RELATIONSHIPS BETWEEN PARENTS’ AND CHILDREN’S ...independence, joie de vivre, and rationality are more appreciated by youngsters than by adults – while responsibility, tolerance

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


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


(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


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


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-


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


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


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),


(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


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


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.


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


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


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


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.


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.


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


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


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


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


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


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.


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.


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


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.


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.


(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


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.


(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


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:


(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.


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.

REFERENCES

Aburckle, J. L.: 1996, ‘Full information estimation in the presence of incompletedata’, in G. A. Marcoulides and R. E. Schumacker (eds.), Advanced Structural

Equation Modeling (Erlbaum, Mahwah, NJ), pp. 243–277.Aburckle, J. L. and W. Wothke: 1999, Amos (Version 4.0) (Smallwaters, Chicago,IL).

Arminger, G. and M. E. Sobel: 1990, ‘Pseudo maximum likelihood estimation ofmean and covariance structures with missing data’, Journal of the AmericanStatistical Association 85, pp. 195–203.

Batista-Foguet, J. M. and G. Coenders: 2000, Modelos de Ecuaciones Estructurales(La Muralla, Madrid).

Bentler, P. M.: 1990, ‘Comparative fit indexes in structural modeling’, PsychologicalBulletin 107, pp. 238–246.

Bentler, P. M.: 2000, EQS 6.0 (Multivariate Software, Encino, CA).


Bollen, K. A.: 1989, Structural Equations with Latent Variables (Wiley, New York).

Bollen, K. A. and J. S. Long: 1993, Testing Structural Equation Models (Sage,Newbury Park, CA).

Brannick, M. T.: 1995, ‘Critical comments on applying covariance structure mod-

eling’, Journal of Organizational Behaviour 16, pp. 201–213.Byrne, B. M., R. J. Shavelson and B. Muthén: 1989, ‘Testing for the equivalence offactor covariance and mean structures: The issue of partial measurement invari-

ance’, Psychological Bulletin 105, pp. 456–466.Campbell, A., P. E. Converse and W. L. Rodgers: 1976, The Quality of AmericanLife: Perceptions, Evaluations and Satisfactions (Russell Sage Foundation, NewYork).

Casas, F., C. Figuer, M. González and G. Coenders: 2003, ‘Satisfacion with lifedomains and salient values for future among children and their parents in 5different countries’, paper presented at the 5th Conference of the International

Society for Quality-of-Life Studies, Johann Wolfgang Goethe-University,Frankfurt am Main, 20–24 july 2003.

Casas, F., M. González, C. Figuer and G. Coenders: 2004, ‘Subjective well-being,

values and goal achievement: The case of planned versus by chance searches on theinternet’, Social Indicators Research 66, pp. 123–142.

Casas, F., M. Rosich and C. Alsinet: 2001, ‘El bienestar psicológico de los prea-

dolescentes’, Anuario de Psicologı́a 31, pp. 73–86.Cheung, G. W. and R. B. Rensvold: 2002, ‘Evaluating goodness-of-fit indexes fortesting measurement invariance’, Structural Equation Modeling 9, pp. 233–255.

Coenders, G. and W. E. Saris: 1995, ‘Categorization and measurement quality. The

choice between pearson and polychoric correlations’, in W.E. Saris and Á.Münnich (eds.), The Multitrait-Multimethod Approach to Evaluate MeasurementInstruments (Eötvös University Press, Budapest ), pp. 125–144.

Coenders, G., A. Satorra and W. E. Saris: 1997, ‘Alternative approaches to struc-tural modeling of ordinal data, a Monte Carlo study’, Structural EquationModeling 4, pp. 261–282.

Collins, L. M., J. L. Schafer and C. M. Kam: 2001, ‘A comparison of inclusive andrestrictive strategies in modern missing data procedures’, Psychological Methods6, pp. 330–351.

Commission of the European Communities: 1990, Public opinion in the European

Community. Eurobarometer, 34. August. Brussels.Commission of the European Communities: 1993, Les européens et la famille. Eu-robarometer, 39. December. Brussels.

Connell, R. W.: 1972, ‘Political socialization in the American family: The evidencereexamined’, Public Opinion Quarterly 36, pp. 321–333.

Corten, I. W., W. E. Saris, G. Coenders, W. van der Veld, C. E. Aalberts and C.

Kornelis: 2002, ‘Fit of different models for multitrait-multimethod experiments’,Structural Equation Modeling 9, pp. 213–232.

Csikzentmihalyi, M.: 1997, Finding Flow: The Psychology of Engagement with

everyday Life’ (New York, Basic Books).Diener, E. and F. Fujita: 1995, ‘Resources, personal strivings, and subjective well-being: A nomothetic and idiographic approach’. Journal of Personality and SocialPsychology 68, pp. 926–935.

Diener, E., E. Suh and S. Oishi: 1998, ‘Recent studies on subjective well-being’,Indian Journal of Clinical Psychology 24, pp. 25–41.


Du Toit, M. and S. du Toit: 2001, Interactive LISREL, User’s Guide (Scientific

Software International, Chicago, IL).Enders, C. K.: 2001, ‘The impact of non-normality on full information maximumlikelihood estimation for structural equation models with missing data’, Psycho-

logical Methods 6, pp. 352–370.Enders, C. K. and D. L. Bandalos: 2001, ‘The relative performance of full infor-mation maximum likelihood estimation for missing data in structural equation

models’, Structural Equation Modeling 8, pp. 430–457.Finkbeiner, C.: 1979, ‘Estimation for the multiple factor model when data aremissing’, Psychometrika 44, pp. 409–420.

Gold,M.S., P.M.Bentler andK.H.Kim: 2003, ‘A comparisonofmaximum likelihood

and asymptotically distribution free methods of treating incomplete non-normaldata’, Structural Equation Modeling 10, pp. 47–79.

Graham, J. W.: 2003, ‘Adding missing-data-relevant variables to FIML-based

structural equation models’, Structural Equation Modeling 10, pp. 80–100.Graham, J. W., S. M. Hofer and D. P. Mackinnon: 1996, ‘Maximizing the usefulnessof data obtained with planned missing data patterns, an application of maximum

likelihood procedures’, Multivariate Behavioral Research 31, pp. 197–218.Graham, J. W., S. M. Hofer and A. M. Piccinin: 1994, ‘Analysis with missing data indrug prevention research, in L. M. Collins and L. Seitz (eds.), Advances in Data

Analysis for Prevention Intervention Research (National Institute on Drug Abuse,Washington, DC), pp. 13–63.

Hess, R. D. and J. V. Torney: 1965, The development of basic attitudes and valuestoward government and citizenships during the elementary school years. Coop-

erative research project, No. 1078. U.S. Office of Education.Huebner, E. S.: 1994, ‘Preliminary development and validation of a multidimen-sional life satisfaction scale for children’, Psychological Assessment 6, pp. 149–158.

Inglehart, R.: 1997, Modernization and Postmodernization. Cultural, Economic andPolitical Change in 43 Societies (Princeton University Press, Princeton).

Jöreskog, K. G.: 1973, ‘A general method for estimating a linear structural equation

system’, in A. S. Goldberger and O. D. Duncan (eds.), Structural Equation Modelsin the Social Sciences (Academic Press, New York), pp. 85–112.

Jöreskog, K. G., D. Sörbom, S. Du Toit and M. Du Toit: 2000, LISREL 8, NewStatistical Features (Scientific Software International, Chicago, IL).

Kasser, T. and A. Ahuvia: 2002, ‘Materialistic values and well-being in businessstudents’, European Journal of Social Psychology 32, pp. 137–146.

Kasser, T. and R. M. Ryan: 1996, ‘Further examining the American dream: Dif-

ferential correlates of intrinsic and extrinsic goals’, Personality and Social Psy-chology Bulletin 22, pp. 280–287.

Kelloway, E. K.: 1995, ‘Structural equation modeling in perspective’. Journal of

Organizational Behaviour 16, pp. 215–224.Lee, S. Y.: 1986, ‘Estimation for structural equation models with missing data’,Psychometrika 51, pp. 93–99.

Little, T. D.: 1997, ‘Mean and covariance structure (MACS) analyses of cross-cul-tural data: Practical and theoretical issues’, Multivariate Behavioral Research 32,pp. 53–76.

Little, R. J. A. and D. B. Rubin: 1987, Statistical Analysis with Missing Data (Wiley,

New York).


Meredith, W.: 1993, ‘Measurement invariance, factor analysis and factorial invari-

ance’, Psychometrika 58, pp. 525–543.Miller, E. B. and E. S. Glass: 1989, ‘Parent-child attitude similarity across the lifecourse’, Journal of Marriage and the Family 51, pp. 991–997.

Musitu, G., S. Buelga, M. J. Cava and M. S. Lila: 2001, Familia y Adolescencia(Sı́ntesis, Madrid).

Muthén, B., D. Kaplan and M. Hollis: 1987, ‘On structural equation modeling with

data that are not missing completely at random’, Psychometrika 52, pp. 431–462.Muthén, L. K. and B. Muthén: 2001, Mplus User’s Guide (Muthén and Muthén, LosAngeles, CA).

Neale, M. C., S. M. Boker, G. Xie and H. H. Maes: 1999, Mx, Statistical Modeling.

5th Edition (Box 126, MCV, Richmond, VA23298, Department of Psychiatry).Orizo, F. A.: 1996, Sistemas de Valores en la España de los 90 (Siglo XXI, Madrid).Park, N., E. S. Huebner, J. E. Laughlin, R. F. Valois and R. Gilman: 2004, ‘A

cross-cultural comparison of the dimensionality of children and adolescents lifesatisfaction reports’, Social Indicators Research 66, pp. 61–79.

Pinillos, J. L.: 1982, ‘El cambio de los sistemas de valores en las sociedades desar-

rolladas y en las sociedades en desarrollo’, Reunión Internacional sobre Psicologı́ade los Valores, Policopied.

Raykov, T. and G. A. Marcoulides: 2000, A First Course in Structural Equation

Modeling (Lawrence Erlbaum, Mahwah, NJ).Reise, S. P., K. F. Widaman and R. H. Pugh: 1993, ‘Confirmatory factor analysisand item response theory: Two approaches for exploring measurement invariance’,Psychological Bulletin 114, pp. 552–566.

Riordan, C. M. and R. J. Vandenberg: 1994, ‘A central question in cross-culturalresearch: do employees of different cultures interpret work-related measures in anequivalent manner?’, Journal of Management 20, pp. 643–671.

Rokeach, M.: 1973, The Nature of Human Values (Free Press, New York).Rubin, D. B.: 1987, Multiple Imputation for Nonresponse in Surveys (Wiley, NewYork).

Satorra, A.: 1992, ‘Asymptotic robust inferences in the analysis of mean andcovariance structures’, in P.V. Marsden (ed.), Sociological Methodology 1992(Basil Blackwell, Cambridge), pp. 249–278.

Satorra, A.: 1993, ‘Asymptotic robust inferences in multi-sample analysis of aug-

mented-moment matrices’, in C. R. Rao and C. M. Cuadras (eds.), MultivariateAnalysis, Future Directions 2 (North-Holland, Amsterdam), pp. 211–229.

Satorra, A. and P. M. Bentler: 1994, ‘Corrections to test statistics and standard

errors in covariance structure analysis’, in A. Von Eye and C. Clogg (eds.), LatentVariables Analysis, Applications to Developmental Research (Sage, ThousandOaks, CA), pp. 399–419.

Steenkamp, J. E. M. and H. Baumgartner: 1998, ‘Assessing measurement invariancein cross-national consumer research’, Journal of Consumer Research 25, pp. 78–90.

Struch, N., S. L. Schwartz and W. A. van der Kloot: 2002, ‘Meanings of basic valuesfor women and men: A cross-cultural Analysis’, Personality and Social PsychologyBulletin 28, pp. 16–28.

Thomas, L. E. and J. Stankiewicz: 1974, ‘Family correlates of parent/child attitude

congruence: Is it time to throw in the towel?’, Psychological Reports 34, pp. 10–38.


Whitbeck, S. B. and V. Gecas: 1988, ‘Value attributions and value transmission

between parents and children’, Journal of Marriage and the Family 50, pp. 829–840.

Wothke, W.: 2000, ‘Longitudinal and multi-group modeling with missing data’, in

T.D. Little, K.U. Schnabel and J. Baumert (eds.), Modeling Longitudinal andMultilevel Data, Practical Issues, Applied Approaches and Specific Examples(Lawrence Erlbaum, Mahwah, NJ), pp. 219-240.

Yuan, K. -H. and P. M. Bentler: 2000, ‘Three likelihood-based methods for meanand covariance structure analysis with nonnormal missing data’, in M. E. Sobeland M. P. Becker (eds.), Sociological Methodology 2000 (American SociologicalAssociation, Washington, DC), pp. 165–200.

Quality of Life Research Institute

University of Girona

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Spain

Faculty Building of Economics and Business Germà Coenders

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