relationships between parents' and children's salient values for ... - UdG

Social Indicators Research (2005) 73: 141–177 DOI 10.1007/s11205-004-3233-0

Springer 2005

GERMA` COENDERS, FERRAN CASAS, CRISTINA FIGUER and MO`NICA GONZA´LEZ

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 of children to children’s values and (b) children’s values to parents’ values. Using confirmatory factor analysis models three dimensions of values (materialistic values, capacities and knowledge values and interpersonal relationship values) consistently emerged in 5 countries (Brazil, South Africa, Norway, Spain and India) for both parents and children. There was a considerable amount of missing data, mainly because the parent’s questionnaire was often not returned. Full information maximum 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. This implies testing the equality of factor loadings and intercepts across groups. This equality is required to ensure that factors have the same interpretation in all groups, which is necessary when comparing any aspect of the factor distribution across groups. 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 models revealed that both parents and children scored higher on most values in Brazil than in Spain. 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 and slightly higher in Brazil. The only value dimension that had some effect on overall life satisfaction was capacities and knowledge, which was so in both countries. KEY WORDS: aspirations, child subjective well being, factor invariance, missing data, 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

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also aspirations of people. Aspirations are a complex concept and social sciences have not yet debated theoretical models and procedures 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 aspirates 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 future. 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

Good manners and politeness Ability to communicate with others Independence Conscientiousness at work A sense of responsibility Imagination

Good manners

Feeling of responsibility Imagination Tolerance and respect for other people

Hard work

Independence

Here is a list of qualities which parents can try to encourage in their children. Which do you consider to be especially important? Please, choose three. Eurobarometer, 34 (Commission of the European Communities, 1990)

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. World Values Survey, 1990, 1995 (Inglehart, 1997)

A sense of responsibility Imagination and creativity Tolerance and respect for others

Hard work

Self-reliance

Good manners

Here is a list of qualities which parents can try to encourage in their children. Which do you consider to be especially important? Please, choose three. Eurobarometer, 39 (Commission of the European Communities, 1993)

His/her way of getting along with people His/her knowledge of computers His/her profession His/her family

His/her practical skills

His/her intelligence

Imagine your son/daughter is 21 years old. At that time, what values would you like him/her to be appreciated for? (Casas et al., 2003)

Lists of desired values and qualities that parents expect from their children

TABLE I PARENTS AND CHILDREN VALUES AND CHILDREN SWB

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Thrift, saving money and things Determination, perseverance Religious faith Unselfishness Obedience

Tolerance and respect for others Thrift, not wasting money and other things Religious faith Obedience Loyalty

Courage A taste of life’s pleasures An appreciation of beauty

Love of life

His/her sympathy

Determination and perseverance Religious faith Generosity Obedience

His/her money His/her power His/her knowledge about the world His/her appearance (his/her image; the way he/she looks to others)

His/her sensitivity

A sense of thrift

Continued

TABLE I

144 GERMA` COENDERS ET AL.

PARENTS AND CHILDREN VALUES AND CHILDREN SWB

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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 formulations 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 satisfaction and their satisfaction with specific domains of life. However, 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). Although 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 development 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, correlations found have often been very modest and lower than expected

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(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 attitudes and values between parents and children are very much influenced 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. However, 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 correlate 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 everyday life contexts experienced by the two generations may influence value structures, it is not unexpected that present values differ between 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


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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 (Diener 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 (interpersonal 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 satisfaction 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 Jo¨reskog (1973) (see Bollen, 1989; Raykov and Marcoulides, 2000; or Batista-Foguet and Coenders, 2000 as introductory manuals)]. First, the comparability of factor structures across

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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 situations may be very different across countries and therefore noncomparable. 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 research, 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-


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operation and were informed that data would be treated confidentially 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 approximately 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

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TABLE II Sample sizes and response rate for parents Children Count

Parents Percent

Count

Percent

Resp. rate

Spain South Africa Norway India Brazil

3118 997

44.7 14.3

1626 565

45.6 15.8

52.1 56.7

893 1115 860

12.8 16.0 12.3

347 763 263

9.7 21.4 7.4

38.9 68.4 30.6

Total

6983

100.0

3564

100.0

51.0

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 email 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’ questionnaire. 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 adolescents’ questionnaire, measured through a five-point Likert scale, 1 meaning ‘‘very dissatisfied’’ and 5 ‘‘very satisfied’’. The concrete


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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 concerned 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 different 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 distributed 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 developing 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 following dimensions could be established (variable names in brackets): (1) Indicators of capacities and knowledge values (capacity): (a) intelligence (intellig),

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(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 confirmatory 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 cultures being so different that the sheer meaning of constructs is different, 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


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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 requires 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 particular, for all materialistic value variables, children scored higher for parents who did not return the questionnaire. One interpretations of this result is as follows: parents highly appreciating 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

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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 individual 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 available 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 (Jo¨reskog et al., 2000; du Toit and du Toit, 2001) and MPLUS 2.1 (Mutheń and Mutheń, 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 nonignorable 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 (Mutheń 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.


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TABLE III Descriptive statistics and valid cases

Intelligence Practical skills Computer knowledge Professional status Knowledge of the world Family Sensitivity Sympathy Social skills Money Power Image Parents Intelligence Practical skills Computer knowledge Professional status Knowledge of world Family Sensitivity Sympathy Social skills Money Power Image Satisfaction with life as a whole Valid N (listwise)

N

Mean

Std. dev.

Skewness Kurtosis

6872 6837 6804 6831 6795 6846 6805 6802 6792 6796 6774 6818

3.82 3.78 3.44 3.94 3.59 3.98 3.72 3.99 3.96 2.85 2.90 3.57

0.964 0.961 1.193 1.030 1.127 1.056 1.069 1.000 0.945 1.380 1.376 1.231

)0.542 )0.511 )0.295 )0.817 )0.435 )0.864 )0.557 )0.874 )0.717 0.135 0.088 )0.495

)0.014 )0.043 )0.739 0.178 )0.485 0.124 )0.273 0.316 0.197 )1.157 )1.155 )0.678

3367 3350 3354 3337 3328 3364 3350 3345 3376 3324 3305 3329 6661

4.20 4.05 3.81 4.23 4.09 4.26 4.24 4.26 4.35 2.83 2.79 3.60 3.99

0.814 0.819 0.982 0.931 0.932 0.882 0.865 0.822 0.790 1.292 1.292 1.139 1.034

)1.007 )0.696 )0.566 )1.248 )0.988 )1.270 )1.156 )1.177 )1.370 0.122 0.149 )0.558 )0.950

1.371 0.644 0.030 1.417 0.845 1.658 1.338 1.743 2.426 )0.923 )0.955 )0.310 0.469

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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 distributed. From the skewness and kurtosis in Table III it can be seen that departures from normality are quite pronounced in our case. Statistical tests that are robust to non-normality are thus required. Satorra (1992, 1993) and Satorra and Bentler (1994) developed robust

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standard errors and test statistics under arbitrary distributions for the complete data case. The missing data case is slightly more complicated 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 (Mutheń and Mutheń, 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 hypothesis that 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 account, 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: sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi count of sample moments PSRMR ¼ SRMR model degrees of freedom where the count of sample moments includes variances, non-duplicated 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


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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 statistic of 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 suggested 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 rejected, 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 pvalues 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 described 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

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significant and large error covariances. After dropping these problematic variables and adding an error covariance between intelligence 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 depicted 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, sensitivity, 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 for pooled data. Children’s values.


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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 ¼ 0.044; 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

Loadings and Intercepts

Factor Means and Variances

Intellig Practic Professi Sensitiv Sympathy Money Power Capacity Personal Material

Intercept

Loading

0 0.011 )0.405 0 0.622 0 )0.133

1 0.986 1.138 1 0.907 1 1.065

Mean

Variance

3.819 3.718 2.850 Capacity

Factor Correlations a

Personal Material

0.662 0.451

Standardized loading 0.645 0.638 0.687 0.756 0.734 0.846 0.904

0.387 0.654 1.363 Personal 0.228

Error variances and covariances are omitted for the sake of simplicity.

160


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 constructed had 58 degrees-of-freedom. The v2 differences (8 d.f.) and the CFI differences with respect to the unconstrained model can thus be computed. Unfortunately, v2 differences are not robust even if computed from robust v2 statistics, and thus standard ML v2 differences are reported in Table VI. The critical values for a v2 distribution with 8 d.f. are 15.5 at the 5% level and 21.7 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 Brazil

1+0 1+1 1+1 0+0

= = = =

1 2 2 0

South Africa

Norway

India

0+0 = 0 2+0 = 2 1+0 = 1

2+3 = 5 2+0 = 0

0+0 = 0


161

TABLE VI 2

Standard ML v difference (8 d.f.) and CFI difference when introducing strong factor invariance constraints for one specific pair of countries. Children’s values Spain 2

South Africa Norway India Brazil

South Africa

v

CFI

281.4

)0.017

52.9 164.0 20.01

)0.003 )0.010 )0.001

2

Norway 2

v

CFI

v

CFI

93.77 45.94 193.29

)0.005 )0.002 )0.012

89.57 )0.005 21.24 )0.001

India v2

CFI

102.50

)0.006

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 interpersonal 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.

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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 configural 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 respect to 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 factor invariance constraints for one specific pair of countries. Parents’ values Spain 2

South Africa Norway India Brazil

South Africa

v

CFI

164.74

)0.021

183.57 128.33 63.17

)0.023 )0.016 )0.008

2

Norway 2

India

v

CFI

v

CFI

v2

CFI

39.50 27.16 72.04

)0.004 )0.002 )0.008

116.11 54.92

)0.014 )0.006

41.24

)0.004


163

Starting with the restricted model and relaxing constraints selectively 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 invariance 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 factor invariance constraints. For what now is a two-group model, the difference in the CFI was small enough at )0.003 to make the factor 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 correlations are (1) between interpersonal and capacities values (for both parents and children), (2) between materialistic and capacities values (for both parents and children), (3) between interpersonal and materialistic values (for both parents and children) and (4) between each children’s value and the same value of their parents.

164


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 ¼ 0.041; SRMR ¼ 0.083; PSRMR ¼ 0.108). In order to attribute this misfit to each particular factor, we performed 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 6.63 at 1%. They were in increasing order of size 0.15 for parents interpersonal 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 nonnormality. 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 relationship 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 systematic 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.


165

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_’’ Group Spain

Intercept

Loading

Standardized loading

Loadings and intercepts

Intellig Practic Professi Sensitiv Sympathy Money Power p_intell p_practi p_profes p_sensit p_sympat p_money p_power

0 )0.117 )0.368 0 0.701 0 0.088 0 0.005 )1.508 0 )0.055 0 )0.236

1 1.013 1.140 1 0.926 1 0.960 1 0.934 1.412 1 1.002 1 1.076

0.637 0.641 0.670 0.675 0.739 0.901 0.874 0.433 0.407 0.586 0.706 0.743 0.885 0.929

Mean

Variance

Factor means and variances

Capacity Personal Material p_capaci p_person p_materi

3.742 3.737 2.681 4.196 4.363 2.531

0.346 0.486 1.412 0.097 0.270 0.984

Capacity

Personal

Material

p_capaci

p_person

Personal Material p_capaci p_person p_materi

0.775 0.351 0.230 0.117 0.067

0.184 0.135 0.174 0.007

0.134 0.011 0.224

0.868 0.389

0.167

Group Brazil

Intercept

Loading


0 )0.117 )0.368 0 0.701 0 0.088

1 1.013 1.140 1 0.926 1 0.960

0.572 0.560 0.645 0.702 0.717 0.868 0.844

Factor correlations

Loadings Intellig and inter- Practic cepts Professi Sensitiv Sympathy Money Power

166


TABLE VIII Continued

Factor means and variances

Factor correlations

a

Group Brazil

Intercept

Loading


p_intell p_practi p_profes p_sensit p_sympat p_money p_power

0 0.005 )1.141 0 )0.055 0 )0.236

1 0.934 1.252 1 1.002 1 1.076

0.595 0.448 0.699 0.621 0.746 0.824 0.916

Mean

Variance

4.122 3.802 3.198 4.449 4.421 2.927

0.255 0.571 1.511 0.164 0.299 1.244

Capacity

Personal

Material

p_capaci

p_person

0.714 0.588 0.389 0.134 0.120

0.376 0.298 0.292 )0.002

0.144 )0.023 0.259

0.683 0.452

0.239

Capacity Personal Material p_capaci p_person p_materi

Personal Material p_capaci p_person p_materi

Error variances and covariances are omitted for the sake of simplicity.

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 capacities/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).


167

Figure 2. Path diagram of final CFA model for Brazil and Spain. Parents’ and Children’s values.

Figure 3. Path diagram of the first specification of the structural part of the model for Brazil and Spain.

The path diagram is displayed in Figure 4 and the estimates in Table IX. Only estimates of regression equation parameters are displayed. 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, 176 d.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 Lagrange multiplier tests for the equality of each of the regression slopes

168


Figure 4. Path diagram of the final specification of the structural part of the model for Brazil and Spain.

TABLE IX Estimates and standard errors (in parenthesis) of the final specification of the structural part of the model for Spain and Brazil Group Spain Capacity= Personal= Material= Oversat=

2.289(0.304) 2.673 (0.212) 1.199 (0.085) 2.790 (0.165)

+0.346 +0.244 +0.270 +0.330

· · · ·

p_capaci (0.072) p_person (0.048) p_materi (0.032) capacity (0.043)

R2 R2 R2 R2

= = = =

0.035 0.034 0.051 0.033

Brazil Capacity= Personal= Material= Oversat=

2.267 1.678 2.304 3.145

+0.417 +0.481 +0.306 +0.267

· · · ·


R2 R2 R2 R2

= = = =

0.113 0.122 0.077 0.018

(0.582) (0.604) (0.240) (0.361)

(distributed as a v2 with 1 d.f., with critical values 3.84 at 5% and 6.63 at 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


169

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 measuring 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 that comparability 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 between 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.

170


(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 satisfaction. 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 relationships 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


171

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 cultural 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’ interests. In the future it would be of interest to use longer lists of salient values for future, including those used in other international 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 considered more salient. Our procedure does not seem to have increased 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 comparability 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 extreme caution for two reasons:

172


TABLE A.1 Estimates and standard errors (in parenthesis) of regression models for all Five countriesa Group

a

Spain Capacity= Personal= Material= Oversat=

2.888 3.238 2.115 3.259

(0.177) (0.155) (0.081) (0.110)

+0.212 +0.165 +0.223 +0.203

· · · ·


R2 R2 R2 R2

= = = =

0.023 0.016 0.038 0.021

Brazil Capacity= Personal= Material= Oversat=

3.270 2.865 2.393 3.558

(0.454) (0.430) (0.237) (0.238)

+0.205 +0.260 +0.272 +0.165

· · · ·


R2 R2 R2 R2

= = = =

0.029 0.039 0.067 0.013

South Africa Capacity= Personal= Material= Oversat=

3.831 3.325 2.125 3.250

(0.218) (0.263) (0.156) (0.207)

+0.069 · p_capaci (0.050) +0.135 · p_person (0.059) +0.309· p_materi (0.046) +0.145 · capacity (0.050)

R2 R2 R2 R2

= = = =

0.004 0.011 0.099 0.010

Norway Capacity= Personal= Material= Oversat=

2.967 3.222 1.989 3.769

(0.268) (0.331) (0.163) (0.147)

+0.127 +0.093 +0.251 +0.050

· · · ·


R2 R2 R2 R2

= = = =

0.011 0.005 0.033 0.002

India Capacity= Personal= Material= Oversat=

3.169 2.885 2.485 3.397

(0.187) (0.183) (0.170) (0.160)

+0.171 +0.183 +0.230 +0.105

· · · ·


R2 R2 R2 R2

= = = =

0.025 0.029 0.041 0.007

Non-significant relations (a=0.05) are bold-faced.

(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 comparable.


173

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 unrelated 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 (Pontifı´ cia Universidade Cato´lica do Rio de Janeiro, Brazil), Rose September (Western Cape University, South Africa) and Ferran Casas (Catalan Network of Child 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 across countries, 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 the square 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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