GERMÀ COENDERS, FERRAN CASAS, CRISTINA FIGUER andMÒNICA GONZÁLEZ
RELATIONSHIPS BETWEEN PARENTS’ AND
CHILDREN’S SALIENT VALUES FOR FUTURE AND
CHILDREN’S OVERALL LIFE SATISFACTION.
A COMPARISON ACROSS COUNTRIES1
(Accepted 8 September 2004)
ABSTRACT. In this paper, a model is set forth relating (a) overall life satisfaction ofchildren to children’s values and (b) children’s values to parents’ values. Usingconfirmatory factor analysis models three dimensions of values (materialistic values,
capacities and knowledge values and interpersonal relationship values) consistentlyemerged in 5 countries (Brazil, South Africa, Norway, Spain and India) for bothparents and children. There was a considerable amount of missing data, mainly
because the parent’s questionnaire was often not returned. Full information maxi-mum likelihood estimators with missing data were thus used.Multiple-group analyses were next performed to assess factor invariance of the
three value dimensions across the five countries for both parents and children. Thisimplies testing the equality of factor loadings and intercepts across groups. Thisequality is required to ensure that factors have the same interpretation in all groups,
which is necessary when comparing any aspect of the factor distribution acrossgroups.The only two countries for which the interpretation of value dimensions was
invariant for both parents and children were Brazil and Spain. The results of other
countries could thus not be compared. Multiple-group structural equation modelsrevealed that both parents and children scored higher on most values in Brazil than inSpain. In both countries, each child value dimension was only significantly predicted
by the same value dimension of the parents. R-squares were in the 4–12% range andslightly higher in Brazil. The only value dimension that had some effect on overall lifesatisfaction was capacities and knowledge, which was so in both countries.
KEY WORDS: aspirations, child subjective well being, factor invariance, missingdata, structural equation models, values
INTRODUCTION
From the point of view of Campbell et al. (1976), quality of life
studies should consider not only perceptions and evaluations, but
Social Indicators Research (2005) 73: 141–177 � Springer 2005DOI 10.1007/s11205-004-3233-0
also aspirations of people. Aspirations are a complex concept and
social sciences have not yet debated theoretical models and proce-
dures sufficiently to get good measures of people’s aspirations in
different contexts.
We may consider aspirations on at least two very different levels:
(1) General aspirations, which are usually formulated in abstract
terms. General aspirations, particularly among pedagogues,
have often been related to values, i.e. values the subject aspi-
rates to be appreciated for in his or her future life. In our
present research we are interested in aspirations as conscious
goals of people and not just dreams.
(2) Concrete aspirations, which are often related to concrete goals
the subject wants to achieve, in the immediately foreseeable fu-
ture.
In the quality of life studies tradition we can meet a good number of
researches exploring the relationship between the pursuit of concrete
goals and subjective well-being (many studies by T. Kasser and by
R.M. Ryan are good examples, as for instance Kasser and Ryan,
1996).
Additionally, we can also find a certain number of studies which
explore general aspirations in very concrete domains – which in fact
share characteristics of the two quoted levels – but, from our point of
view, they are clearly more related to values than to concrete goals,
because of the general terms used to find out the position of the
surveyed subjects. Clear examples are some studies on desired values
or qualities to be fulfilled in children’s growing up and education, as
for example in the different questionnaires of the World Values
Survey (WVS). In the 1990, 1995 and 1999–2002 WVS, the following
item was included: Here is a list of qualities which children can be
encouraged to learn at home. Which, if any, do you consider to be
especially important? Please, choose up to five (see the list of values
following this item in Table I). In the 1999–2002 questionnaire this
list of 11 values was reduced to 10 – good manners being excluded of
the list.
A similar item – with slight but interesting differences (see Table I)
– was included in some Eurobarometer surveys, the first time in
number 34 (Commission of the European Communities, 1990) under
the label What parents expect from their children. The list was also of
GERMÀ COENDERS ET AL.142
TABLE
I
Lists
ofdesired
values
andqualities
thatparents
expectfrom
theirchildren
Hereisalist
ofqualities
whichchildrencanbe
encouraged
tolearn
at
home.
Which,ifany,do
youconsider
tobe
especiallyim
portant?
Please,choose
upto
five.
WorldValues
Survey,1990,1995
(Inglehart,1997)
Hereisalist
ofqualities
whichparents
cantryto
encouragein
theirchildren.
Whichdoyouconsider
tobeespeciallyim
portant?
Please,choose
three.
Eurobarometer,34
(Commissionofthe
EuropeanCommunities,
1990)
Hereisalist
ofqualities
whichparents
cantryto
encouragein
theirchildren.
Whichdoyouconsider
tobeespeciallyim
portant?
Please,choose
three.
Eurobarometer,39
(Commissionofthe
EuropeanCommunities,
1993)
Imagineyourson/daughteris
21
years
old.At
that
time,
what
values
would
you
like
him
/her
tobe
appreciated
for?
(Casas
etal.,2003)
Goodmanners
Goodmannersand
politeness
Goodmanners
His/her
intelligence
Independence
Abilityto
communicate
withothers
Self-reliance
His/her
practicalskills
Hard
work
Independence
Hard
work
His/her
wayofgettingalongwith
people
Feelingofresponsibility
Conscientiousnessatwork
Asense
ofresponsibility
His/her
knowledgeofcomputers
Imagination
Asense
ofresponsibility
Imaginationandcreativity
His/her
profession
Tolerance
andrespect
forother
people
Imagination
Tolerance
andrespect
forothers
His/her
family
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 143
TABLE
I
Continued
Thrift,savingmoney
and
things
Tolerance
andrespect
forothers
Asense
ofthrift
His/her
sensitivity
Determination,
perseverance
Thrift,notwastingmoney
andother
things
Determinationand
perseverance
His/her
sympathy
Religiousfaith
Religiousfaith
Religiousfaith
His/her
money
Unselfishness
Obedience
Generosity
His/her
power
Obedience
Loyalty
Obedience
His/her
knowledge
about
the
world
Loveoflife
His/her
appearance
(his/her
im-
age;
the
way
he/she
looks
toothers)
Courage
Atasteoflife’spleasures
Anappreciationofbeauty
GERMÀ COENDERS ET AL.144
11 qualities or values, but only 4 of them are formulated in exactly
the same way than in the WVS. In Eurobarometer 39 (Commission of
the European Communities, 1993) the list was expanded to 14 values
–among them only four being formulated in exactly the same way as
in number 34 (Table I). As a consequence of the different formula-
tions of the question, results present clear differences, although they
offer similarities as well.
In the psychological tradition there are many value studies using
similar lists, which sometimes have been administered to adolescents
in order to explore their own desired values. However, the general
question is always referred to present time, not to future. For
example, in Schwartz’s studies, the question As a guiding principle in
my life, this value is. . ., is used before presenting a list of 56 values
(Struch et al., 2002). In order to distinguish our formulation from the
ones referred to present time, we will use the concept salient values for
future.
As many authors have pointed out, along the last decade a
large number of studies on children’s and adolescents’ subjective
well being (SWB) have been published, although this field of study
is still in its infancy compared to adult’s SWB studies (Huebner,
1994; Casas et al., 2001). Such studies have usually tested measures
of how children and adolescents evaluate their overall life satis-
faction and their satisfaction with specific domains of life. How-
ever, there is little research of under 16s in which life satisfaction is
studied in relation to general life aspirations, as for example,
values.
Often, beliefs and value systems have a hierarchical structure,
some of them being more nuclear than others (Rokeach, 1973). Al-
though the value system of each individual is relatively stable, it may
change in different social contexts and in different cultural conditions,
and it is particularly influenced by the social and political develop-
ment of each society (Pinillos, 1982).
A relationship between parents’ personal values and parent’s
values oriented to the growing-up of their own children (educational
values, according some authors) has been often identified in scientific
literature. However, surprisingly, very often scientific research has
concluded that it is very difficult to empirically demonstrate the
influence of parents’ values on children’s values – traditionally, cor-
relations found have often been very modest and lower than expected
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 145
(Hess and Torney, 1965; Connell, 1972; Thomas and Stankiewicz,
1974).
Two theories have been developed trying to explain that situation
(Musitu et al., 2001), which make opposite predictions: (a) The
evolutional hypothesis stands for little direct influence. Similar atti-
tudes and values between parents and children are very much influ-
enced by a shared context, and will increase only when they have to
deal together with similar situations and similar crises. Parents’ and
children’s values will approach only when children become adults. (b)
The socialization hypothesis stands for a direct influence, but in
competition with different socialization agents. Therefore, the older a
child is, the smaller the direct influence of parents and the larger the
influence from other agents, which will produce larger differences
between parents’ and children’s values.
Longitudinal studies tend to refuse the first hypothesis. How-
ever, they do not give a clear support to the second either (Miller
and Glass, 1989). In consequence, the idea of a direct, simple and
clear influence has to be avoided, because the interrelationships
seem to be more complicated than expected. Explained variances
of children values remain in any case low.
Some sociological research has found that some values do cor-
relate with age, and appear to be more or less appreciated among
youngsters than among adults. Orizo (1996) found in a Spanish
sample that honesty and religious faith are less appreciated and
independence, joie de vivre, and rationality are more appreciated by
youngsters than by adults – while responsibility, tolerance or good
manners did not correlate with age. Also Whitbeck and Gecas
(1988) point out age as a mediating factor together with the nature
of values transmitted, the perceptions and attributions that children
have about parents’ values and the quality of parent–children
interactions.
All the comparisons between parents and children that we have
been able to find in the scientific literature develop evaluations of
present values. Because both age differences and the different every-
day life contexts experienced by the two generations may influence
value structures, it is not unexpected that present values differ be-
tween parents and children in the same family. In order to explore a
different perspective, we designed our research based in a commonly
imagined future (when the child becomes 21 years old) and as a
GERMÀ COENDERS ET AL.146
common aspiration (values the child is desired to be appreciated for).
In that way we assume that we do not compare present values, but
salient values for future, that is to say, a kind of general aspirations.
The relationship between salient values for future held by children
and their SWB has the potential to raise rather new debates. Do
adolescents high in materialistic aspirations tend to be more or less
satisfied with life than those high in more humanistic aspirations? Or
perhaps salient values for future make no major difference to people’s
life satisfaction? Does the fact of being satisfied with life or with a
specific life domain bear any relation to different desired values? One
of our basic hypotheses is that aspirations and SWB of children are
related in some way or another.
The study of values in relation to children’s SWB has been,
compared to adults, hardly considered by the research community,
although its inclusion is defended, however, by several authors (Di-
ener and Fujita, 1995; Csikzentmihalyi, 1997; Diener et al., 1998).
Therefore, it is not strange that we have, as quoted, research on
adults opinions on salient values for children’s future, but we scarcely
have available research on children’s or adolescents’ opinions about
salient values for their own future.
According our previous own research, some salient values for
future seem to contribute to SWB of adolescents (Casas et al., 2004).
Moreover, there is increasing evidence that those people that give
more importance to the so called extrinsic or materialistic values
(fame, money, power, etc.), as opposed to the intrinsic ones (inter-
personal relationships, feelings of community belonging, and so on)
show lower overall life satisfaction (Kasser and Ryan, 1996; Kasser
and Ahuvia, 2002) – and we have already collected similar empirical
evidence also among adolescents (Casas et al., 2004).
In this paper, a model is set forth relating (a) overall life satis-
faction of adolescents (12–16 years old) to their own salient values
for future and (b) adolescents’ salient values for future to parents’
desired values for their own children’s future, in five countries
(Brazil, South Africa, Norway, Spain and India) using structural
equation models – SEM [sometimes called LISREL models, after
the name of the first commercial software that became available,
developed by Jöreskog (1973) (see Bollen, 1989; Raykov and Mar-
coulides, 2000; or Batista-Foguet and Coenders, 2000 as introduc-
tory manuals)]. First, the comparability of factor structures across
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 147
countries will be assessed and then comparisons will be made
among the set of countries that are comparable.
SUBJECTS AND MEASUREMENT INSTRUMENTS
Sample Selection
In each country, we selected a town or a region, and then we obtained
a list of all schools with pupils within the age range between 12 and
16 years old, that is in their late childhood or early adolescence. Next,
we selected those schools whose pupil population could be considered
most representative of the characteristics of the majority of the
families in the town. In practice, that means we excluded a few
schools of the list: the elite schools with disproportionally large
numbers of rich people and those schools from the communities with
the lowest socio-economic status. Thus, we intended to compare
across countries a sample with a majority of children of middle class
families according the standards of each country or culture (low,
medium and high middle class). We assumed that the extreme situ-
ations may be very different across countries and therefore non-
comparable. For example, consider the different situations of the
lowest classes in countries like Norway and India.
From the final list of schools we randomly selected a sufficient
number for an overall sample size of between 600 and 1200 children.
As expected, a number of schools refused to participate in our re-
search, and we attempted to randomly substitute them. However in
areas with a low population, sometimes we were forced to select only
the school or schools willing to cooperate. Even if our samples are
not nationally representative, they represent well city middle class
children and heterogeneity is enough to estimate relationships among
variables.
In each school, we reported our aims to the director and to the
parents association, and we proceeded in accordance with regular
ethical guidelines for questionnaire administration to children in each
country.
When participation in our research had been agreed, we randomly
selected a number of classes, until we had fulfilled a quota for each
age group from each school, and we asked for cooperation from the
responsible teachers. After that, children were carefully asked for co-
GERMÀ COENDERS ET AL.148
operation and were informed that data would be treated confiden-
tially and that they were free to refuse. The questionnaires were
administered in their regular classroom to the whole group. One of
their usual teachers and one or two researchers were present during
the administration, and clarified any of the children’s questions that
arose. The session was usually about 1 h long for the youngest and
about 35–40 min for the oldest.
At the end of the session, we gave each child a letter and a
questionnaire for their parents in a sealed envelope, to be delivered by
hand. They were asked to return it to the teacher within approxi-
mately one week, also in a sealed envelope. The questionnaire could
be answered by either of the parents or by both together, and a record
was kept of that important variable. Each parent’s questionnaire was
coded, so that it could be paired with their child’s.
In their questionnaire, parents were requested to answer with only
the child who had answered our school questionnaire in mind. The
name of the child was marked on the form.
After deleting 87 cases with many missing values, the final usable
sample sizes for both parents and children, and the parent response
rate are in Table II. By age and gender, 51.2% were boys and 48.8%
girls, 11.1% aged 12, 28.1% aged 13, 29.1% aged 14, 22.5% aged 15
and 9.2% aged 16.
Measurement Instruments
The original questionnaires were in Castilian Spanish and in Catalan
languages and had already been tested in previous studies. In the
Spanish region where the questionnaires were administered, both
languages are official, and all children speak both of them fluently,
with the exception of recently arrived immigrants. However, that is
not the case for all parents, so they could choose the language version
with which they felt more comfortable.
For the international study, the Spanish version was translated
into English and participants from all research teams across the five
countries discussed the translation at an international meeting, where
many cultural and social specific factors were taken into account. As
a result of this discussion, some items of the original questionnaires
were changed and a few new ones were added. Then, the English
version was translated into all other languages. In the cases of
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 149
Brazilian-Portuguese and Norwegian, the translations also used the
Spanish version, because at least one team member was fluent in
Spanish. All translations were tested in each country, and a long e-
mail discussion developed among all research teams, until agreement
was reached on a new English standard version, and all translated
questionnaires were re-adapted to that version.
One method that has been used for exploring children’s and
adolescents’ salient values for future is to ask them to what extent
they would like to be appreciated for some concrete values, when
they get older. We have used this technique in previous research in
order to identify different value structures between parents and
children (Casas et al., 2004). For the present study, we designed a
closed set of salient values particularly thinking in adolescents’
perspectives and desired values. We did not ask them to select a
number of values in the list but to evaluate each value of the list
through a five-point Likert scale, 1 meaning ‘‘not at all’’ and 5
‘‘very much’’. The question was Imagine you are 21 years old. At
that time how much would you like people to appreciate the following
aspects about you?
The same set of values was introduced into the parents’ ques-
tionnaire. In this case, they were asked to indicate to what extent they
would like their children to be appreciated by other people in the
future on the same twelve values (Table I).
An item on overall life satisfaction was also included in adoles-
cents’ questionnaire, measured through a five-point Likert scale, 1
meaning ‘‘very dissatisfied’’ and 5 ‘‘very satisfied’’. The concrete
TABLE II
Sample sizes and response rate for parents
Children Parents
Count Percent Count Percent Resp. rate
Spain 3118 44.7 1626 45.6 52.1
SouthAfrica
997 14.3 565 15.8 56.7
Norway 893 12.8 347 9.7 38.9India 1115 16.0 763 21.4 68.4
Brazil 860 12.3 263 7.4 30.6
Total 6983 100.0 3564 100.0 51.0
GERMÀ COENDERS ET AL.150
question was: At present, how satisfied are you with your life as a
whole?
The questionnaire was a part of a larger research project aimed at
exploring different activities, perceptions and evaluations related to
the use of audiovisual instruments by children. This article is con-
cerned only with the comparison of the value dimensions across the
five countries mentioned, both for parents and children, and with
overall life satisfaction of children.
Cross-cultural Comparability
Factor invariance, also called measurement invariance, measurement
equivalence, factor equivalence, and construct comparability, refers
to the extent to which items used in survey-type instruments and the
dimensions they measure mean the same thing to members of dif-
ferent groups. It is thus clear that factor invariance is needed before
the groups can be compared in a meaningful way, as otherwise, group
differences in means or regression coefficients could be attributable to
true differences in group distributions or to a different meaning of
variables (Meredith, 1993; Little, 1997). This is especially relevant in
cross-cultural research like ours, in which translated versions of the
questionnaire are administered to different groups (e.g. Reise et al.,
1993; Steenkamp and Baumgartner, 1998). In quality of life research,
this problem has often been overlooked in the past, but research
practice is rapidly changing (e.g. Park et al., 2004).
Data were first explored with different statistical tests and dis-
tributed to all teams for their analysis. An international 3-day
meeting was organized to discuss the results. After this discussion, we
concluded that some results might be cross-culturally comparable
and others were not. Fortunately, among the former were values,
which showed a very similar structure across countries after devel-
oping a principal component analysis in each country (Casas et al.,
2003). In all countries, acceptable solutions with three dimensions
were found. Even if some items had more than one substantial
principal component loading in some countries, in general the fol-
lowing dimensions could be established (variable names in brackets):
(1) Indicators of capacities and knowledge values (capacity):
(a) intelligence (intellig),
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 151
(b) practical skills (practic),
(c) computer knowledge (computer),
(d) profession (professi),
(e) knowledge of the world (world).
(2) Indicators of interpersonal relationships values (personal):
(a) family (family),
(b) sensitivity (sensitiv).
(c) sympathy (sympathy),
(d) social skills or way of getting along with people (social).
(3) Indicators of materialistic values (material):
(a) money (money),
(b) power (power),
(c) own image (image).
This multivariate descriptive analysis is not sufficient to assess factor
invariance: multiple-group structural equation models (SEM) are
required. Under this approach the same SEM, usually a confirma-
tory factor analysis model, is simultaneously fitted to the data of
several populations constraining certain parameters to be equal
across populations, as explained below.
A first requisite for factor invariance is the so-called configural
invariance that is defined as the fact that individuals of different
groups conceptualise the constructs in the same way (Meredith, 1993;
Riordan and Vandenberg, 1994). Its assessment consists of checking
that in all groups the same numbers of factors are associated with the
same items. Configural invariance may fail for instance due to cul-
tures being so different that the sheer meaning of constructs is dif-
ferent, due to translation problems, or due to a different
understanding of questions.
A second requisite is metric invariance, which implies that in
addition to configural invariance all factor loading parameters be
equal across groups. Thus, not only the items composing each
dimension but also the strength of the relationship between items
and factors must be constant. Metric invariance is a requisite for
cross-group comparison of factor variances, and of covariances and
regression slopes relating different factors. The metric invariance
requisite is often not completely satisfied in practice as it may fail
for even more reasons than configural invariance (for example,
different meanings of the translated response categories of some
GERMÀ COENDERS ET AL.152
questions might suffice). It is argued that if it holds only for a set of
items, it is enough to constrain the loadings of these to anchor a
common meaning of the factors across groups (Byrne et al., 1989).
This is the so-called partial measurement invariance.
A third requisite is called strong factor invariance (Meredith,
1993). In addition to metric invariance, strong factor invariance re-
quires that measurement intercepts (values of the item corresponding
to the zero value of the construct) also be constrained across groups.
Strong factor invariance is a prerequisite for comparing factor means.
This type of invariance can also hold only partially, that is for a
subset of items of each dimension (Byrne et al., 1989), and yet make
comparisons of factor means possible.
ESTIMATION AND TESTING
From the descriptive statistics in Table III it can be seen that there
are a large number of missing values, especially for the parents’
variables, as could be expected from Table II. Missing data are
treated in several alternative ways within the context of SEM.
(1) The classic procedures of listwise deletion, pairwise deletion and
mean substitution. These procedures are only justified if the
data are missing completely at random (Little and Rubin,
1987). Data are said to be missing completely at random when
the probability that a datum is missing is independent of any
characteristic of the individual. Even under this unrealistic
assumption, these approaches have a number of serious
problems (Graham et al., 1994; Graham et al., 1996; Enders
and Bandalos, 2001; Enders, 2001). In our case, we found
evidence that data are not missing completely at random.
Differences in children’s responses were detected depending on
whether parents returned the questionnaires or not. In partic-
ular, for all materialistic value variables, children scored higher
for parents who did not return the questionnaire. One inter-
pretations of this result is as follows: parents highly appreci-
ating materialistic values may be busier, may spend less time at
home, or may more frequently have an attitude that considers
such things as answering a questionnaire that has been sent
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 153
home as ‘‘non-productive’’ or ‘‘of no value’’. Children high in
materialistic values may have internalised such values from
their parents.
(2) Imputation. This approach has the advantage of providing a
complete data set on which standard estimation procedures
could in principle be used. Imputation can be justified both if
the data are missing at random or completely at random. Data
are said to be missing at random when the probability that a
datum is missing depends only on characteristics of the indi-
vidual that are observed (not missing). However, simple
imputation procedures lead to biased estimates and standard
errors. Multiple imputation (Rubin, 1987) does not have these
drawbacks but it is cumbersome to perform unless special
software is available.
(3) Direct Maximum Likelihood (ML) assuming that the data are
normally distributed and missing at random (Finkbeiner, 1979;
Lee, 1986; Aburckle, 1996). This procedure is currently avail-
able in most of the latest commercial software packages for
SEM like Mx (Neale et al., 1999), EQS 6.0 (Bentler, 2000),
AMOS 4.0 (Aburckle and Wothke, 1999), LISREL 8.51
(Jöreskog et al., 2000; du Toit and du Toit, 2001) and MPLUS
2.1 (Muthén and Muthén, 2001). This procedure is consistent,
efficient and leads to correct standard errors and test statistics if
the data are normal and missing at random (Aburckle, 1996;
Wothke, 2000; Enders, 2001; Enders and Bandalos, 2001).
When data are missing not at random (what is also called non-
ignorable missing data) none of the procedures is consistent. This is
the case when the probability that a datum is missing depends on
characteristics of the individual that are missing, for instance on the
same variable that is missing for the individual. Unfortunately it
cannot be tested whether the data are missing at random or not at
random. However, ML with missing data is reported to be less biased
than the alternative approaches (Muthén et al., 1987). Besides, bias
can be further reduced by the addition of more observed variables
that can help predict missingness, which brings the situation closer to
missing at random (Collins et al., 2001; Graham, 2003). Thus, large
models like ours, with many observed variables will be likely to be
less prone to bias.
GERMÀ COENDERS ET AL.154
Five-point response scales such as the ones used in this article must
be considered to be of an ordinal nature. However, it has been shown
that factor analysis models’ capability to take measurement error into
account makes them hardly vulnerable to ordinal measurement, so
that the analysis of this type of ordinal data is admissible with
standard estimation procedures (Coenders and Saris, 1995; Coenders
et al., 1997). However, 5-point data can never be normally distrib-
uted. From the skewness and kurtosis in Table III it can be seen that
departures from normality are quite pronounced in our case. Statis-
tical tests that are robust to non-normality are thus required. Satorra
(1992, 1993) and Satorra and Bentler (1994) developed robust
TABLE III
Descriptive statistics and valid cases
N Mean Std. dev. Skewness Kurtosis
Intelligence 6872 3.82 0.964 )0.542 )0.014Practical skills 6837 3.78 0.961 )0.511 )0.043Computer knowledge 6804 3.44 1.193 )0.295 )0.739Professional status 6831 3.94 1.030 )0.817 0.178Knowledge of the world 6795 3.59 1.127 )0.435 )0.485Family 6846 3.98 1.056 )0.864 0.124Sensitivity 6805 3.72 1.069 )0.557 )0.273Sympathy 6802 3.99 1.000 )0.874 0.316Social skills 6792 3.96 0.945 )0.717 0.197Money 6796 2.85 1.380 0.135 )1.157Power 6774 2.90 1.376 0.088 )1.155Image 6818 3.57 1.231 )0.495 )0.678Parents
Intelligence 3367 4.20 0.814 )1.007 1.371Practical skills 3350 4.05 0.819 )0.696 0.644Computer knowledge 3354 3.81 0.982 )0.566 0.030Professional status 3337 4.23 0.931 )1.248 1.417Knowledge of world 3328 4.09 0.932 )0.988 0.845Family 3364 4.26 0.882 )1.270 1.658Sensitivity 3350 4.24 0.865 )1.156 1.338Sympathy 3345 4.26 0.822 )1.177 1.743Social skills 3376 4.35 0.790 )1.370 2.426Money 3324 2.83 1.292 0.122 )0.923Power 3305 2.79 1.292 0.149 )0.955Image 3329 3.60 1.139 )0.558 )0.310Satisfaction with life
as a whole
6661 3.99 1.034 )0.950 0.469
Valid N (listwise) 2739
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 155
standard errors and test statistics under arbitrary distributions for the
complete data case. The missing data case is slightly more compli-
cated but robust test statistics are also available. These are Yuan and
Bentler’s (2000) T2* and sandwich standard errors (Arminger and
Sobel, 1990). The few studies conducted to date report that these
robust methods perform quite well (Enders, 2001; Gold et al., 2003).
All estimations were carried out with the M-PLUS 2.13 program
(Muthén and Muthén, 2001) using robust maximum likelihood with
missing data.
Several goodness of fit measures are usually considered in SEM
(Bollen and Long, 1993). A likelihood ratio v2 test of the hypothesisthat all model constraints hold in the population is usually performed
first. Usually researchers are not so interested in exactly fitting
models, so that quantitative measures of misfit are preferred to tests
of exact fit. A wealth of such fit measures has been suggested. Among
the most widely used are the Root Mean Squared Error of
Approximation (RMSEA), the Tucker and Lewis Index (TLI), also
known as Non Normed Fit Index, Bentler’s comparative fit index
(CFI) and the Standardized Root Mean Squared Residual (SRMR).
RMSEA, CFI and TLI take the parsimony of the model into ac-
count, so that the releasing of approximately correct constraints does
not necessarily improve the values of these indices. SRMR does not
take parsimony into account, but a modification of it, the so-called
Parsimony Standardized Root Mean Squared Residual (PSRMR, see
Corten et al., 2002) does. It can be computed from the standard
SRMR as:
PSRMR ¼ SRMR
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
count of sample moments
model degrees of freedom
s
where the count of sample moments includes variances, non-dupli-
cated covariances and means (if a mean structure is included in the
model), taking all groups into account.
Values of RMSEA and SRMR below 0.05 and values of TLI
above 0.95 are usually considered acceptable, though the debate
concerning which goodness of fit measures to use and what the
threshold for a good model can be is far from resolved (see Bollen
and Long, 1993 for details). For the PSRMR we recommend a
threshold of 0.07, which is equivalent to a threshold of 0.05 for
GERMÀ COENDERS ET AL.156
SRMR when the model has twice as many sample moments as
parameters.
Testing factor invariance constraints implies comparing nested
models with and without the factor invariance constraints. For
some reason, tests of factor invariance are often carried out by
means of likelihood ratio tests alone, by comparing the v2 statisticof both models and ignoring the change in other goodness of fit
measures (e.g. Byrne et al., 1989; Reise et al., 1993; Steenkamp and
Baumgartner, 1998). Brannick (1995), Kelloway (1995) and Cheung
and Rensvold (2002) warn against this incoherent practice. Cheung
and Rensvold (2002), based on a large-scale simulation study,
showed that for models testing measurement invariance, the CFI
(Bentler, 1990) was especially well suited. In particular, they sug-
gested computing the difference in CFI between two nested models.
According to these authors, if this difference is larger than 0.01 in
favour of the less restricted model, then restrictions should be re-
jected, although the authors recognize that this threshold may be
appropriate for two-group models only.
Finally, it must be taken into account that standard errors and p-
values must be interpreted with caution due to the cluster sample
used (students are nested within classrooms and thus students in the
same classroom fail to be independent). SRMR and PSRMR are
the only of the reported fit measures not to be affected by data
dependence.
MODEL FOR CHILDREN’S AND ADOLESCENTS’ VALUES
One-group Model on the Pooled Data of all Groups
A confirmatory factor analysis imposing the factor structure de-
scribed in the cross-cultural comparability section was first specified
for the pooled data of children and adolescents of all countries. The
fit of the model was very bad according to all usual goodness of fit
measures (v2 ¼ 2423.37, with 51 d.f.; CFI ¼ 0.886; TLI ¼ 0.853;RMSEA ¼ 0.082; SRMR ¼ 0.067; PSRMR ¼ 0.089).
There were many sources of misfit in this model. Knowledge of
the world and knowledge of computers would load significantly and
substantially on materialistic values, and social skills and image on
interpersonal relationships values. Family was involved in three
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 157
significant and large error covariances. After dropping these prob-
lematic variables and adding an error covariance between intelli-
gence and practical skills, the model had an acceptable fit to the
data (v2 ¼ 122.59, with 10 d.f.; CFI ¼ 0.989; TLI ¼ 0.977;RMSEA ¼ 0.040; SRMR ¼ 0.019; PSRMR ¼ 0.036) and is de-picted in Figure 1. The specification included the variances and
means of the three value factors, as required for multiple group
comparisons. The first item in each dimension (intelligence, sensi-
tivity, and money) was used to fix the scale of the value factor by
fixing the loading to 1 and the intercept to 0. The estimates are
displayed in Table IV. Measurement quality estimates in the form
of standardized loadings are of reasonable magnitude and all factor
correlations were lower than one, thus arguing for convergent and
discriminant validity. The fact that intelligence and practical skills
measure the same specific component of capacities seems to make
theoretical sense and seems to be no threat to validity. Anyway,
three items are too few to fit a two-dimensional model for capacities
and knowledge values.
Figure 1. Path diagram of final Confirmatory Factor Analysis (CFA) model forpooled data. Children’s values.
GERMÀ COENDERS ET AL.158
Factor Invariance Tests: Multiple Group Analyses
In order to test for configural invariance, the model was fitted to data
of all countries without parameter constraints across countries. The
same model seemed to fit the data of all countries relatively well
(v2 ¼ 187.01, with 50 d.f.; CFI ¼ 0.987; TLI ¼ 0.972; RMSEA ¼0.044; SRMR ¼ 0.026; PSRMR ¼ 0.049).
When we introduce the strong factor invariance assumptions, that
is, the equality of both intercepts and factor loadings across groups
the fit of the model gets much worse (v2 ¼ 594.01, with 82 d.f.;CFI ¼ 0.950; TLI ¼ 0.936; RMSEA ¼ 0.067; SRMR ¼ 0.048;PSRMR ¼ 0.070).
In order to look for pairs or triplets of countries for which the
assumption would hold we attempted different types of specification
searches:
(1) Starting with the unrestricted model:
(a) We tested the equality of each free loading and intercept
(eight tests in all) by means of t-tests for each pair of
TABLE IV
Estimates of final CFA model for pooled data. Children’s valuesa
Intercept Loading Standardizedloading
Loadings and Intellig 0 1 0.645Intercepts Practic 0.011 0.986 0.638
Professi )0.405 1.138 0.687Sensitiv 0 1 0.756Sympathy 0.622 0.907 0.734
Money 0 1 0.846Power )0.133 1.065 0.904
Mean Variance
Factor Capacity 3.819 0.387Means and Personal 3.718 0.654Variances Material 2.850 1.363
Capacity Personal
Factor Personal 0.662Correlations Material 0.451 0.228
aError variances and covariances are omitted for the sake of simplicity.
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 159
countries by combining their two robust standard
errors.2 The number of significant differences at 5%, at
1% and the total are displayed in Table V. Factor
invariance seems to hold for the following three pairs:
Spain and Brazil, South Africa and Norway, India and
Brazil.
(b) We performed hierarchical cluster analysis of countries
using loading and intercept values as variables and the
Euclidean distance as the dissimilarity measure. Spain,
Brazil and India clustered together both when using
intercepts and loadings and both when using single
linkage and complete linkage cluster analysis. South
Africa and Norway formed a much more heterogeneous
cluster.
(c) We introduced strong factor invariance constraints for
all possible pairs of countries. The models so con-
structed had 58 degrees-of-freedom. The v2 differences (8d.f.) and the CFI differences with respect to the
unconstrained model can thus be computed. Unfortu-
nately, v2 differences are not robust even if computedfrom robust v2 statistics, and thus standard ML v2 dif-ferences are reported in Table VI. The critical values for
a v2 distribution with 8 d.f. are 15.5 at the 5% level and21.7 at the 1% level. Thus statistically speaking, exact
invariance is rejected for all pairs of countries (except
for two if we use a 1% level). However, if we take the
small differences in CFI into account, we could say that
factor invariance approximately holds for the Spain,
TABLE V
t-tests of equality of factor loadings and intercepts for each pair of countries.Number of significant differences at 5%, 1% and total
Spain South Africa Norway India
South Africa 1+0 = 1Norway 1+1 = 2 0+0 = 0
India 1+1 = 2 2+0 = 2 2+3 = 5Brazil 0+0 = 0 1+0 = 1 2+0 = 0 0+0 = 0
GERMÀ COENDERS ET AL.160
Brazil and Norway triplet and for the South Africa and
India pair.
The above results show how different conclusions can be
reached depending on the approach taken. All approaches
coincide only in the finding that factor invariance holds for
Spain and Brazil.
(2) Starting with the restricted model:
(a) we relaxed constraints selectively based on modification
indices.
(b) we relaxed joint constraints of the five countries one by
one.
After all these specification searches, we arrive at a model in
which scale invariance held for Spain and Brazil for all factors,
for Spain, Brazil and India for the abilities factor, and for
Spain, Brazil and Norway for the materialistic and interper-
sonal factors, though Norway required an extra loading of
profession on materialistic values (and thus, even the mildest
requirement, i.e. configural invariance does not hold for this
country as this loading was substantial at 0.33, with a t-value of
7.109). The fit of such a model was more than acceptable
(v2 ¼ 209.90, with 68 d.f.; CFI ¼ 0.986; TLI ¼ 0.978;RMSEA ¼ 0.039; SRMR ¼ 0.025; PSRMR ¼ 0.040).
Thus, both when starting with the unrestricted and restricted models,
we arrive at the same conclusion: scale invariance holds for Brazil
and Spain only.
TABLE VI
Standard ML v2 difference (8 d.f.) and CFI difference when introducing strong factorinvariance constraints for one specific pair of countries. Children’s values
Spain South Africa Norway India
v2 CFI v2 CFI v2 CFI v2 CFI
South
Africa
281.4 )0.017
Norway 52.9 )0.003 93.77 )0.005India 164.0 )0.010 45.94 )0.002 89.57 )0.005Brazil 20.01 )0.001 193.29 )0.012 21.24 )0.001 102.50 )0.006
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 161
MODEL FOR PARENTS’ VALUES
One-group Model on the Pooled Data of all Groups
After some specification searches, the same model as for children was
found to have an acceptable fit to the parents’ sample (v2 ¼ 57.86,with 10 d.f.; CFI ¼ 0.990; TLI ¼ 0.980; RMSEA ¼ 0.038;SRMR ¼ 0.018; PSRMR ¼ 0.034).
Factor Invariance Tests: Multiple Group Analyses
A multiple group model without constraints also yielded a relatively
good fit, at least in terms of CFI and SRMR, thus arguing for con-
figural invariance (v2 ¼ 195.43, with 50 d.f.; CFI ¼ 0.972;TLI ¼ 0.940; RMSEA ¼ 0.065; SRMR ¼ 0.034; PSRMR ¼ 0.064).On the contrary, a model with strong factor invariance constraints
across all groups was clearly rejected (v2 ¼ 629.53, with 82 d.f.;CFI ¼ 0.893; TLI ¼ 0.863; RMSEA ¼ 0.099; SRMR ¼ 0.079;PSRMR ¼ 0.115).
Starting with the unrestricted model and introducing strong factor
invariance constraints for all possible pairs of countries, non-robust
standard ML v2 differences (8 d.f.) and CFI differences with respectto the unconstrained model are presented in Table VII.
According to this table, the only pairs of countries for which
strong factor invariance would more or less hold would be South
Africa and India, South Africa and Norway and India and Brazil.
TABLE VII
Standard ML v2 difference (8 d.f.) and CFI difference when introducing strong factorinvariance constraints for one specific pair of countries. Parents’ values
Spain South Africa Norway India
v2 CFI v2 CFI v2 CFI v2 CFI
SouthAfrica
164.74 )0.021
Norway 183.57 )0.023 39.50 )0.004India 128.33 )0.016 27.16 )0.002 116.11 )0.014Brazil 63.17 )0.008 72.04 )0.008 54.92 )0.006 41.24 )0.004
GERMÀ COENDERS ET AL.162
Starting with the restricted model and relaxing constraints selec-
tively based on modification indices we arrive at a model in which the
strong factor invariance constraints hold for South Africa, Brazil,
India and Spain, except for the loading and intercept of profession.
Thus, we found a case of partial invariance for the capacities and
knowledge values. The fit of the model is on the borderline of being
acceptable (v2 ¼ 282.65, with 68 d.f.; CFI ¼ 0.958; TLI ¼ 0.935;RMSEA ¼ 0.068; SRMR ¼ 0.046; PSRMR ¼ 0.074).
If we combine the results of parents’ and children’s values, the
only comparable pair is Brazil and Spain, while only partial invari-
ance holds for the parents’ capacities and knowledge values, which is
enough for comparisons to be made (Byrne et al., 1989).
COMPLETE MEASUREMENT MODEL WITH BRAZIL AND
SPAIN, FOR PARENTS’ AND CHILDREN’S VALUES.
INVARIANCE CONSTRAINTS
A factor analysis model with all six dimensions (both parents’ and
children’s value dimensions) was fitted only on the samples of
Brazil and Spain with (v2 ¼ 375.44, with 134 d.f.; CFI ¼ 0.976;TLI ¼ 0.967; RMSEA ¼ 0.030; SRMR ¼ 0.035; PSRMR ¼ 0.049)and without (v2 ¼ 325.97, with 120 d.f.; CFI ¼ 0.979; TLI ¼ 0.969;RMSEA ¼ 0.029; SRMR ¼ 0.029; PSRMR ¼ 0.038) strong factorinvariance constraints. For what now is a two-group model, the
difference in the CFI was small enough at )0.003 to make thefactor invariance assumption tenable according to the guidelines
given by Cheung and Rensvold (2002). Only partial invariance was
imposed on the capacities value for parents. The estimates are
shown in Table VIII and the path diagram in Figure 2. Note the
equality of certain loadings and intercepts (not when standardized,
though).
It can be seen that for both countries the highest factor cor-
relations are (1) between interpersonal and capacities values (for
both parents and children), (2) between materialistic and capaci-
ties values (for both parents and children), (3) between interper-
sonal and materialistic values (for both parents and children) and
(4) between each children’s value and the same value of their
parents.
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 163
A model with strong factor invariance constraints provides factor
mean estimates and can be used to test their equality across countries.
A model assuming all six factors means to be equal across countries
yields a rather bad fit, at least in terms of TLI and SRMR
(v2 ¼ 605.26, with 140 d.f.; CFI ¼ 0.954; TLI ¼ 0.940; RMSEA ¼0.041; SRMR ¼ 0.083; PSRMR ¼ 0.108).
In order to attribute this misfit to each particular factor, we per-
formed Lagrange multiplier tests (sometimes called modification
indices) for the equality of each of the means. These test statistics are
distributed as a v2 with 1 d.f., with critical values 3.84 at 5% and 6.63at 1%. They were in increasing order of size 0.15 for parents inter-
personal relationship values, 3.07 for parents’ materialistic values,
7.74 for parents’ capacity values, 16.74 for children’s materialistic
values, 21.87 for children’s interpersonal relationship values and
114.58 for children’s capacity values. Lagrange multiplier tests are
computed under standard ML theory and are thus not robust to non-
normality. However, the very large values of 4 of these statistics are
definitely significant.
Thus, the equal mean assumption was only supported for the
parents’ materialistic values and the parents’ interpersonal relation-
ship values. On the remaining values, Brazilian parents and children
scored higher than their Spanish counterparts, though the mean
difference for children regarding interpersonal relationship values was
small (3.862-3.737 ¼ 0.125 on a 5-point scale).
STRUCTURAL MODEL WITH BRAZIL AND SPAIN,
INCLUDING OVERALL LIFE SATISFACTION OF
CHILDREN. INVARIANCE CONSTRAINTS
The model was extended to include overall life satisfaction. In this
model, each child’s value was regressed on all parents’ values, and
overall life satisfaction was regressed on all values, both parents’ and
children’s. The disturbances of children’s values were allowed to
correlate as we could not assume parents’ values to explain all sys-
tematic variance in children’s values (see Figure 3). This model has a
good fit to the data (v2 ¼ 406.96, with 150 d.f.; CFI ¼ 0.975;TLI ¼ 0.965; RMSEA ¼ 0.029; SRMR ¼ 0.034; PSRMR ¼ 0.045)but contains many insignificant regression coefficients among factors.
GERMÀ COENDERS ET AL.164
TABLE VIII
Estimates of final CFA model for Spain and Brazila. Parents’ and children’s values.Partial invariance constraints. Parent’s values are preceded by ‘‘p_’’
GroupSpain
Intercept Loading Standar-dized
loading
Loadings
and in-tercepts
Intellig 0 1 0.637
Practic )0.117 1.013 0.641Professi )0.368 1.140 0.670Sensitiv 0 1 0.675
Sympathy 0.701 0.926 0.739Money 0 1 0.901Power 0.088 0.960 0.874p_intell 0 1 0.433
p_practi 0.005 0.934 0.407p_profes )1.508 1.412 0.586p_sensit 0 1 0.706
p_sympat )0.055 1.002 0.743p_money 0 1 0.885p_power )0.236 1.076 0.929
Mean Variance
Factormeans
andvariances
Capacity 3.742 0.346Personal 3.737 0.486
Material 2.681 1.412p_capaci 4.196 0.097p_person 4.363 0.270
p_materi 2.531 0.984
Capacity Personal Material p_capaci p_person
Factor
correla-tions
Personal 0.775
Material 0.351 0.184p_capaci 0.230 0.135 0.134p_person 0.117 0.174 0.011 0.868p_materi 0.067 0.007 0.224 0.389 0.167
GroupBrazil
Intercept Loading Standar-dized
loading
Loadingsand inter-cepts
Intellig 0 1 0.572Practic )0.117 1.013 0.560Professi )0.368 1.140 0.645Sensitiv 0 1 0.702Sympathy 0.701 0.926 0.717Money 0 1 0.868
Power 0.088 0.960 0.844
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 165
After a short specification search, we arrived at a model in
which each value dimension of the children depends only on the
same value dimension of their parents, and only children’s capac-
ities/knowledge values affect children’s overall life satisfaction, thus
dropping 11 insignificant regression coefficients out of the 15 initial
ones. Interestingly, the same significant effects were encountered in
Brazil and in Spain. The fit of the model is at least equal to or even
better than that of the unrestricted model with all possible
regression coefficients, except with respect to SRMR, which does
not take parsimony into account (v2 ¼ 439.33, with 172 d.f.;CFI ¼ 0.975; TLI ¼ 0.969; RMSEA ¼ 0.028; SRMR ¼ 0.036;PSRMR ¼ 0.045).
TABLE VIII
Continued
GroupBrazil
Intercept Loading Standar-dizedloading
p_intell 0 1 0.595p_practi 0.005 0.934 0.448
p_profes )1.141 1.252 0.699p_sensit 0 1 0.621p_sympat )0.055 1.002 0.746p_money 0 1 0.824p_power )0.236 1.076 0.916
Mean Variance
Factormeansand
variances
Capacity 4.122 0.255Personal 3.802 0.571Material 3.198 1.511
p_capaci 4.449 0.164p_person 4.421 0.299p_materi 2.927 1.244
Capacity Personal Material p_capaci p_person
Factor
correla-tions
Personal 0.714
Material 0.588 0.376p_capaci 0.389 0.298 0.144p_person 0.134 0.292 )0.023 0.683p_materi 0.120 )0.002 0.259 0.452 0.239
aError variances and covariances are omitted for the sake of simplicity.
GERMÀ COENDERS ET AL.166
The path diagram is displayed in Figure 4 and the estimates in
Table IX. Only estimates of regression equation parameters are dis-
played. All effects are statistically significant but R2 values are not
high, and even less so for the Spanish sample and for the overall
satisfaction variable.
The fact that factor invariance holds and the model specification is
the same in both groups makes it possible to test the equality of
regression slopes across groups. A model with equal regression slopes
fits even better than the model with unequal regression slopes, which
leads to maintaining the equal slopes constraints (v2 ¼ 429.44, 176d.f.; CFI ¼ 0.975; TLI ¼ 0.971; RMSEA ¼ 0.027; SRMR ¼ 0.037;PSRMR ¼ 0.046).3 We also performed standard ML Lagrangemultiplier tests for the equality of each of the regression slopes
Figure 2. Path diagram of final CFA model for Brazil and Spain. Parents’ andChildren’s values.
Figure 3. Path diagram of the first specification of the structural part of the model
for Brazil and Spain.
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 167
(distributed as a v2 with 1 d.f., with critical values 3.84 at 5% and 6.63at 1%) and none was individually rejected.
The appendix shows the same results of Table IX obtained by
means of regression models for all five countries. As stated there,
these results are to be interpreted with the greatest caution. For this
reason, this interpretation is not done in the main text but in the
appendix itself.
DISCUSSION
Our research aim was to cross-culturally compare the influence of
parents’ desired values for their own children and children’s salient
Figure 4. Path diagram of the final specification of the structural part of the modelfor Brazil and Spain.
TABLE IX
Estimates and standard errors (in parenthesis) of the final specification of thestructural part of the model for Spain and Brazil
Group
SpainCapacity= 2.289(0.304) +0.346 · p_capaci (0.072) R2 = 0.035Personal= 2.673 (0.212) +0.244 · p_person (0.048) R2 = 0.034Material= 1.199 (0.085) +0.270 · p_materi (0.032) R2 = 0.051Oversat= 2.790 (0.165) +0.330 · capacity (0.043) R2 = 0.033
BrazilCapacity= 2.267 (0.582) +0.417 · p_capaci (0.130) R2 = 0.113Personal= 1.678 (0.604) +0.481 · p_person (0.135) R2 = 0.122Material= 2.304 (0.240) +0.306 · p_materi (0.082) R2 = 0.077Oversat= 3.145 (0.361) +0.267 · capacity (0.087) R2 = 0.018
GERMÀ COENDERS ET AL.168
values for future on SWB. To develop such a comparison we needed
evidence that our samples could be compared in a meaningful way
across cultures, in other words, that we could assume we were mea-
suring exactly the same phenomena. In order to obtain such evidence
we checked factor invariance of the results in each country for both
subgroups of subjects (children and parents). We have checked
configural invariance, metric invariance and strong factor invariance
using the ML procedure to deal with missing data. For that purpose,
CFI, TLI, RMSEA, SRMR and a newly developed parsimony
adjusted version of SRMR have been systematically explored and
preferred to the v2 test. This procedure led to the conclusion thatcomparability of results among Brazilian and Spanish data can be
assumed for both parents and children. This did not hold for any
other pair of countries, which cannot thus be compared in a formal
statistical manner.
An interpretation of the comparability of results from these
countries might be that the languages belong to the same family of
languages. However, it is difficult to state that Catalan or Spanish are
more similar to Brazilian-Portuguese than Norwegian is to English.
And in any case, the Indian sample was collected from a bilingual
middle-class population speaking fluent English, with exactly the
same wording as in South Africa. Therefore, the best explanation
seems to be the cultural one: the Spanish and Brazilian cultures have
deep common roots in the Latin culture.
Because there is a rationale for the comparability of results be-
tween these two countries, we then developed our structural model to
include overall life satisfaction of children. That model shows us that
in both cultures:
(1) Salient values for future of parents and of children can be
grouped into three factors: materialistic values, capacities and
knowledge related values, and interpersonal relationships
values.
(2) Both for parents and for children interpersonal relationships
values are very strongly correlated to capacities and knowledge
related values.
(3) Both for parents and for children, capacity and knowledge
values on the one hand and material values on the other are
highly correlated.
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 169
(4) Both for parents and for children personal relationships values
and materialistic values are correlated but not strongly.
(5) Each value factor of parents is correlated with the same value
factor of their children but not strongly.
When taking into account only the significant regression coefficients
among factors we reached a model in which each value dimension of
children depends on the same value dimension of parents, thus
showing the interactional socialization process in which values go
from parents to children in both cultures. The fact that the regression
coefficients did not significantly differ between Brazil and Spain, may
suggest a common trait of Latin cultures, in which strong family links
are characteristic.
In the model only adolescents’ capacities and knowledge salient
values for future significantly affect their own overall life satisfaction
in both countries. Such results suggest a key role that salient values
for future related to adolescents’ capacities and knowledge play in
their own life satisfaction in the two studied cultures, at an age at
which attending school is their main occupation.
However, the high correlation of capacities and knowledge related
values with the other considered values makes collinearity a plausible
explanation for the lack of significant effects on overall life satisfac-
tion. In fact, the estimated correlations between children’s salient
values for future and life satisfaction were for capacities and
knowledge 0.175 in Spain and 0.150 in Brazil; for interpersonal
relationships 0.173 in Spain and 0.109 in Brazil; and for materialistic
values 0.025 in Spain and 0.044 in Brazil. Thus, interpersonal rela-
tionships values are almost as highly correlated with satisfaction as
capacities and knowledge values. This suggests that capacities and
knowledge values could constitute a mediating factor in the effect of
interpersonal relationships values on satisfaction.
As a consequence of our results several additional ideas can be
discussed, some of which are related to limitations of our present
research:
(1) Findings of traditional research which usually did not identify
strong relationships between parents’ and children’s values are
also confirmed when using methods that ensure comparability
of results while correcting measurement error and missing
GERMÀ COENDERS ET AL.170
data bias. Traditional regression models (see appendix) offer
us a weak relationship between materialistic values of the two
generations in all countries, and even weaker relationships for
other value factors, which depend to some extent on the cul-
tural context. Non-linear relationships may also exist among
the studied factors and such possibility should be explored in
the future.
(2) We selected a list of values having in mind adolescents’ inter-
ests. In the future it would be of interest to use longer lists of
salient values for future, including those used in other inter-
national surveys, in order to explore their factor structure.
Although such lists have already been explored among adults,
perhaps they may offer additional information when applied to
adolescents.
(3) Results using a Likert like scale for each value have provided us
with more information than previous research in which
respondents were just asked to select the 3 or 5 values consid-
ered more salient. Our procedure does not seem to have in-
creased the number of missing answers. In future research still
less crude scales could be used, as for example 10-point rating
scales.
(4) We are convinced that placing the desired values in a concrete
future moment of the adolescent’s life has improved compara-
bility between generations. However, we still think that other
alternative formulations of the question should be explored.
APPENDIX. RESULTS OF REGRESSION MODELS IN ALL
COUNTRIES
The same models of Table IX were estimated as linear regression
models for all five countries. Scales were constructed by averaging
the items of each value dimension, while the overall satisfaction
item was used as is. The only difference with respect to a plain
regression model is that robust maximum likelihood with missing
data was used instead of ordinary least squares.
Results are shown in Table A.1 and must be interpreted with ex-
treme caution for two reasons:
PARENTS AND CHILDREN VALUES AND CHILDREN SWB 171
(1) The scales are measured with error. Thus, relationships are
attenuated and standard errors are biased. In particular, it can be
seen that for Brazil and Spain, R2 are in general lower than those
in Table 9.
(2) The results in the paper have shown that scales may not
measure the same construct in all countries, except for the
Brazil/Spain pair. Thus, equations of different countries may be
regressing different things and are, strictly speaking, not com-
parable.
TABLE A.1
Estimates and standard errors (in parenthesis) of regression models for all Fivecountriesa
Group
SpainCapacity= 2.888 (0.177) +0.212 · p_capaci (0.042) R2 = 0.023Personal= 3.238 (0.155) +0.165 · p_person (0.036) R2 = 0.016Material= 2.115 (0.081) +0.223 · p_materi (0.031) R2 = 0.038Oversat= 3.259 (0.110) +0.203 · capacity (0.028) R2 = 0.021
BrazilCapacity= 3.270 (0.454) +0.205 · p_capaci (0.104) R2 = 0.029Personal= 2.865 (0.430) +0.260 · p_person (0.097) R2 = 0.039Material= 2.393 (0.237) +0.272 · p_materi (0.081) R2 = 0.067Oversat= 3.558 (0.238) +0.165 · capacity (0.056) R2 = 0.013
South AfricaCapacity= 3.831 (0.218) +0.069 · p_capaci (0.050) R2 = 0.004Personal= 3.325 (0.263) +0.135 · p_person (0.059) R2 = 0.011Material= 2.125 (0.156) +0.309· p_materi (0.046) R2 = 0.099Oversat= 3.250 (0.207) +0.145 · capacity (0.050) R2 = 0.010
NorwayCapacity= 2.967 (0.268) +0.127 · p_capaci (0.076) R2 = 0.011Personal= 3.222 (0.331) +0.093 · p_person (0.080) R2 = 0.005Material= 1.989 (0.163) +0.251 · p_materi (0.076) R2 = 0.033Oversat= 3.769 (0.147) +0.050 · capacity (0.042) R2 = 0.002
IndiaCapacity= 3.169 (0.187) +0.171 · p_capaci (0.043) R2 = 0.025Personal= 2.885 (0.183) +0.183 · p_person (0.045) R2 = 0.029Material= 2.485 (0.170) +0.230 · p_materi (0.046) R2 = 0.041Oversat= 3.397 (0.160) +0.105 · capacity (0.040) R2 = 0.007
aNon-significant relations (a=0.05) are bold-faced.
GERMÀ COENDERS ET AL.172
In all countries, material values seem to be the most transmitted from
parents to children. Brazil is the country in which values in general
are most transmitted from parents to children. Next India and Spain
would come. South Africa (with the exception of material values) and
Norway are the countries in which children’s values are most unre-
lated to their parents’.
Predictive power of the capacity values on global satisfaction is
generally low, but even more so in India and Norway.
NOTES
1 Acknowledgements are due to the country project directors and their associates
Per Egil Mjaavatn (Norwegian University of Science and Technology, Trondheim,Norway), Usha Nayar (Tata Institute of Social Sciences, India), Irene Rizzini(Pontifı́cia Universidade Católica do Rio de Janeiro, Brazil), Rose September
(Western Cape University, South Africa) and Ferran Casas (Catalan Network ofChild Researchers – XCIII – in co-operation with the University of Girona, Spain)for permitting us to use part of their project and to Childwatch International, Oslo,
for sponsorship.2 As the samples are independent and the model does not contain constraints acrosscountries, estimates of different countries are independent. Thus, the standard error
of the difference of the values of a parameter in two countries can be computed as thesquare root of the sum of both squared standard errors.3 Unlike the case is with a standard ML v2 statistic, a slight decrease of a robust v2
statistic can occur when imposing constraints. In any case, differences in robust v2
statistics are not interpretable.
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Quality of Life Research Institute
University of Girona
Girona
Spain
Faculty Building of Economics and Business Germà Coenders
Campus Montilivi
17071 Girona
Spain
E-mail: [email protected]
Fax: +34972418032
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