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ORIGINAL PAPER
Pathways from adult education to well-being:The Tuijnman model revisited
Andrew Jenkins • Richard D. Wiggins
Published online: 6 March 2015
� Springer Science+Business Media Dordrecht and UNESCO Institute for Lifelong Learning 2015
Abstract There is a growing interest among researchers and policy-makers in the
influence of adult learning on a range of outcomes, notably health and well-being.
Much of the research to date has tended to focus on younger adults and the im-
mediate benefits of course participation. The longer-term outcomes, such as the
potential of accumulated learning experience for enriching later life, have been
neglected. The study presented in this article adopts a lifecourse approach to par-
ticipation in learning and the potential benefits of learning. The authors concentrate
on adult education in mid-life, that is between the ages of 33 and 50, as the measure
of learning participation. Their research draws upon previous work conducted by
Albert Tuijnman which used Swedish data and which was published a quarter of a
century ago in the pages of the International Review of Education. The authors of
this paper seek to replicate and extend his pioneering work, using data from the
National Child Development Study (NCDS), a large-scale survey containing in-
formation on all those born in Britain in one week in 1958. Follow-up data were
collected at various points in childhood and adulthood, most recently when the
cohort reached the age of 50, thus enabling insights into long-term developments.
The authors analyse well-being at age 50 as an outcome in structural equation
models (SEM). This approach helps to understand the pathways through which adult
education has an impact on well-being. The estimated models show how adult
education in mid-life has an influence on the type and quality of jobs which are
accessible to individuals, and how this in turn can contribute to higher well-being at
age 50.
A. Jenkins (&) � R. D. Wiggins
Department of Quantitative Social Science, UCL Institute of Education, University College London,
20 Bedford Way, London WC1H 0AL, UK
e-mail: a.jenkins@ioe.ac.uk
R. D. Wiggins
e-mail: r.wiggins@ioe.ac.uk
123
Int Rev Educ (2015) 61:79–97
DOI 10.1007/s11159-015-9468-y
Keywords Adult education � Well-being � Qualifications � Mid-life � Structuralequation models
Resume Parcours pour passer de l’education des adultes au bien-etre : le modele
de Tuijnman revisite – Les chercheurs et decideurs montrent un interet croissant
pour l’influence positive de l’apprentissage a l’age adulte sur divers domaines, en
particulier la sante et le bien-etre. La recherche a en grande partie tendance a se
concentrer sur les jeunes adultes et sur les bienfaits immediats de leur participation.
Les repercussions a long terme, telles que le potentiel de l’experience educative
accumulee qui enrichit l’age mur, ont jusqu’alors ete negligees. L’etude presentee
ici adopte une approche axee sur les parcours de vie de la participation a l’ap-
prentissage et de ses bienfaits potentiels. Les auteurs se penchent sur l’education des
adultes en milieu de vie, a savoir entre 33 et 50 ans, qui constitue la mesure de la
participation a l’apprentissage. Leur recherche s’appuie sur le travail d’Albert
Tuijnman effectue a partir de donnees collectees en Suede et publie il y a 25 ans
dans la Revue internationale de l’education. Ils tentent de reproduire et d’etendre ce
travail de pionnier a partir des donnees de l’enquete nationale britannique sur le
developpement infantile realisee a grande echelle, qui fournissent des renseigne-
ments sur toutes les personnes nees en Grande-Bretagne au cours d’une semaine de
l’annee 1958. Les donnees ulterieures ont ete collectees a diverses etapes de l’en-
fance et de l’age adulte, plus recemment lorsque la cohorte a atteint l’age de 50 ans,
livrant ainsi des eclaircissements sur l’evolution a long terme. Les auteurs analysent
le critere du bien-etre a l’age de 50 ans, resultant de modeles d’equations struc-
turelles. Cette methode contribue a cerner les parcours favorisant l’impact de
l’education des adultes sur le bien-etre. Les modeles evalues signalent que
l’education des adultes accomplie en milieu de vie exerce une influence sur le type
et la qualite des activites professionnelles accessibles aux individus, et que ce critere
peut contribuer a son tour a un bien-etre accru a l’age de 50 ans.
Introduction
In recent years it has been increasingly recognised that, at least in developed
economies, growth in real income per head of population has only a very marginal
impact on the happiness of the citizenry. Consequently, there has been a growing
interest in well-being and the factors which have an impact on it (Layard 2011). As
part of this research agenda, work has been produced on the relationships between
participation in learning in adulthood and subsequent well-being (Field 2009).
While of great interest and value, the research in this field also has some
acknowledged limitations. It has tended to focus mainly on younger adults. Much of
it has been short-term in nature, typically investigating the impact of learning on the
measurement of well-being at the end of a course of study or between two waves of
a panel survey (Schuller et al. 2004; Feinstein et al. 2008). While these analyses
have found evidence of associations between engagement in learning and certain
outcomes, the processes and pathways through which learning has an impact are not
entirely clear (Desjardins 2008).
80 A. Jenkins, R. D. Wiggins
123
In this paper, we report new empirical analyses which seek to address these
limitations. The data we used are for adults in early old age (33 to 50), rather than
young adults. The analyses focus on the longer-term well-being outcomes of
participation in adult education. The modelling techniques we applied are able to
yield insights on the pathways through which learning does, or does not, have an
impact on well-being. In undertaking this work, we have drawn upon previous work
conducted by Albert Tuijnman (1989, 1990) which appeared initially as a Swedish
PhD thesis and was then published in the pages of this journal, the International
Review of Education. His work appeared in print a quarter-century ago, many years
prior to the current wave of studies on the nature of well-being, and since then (to
the best of our knowledge) no-one has attempted to build on Tuijnman’s research. In
this paper we replicate and extend his pioneering work. As to the structure of our
paper, we begin with a review of relevant literature, which is followed by an outline
of the methodology. The next section then sets out the results and discusses their
implications. The paper ends with conclusions.
Literature review
Our research concerns the relationships between adult learning, job quality and
well-being and so our literature review focuses on these topics. As work occupies a
large place in many people’s lives, it might be anticipated that there would be an
association between job satisfaction and overall well-being, and this has been
confirmed by a substantial body of research. Drawing on several works of synthesis
such as those by Alex Michalos (1986) and Robert Rice et al. (1980), Tuijnman
(1989, p. 79) showed that such a relationship had been found in a large number of
studies up to the 1980s. This has continued to be so in more recent analyses,
summarised by Jonathan Westover (2011) and Nathan Bowling et al. (2010), which
confirm that job satisfaction has an impact on measures of life satisfaction and
subjective well-being.
It is also well-established that higher levels of education are associated with
favourable employment outcomes such as higher wages and better chances of being
in work (Blundell et al. 2005). Since the late 1990s there has moreover been a
growing body of work on the wider, or non-economic, benefits of learning,
including well-being or closely related outcomes such as life satisfaction. For
example, Leon Feinstein and Cathie Hammond (2004) used a longitudinal cohort
from Britain to examine the links between adult learning and life satisfaction or
happiness. They considered how the learning of adults in their 30s and early 40s
affected changes in life satisfaction over the same period, controlling for level of
prior education and a range of other relevant factors. Their key finding was that
adult learning did have an influence on life satisfaction. The effects did not look
particularly large, but as there were few changes in life satisfaction for people in
their 30s and early 40s, the effect of adult learning was nonetheless important. There
is evidence that participation in adult education is associated with improvements in
aspects of psychological well-being, especially self-esteem and self-confidence. The
analyses by Feinstein and Hammond (2004) and Hammond and Feinstein (2006)
Pathways from adult education to well-being 81
123
found robust associations between participation in adult learning in Britain and
increases in self-efficacy, even after controlling for a range of variables reflecting
family and social background, prior education level and current circumstances. The
large-scale qualitative research exercise of Tom Schuller et al. (2002), which
involved interviews with British adults who were participants in adult education,
showed that many respondents reported improvements in psychological well-being
stemming from their engagement in adult learning.
The analyses of relationships between education and well-being have reached a
sufficient stage of maturity that several reviews on this topic have now been
published (Desjardins 2008; Field 2011). This line of study has made much progress
in that time. One key area of strength is that much of the research has been able to
draw on high-quality longitudinal datasets and so the findings are robust and
persuasive (Bynner 2010). Moreover, this longitudinal quantitative research on
learning benefits has had an impact outside academia amongst practitioner
organisations and policy-makers (Field 2011).
Nevertheless, some limitations of research on this topic to date should also be
acknowledged. The overwhelming majority of studies have been on younger adults,
in their 30s to early 40s, or younger. There are very few quantitative studies on
middle-aged or older adults (Field 2011). Research has also tended to be short-term
in nature, looking at the benefits of learning immediately at the end of a course of
study, or else between two waves of a longitudinal survey. The longer-term benefits
of learning over the adult lifecourse, or substantial phases of it, have received much
less attention in the literature to date. A further important limitation is the relative
lack of evidence on the pathways and processes through which learning has an
impact. Typically, quantitative analyses have yielded precise estimates of the effects
of learning but have provided rather little insight into how and why it might be
doing so.
Many years prior to the recent upsurge of research on well-being, Tuijnman
(1989, 1990) produced pioneering work on the long-term impact of adult education
on well-being. This research also used techniques which were of value for
understanding processes and pathways. It was based on data from the so-called
‘‘Malmo longitudinal study’’ in Sweden, it observed a cohort born around 1928
whose lives were followed from the age of about 10. Tuijnman developed a model
in which adult education had an impact on the type and nature of job in terms of
occupational status, earnings, job satisfaction and employment prospects and these
in turn were hypothesised to have an impact on well-being (an indirect pathway).
The model also allowed a for a direct pathway from adult learning to well-being.
Structural equation modelling (SEM) was used to estimate these pathways.
Data were drawn from the follow-up of the Malmo study which occurred in 1984,
at which point respondents had reached their mid-50s, and information was gathered
on adult educational activities respondents had undertaken since about age 30. They
were also asked a series of questions about their enjoyment of life and these were
used, via factor analysis, to derive a construct which could be interpreted as a
measure of well-being. Only men were included in the analysis of well-being in
1984, when they were in their mid-50s. Although various forms of adult education
were included in the survey, the analysis focused on job-related adult education.
82 A. Jenkins, R. D. Wiggins
123
In Tuijnman’s empirical results, adult education was found to have an impact on
occupational status, which in turn had an impact on well-being. The link from adult
education via occupational status to career prospects to well-being was also
significant. And participation in adult education itself had a small but statistically
significant direct effect on well-being. This research was remarkable and pioneering
in its application of structural equation models to the relationship between adult
education and well-being in the longer term. By this means valuable information
was provided, too, on the various job-related indirect and direct pathways through
which adult education had an effect on well-being.
Method, data and measurement
Method
The model we use in this paper is similar in spirit, if not identical in specification, to
that of Tuijnman. Our definition of adult education focuses solely on learning which
leads to qualifications. It has been argued by policy-makers that qualifications, as
opposed to uncertified training, have greater potential to enhance individual
earnings and career prospects because they provide clear signals to employers about
the skills and potential productivity of the person holding the qualification (Jessup
1991; Jenkins and Wolf 2005). Until recently, targets for the numbers of people
gaining qualifications at specific levels in Britain formed a key component of adult
skills policy (Wolf et al. 2006). There is, then, considerable interest in the extent to
which gaining qualifications leads to beneficial outcomes for the individuals who
acquire them. In our model, qualifications obtained during initial education (up to
age 23) and post-initial education (between the ages of 23 and 32) are hypothesised
to determine career position at age 33. This variable plus any qualifications gained
in mid-life (ages 33 to 50) determine job quality at age 50. Well-being at age 50
depends on job quality. Fig. 1 shows the model in the form of a path diagram.
Job quality was hypothesised to consist of three components: First, the status of
the job, defined both in terms of its position in the occupational hierarchy and
whether it involved managerial or supervisory responsibilities; second, the security
and satisfaction of the job; and third, the extent to which the job spills over to affect
other aspects of life such as having an adverse impact on family life or via long
hours at work. These three components and their various manifestations are shown
in Fig. 2.
Our model requires that the pathways between several variables be estimated
simultaneously. Some of the variables are latent, rather than directly observed,
factors. An appropriate methodology for estimating models such as this is structural
equation modelling (SEM). So, in order to analyse the relationships between
learning in mid-life, job quality and well-being, we used SEM. A full structural
equation model consists of two parts: a measurement model, describing the way in
which observed variables load onto latent factors, and a structural model, which
estimates the pathways among all the variables, including the latent factors
(Joreskog and Sorbom 1979; Kaplan 2009; Byrne 2012).
Pathways from adult education to well-being 83
123
Data
Our data source is the National Child Development Study (NCDS). This began as a
perinatal mortality survey of every baby born in Britain in a single week in 1958.
Follow-up data collection took place at several points in childhood up to age 16, and
in adulthood at ages 23, 33, 42, 46 and 50. Originally, more than 17,000 people were
in the study and over 9,000 of these were still in the sample by age 50.1 During the
cohort members’ childhoods, data were collected by health visitors from the parents
and from the children through educational and medical assessments. Some
information was also gathered from teachers. In adulthood, data have been obtained
directly from cohort members themselves via structured interviews. We use data
from several of the surveys which occurred in adulthood through to the 50-year
follow-up in 2008. Since in the model which we wish to estimate, adult learning,
Career posi�onat age 33
Ini�al educa�on(highest
qualifica�on by age 23)
Further qualifica�ons
acquired between ages 23 and 32
Qualifica�ons in mid-life (ages 33
to 50)
Job quality
at age 50
Well-being
at age 50
Fig. 1 Initial education, obtaining qualifications, career pathways, and well-being at 50: hypothesisedrelationships
Job status and
influence
Manager or Supervisor
roles
Occupa-�on
status
Job stress
Long hours
Conflict with
family life
Job security and
sa�sfac�on
Job security
Job sa�sfac-
�on
Likely to remain in same job
Fig. 2 Components of job quality
1 For further information about the National Child Development Study (NCDS) see http://www.cls.ioe.
ac.uk/.
84 A. Jenkins, R. D. Wiggins
123
measured in terms of obtaining qualifications, is hypothesised to affect job quality,
the sample was confined to people who were working (either employed or self-
employed, full-time or part-time) at the age of 50 and for whom information was
available on whether or not they obtained qualifications. There were 7,447 cases
meeting these criteria, of whom 6,630 (89%) had complete data and the remaining
817 cases had missing data on one or more of the variables in the model. Of the
7,447 cases, 3,833 (51.5%) were male and 3,614 (48.5%) were female.
In longitudinal surveys, people may be present at certain waves of the survey and
not present at others (wave non-response) or they may drop out of the survey never
to return (attrition). While missing data is a problem in surveys of all kinds, then, it
is a particular issue when using longitudinal data. In recent decades, statistical
methodologists have replaced ad hoc approaches for dealing with missing data with
statistically principled methods (Little and Rubin 1987). The latter include
maximum likelihood methods, weighting for non-response, and multiple imputa-
tion, and these approaches have gradually been filtering into applied work (Enders
2010; Carpenter and Plewis 2011). In this paper we use full information maximum
likelihood (FIML) to incorporate cases with missing data into our results. Whereas
in a complete case analysis only respondents with no missing data on any variable
can be included in the results, FIML enables parameter estimates to be obtained
which include cases with missing observations. This is done essentially by
constructing maximum likelihood estimates for each pattern of missing data in the
dataset and then combining the likelihood from each pattern to construct an overall
maximum likelihood estimate (Arbuckle 1996; Enders 2010). So in our results
below we usually report both findings from a complete case sample and also from a
larger sample, including some additional cases with missing data where the
estimates were obtained via FIML.
Measurement
The phase of adult education begins once initial education has been completed.
There is scope for debate about when exactly initial education comes to an end. It is
usually assumed to be somewhere in the early or mid-20s. Since NCDS cohort
members were interviewed at age 23, it is convenient to take that age as the terminal
point of initial education. The phase of adult education therefore occurs from age 23
onwards and, with the data available in NCDS, it is possible to observe whether
people obtained qualifications between the ages of 23 and 50. This period of adult
education can be broken down into an immediate post-initial phase, from ages 23 to
32 inclusive, and mid-life learning, which occurs between ages 33 and 50.
Information on qualifications has been gathered in all the adult waves of the survey.
This information was used to map qualifications obtained, and the highest level of
qualification, of respondents at ages 23, 33 and 50. The qualifications obtained by
cohort members were coded to the levels of the National Qualifications Framework
(NQF).2 Highest qualification was measured on a six-point scale where:
2 More information about the British National Qualifications Framework (NQF) is available at www.gov.
uk/ofqual [accessed 31 December 2014].
Pathways from adult education to well-being 85
123
0 = no qualifications;
1 = qualifications below GCSE3/O level (roughly lower secondary schooling);
2 = qualifications at O level A–C or equivalent (secondary schooling);
3 = A levels (post-compulsory education);
4 = degrees and equivalent; and
5 = higher degrees.
As can be seen in Fig. 2, the three components of job quality were status/influence,
security/satisfaction and stress. All items were drawn from the age 50 wave of the
NCDS. Job stress consisted of an item on hours worked and was coded 0 for less than
35 hours per week, 1 for 35 to 49 hours, and 2 for 50? hours. It was calculated
separately for the employed and the self-employed. A second item concerned
responses to the question whether work interferes with family life. It was coded 0 for
those answering ‘‘no’’ to this question and 1 for those who said ‘‘yes’’. Respondents in
NCDS at age 50were asked a question about their perceptions of job security. Thiswas
coded on a 3-point scale: 0 for not secure, 1 for fairly secure, 2 for very secure. Two
items were used to assess job satisfaction. Own perception of job satisfaction was
coded from 0, very dissatisfied to 4, very satisfied. A second item used the question on
whether or not the person thought it likely that theywould be in the same job in a year’s
time, coded 0 for ‘‘no’’ and 1 for ‘‘yes’’. For job status and influencewe used two items.
The first was a question on the extent of influence in the job. This was coded 0 if no
supervisory responsibilities, 1 if foreman/supervisor, or ran small business (no
employees), 2 if had managerial responsibilities, or ran their own business and had
employees. Second, we used an item on occupational status, ranked on a 5-point scale
from unskilled to professional. Fig. 2 is a diagram of how these items were
hypothesised to load onto the job quality variables.
To measure well-being, we used a version of ‘‘Control Autonomy Self-realisation
Pleasure’’ (CASP). This was developed specifically for use with those in middle age
and beyond (Hyde et al. 2003). The measure is called CASP because well-being was
theorised as the satisfaction of needs in four areas: control (C), the need to be able to
act freely in one’s environment; autonomy (A), the need to be free from undue
interference by others; the need for self-realisation (S); and pleasure (P), the need
for enjoyment in life (Wiggins et al. 2004).
The measure was designed with the clear premise of a separation of well-being
itself from the factors which might influence it, such as health, poverty and social
engagement. A further principle underpinning the measure is that it is based on the
perception of the respondent, on the experience of the individual themselves rather
than some external assessment. Since it is based on a theory of needs satisfaction,
people attain well-being in the extent to which needs are satisfied (Wiggins et al.
2008). Moreover, it was designed to include positive factors, not just the absence of
negative ones, in the measure of well-being. Measurement should be able to pick up
the positive aspects of later life as well as the negative ones. Finally, CASP aims to
capture an overall assessment, not just one domain of the quality of life. The notion
of using a single item, such as asking the respondent ‘‘How satisfied are you with
3 GCSE stands for General Certificate of Secondary Education.
86 A. Jenkins, R. D. Wiggins
123
your life overall?’’, is rejected in favour of the psychometric tradition that multi-
item scales offer better reliability and are more likely to capture all aspects of the
concept which it is wished to measure.
CASP was originally designed during the follow-up of the Boyd-Orr survey4 in
the late 1990s. There are 19 items in the full version of the instrument, it is therefore
often referred to as CASP-19. Versions of CASP have been adopted in several major
surveys including the English Longitudinal Study of Ageing (ELSA), the British
Household Panel Survey (BHPS), the American Health and Retirement Survey and
the Survey of Health Ageing and Retirement in Europe (SHARE). It is a widely-
used measure of well-being. The measure of well-being used in our analyses was
CASP-12v2. This is a shortened version of the original CASP-19 subjective quality
of life measure and is available in NCDS at age 50.
Descriptive statistics
Among the sample of 7,447 NCDS respondents, some 54 per cent obtained a
qualification between the ages of 33 and 50. These qualifications were mainly
vocational. Table 1 shows the level of the highest qualification obtained, broken down
by gender. Women were more likely than men to obtain qualifications in mid-life: 60
per cent of women gained at least one qualification between the ages of 33 and 50,
while just 48 per cent ofmen did so. The gap betweenmen andwomenwas particularly
noticeable for higher-level qualifications. Very similar proportions (28 per cent of
men, 30 per cent of women) obtained qualifications at Level 2 or below as their highest
Table 1 Highest overall qualification obtained between ages 33 and 50
Males Females All
No qualifications
%
1,995
52.1
1,435
39.7
3,430
46.1
Level 1
%
798
20.8
648
18.0
1,446
19.4
Level 2
%
292
7.6
424
11.7
716
9.6
Level 3
%
185
4.8
269
7.4
454
6.1
Level 4
%
324
8.5
555
15.4
879
11.8
Level 5
%
239
6.2
283
7.8
522
7.0
Total
%
3,833
100.0
3,614
100.0
7,447
100.0
Note Levels are NQF levels; see previous section on measurement
4 The Boyd-Orr survey, also referred to as the ‘‘Carnegie United Kingdom Trust’s study of family diet
and health in pre-war Britain’’ was first carried out in 1937–1939. The follow-up collected data on the
later life of 4,999 children surveyed in the original study.
Pathways from adult education to well-being 87
123
new qualification in mid-life; almost a third of women but just under a fifth of men
gained a qualification at Level 3 or above in this stage of the lifecourse.
CASP-12v2 had a mean of 26.7 (standard deviation = 5.35), which was
somewhat higher at age 50 amongst women than amongst men (see Table 2). Well-
being was also greater at higher levels of occupational status. The difference in
well-being between professional and unskilled workers was about 2.4 points on the
CASP score, or approximately 45 per cent of a standard deviation (SD). There was
no difference in CASP score between those who obtained a qualification in mid-life
(i.e. between ages 33 and 50) and those who did not obtain a qualification.
Obtaining qualifications in mid-life was associated with moving to higher levels in the
occupational hierarchy. Amongst those who gained at least one qualification between the
ages of 33 and 50, over 30 per cent were in a higher-status occupation compared to about
23 per cent for those who did not obtain a qualification in mid-life (see Table 3).
Among the key points to emerge from these descriptive statistics, then, was an
association between gaining qualifications in mid-life and occupational status at age
Table 2 Means and standard deviations of the well-being measure (CASP-12v2)
N Mean SD
All 6,672 26.68 5.35
By gender:
Males 3,367 26.48 5.34
Females 3,305 26.88 5.35
By Occupational level:
Unskilled 174 25.10 5.74
Semi-skilled 738 25.66 5.65
Skilled (manual or non-manual) 2,551 26.27 5.38
Managerial/technical 2,783 27.30 5.17
Professional 426 27.50 5.00
By whether obtained qualification between ages 33 and 50:
No qualification obtained 3,037 26.68 5.39
Obtained qualification 3,635 26.68 5.32
Note SD = standard deviation
Table 3 Change in occupational status by whether obtained qualification in mid-life
Change in occupational status between ages 33 and 50 Obtained qualification
between ages 33 and 50
All
No Yes
% % %
Moved downwards 18.3 17.2 17.7
Remained at same level 58.5 52.6 55.3
Moved upwards 23.2 30.2 27.0
ALL 100.0 100.0 100.0
N 3,430 4,017 7,447
88 A. Jenkins, R. D. Wiggins
123
50. There was also an association between occupational status and well-being, but
no readily discernible link between obtaining qualifications and well-being.
Differences by gender were also noticeable, with women markedly more likely
than men to obtain qualifications in mid-life.
Results
Model fit
The first step was to validate the measurement model for job quality, using
confirmatory factor analysis (CFA). We then proceeded to estimate the full
structural model which provides answers to the key research questions about the
impact of adult learning on well-being and the pathways through which adult
learning operates.
The analyses were carried out using Mplus 6 (Muthen and Muthen 2010), which
allows the estimation of models with both categorical and continuous variables.
Several criteria were used to assess the overall fit of the models. Because of the
known sensitivity of the likelihood-ratio Chi squared statistic in large samples, a
range of alternative model fit indicators have been developed by methodologists
(Kaplan 2009, pp. 110–121) and we used several of these, including (i) the
Comparative Fit Index (CFI), and the Tucker-Lewis Index (TLI), where values
above 0.95 indicate an excellent fit and values above 0.90 an adequate fit; and (ii)
the Root Mean Square Error of Approximation (RMSEA), where values below 0.05
are considered as indicative of good fit and below 0.08 indicative of adequate fit (Hu
and Bentler 1995).
In the measurement model, as some of the items could take only a small number
of values, normality and hence multivariate normality did not seem a reasonable
assumption, and so for estimation the robust maximum likelihood estimator (MLM)
in M-Plus was used. We found that all parameter estimates were significant at the
p\ 0.05 level. Indicators of model fit were: Chi square 212.8, df 11; CFI: 0.957;
TLI: 0.918; RMSEA: 0.049 with 90% confidence interval of 0.043–0.055. Overall,
and based on the criteria listed above, the conclusion was that these figures were
consistent with a well-fitting model. The full structural equation model was then
obtained using the Robust Weighted Least Squares (WLSMV) estimator available
in Mplus 6. This allowed for the presence of non-normal and categorical data.
Again, the criteria for adequate model fit appeared to be met.5
Main results
The full structural equation model for all cases in the dataset estimated by FIML is
reported in Table 4. This shows the key structural regression path estimates for the
5 For the complete case dataset: Chi square 186.6 on 63 df; CFI = 0.975; TLI = 0.951;
RMSEA = 0.017. For all cases (including missing data): Chi square 214.4 on 63 df; CFI = 0.973;
TLI = 0.947; RMSEA = 0.018.
Pathways from adult education to well-being 89
123
Table 4 Parameter estimates from full structural equation model
Estimate S.E. Est./S.E. p-value
Regression of Well-being (CASP) at age 50 on:
Job stress -0.005 0.561 -0.009 0.993
Job security/satisfaction 12.052 0.624 19.308 0.000
Job status/influence 1.444 0.302 4.776 0.000
Mid-life qualifications 0.007 0.073 0.098 0.922
Occupational status at age 33 -0.037 0.083 -0.447 0.655
Regression of Job stress (at age 50) on:-
Occupational status at age 33 0.040 0.013 3.068 0.002
Mid-life qualifications 0.000 0.004 -0.051 0.960
Highest qualification by age 23 (base none)
Level 1 -0.003 0.016 -0.179 0.858
Level 2 -0.015 0.014 -1.086 0.278
Level 3 0.009 0.017 0.501 0.616
Level 4-plus -0.025 0.019 -1.333 0.183
Highest qualification between ages 23 and 32 (base none)
Level 1 0.024 0.014 1.697 0.090
Level 2 0.012 0.014 0.832 0.406
Level 3 0.033 0.018 1.841 0.060
Level 4-plus 0.023 0.014 1.623 0.105
Regression of Job security/satisfaction (at age 50) on:
Occupational status at age 33 0.005 0.004 1.233 0.218
Mid-life qualifications 0.005 0.004 1.272 0.204
Highest qualification by age 23 (base none)
Level 1 0.011 0.014 0.776 0.438
Level 2 -0.013 0.012 -1.106 0.269
Level 3 -0.012 0.014 -0.866 0.386
Level 4-plus 0.011 0.015 0.755 0.450
Highest qualification between ages 23 and 32 (base none)
Level 1 0.013 0.011 1.210 0.226
Level 2 -0.001 0.012 -0.087 0.931
Level 3 0.016 0.015 1.102 0.270
Level 4-plus 0.006 0.011 0.571 0.568
Regression of Job status/influence (at age 50) on:
Occupational status at age 33 0.165 0.009 17.396 0.000
Mid-life qualifications 0.046 0.006 7.419 0.000
Highest qualification by age 23 (base none)
Level 1 0.065 0.023 2.786 0.005
Level 2 0.152 0.022 7.008 0.000
Level 3 0.234 0.026 8.932 0.000
Level 4-plus 0.314 0.029 10.956 0.000
90 A. Jenkins, R. D. Wiggins
123
model along with the standard errors and the level of statistical significance (i.e. p-
value).6 Highest qualification obtained in initial education and further qualifications
obtained between the ages of 23 and 33 determined occupational status at age 33.
This in turn had a significant impact on some indicators of job quality at age 50,
especially the status/influence level of the job. Obtaining qualifications in mid-life
(ages 33 to 50) was positively and significantly associated with higher-status jobs at
age 50, but not significantly associated with the stress level of the job, nor with the
job satisfaction/security factor. Well-being at age 50 was strongly and positively
associated with the job satisfaction/security and the job status/influence factors.
Table 4 continued
Estimate S.E. Est./S.E. p-value
Highest qualification between ages 23 and 32 (base none)
Level 1 0.034 0.018 1.864 0.062
Level 2 -0.001 0.021 -0.036 0.971
Level 3 0.096 0.025 3.834 0.000
Level 4-plus 0.127 0.018 7.074 0.000
Regression of Occupational status at 33 on:
Highest qualification by age 23 (base none)
Level 1 0.268 0.051 5.234 0.000
Level 2 0.669 0.043 15.466 0.000
Level 3 1.085 0.049 22.134 0.000
Level 4-plus 1.776 0.049 36.182 0.000
Highest qualification between ages 23 and 32 (base none)
Level 1 0.061 0.041 1.495 0.135
Level 2 0.044 0.048 0.912 0.362
Level 3 0.165 0.057 2.881 0.004
Level 4-plus 0.681 0.037 18.549 0.000
Regression of Mid-life qualifications on:
Occupational status at age 33 -0.021 0.014 -1.552 0.121
Highest qualification by age 23 (base none)
Level 1 0.159 0.060 2.642 0.008
Level 2 0.329 0.053 6.247 0.000
Level 3 0.393 0.058 6.789 0.000
Level 4-plus 0.530 0.060 8.816 0.000
Highest qualification between ages 23 and 32 (base none)
Level 1 0.173 0.043 3.989 0.000
Level 2 0.264 0.050 5.246 0.000
Level 3 0.275 0.057 4.789 0.000
Level 4-plus 0.387 0.035 10.988 0.000
Notes All cases. Sample size: 7,447
S.E. stands for standard error
6 The estimates for the complete case dataset were very similar in all respects.
Pathways from adult education to well-being 91
123
Pathways from adult learning to well-being
One of the advantages of structural equation modelling is that it is possible to
distinguish direct and indirect pathways among variables, in this instance from
participation in learning to well-being at 50. Table 5 summarises the direct and
indirect routes from mid-life learning, defined throughout the paper as obtaining a
qualification between ages 33 and 50, to well-being.
The first column shows results for cases with complete data, while the second
column also includes cases which had missing data on one or more of the variables.
The results were, for the most part, very similar, irrespective of whether the cases
with missing data were included or not. Mid-life learning was important directly in
determining job status at age 50. Since the significant determinants of well-being
(CASP) in the model included job status, there was, it emerged, a statistically
significant pathway from obtaining qualifications in mid-life to well-being via job
status. Conflating the various paths in which mid-life qualifications impact on well-
being, it is apparent that the size of the effect was quite small, at about 0.14 units of
our well-being measure. Also, the direct pathway from obtaining qualifications in
mid-life to well-being at age 50 was not statistically significant. In other words,
there was no evidence that obtaining qualifications in mid-life had any direct effect
on well-being.
Table 5 Direct and indirect pathways from mid-life learning to well-being (unstandardised estimates)
Complete case data All cases
Pathways:
Specific Indirect
Mid-life learning ? job stress
?well-being
0.000
(0.001)
0.000
(0.001)
Mid-life learning ? job satisfaction/security
? well-being
0.037
(0.050)
0.060
(0.048)
Mid-life learning ? job status
?well-being
0.071**
(0.017)
0.067**
(0.016)
Total Indirect 0.108*
(0.053)
0.127**
(0.050)
Direct (mid-life learning ? well-being) 0.033
(0.073)
0.007
(0.072)
Total 0.140
(0.072)
0.135
(0.072)
Sample size 6,630 7,447
Notes Standard errors in parentheses
Significant at *5%; **1%
Here mid-life learning means obtaining a qualification between the ages of 33 and 50
92 A. Jenkins, R. D. Wiggins
123
Results for sub-groups
Some analyses were also conducted on sub-groups within our sample. We
considered whether there were differences between results for males and females,
and also whether the level of the qualification obtained in mid-life made a difference
to its impact on well-being.
Comparing males and females
At age 50, men were more somewhat more likely than women to be in higher-status
occupations. For instance, 50 per cent of the men in the sample, but only 46 per cent
of the women were in professional, managerial or technical jobs. On the other hand,
women tended to report better job security and job satisfaction. At age 50, 31 per
cent of men, and 43 per cent of women felt that their current job was ‘‘very secure’’.
The proportion of men reportedly ‘‘very satisfied’’ with their current job was 39 per
cent, but this proportion rose to 45 per cent amongst women. Women also scored
better on the job stress measure, on average. Some 43 per cent of men, but just 34
per cent of women, said that their work ‘‘interfered with family life’’. This was most
likely due to the much higher proportions of women working part-time, with men
more likely to be working full-time.
It therefore seemed relevant to conduct some analyses in which parameter
estimates were allowed to differ for males and for females. It emerged, in fact, that
the pathways through which obtaining qualifications had an impact on well-being
were the same for males and females: for both groups, obtaining qualifications in
mid-life had an impact on job status at age 50, which in turn had a significant effect
on well-being. Other pathways from mid-life learning to well-being were not
Table 6 Direct and indirect pathways from mid-life learning to well-being: comparing men and women
(unstandardised estimates)
Model Complete case data All cases
Males Females Males Females
Pathways:
Specific Indirect
Mid-life learning ? job stress ? well-being -0.004 -0.048 -0.005 -0.041
Mid-life learning ? job satisfaction/security ? well-being -0.079 0.037 -0.050 0.040
Mid-life learning ? job status ?well-being 0.037* 0.150** 0.037* 0.135**
Total Indirect -0.046 0.139 -0.018 0.134
Direct (mid-life learning ? well-being) 0.088 0.031 0.056 0.030
Total 0.042 0.170 0.038 0.164
Sample size 3,345 3,285 3,833 3,614
Notes significant at *5%; **1%
Here mid-life learning means obtaining a qualification between the ages of 33 and 50
Pathways from adult education to well-being 93
123
statistically significant. However, while the pathways were very similar, effect sizes
were not. It was noticeable that the effects of mid-life learning were much larger for
women than for men. In Table 6, the size of the overall effect of mid-life learning
on well-being for women was about four times as large than that for men.
Comparing the effects of low-level and high-level qualifications in mid-life
The sample was split into two groups: those who obtained qualifications at level 2 or
below, and those who obtained qualifications at level 3 and above. We then ran the
model separately for each of these groups, thereby examining the effects of gaining
qualifications at these different levels. Some quite stark differences were found. For
those who gained low-level qualifications in mid-life there was no significant
overall effect on well-being (p[0.05), whereas for those who obtained higher-level
qualifications in mid-life there was a significant effect on well-being (p\ 0.01),
which was about twice the size of the effects for the sample as a whole (as reported
in Table 4 earlier). It seems that the acquisition of low-level qualifications in mid-
life may have little impact on job quality and hence no significant effect on well-
being.
Conclusions
The importance of our research lies mainly in analysing the links between learning,
job quality and well-being. Our research question concerned whether gaining
qualifications in mid-life was associated with higher well-being at age 50. In the
structural equation model, those acquiring qualifications in mid-life were likely to
have better jobs at age 50, compared to those who did not obtain qualifications. The
quality of the jobs tended to be higher in terms of status and influence, but they were
not necessarily more secure, satisfying or free from stress. Therefore, the impact of
qualifications on well-being via improved job quality tended to be a modest one,
statistically significant but substantively quite small.
Some limitations of our study should also be acknowledged. We have not
discussed non-vocational learning because it is unlikely to affect job quality. But
this type of learning could have effects on well-being in other ways – as discussed
elsewhere (Jenkins 2011) and also, for example, in an article on the topic by John
Field (2009) – and there may be scope for more research on this topic. The focus
was on qualifications; therefore we have not attempted to look at other forms of
vocational training or, indeed, at informal learning at work. Nor do we have
information on very specific questions such as the location of the course, or the
nature of the funding for it and so on. These questions might be addressed using
very specific cross-sectional surveys of training. Given the research question and
our wish to replicate an earlier Swedish study, we chose to use a longitudinal data
source in order to track the long-term benefits of (mainly vocational) qualifications
gained in mid-life. There may also be scope for further investigation of why the
relationship between job quality and well-being was found to be quite weak. Some
have suggested (e.g. Green 2006, 2008) that higher job quality has been associated
94 A. Jenkins, R. D. Wiggins
123
with greater intensification of effort and hence does not lead to much improvement
in job satisfaction.
The model that we used in this paper was derived from the pioneering work of
Albert Tuijnman (1990) and it is therefore interesting to make comparisons between
our results and those Tuijnman arrived at nearly a quarter-century ago. Both studies
found a positive impact of a measure of adult education on well-being. Both found
the size of the effect to be, in some sense, small. The main difference in results is
that Tuijnman identified a statistically significant direct pathway, i.e. adult
education had a direct impact on well-being, while in our work there was a
positive direct association but it was not statistically significant.
Our study is not identical to that of Tuijnman. Perhaps the major difference is
that we used data on males and females, while Tuijnman focused just on men. Our
results showed that women, who were more likely to gain qualifications in mid-life,
were correspondingly more likely to move to a higher occupational status by age 50
than they had held at age 33. Of course overall men were still more likely than
women to be in high-status jobs at the age of 50, but women had caught up
somewhat relative to their position at the age of 33. Some 30 percent of the women
in our sample, but only 24 per cent of the men, climbed the occupational hierarchy
between the ages of 33 and 50. The gains in well-being for women were also
noticeably larger than for men.
Furthermore, we made distinctions in terms of the level of qualification obtained
and found that there was little evidence that low-level qualifications had an effect on
well-being. This is fully consistent with previous research (Wolf et al. 2006; Jenkins
et al. 2007), which showed that the acquisition of low-level qualifications in
adulthood had little or no impact on the earnings of individuals in the British labour
market. The adult education programmes in Sweden analysed by Tuijnman had a
clear economic rationale, aiming at improving skills relevant to the workplace
(Tuijnman 1989, p. 49; Rubenson 1997, p. 72). However, there have been concerns
that economic benefits for many participants in such programmes have been quite
limited (Ekstrom 2003). This previous research suggests, then, similarities between
Sweden and Britain, and this may help to explain why our findings on the outcomes
of participation in vocational learning in Britain are mostly quite comparable with
Tuijnman’s earlier findings on Sweden.
A major theme in both our results and those of Tuijnman is the need for earlier
cohorts to compete with more recent cohorts who had obtained more and higher
qualifications from their initial education. The respondents in the Malmo study used
by Tuijnman typically had about 8 years of full-time initial education. They were in
a job market competing with younger cohorts who tended to have more by way of
initial education. Adult education played a role in helping them to secure better-
status jobs, which in turn had an impact on their well-being. Our NCDS cohort were
born in 1958, some 30 years later, and in another country. They had rather more by
way of initial education, typically 11 or 12 years of schooling, leaving compulsory
education at the age of 16. But, as with the Malmo respondents, the NCDS sample
were also competing with more recent cohorts who tended to stay in initial
education for longer, and who therefore had a greater likelihood of proceeding to
university and obtaining higher-level qualifications by their early 20s. Thus adult
Pathways from adult education to well-being 95
123
education was important for the 1958 cohort to catch up on the qualifications of
their younger counterparts, and this again played a part in helping them to obtain
better-status jobs, which in turn had some impact on their well-being.
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The authors
Andrew Jenkins is a senior researcher in the Department of Quantitative Social Science at the UCL
Institute of Education in London. He specialises in the secondary analysis of large-scale longitudinal
datasets and was recently a British Academy mid-career research fellow. His research interests are mainly
about learning in adulthood, including participation in different types of learning during distinct phases of
the lifecourse, the effects of learning on labour market outcomes, and the mental health benefits of
learning for older adults.
Richard D. Wiggins is a professor in the Department of Quantitative Social Science at the UCL Institute
of Education. His methodological interests include the longitudinal analysis of secondary data, mixed
methods, survey design, attitude measurement and sampling methodology, evaluation research and policy
analysis. His current research covers the exploration of structure and agency in the context of ageing,
poverty, physical and mental health and well-being, cross-national differences in health, multilingual
capital, ethnicity and socio-economic aspects of education.
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