ART - Jenkins & Wiggins - Pathways From Adult Education to Well-being - The Tuijnman Model Revisited

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: [email protected]

R. D. Wiggins

e-mail: [email protected]

123

Int Rev Educ (2015) 61:79–97

DOI 10.1007/s11159-015-9468-y

http://crossmark.crossref.org/dialog/?doi=10.1007/s11159-015-9468-y&domain=pdf

http://crossmark.crossref.org/dialog/?doi=10.1007/s11159-015-9468-y&domain=pdf

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.


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).


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/.


123

http://www.cls.ioe.ac.uk/

http://www.cls.ioe.ac.uk/

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].


123

http://www.gov.uk/ofqual

http://www.gov.uk/ofqual

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.


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.


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


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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.


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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:




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


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:




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


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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


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:


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


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


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


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.


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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


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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


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


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


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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.

References

Arbuckle, J. L. (1996). Full information estimation in presence of incomplete data. In G. A. Marcoulides

& R. E. Schumaker (Eds.), Advanced structural equation modelling: Issues and techniques (pp.

243–277). Mahwah, NJ: Erlbaum.

Blundell, R., Dearden, L., & Sianesi, B. (2005). Measuring the returns to education. In S. Machin & A.

Vignoles (Eds.), What’s the good of education? The economics of education in the UK. Princeton,

NJ: Princeton University Press.

Bowling, N., Eschleman, K., & Wang, Q. (2010). A meta-analytic examination of the relationship

between job satisfaction and subjective well-being. Journal of Occupational and Organizational

Psychology, 83(4), 915–934.

Bynner, J. (2010). Lifelong learning, the life course and longitudinal study: Building the evidence base. In

J. Derrick, et al. (Eds.), Remaking adult learning: Essays on adult education in honour of Alan

Tuckett. London: Institute of Education, University of London.

Byrne, M. B. (2012). Structural equation modelling with Mplus: Basic concepts, applications and

programming., Multivariate applications series New York: Routledge.

Carpenter, J., & Plewis, I. (2011). Analysing longitudinal studies with non-response: Issues and statistical

methods. In M. Williams & W. P. Vogt (Eds.), The Sage handbook of innovation in social research

methods (pp. 498–523). London: Sage.

Desjardins, R. (2008). Researching the links between education and well-being. European Journal of

Education, 43(1), 23–35.

Ekstrom, E. (2003). Earning effects of adult secondary education in Sweden, Working paper 2003:16.

Stockholm: Institute for Labour Market Policy Evaluation.

Enders, C. (2010). Applied missing data analysis. London: Guilford Press.

Feinstein, L., Budge, D., Vorhaus, J., & Duckworth, K. (2008). The social and personal benefits of

learning: A summary of key research findings. London: Centre for Research on the Wider Benefits

of Learning, Institute of Education, University of London.

Feinstein, L., & Hammond, C. (2004). The contribution of adult learning to health and social capital.

London: Institute of Education, University of London.

Field, J. (2009). Good for your soul? Adult learning and mental well-being. International Journal of

Lifelong Education, 28(2), 175–191.

Field, J. (2011). Researching the benefits of learning: The persuasive power of longitudinal studies.

London Review of Education, 9(3), 283–292.

Green, F. (2006). Demanding work. The paradox of job quality in the affluent economy. Princeton:

Princeton University Press.

Green, F. (2008). Work effort and worker well-being in the age of affluence. In R. J. Burke & C.

L. Cooper (Eds.), The long work hours culture: Causes, consequences and choices (pp. 115–136).

Bingley: Emerald.

Hammond, C., & Feinstein, L. (2006). Are those who flourished at school healthier adults? What role for

adult education?. London: Centre for Research on the Wider Benefits of Learning, Institute of

Education, University of London.

Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling:

Concepts, issues and applications. Thousand Oaks, CA: Sage.

Hyde, M., Wiggins, R. D., Blane, D., & Higgs, P. (2003). A measure of quality of life in early old age:

The theory, development and properties of a needs satisfaction model (CASP-19). Aging and Mental

Health, 7(3), 186–194.

Jenkins, A. (2011). Participation in learning and wellbeing among older adults. International Journal of

Lifelong Education, 30(3), 403–420.

Jenkins, A., Greenwood, C. & Vignoles, A. (2007). The returns to qualifications in England (Report no.

89). London: Centre for the Economics of Education, LSE.


123

Jenkins, A., & Wolf, A. (2005). Employers’ selection decisions: The role of qualifications and tests. In S.

Machin, A. Vignoles, & A. Vignoles (Eds.), What’s the good of education? The economics of

education in the UK. Princeton, NJ: Princeton University Press.

Jessup, G. (1991). Outcomes: NVQs and the emerging model of education and training. London: Falmer.

Joreskog, K. G., & Sorbom, S. D. (1979). Advances in factor analysis and structural equation modelling.

Cambridge, MA: Abt Books.

Kaplan, D. (2009). Structural equation modeling: Foundations and extensions. London: Sage.

Layard, R. (2011). Happiness: Lessons from a new science (2nd ed.). London: Penguin.

Little, R., & Rubin, D. (1987). Statistical analysis with missing data. New York: Wiley.

Michalos, A. C. (1986). Job satisfaction, marital satisfaction and the quality of life: A review and a

preview. In E. M. Andrews (Ed.), Research on the quality of life (pp. 57–83). Ann Arbor, MI:

Institute for Social Research, The University of Michigan.

Muthen, L., & Muthen, B. (2010). Mplus statistical analysis with latent variables. User’s guid (6th ed.).

Los Angeles, CA: Muthen & Muthen.

Rice, R. W., Hunt, R. G., & Near, J. P. (1980). The job-satisfaction/life-satisfaction relationship: A review

of empirical research. Basic and Applied Social Psychology, 1(1), 37–64.

Rubenson, K. (1997). Sweden: The impact of the politics of participation. In P. Belanger & S. Valdivielso

(Eds.), The emergence of learning societies: Who participates in adult learning? (pp. 91–93).

Oxford: Pergamon.

Schuller, T., Brassett-Grundy, A., Green, A., Hammond, C., & Preston, J. (2002). Learning, continuity

and change in adult life. London: Institute of Education.

Schuller, T., Preston, J., Hammond, C., Brassett-Grundy, A., & Bynner, J. (2004). The benefits of

learning: The impact of education on health, family life and social capital. London: Routledge

Falmer.

Tuijnman, A. (1989). Recurrent education, earnings and well-being: A Fifty-year longitudinal study of a

cohort of Swedish men. Stockholm studies in educational psychology Stockholm: Almqvist &

Wiksell International.

Tuijnman, A. (1990). Adult education and the quality of life. International Review of Education, 36(3),

283–298.

Westover, J. (2011). The relationship between job satisfaction and other important individual,

organizational, and social outcomes. The International Journal of Science in Society, 2(1), 63–76.

Wiggins, R. D., Higgs, P., Hyde, M., & Blane, D. (2004). Quality of life in the third age: Key predictors

of the CASP-19 measure. Ageing & Society, 24(5), 693–708.

Wiggins, R. D., Netuveli, G., Hyde, M., Higgs, P., & Blane, D. (2008). The evaluation of a self-

enumerated scale of quality of life (CASP-19) in the context of research on ageing: A combination

of exploratory and confirmatory approaches. Social Indicators Research, 89(1), 61–77.

Wolf, A., Jenkins, A., & Vignoles, A. (2006). Certifying the workforce: Economic imperative or failed

social policy? Journal of Education Policy, 21(5), 535–565.

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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Documents

ART - Jenkins & Wiggins - Pathways From Adult Education to Well-being - The Tuijnman Model Revisited