescents on the acquisition of digital competence.
Existing empirical evidence on the impact of individual factors is reported in the follow-
ing section.
Determinants ofdigital competence ofadolescents: empirical evidence
e International Computer and Information Literacy Study (ICILS) repeatedly reports
deficits regarding digital competences among German adolescents (Bos etal. 2014; Eick-
elmann etal. 2019). In this comparative international study, German 8th grade students
Fig. 1 Theoretical model of the acquisition of computer and information literacy ( adapted from Fraillon et al.
2020, p. 7)
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
demonstrate only rudimentary competence levels. Compared to other countries repre-
sented in the ICILS, Germany ranks in the middle range. However, there is no significant
change regarding digital competences between the 2013 and the 2018 survey.
When it comes to determining factors of digital competences, several studies have
examined the effects of gender. e ICILS finds that for the German sample, girls possess
significantly higher digital competences than boys. In fact, none of the other countries
that are part of the study report advantages of male participants compared to females
(Gerick etal. 2019). ese findings are supported by other studies that also find advan-
tages for females regarding digital competences (e.g. Siddiq and Scherer 2019). However,
there is also empirical evidence suggesting higher digital competences of males (e.g.
Goldhammer etal. 2013) as well as studies finding no gender effect (Hatlevik and Chris-
tophersen 2013).
Moreover, education seems to be related to digital competence. As Hatlevik and
Christophersen (2013) demonstrate for a group of students in upper secondary schools
in Norway (N = 4087), the study program (vocational vs. general education) significantly
predicts digital competences. e authors perceive the study program as an indicator
of academic aspiration and illustrate that students in general educational tracks signifi-
cantly outperform vocational track students with regard to digital competence. is rep-
licates findings of previous studies (Calvani etal. 2012; Hatlevik 2010). For the German
context, Wild and Schulze Heuling (2020) indicate that students in cooperative higher
education programs demonstrate higher digital competences than students in VET. Fur-
thermore, Hatlevik etal. (2015b) find that for a sample of ninth grade students in Nor-
way (N = 852)—apart from family background—prior academic achievement (grades
achieved in the most important school subjects) is the most important predictor of digi-
tal competence.
When it comes to the effect of learning opportunities, Zhong (2011) finds from PISA
data that both ICT access at home and at school significantly predict adolescents’ self-
reported digital competence. However, the effect of ICT access at home is higher than
the school effect and the students’ previous experience in using a computer significantly
predicts digital competence.
Empirical evidence on digital competences of trainees in VET is still scares. ere is,
however, one study from 2013 on the internet use of German trainees, finding that par-
ticipants do well when it comes to navigating through the internet (orienting themselves
on an unknown website, registering for a platform with their email address) (Burchert
etal. 2013). With respect to those tasks, the authors found no differences between dif-
ferent types of VET programs (technical vs. commercial trainees). However, all the
trainees experienced difficulties when it comes to searching for information and reading
web content. Furthermore, the results reveal that the trainees use the internet mainly
for communication and information. While there is a high affinity to use the internet
for private purposes, internet use for professional reasons in the workplace is less com-
mon. is is true for the search of information and even more so for the use of internet
forums, blogs, or online videos. When facing a problem, the trainees prefer consulting
experienced colleagues or other trainees before trying internet searches.
Furthermore, in a small interview study among trainees in healthcare profes-
sions (N = 3), Evangelinos and Holley (2015) demonstrate-based on the DigComp
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
framework—that trainees perceive themselves as fairly capable with respect to ICT
tasks. However, their activities cover only a narrow field of technology use, mainly for
private purposes (e.g. communication via social media), and they overestimate their dig-
ital competence and fail to recognize skills necessary for the workplace.
Research questions
e empirical evidence described above mainly focuses on general education programs.
For the context of VET, reliable findings are missing. Moreover, in face of the repeatedly
reported deficits regarding general digital competences of adolescents (in Germany), it
seems worthwhile to examine digital competences of beginning trainees in VET as well
as factors predicting different competence profiles. is is the purpose of our study. We
focus on beginning trainees in commercial VET in Germany. e professional field of
Commercial Services, Trade, Distribution, Hotel and Tourism is the largest field in terms
of the number of beginning trainees in the federal state of Baden-Wuerrtemberg (2018:
11,914 beginning trainees). Within this field we focused on the three professions with
the highest numbers of beginning trainees: industrial clerks [Industriekaufmann/frau]
(2018: 3,219 beginning trainees), retail salespersons [Kaufmann/frau im Einzelhandel]
(3,649 beginning trainees), and salespersons [Verkäufer/in] (2,575 beginning trainees).
is allows us to compare trainees in three different, yet similar professions. Hence, we
can, for instance, expect fairly similar interests of adolescents applying to these training
programs (e.g. affinity towards the use of digital media). At the same time, these three
professions typically vary with regard to trainees’ characteristics, especially regard-
ing school leaving qualification, and to some extent gender and age (see Table7 in the
Appendix). Hence, it is of interest to analyze whether trainees in these professions differ
regarding digital competence and to what extend differences can be explained by indi-
vidual characteristics.
is study aims to answer the following research questions:
1. Which digital competence profiles do trainees in commercial VET possess at the
beginning of their VET program?
2. Which factors predict general digital competences of beginning trainees in commer-
cial VET?
Research Question 1 focuses on the identification of heterogeneity among beginning
trainees. Research Question 2 focusses on predictors of the level of general digital com-
petence. Based on the theoretical framework depicted in Fig.1, there are several pos-
sible predictors. is study focuses on analyzing factors on the student level. Hence, we
expect trainees’ competence profiles to be influenced by (1) individual characteristics
and (2) trainees’ learning processes related to digital activities. Regarding individual
characteristics, we examine the effect of trainees’ age, gender and educational qualifica-
tion. Furthermore, we are interested in differences between the three training profes-
sions we include in our analysis. Among the three professions, a training program for
industrial clerks is the most highly regarded. For instance, training companies typically
select applicants with higher educational qualifications for this program (two thirds
possess higher education entrance qualifications; see Appendix Table7) than for the
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
other two training programs. Our study aims to examine, whether differences between
trainees in these three training programs also occur with respect to general digital com-
petences. Also, we examine, to what extend these differences can be explained by indi-
vidual characteristics.
Apart from individual characteristics, we analyze the impact of adolescents’ learn-
ing opportunities related to digital activities. Since we focus on beginning trainees in
the first couple of months into the training program, participants did not yet have the
opportunity to significantly benefit from profession-specific learning processes in both
the training company and the vocational school. Hence, we focus on general digital com-
petences as well as learning opportunities prior to/outside of the VET program (experi-
ences from digital activities at home or in school). As it is not uncommon to complete
more than one training program, we control for previously completed training programs
of trainees. In doing so, we can take into account if trainees did have access to vocational
learning opportunities regarding digital activities.
Methodology
Research design andinstruments
We collected data from 480 trainees in commercial VET programs during their first
months into a vocational training program. Data collection lasted from October 2018
to February 2019 and covered five vocational schools and 22 classes in the federal state
of Baden-Wuerttemberg (convenience sampling). Participation was voluntary, and a pri-
vacy policy was adhered to. ere were no incentives for participation.
During the survey, participants answered a modified instrument designed by Müller
etal. (2018) with 24 items based on the DigComp framework (Ferrari 2013) to meas-
ure the following five components of digital competence: (1) Information, (2) Commu-
nication, (3) Content creation, (4) Safety, and (5) Problem solving. e items for each
dimension are displayed in Table1. For each item, the trainees indicate whether they are
able to complete the task described (e.g. online transfer of money) or how they would
describe their behavior (e.g. changing passwords regularly). Hence, each item is assessed
dichotomously (0: I am not able to complete this task/I do not do this regularly/I do
not recognize this; 1: I am able to complete this task/I do this regularly/I do recognize
this). Since the questionnaire aims at a general assessment of digital competence and is
designed to be applicable to a wide range of individuals, the survey participants assess
their digital competence on a rather broad level. Hence, they are not asked, for instance,
to distinguish between private and professional behavior.
e use of self-reports, of course, falls short of elaborate performance-based compe-
tence measures that are increasingly state-of-the-art in commercial vocational education
and training research (e.g. Seeber 2016; Seifried etal. 2020). However, due to limited
testing time, a thorough performance-based assessment of trainees’ digital competence
was not possible in this study. A self-report questionnaire had the advantages of time
and cost effectiveness. Additionally, in other fields of vocational education (e.g. health
care; see Evangelinos and Holley 2014, 2015), the DigComp framework was used as well.
Overall, in view of the focus of our study, this approach seems to be suitable for the
assessment of general digital competences.
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
To evaluate the measurement quality of the used instrument, we applied the IRT-
based approach by Birnbaum (1968). In detail, we used Yen’s Q3-Index with a cut-off
point of 0.2 to check the local independence assumption (Yen 1993). For the items
Designing web applications and Programming, this assumption was violated (Yen’s
Q3-Index = 0.34). However, as the knowledge necessary for designing web applications
is not identical to the knowledge required for programming in general, from a content
point of view, we decided against excluding either of the items. All the other items were
below the cut-off point (Yen’s Q3-Index < 0.2). Moreover, the scales revealed fair relia-
bility (see also Table2): Information (EAP/PV-Reliability = 0.74; 5 Items; example of an
item: ‘Data transmission between devices’), Communication (EAP/PV-Reliability = 0.68;
4 items; example of an item: ‘Recognizing fake news’), Content creation (EAP/PV-Relia-
bility = 0.74; 5 items; example of an item: ‘Designing web applications’), Safety (EAP/PV-
Reliability = 0.63; 3 items; example of an item: ‘Regular updates of antivirus software’)
and Problem solving (EAP/PV-Reliability = 0.73; 5 items; example of an item: ‘Learning
to use new program versions’).
Next, we checked for the multidimensionality of the instrument by the estimation of
four different models: (1) a one-dimensional 1 PL model, (2) a one-dimensional 2 PL
Table 1 DigComp dimensions and items (translation and original wording)
Text in the introduction: ‘ Think about your digital skills. What can you do, recognize and what is your behavior?’
Item in English Item in German
Information Informationsverarbeitung
Internet research Internetrecherchen
Data transmission between devices Datenübertragung zwischen Geräten
Use of multiple sources Nutzung mehrerer Quellen
Recognition of advertisements Erkennen von Werbeanzeigen
Considering search results, beyond the first page Beachtung von Suchtreffern über die erste Seite hinaus
Communication Kommunikation
Online bank transfers Online-Überweisung
Recognizing fake news Erkennen von Fake News
Posting information on social networks Inhalte in soziale Netzwerke einstellen
Handling hostility on social networks Umgang mit Anfeindungen über soziale Netzwerke
Content creation Erstellen von Inhalten
Creating texts (word processing programs) Texte erstellen (Textprogram)
Performing calculations (spreadsheet program) Berechnungen erstellen (Tabellenprogram)
Creating a presentation Präsentationserstellung
Designing web applications Webanwendungen gestalten
Programming Programmieren
Safety Schutz und Sicherheit
Awareness that services/apps transfer data Bewusstsein, dass Dienste/Apps Daten weitergeben
Regular updates of antivirus software Regelmäßiges Update der Antivirensoftware
Changing passwords regularly Regelmäßiger Passwortwechsel
Problem solving Problemlösung
Installation of devices Installation von Geräten
Establishment of a (home)network Einrichtung (Heim-)Netzwerk
Helping others with internet/computer problems Anderen bei Internet- und PC-Problemen helfen
Connecting hardware to a device Hardware anschließen
Learning to use new program versions Mich in neue Programmversionen einarbeiten
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
model, (3) a five-dimensional 1 PL model (competence structure given in Table2), and (4)
a five-dimensional 2 PL model. We used the Akaike information criterion (AIC), Bayes-
ian information criterion (BIC), Log-likelihood (LL), and Deviance to analyze model fit.
e different models analyzed and the corresponding fit indices are displayed in Table3.
Based on χ2-difference tests, we specifically tested the multidimensional model with five
competence dimensions (2 PL model) against the three other models: (1) the model with
one competence dimension (1 PL model) (χ2 = 382.31; df = 31; p < 0.001), (2) the model
with one competence dimension (2 PL model) (χ2 = 197.87; df = 10; p < 0.001), and at last
(3) the five-competence dimension 1 PL model (χ2 = 115.72; df = 17; p < 0.001). We found
that model fit was significantly higher for the five-competence dimension 2 PL model
compared to all other alternative models. Hence, further analyses were based on the
model with five dimensions and a 2 PL structure.
Spearman intercorrelations (rs) between the five dimensions varied between rs = 0.46
and rs = 0.69 (see Table2). e lowest correlation existed between Content creation and
Safety (rs = 0.46). e highest correlation was between Information and Communication
(rs = 0.69).1
To assess trainees’ learning processes and learning opportunities (see Research Ques-
tion 2), we also used an instrument by Müller etal. (2018). Here, the participants indi-
cated which activities they performed regularly (once or several times a week). Based on
face validity aspects we categorized the four items ‘using searching tools on the internet
to find content/information, ‘viewing online videos (e.g. YouTube)’, ‘using digital maps
Table 2 Reliability of and intercorrelations (Spearman) between the scales of digital competence
EAP/PV = Expected-a-posteriori/plausible value reliability
All correlations are signicant (p < 0.01)
Scales Number of
items EAP/PV 1 2 3 4 5
1. Information 5 0.74
2. Communication 4 0.68 0.69
3. Content creation 5 0.74 0.57 0.59
4. Safety 5 0.63 0.49 0.50 0.46
5. Problem solving 5 0.73 0.56 0.55 0.58 0.52
Table 3 Confirmatory factor analysis nested model comparisons
AIC Akaike information criterion, BIC Bayesian information criterion, LL log-likelihood, 1 PL Rasch Model, 2 PL Birnbaum
Model
AIC BIC LL Deviance df Δχ2 Δdf p
One dimension 1 PL model 10,479.46 10,575.46 5216.73 10,433.46 23
Five dimensions 2 PL model 10,159.15 10,384.53 5025.57 10,051.15 54 382.31 31 < 0.001
One dimension 2 PL model 10,337.01 10,520.66 5124.50 10,249.01 44
Five dimensions 2 PL model 10,159.15 10,384.53 5025.57 10,051.15 54 197.87 10 < 0.001
Five dimensions 1 PL model 10,240.87 10,395.30 5083.43 10,166.87 37
Five dimensions 2 PL model 10,159.15 10,384.53 5025.57 10,051.15 54 115.72 17 < 0.001
1 In a prior study, the instrument was also tested on a sample of more than 1,000 persons in VET and higher education
institutions with similar measurement quality (Wild and Schulze Heuling 2021).
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
and route guidance systems (e.g. Google Maps)’, and ‘using learning opportunities on the
internet (e.g. online course, learning languages online)’ as Collecting information and
learning. Moreover, we clustered three items to the aspect Communication and collab-
oration (‘using instant messaging services (e.g. WhatsApp, reema, Telegram)’, ‘using
cloud services (e.g. Dropbox, Google Drive, Amazon Drive)’, and ‘collaborating within a
team via online tools (e.g. Google Docs, Microsoft SharePoint)’). In the category Gener-
ating content, we summarized the items ‘using office programs (e.g. Word, Excel, Pow-
erPoint)’ and ‘reading blogs and forums or creating blog entries’. e participants were
asked to select all activities they regularly perform and leave unchecked the activities
they do not perform (regularly) (dichotomous classification). Again, the questionnaire
did not explicitly distinguish between private learning processes and work-related learn-
ing processes, however, since the trainees were only a couple of weeks/months into the
training program when they filled out the questionnaire, we expect learning processes
regarding digital activities to rather occur during their leisure time.
Sample
Table4 gives a summary of the sample of 480 trainees collected in the course of this
study. e sample consists of 205 industrial clerks, 145 retail salespersons, and 130 sales-
persons. Of all participants, 61% were female, 37% male, and 2% could not be assigned
to either male or female. On average, the trainees were M = 19.38 (SD = 2.35) years old.
Eleven percent of the trainees successfully completed a VET program in a different pro-
fession before starting the current program. e trainees are trained in three different
commercial professions: School leaving certificates varied. Almost 43% had a General
Certificate of Secondary Education [Realschule]. About 20% had a lower school leaving
certificate [Hauptschule]. e rest gained a higher education entrance qualification (Abi-
tur: 18%) or technical college entrance qualification (Fachhochschulreife: 18%).2
We found a significant difference in the training professions with respect to gender
(χ2 (4) = 13.75, p 0.01, Cramér’s V = 0.12). e ratio of female trainees is higher among
industrial clerks (69%) and retail salespersons (59%) than among salespersons (52%).
Further analyses reveal significant differences between the trainees’ school leaving cer-
tificate in different professions (χ2 (8) = 197.67, p 0.001, Cramér’s V = 0.45). An equal
share (33%) of industrial clerks had a school leaving certificate at General Certificate
of Secondary, advanced technical college entrance qualification, and general univer-
sity entrance qualification (Abitur). Most retail salespersons had a General Certificate
of Secondary Education (61%). For salespersons, there was a higher frequency of lower
school leaving certifications (48%) and General Certificate of Secondary Education
(40%). Finally, there were differences between the training professions in relation to for-
mer vocational apprenticeships (χ2 (2) = 6.50, p 0.05, Cramér’s V = 0.12). A successfully
completed VET program was most common among salespersons (16%), compared to
12% for retail salespersons and 7% for industrial clerks. More detailed information for
2 Comparing the sample characteristics with official data on beginning trainees by the German “Berufsbildungsinsitut”
(BIBB; https:// www. bibb. de/ dienst/ dazubi/ de/ 1871. php) shows that the sample—apart from the share of trainees who
completed a prior apprenticeship—seems to be highly representative of the population of beginning trainees for the
three professions (see Table7 in the Appendix for details), which overall justifies the convenience sampling approach
chosen in this study.
Page 11 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
each professional path—also regarding digital activities and learning opportunities—is
presented in Table4.
Data analysis
In the first step, we analyzed trainees’ digital competences separately for each profes-
sion. We report descriptive data based on boxplots. To test the differences of the five
competence dimensions between different training professions, we used the Kruskal–
Wallis-tests and the post-hoc-tests of Dunn (1964) with Bonferroni correction. Next, we
applied a latent profile analysis (LPA) with the aim of grouping homogenous participants
into heterogeneous groups (Oberski 2016; Vermunt and Magidson 2002). As decision
Table 4 Sample characteristics for different training professionals (N = 480)
Proportion/mean (M) with standard deviation (SD) in parenthesis
Industrial
clerks
(n = 205)
Retail sales-
persons
(n = 145)
Sales-
persons
(n = 130)
Total sample (n = 480)
Sex
Male 31% 38% 46% 37%
Female 69% 59% 52% 61%
Diverse 0% 3% 2% 2%
Age 19.21 (1.97) 19.38 (2.62) 19.66 (5.59) 19.38 (2.35)
Prior vocational apprenticeship
Yes 7% 12% 16% 11%
No 93% 88% 84% 89%
School leaving certificate
Dropout 0% 1% 3% 1%
Lower school certification (Haupts-
chule) 1% 22% 48% 20%
General Certificate of Secondary
Education (Realschule) 33% 61% 40% 43%
Advanced technical college
entrance qualification (Fachhochs-
chulreife)
33% 8% 4% 18%
University entrance qualification
(Abitur) 33% 8% 5% 18%
Collecting information and learning
Using searching tools on the inter-
net to find content/information 76% 55% 39% 59%
Viewing online videos 68% 70% 69% 69%
Using digital maps and route guid-
ance systems 49% 41% 35% 43%
Using learning opportunities on the
internet 10% 17% 15% 14%
Communication and collaboration
Using instant messaging services 83% 73% 67% 76%
Using cloud services 21% 17% 18% 19%
Collaborating within a team via
online tools 6% 8% 12% 8%
Generating content
Using office programs 72% 23% 20% 43%
Reading blogs and forums or creat-
ing blog entries 19% 21% 15% 19%
Page 12 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
criteria, we used the Aikake information criterion (AIC) and the Bayesian information
criterion (BIC). In addition, we checked for the entropy values closest to 1 (Asparouhov
and Muthen 2018; Celeux and Soromenho 1996), and also used the diagonal of the aver-
age latent class probabilities for most likely class membership as a selection criterion.
For the latter, the cut-off criterion was an assigned class of above 80 percent (Jung and
Wickrama 2008; Rost 2006).
To analyze the research questions described in Sect.2, we used an ordinal regression
(Hosmer etal. 2013). is type of regression is a sub-type of logistic regression where
the dependent variable is ordered. ese analyses differ with regard to calculations of
probabilities. While a logistic regression provides probabilities that a variable will take
on a specific value, ordered logit provides probabilities that values will fall below a cer-
tain threshold. To check the robustness of the results, we estimated nested models with
500 bootstraps. Multicollinearity was not a problem in the estimated models (VIF 1.48
for all variables used).
Five participants provided no information on socio-demographic data; these partic-
ipants were excluded from the regression analysis. Apart from that, the data set con-
tained only single missing values regarding the variables age and gender (nine missings
each). Hence, we decided not to impute missing data (Tabachnick and Fidell 2013).
When analyzing the effect of school leaving certificates, we excluded dropouts (n = 6)
and grouped together the two different types of higher education entrance qualifica-
tion (general university entrance qualification [Abitur] and advanced technical college
entrance qualification [Fachhochschulreife]). We estimated the LPA using the software
R with packages ‘idyLPA’ and ‘tidyverse’. All other analyses were carried out in STATA
(Version 14) and SPSS (Version 27).
Results
Preliminary analysis andlatent prole analysis
Figure2 shows boxplots for the five digital competences and the three different training pro-
grams (N = 480). In detail, the analyses show that industrial clerks tend to have the highest
standardized theta scores of digital competences, retail salespersons rank second, and sales-
persons show the lowest scores. is order holds for three of the five competence dimensions:
Information (MdSalespersons = 0.47 < MdRetail_salespersons = 0.19 < MdIndustrial_clerks = 0.41), Com-
munication (MdSalespersons = 0.36 < MdRetail_salespersons = 0.12 < MdIndustrial_clerks = 0.21), and
Content creation (MdSalespersons = 0.31 < MdRetail_salespersons = 0.16 < MdIndustrial_clerks = 0.16).
For the dimensions Safety, salespersons reach the lowest score (MdSalespersons = 0.15), but
retail salespersons and industrial clerks are on the same level (Md = 0.25). However, regard-
ing the dimension Problem solving, retail salespersons reach the highest median (MdRe-
tail_Salespersons = 0.16), before industrial clerks (MdIndustrial_clerks = 0.11) and salespersons
(MdSalespersons = 0.23). e Kruskal–Wallis-tests indicate significant differences between the
training professions for all five dimensions: Information (H(2) = 40.20, p < 0.001), Communi-
cation (H(2) = 27.87, p < 0.001), Content creation (H(2) = 24.92, p < 0.001), Safety (H(2) = 23.64,
p < 0.001) and Problem solving (H(2) = 11.31, p < 0.01). A pairwise comparison according
to Dunn (1964) was used to test differences between the three professions in detail. For the
dimension Information, the results show significant differences between salespersons and
retail salespersons (z = 2.99, p < 0.01), salespersons and industrial clerks (z = 6.30, p < 0.001)
Page 13 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
as well as retail salespersons and industrial clerks (z = 3.18, p < 0.01). For the dimension Com-
munication significant differences are revealed between salespersons and industrial clerks
(z = 5.14, p < 0.001) as well as between retail salespersons and industrial clerks (z = 3.15,
p < 0.01). For Content creation we again find significant differences between salespersons and
industrial clerks (z = 4.99, p < 0.001) as well as between salespersons and retail salespersons
(z = 2.89, p < 0.05). Similar results are found for Safety (salespersons and industrial clerks:
z = 4.82, p < 0.001; salespersons and retail salespersons: z = 3.26, p < 0.01) as well as Problem
solving (salespersons and industrial clerks: z = 3.15, p < 0.01; salespersons and retail salesper-
sons: z = 2.75, p < 0.05).
Using a latent profile analysis (LPA), we identify profiles of digital competences. In the
analysis, we test different amounts of profiles (one to five profiles) against each other. Table5
provides the fit statistics. e results show that AIC and BIC decrease from the solution with
one profile to five profiles. However, entropy suggests a solution with three profiles (highest
entropy value = 0.93 for a three profile solution). e same is true for the diagonal of the aver-
age latent class probabilities for most likely class membership. e highest minimum (96%)
and highest maximum (98%) are reached in the solution with three profiles. Based on these
results we decided to distinguish three competence profiles for further analysis.
Figure3 depicts the three profiles for the five digital competences. e first profile
(line dashed dotted) comprises 22% of the sample and shows the lowest digital compe-
tences in all five dimensions. For further analysis, we call this profile low competence
level profile. A second profile (43% of the sample) achieves the highest values in all five
competence dimensions. We call this profile high competence level profile. e values of
a third profile (35% of the sample) lie between the two previous described profiles in all
dimensions (dotted line). We name this profile medium competence level profile.
Fig. 2 Distribution of digital competences by profession (standardised score of theta)
Page 14 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
Regression results
To examine our research questions, we applied ordinal regression analyses (n = 460).
Table6 reports the regressions results. Model 1 includes trainees’ professional path as
well as their individual characteristics (Pseudo R2 = 0.05; Nagelkerke R2 = 0.10; Cox &
Snell R2 = 0.11). In this model, we find a significant effect of the trainees’ profession on
general digital competences. e probability of being in a higher profile of digital com-
petences (odds ratio [OR]) is almost four times higher for industrial clerks (OR = 4.22;
p < 0.001) and about twice as high for retail salespersons (OR = 2.26; p < 0.01), as com-
pared to salespersons. Participants’ gender does not significantly affect their digital com-
petences. Furthermore, there is a marginally significant effect of the trainees’ age. Each
additional year increases the odds ratio to belong to a higher profile by 9% (OR = 1.09;
p < 0.10). However, as the results of Model 2 indicate, the effect of both age and train-
ing profession can be explained by differences in school leaving certification. e age
effect becomes insignificant in Model 2, when controlling for school leaving certificates,
and the differences in probability to belong to a higher profile are reduced to OR = 2.06
(p < 0.05) for industrial clerks. A likelihood ratio test between Model 1 and Model 2
Table 5 Fit statistics of latent profile analysis (N = 480)
AIC Akaike information criterion, BIC Bayesian information criterion
Number
of
proles
AIC BIC Entropy Minimum average latent class
probabilities for most likely
latent class membership
Maximum average latent class
probabilities for most likely
latent class membership
1 6292.033 6333.771 –
2 5502.042 5568.822 0.874 0.947 0.975
3 5333.306 5425.129 0.933 0.957 0.983
4 5110.385 5227.251 0.863 0.873 0.943
5 5034.353 5176.262 0.845 0.783 0.980
Fig. 3 Latent profile analysis of digital competences with three profile solution
Page 15 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
yields evidence of a modest improvement in model fit (Pseudo R2 = 0.06; Nagelkerke
R2 = 0.14; Cox & Snell R2 = 0.13; χ2 (2) = 14.88, p < 0.001). In Models 1 and 2, a former
degree in another VET program significantly decreases the odds to belong to a higher
profile by 50 percent (Model 2: OR = 0.50; p < 0.05).
In Model 3, we included different digital activities (learning processes) (Research
Question 2). A likelihood ratio test between Model 2 and Model 3, again, shows a
modest improvement in model fit (Pseudo R2 = 0.17; Nagelkerke R2 = 0.35; Cox & Snell
R2 = 0.30; χ2 (9) = 104.46, p < 0.001). In Model 3 (see also Table 6), the effect of the
trainees’ profession becomes entirely insignificant. Instead, school leaving certificates
explain a significant amount of the differences in digital competence. Compared to a
lower school leaving certificate, the trainees with a certificate of secondary education are
twice as likely (OR = 2.01, p < 0.05) and trainees with a higher education entrance quali-
fication are three times as likely (OR = 3.06, p < 0.01) to belong to a higher competence
profile. Compared to Model 1 and 2, the effect of a former training program becomes
insignificant.
Table 6 Ordinal regression of digital competence profile with 500 bootstraps (n = 460)
Standard errors in parenthesis; #p < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001
Model 1 Model 2 Model 3
Odds ratio Odds ratio Odds ratio
Professional path (ref. = salespersons)
Industrial clerks 3.93 (0.90)*** 2.06 (0.64)* 1.03 (0.35)
Retail salespersons 2.10 (0.52)** 1.59 (0.46) 1.42 (0.43)
Sex (ref. = male)
Female 0.89 (0.17) 0.92 (0.19) 0.96 (0.20)
Diverse 2.11 (5.87) 2.01 (5.50) 3.07 (10.50)
Age 1.09 (0.05)# 1.03 (0.05) 0.99 (0.05)
Vocational apprenticeship 0.48 (0.15)* 0.50 (0.17)* 0.71 (0.27)
School leaving certificates (ref. = lower school leaving certificate
[Hauptschule])
Certificate of secondary education [Realschule] 2.25 (0.68)** 2.01 (0.59)*
Higher education entrance qualification [Fachhochschulreife or
Abitur]3.58 (1.38)** 3.06 (1.16)**
Collecting information and learning
Using searching tools on the internet to find content/information 1.98 (0.44)**
Viewing online videos 1.23 (0.32)
Using digital maps and route guidance systems 1.43 (0.33)
Using learning opportunities on the internet 0.53 (0.17)*
Communication and collaboration
Using instant messaging services 2.82 (0.79)***
Using cloud services 1.54 (0.42)
Collaborating within a team via online tools 0.87 (0.36)
Generating content
Using office programs 2.63 (0.62)***
Reading blogs and forums or creating blog entries 2.16 (0.58)**
Pseudo R20.05 0.06 0.17
Nagelkerke R20.10 0.14 0.35
Cox & Snell R20.11 0.13 0.30
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Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
When it comes to digital activities and learning opportunities, for the dimension Col-
lecting information and learning, we find a significant positive effect for ‘using searching
tools on the internet to find content/information’ (OR = 1.98; p < 0.01) and surprisingly,
a significant negative effect for ‘using learning opportunities on the internet’ (OR = 0.53;
p < 0.05). Regarding the dimension Communication and collaboration, the item ‘using
instant messaging services’ has a significant positive effect (OR = 2.82; p < 0.001). Finally,
the items ‘using office programs’ (OR = 2.63; p < 0.001) and ‘reading blogs and forums or
creating blog entries’ (OR = 2.16; p < 0.01) of the dimension generating content have a
positive effect.
Table 6. Ordinal regression of digital competence profile with 500 bootstraps
(n = 460).3
Discussion
General discussion
e aim of this study was to examine profiles of general digital competences of begin-
ning trainees as well as factors (individual characteristics and learning opportunities)
influencing the digital competences of beginning trainees. Against the background that
training companies can benefit from trainees who begin their training program with a
certain level of digital competence, we claim that the competences measured using the
DigComp framework form the basis for a successful start in commercial training pro-
grams and the acquisition of profession-specific digital competences during the VET
program. Our analysis is based on beginning trainees in three different commercial VET
programs and cannot be generalized to other VET programs. For the sample examined,
we identified three different profiles of digital competence that can be characterized
as low (22% of the sample), medium (35%), and high digital competences (43%). Initial
results point towards significant differences regarding digital competences between dif-
ferent training professions. In detail, both industrial clerks and retail salespersons seem
to outperform salespersons for each of the five dimensions of digital competence. How-
ever, further analysis demonstrate that these effects can be explained by differences in
the trainees’ school leaving qualifications. When controlling for school leaving certifi-
cates, the only effect that remains is an advantage of industrial clerks compared to sales-
persons. is effect also becomes insignificant when controlling for learning processes
(digital activities). e finding is also in line with results from research showing that
prior academic achievement is the most relevant predictor of digital competence (e.g.
Hatlevik etal. 2015b).
We find no significant effect of gender on general digital competences of beginning
trainees in commercial VET programs. Although most studies on gender effects pointed
towards significant effects in favor of female students in general education programs,
this result could not be replicated for trainees in VET. is finding might be explained
by the assessment method that is based on self-reports (see also Sect.5.2). With regard
3 Please note that we did not exclude the category diverse (gender) from the analysis although there is only a rather small
number of participants indicating diverse gender. Although there is a risk of high standard error for the category diverse
in the regression analysis, we did not want to systematically exclude trainees with diverse genders due to ethical reasons.
In order to perform a robustness check, we re-ran the regression without the category diverse and the results did not
change. e only two minor changes in the alternative model concern the significance levels of vocational apprentice-
ship (p < 0.10) and higher education entrance qualification (p < 0.001).
Page 17 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
to digital competences, it is well documented in prior research that male participants
report higher self-efficacy regarding advanced digital skills (e.g. Gerick etal. 2019). is
bias could overshadow differences that might exist in favor of female participants.
Moreover, there is no significant effect of the trainees’ age, once we control for school
leaving certificates. Hence, the mere age does not seem to matter with respect to train-
ees’ general digital competence.
Furthermore, our results indicate that trainees who already participated in a prior VET
program do not have a higher probability of belonging to a higher competence profile
once individual learning processes are controlled for. is variable was used as a con-
trol variable to account for prior experiences regarding digital activities in vocational
contexts. An insignificant effect could indicate that participants did not take their digi-
tal activities during prior training programs into account when reporting their learning
processes.
Finally, our results reveal certain effects of the trainees’ digital activities or (gen-
eral) learning opportunities on digital competences. In line with expectations, trainees
who regularly (1) use searching tools on the internet to find content/information, (2)
use office programs, and (3) read blogs and forums or create blog entries reach higher
profiles of digital competences. ese three learning opportunities can be expected to
be directly related to general digital competences. We also find a positive effect of the
regular use of instant messaging services. is finding might be explained by a general
affinity toward the use of digital tools and might therefore be related to general digital
competences. Surprisingly, the regular use of learning opportunities on the internet is
negatively related to general digital competences. is might be attributed to the fact
that trainees do not perceive this activity to be relevant for the development of digital
competence. However, further research is necessary to examine this relationship. Finally,
the regular use of cloud services and the collaboration within a team via online tools
do not significantly affect general digital competences. is finding can probably be
explained by the assessment method. e DigComp framework does not account for
these or similar aspects when assessing general digital competences.
Limitations andfuture research
is study has several limitations that need to be considered when interpreting the
results. First, as already mentioned in Sect.3.1 the use of self-reports contains certain
limitations regarding the validity of digital competence assessment. A major disadvan-
tage of self-reports is that the respondents might have distorted self-perceptions. is
could lead to severe overestimations of their own abilities. However, several studies
reveal that students’ ICT self-efficacy positively predicts digital competence (Hatlevik
etal. 2015a, 2018). Hence, it can be assumed that self-reports can at least be used as an
indicator for actual digital competence.
Moreover, our study focused on trainees’ general digital competences, as we
focused on beginning trainees and aimed at an assessment of their starting condi-
tions. Also, the study focused on the sector of commercial VET. Hence, we are neither
able to draw any conclusions regarding profession-specific digital competences (e.g.
handling big data, using ERP systems that are especially relevant to industrial clerks;
see Sect.1.2) nor regarding other fields of VET or other training professions.
Page 18 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
Another limitation refers to the assessment of learning opportunities. ese were,
again, assessed at a rather general level. Unfortunately, due to limited test time, we
did not gather additional information about thecontext where digital activities took
place. It would have been especially helpful to know, which prior training program
participants completed and which digital activities they performed during prior train-
ing. A more distinct assessment of digital activities would generally be of interest, for
instance, as the results of Burchert etal. (2013) point to differences in the trainees’
internet use for private and professional purposes.
Finally, we do not have information on the trainees’ success during the VET pro-
gram or on their performance on the job. is information could be useful to relate
differences in the level of general digital competence at the start of the training pro-
gram to training outcomes or to competence development during VET.
Overall, future research endeavors should focus on the development of profession-
specific digital competences over the course of the VET program, preferably using
longitudinal designs. Hereby, it would be interesting to examine the role of trainees’
starting conditions (general digital competence) for the acquisition of further profes-
sion-specific digital competences and training success. When it comes to antecedents
and learning processes relevant for the development of digital competence, the theo-
retical framework in Fig.1 would need to be expanded by two additional levels: the
vocational school and the training company.
Implications
Based on the results reported above, there are certain implications for VET programs.
Since, 22% of our sample demonstrate low competence levels regarding general dig-
ital skills at the start of the VET program, there is a need to foster digital compe-
tences during VET. is seems to be especially relevant for salespersons who show
the lowest profile of the professions examined. is would imply implementing new
learning formats into VET. Such learning formats that allow for flexible use and are
independent of time and location include, for instance, mobile learning, social learn-
ing and game-based learning (de Witt 2012; Seufert etal. 2012). ey have the poten-
tial to foster professional competences, to improve the cooperation between different
places of learning in VET, or to enable collaboration between employees working in
separated branch offices (de Witt 2012; Seufert etal. 2012). Consequently, trainers
and teachers in VET also need relevant competences to implement these learning
formats (e.g. Attwell and Gerrard 2019; Wilbers 2012). Additionally, teachers should
be trained to instruct trainees in the use of digital tools for the specific profession.
However, as mentioned in Sect.5.2, before implementing new learning formats and
training teachers and trainers, there is a need for an assessment of domain- or even
job-specific digital competences of trainees that can guide respective changes in VET
programs.
Appendix
See Table7.
Page 19 of 21
Findeisenand Wild Empirical Res Voc Ed Train (2022) 14:2
Acknowledgements
Not applicable.
Authors’ contributions
SF and SW developed the research questions. SF and SW discussed and executed the research design. SW performed
the data analysis. SF wrote the first draft of the manuscript. All authors read and approved the final manuscript.
Funding
No funding was received for this study.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable
request.
Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Economics, University of Konstanz, Universitätsstrasse 10, 78464 Konstanz, Germany. 2 Chair of Psy-
chological Diagnostics, Faculty 13 Rehabilitation Sciences, Dortmund University of Technology, Emil-Figge-Str. 50,
44227 Dortmund, Germany.
Received: 4 August 2021 Accepted: 10 January 2022
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Chapter
Latent class analysis (LCA) is a latent variable modeling technique that used for identifying subgroups of individuals with unobserved but distinct patterns of responses to a set of observed categorical indicators (Lanza et al. 2007).