Soc Indic Res (2011) 101:109–126 DOI 10.1007/s11205-010-9639-y
A Comparison of Alternative Models of Individual Quality of Life for Social Service Recipients ´ ngel Verdugo • Benito Arias Laura E. Go´mez • Miguel A Vı´ctor Arias
•
Accepted: 11 May 2010 / Published online: 20 May 2010 Springer Science+Business Media B.V. 2010
Abstract There is considerable debate in the area of individual quality of life research regarding the factor structure of the QOL construct that is focused on the number and composition of QOL factors and domains, and its hierarchical nature. The main goal of this study involve testing by means of confirmatory factor analyses five models that have been pointed out by recent scientific literature: firstly, an unidimensional model; secondly, a QOL model that consisted of eight inter-correlated domains proposed by Schalock and Verdugo (2002); thirdly, a model composed of these eight 1st-order factors and one 2nd- order factor; the forth and five are model with the eight 1st-order factors and three 2nd-order factor that has been denominated in other studies ‘Salamanca model’ and ‘Schalock model’. Data were collected from 3.029 social service recipients from Catalonia (Spain) who completed the GENCAT Scale, an objective QOL questionnaire. The best fit of the eight inter-correlated and 1st order domains was empirically demonstrated. Implications for future research are also discussed. Keywords Quality of life Quality of life models Structural equation modeling Validity Social service recipients
The overriding purpose of this research is to answer the question of what we understand individual quality of life to be. We refer to ‘‘individual quality of life’’ to distinguish from L. E. Go´mez B. Arias INICO, University of Valladolid, Valladolid, Spain L. E. Go´mez (&) Departamento de Psicologı´a, Facultad de Educacio´n y Trabajo Social, Paseo de Bele´n, 1, 47011 Valladolid, Spain e-mail: lauragomez@psi.uva.es ´ . Verdugo M. A University of Salamanca, Salamanca, Spain V. Arias University of Valladolid, Valladolid, Spain
123
110
L. E. Go´mez et al.
others widely used, such as family quality of life, health-related quality of life or work-related quality of life. So, we do not mean we consider quality of life depends only on the individual variables, but we refer to quality of life of an individual from a comprehensive perspective. The review of the scientific literature reveal that prior to the 1960 s only approximate concepts were to be found that contributed to the birth of the quality of life construct, but which focused on the regulatory and objective evaluations of concepts that are now considered to be quite different. It was not until the end of the 1960 s and during the 70 s that the concept was effectively linked to a notion that incorporated subjective evaluations that included aspects such as personal feelings of happiness or satisfaction, and only in the 80 s did the concept become consolidated with operationalization and evaluation beginning with different models. Nonetheless, the 90 s were the decade of results and the disclosure of findings. The concept was forged when much more detailed work was carried out on the specification and breakdown of the concept and its dimensions, its measurement and its integration within professional practices. Research began to increase rapidly during these years, giving rise to more than 100 definitions (Cummins 1997) and over 1,000 evaluation instruments (Hughes and Hwang 1996). Growing interest in the subject meant that this decade saw the statement of the so-called 12 principles of quality of life, which seek to respond to the pressing need to clarify the concept and which were proposed and disseminated by an international team of researchers and quality of life professionals (Schalock and Verdugo 2002). Based on their work, the decade we are now in has witnessed the emergence of a growing consensus regarding four approaches to the use of quality of life-related personal outcomes (Schalock et al. 2007). This overview of the concept’s historical development leads us to today’s understanding of quality of life, in which there is a prevalence of operational models and in which, thanks to the work undertaken by the Special Interest Research Group on Quality of Life of the International Association for the Scientific Study of Intellectual Disabilities (IASSID), we can affirm that there is international consensus on its essential aspects (Schalock and Verdugo 2007). Amongst the models of individual quality of life that are most widely used, special note should be taken of the Comprehensive Quality of Life Scale by Cummins (1996, 2000, 2005), the model by Felce and Perry (1995, 1996)—also applied to, and adapted for, people with severe and multiple disability (Petry et al. 2005, 2007), and the model proposed by Schalock and Verdugo (Schalock and Verdugo 2002, 2007; Schalock et al. in press; Verdugo 2006; Verdugo et al. 2010)—one of the most cited today and with major impacts on field of intellectual and developmental disabilities. Schalock and Verdugo’s model has a wide number of studies related to its formulation (Aznar and Castan˜o´n 2005; Chou and Schalock 2009; Claes et al. 2009; Felce and Perry 1997; Gardner and Carran 2005; Jenaro et al. 2005; Keith 2007; Keith et al. 1996; Schalock and Bonham 2003; Schalock et al. 2007; Schalock and Keith 1993; Schalock and Verdugo 2002; Schalock et al. 2005; Verdugo et al. 2009; Xu et al. 2005), the validation of the conceptual and measurement framework through the verification of its factor structure (Bonham et al. 2003; Bonham et al. 2004; Jenaro et al. 2005; Verdugo et al. 2010; Wang et al. in press), the determination of the etic and emic properties of its dimensions and indicators (Chou and Schalock 2009; Jenaro et al. 2005; Keith and Schalock 2000; Kober and Eggleton 2002; Schalock and Verdugo 2002; Skevington 2002; Verdugo et al. 2005), and the implementation of the model through dimensions, indicators and mediating and moderating variables (Schalock et al. in press). However, it is much more extensive the literature concerning to the formulation of the model in comparison with the literature related to its validation. Today, it seems clear that quality of life is composed of the eight domains formulated by the model proposed by
123
A Comparison of Alternative Models
111
Schalock and Verdugo (self-determination, social inclusion, interpersonal relations, rights, material wellbeing, emotional wellbeing, physical wellbeing and personal development), and theory tends toward an eight-factor correlated model (Schalock and Verdugo 2002; Schalock et al. 2005; Verdugo et al. 2009, 2010), but some of the most recent studies point out evidences about a possible hierarchical nature of the QOL construct (Wang et al. in press). In this sense, although different instruments developed on the base of the model proposed by Schalock and Verdugo—and therefore, based on the eight correlated domains—were used in recent studies, different hierarchical solutions fitted better to the data than the eight-first-order factors. For example, some authors provided evidences for the existence of a second-order factor (Wang et al. in press) and others suggested the existence of three-second-order factors (Bonham et al. 2003, 2004; Jenaro et al. 2005; Schalock et al. 2005). In conclusion, it seems that there are enough evidences to believe that individual quality of life is composed of the eight domains stated by the model of Schalock and Verdugo, but lots of problems are found to validate its factor structure and authors have no choice but find less parsimonious solutions and combine the eight domains in different ways to achieve the model fit to the data. However, we consider that these hierarchical solutions could be consequence of instruments whose development, validation and psychometric properties can be improved or consequence of non representative and small samples. For this reason, in the present study, the purpose is to compare the alternative models of individual quality of life that has been pointed out in recent literature using a representative sample data of social service recipients in the GENCAT Scale (Verdugo et al. 2008a), an instrument with adequate reliability and enough validity evidences based on the scale content and based on its internal structure (Verdugo et al. 2008b, 2009, 2010). In other words, based on the results of the most recent research, we will examine the following specific research questions: (a) is individual quality of life a unidimensional concept or is it composed of the eight domains proposed by Schalock and Verdugo (2002)? (b) is there a single second-order factor model of the QOL conceptual framework or are there different three-second-order factors that fit better to the data? To achieve that goal, we will check the four alternative models found in the literature review and—with a view to achieving model parsimony—we will also test the unidimensional structure (model 1). These four models found in the literature review are firstly the eight-factor correlated model proposed by Schalock and Verdugo (2002) (model 2 in this paper. Then, the model proposed by Wang et al. (in press), in which quality of life is understood to be a hierarchical structure in which the eight dimensions are drawn together in a second-order one (model 3), will be checked. There now follows a further comparison of hierarchical structures by verifying the data fit to different first-order combinations of eight dimensions grouped into three-second-order ones. These designs are reported in the paper we have just cited by Wang et al. (in press) and involve the so-called ‘Salamanca model’ (model 4) and ‘Schalock model’ (model 5). All the models tested are listed and described forthwith: Model 1. Quality of life is a unidimensional construct. Model 2. Quality of life consists of 8 correlated factors (Schalock & Verdugo, 2002): Self-determination, Social inclusion, Interpersonal relations, Rights, Material wellbeing, Emotional wellbeing, Physical wellbeing and Personal development. Model 3. Quality of life consists of 8 first-order factors and 1 s-order one (quality of life) (Model by Wang et al., in press).
123
112
L. E. Go´mez et al.
Model 4. Quality of life consists of 8 first-order factors and 3 s-order ones (Salamanca Model): ‘Personal Wellbeing’, ‘Empowerment’ and ‘Physical and Material Wellbeing’ (Wang et al., in press). Model 5. Quality of life consists of 8 first-order factors and 3 s-order ones (Schalock Model): Independence, Social Integration and Personal Wellbeing (Wang et al., in press).
1 Method 1.1 Sampling Procedure Involving the recipients of social services provided by the Catalan Welfare and Social Services Institute (‘Instituto Catala´n de Asistencia y Servicios Sociales’) (ICASS), the selection of the sample was undertaken by means of a stratified and polytypic probability sampling. Stratification was performed according to both the nature of the target collective for the services and the geographical area of the centre providing such services. The sampling design described was applied to two differentiated groups. The first case involved a sampling of the population of elderly people (sampling error of 2.43 with 95% confidence and p = q). The second involved a sampling of the population of other collectives at risk of social exclusion: people with intellectual disability, physical disability, mental health problems, problems of substance abuse and those with HIV or AIDS. Sampling errors ranged from 3.99 to 5.51%. 1.2 Participants Our study has involved 608 professional staff from 239 entities providing social services and attached to the ICASS. Their task consisted in completing a scale for the objective evaluation of quality of life for 3,029 users of the aforementioned services in the Autonomous Community of Catalonia. In the 239 centres, the mean number of users evaluated was 12.67 (DT = 7.75) and the mean number of persons evaluated by each professional was approximately five (M = 4.981). Regarding those social services provided by the ICASS, the study involved those that provided services to the elderly (rest homes and day centres), people with intellectual disability, physical disability, mental health problems, problems of substance abuse and those with HIV or AIDS. Related to the main socio-demographic characteristics of professionals, most of them were female (85%), had been working with the client for more than 2 years (55.74%), were psychologists (23.01%) and social workers (18.41%), and had been working in social services more than 5 years (52.80%). Concerning to the social service users, 55.7% were female. Their ages ranged between 16 and 105 (M = 64.72; SD = 21.34). More than half of sample (57.57%; n = 1,711) was older than 60. Actually, the biggest group (n = 791) was composed of 81–90 years old people and only 17.39% (n = 515) were younger than 41. Concerning to people condition, the most representative group was the one composed of elder people living in residence settings (44.70%), followed by people with intellectual disabilities (19.35%), physical disabilities (11.72%), mental health (10.33%), and old people in day centres (8.75%). Percents of people with drug dependences and HIV/AIDS ranged from 2.48 to 2.67%.
123
A Comparison of Alternative Models
113
1.3 Instrument The instrument used was the GENCAT Scale (Verdugo et al. 2008a, 2008b, 2009; Verdugo et al. 2007): an instrument for the objective evaluation of quality of life designed according to the advances made on the eight-dimension model proposed by Schalock and Verdugo (2002). We can speak about objective and subjective measures and measurement instruments, depending on their purpose, content, and respondent. If an evaluator desires to assess personal outcomes and develop person-cantered programs, subjective Likert-type scales answered by the client or user of the service should be applied (Schalock and Felce 2004). In distinction, when the goal is program evaluation, service quality improvement, or to assess organizational changes, it is recommended to use objective questionnaires based on the direct observation of personal experiences and circumstances. The GENCAT Scale (see Appendix A) evaluates a series of observable aspects related to the eight areas that make up a person’s quality of life and which may be the focus of customised support programmes arranged by different types of social services. The validation process used the Catalan language version (Verdugo et al. 2008a). The GENCAT Scale is used for the objective evaluation of the quality of life of adult users of social services. The ones completing the scale are the professional staff of these services who know well the person whose quality of life is to be evaluated and who have had recent opportunities to observe that person over prolonged periods of time and in different facets of their life. In total, the questionnaire has 69 items that cover observable aspects related to quality of life. They were all drafted as statements using the third-person and were randomly arranged around the eight subscales that correspond to the dimensions of the underlying theoretical model. Approximately half the items have a positive valence (n = 35) whilst the other half has a negative one (n = 34). The format for answering involves four frequency options: ‘never or hardly ever’, ‘sometimes’, ‘often’, ‘always or almost always’. Nevertheless, regarding those items that might be difficult to rate with this frequency scale, instructions have been given to answer bearing in mind a four-point Likert scale: ‘strongly agree’, ‘agree’, ‘disagree’, ‘strongly disagree’. Although all the items are observable, specific and easy to understand, below each sub-scale a few simple clarifications were added for some of them. For its correction, different yardsticks were provided for users of social services in general and specific ones for the various collectives: the elderly; people with intellectual disability; and all the other collectives at risk of social exclusion (people with physical disability, problems of mental health, substance abuse and HIV/AIDS). The composite scores recorded in each dimension and for the overall scale are converted according to these yardsticks into standard scores (M = 10; DT = 3), into percentiles and into a Quality of Life Index (M = 100; DT = 15). These scores allow identifying a person’s Quality of Life Profile in order to draw up person-centered support programmes and provide a reliable measure for monitoring the programme’s progress and results. 1.4 Procedure Once a selection had been made of the centres taking part in the process for validating the GENCAT Scale (N = 351) we applied a strict application protocol. Firstly, the Catalan Welfare and Social Services Institute (ICASS) sent them an official letter explaining the purpose of the study and inviting the centres and services to take part. This letter was sent by both standard post and e-mail in order to ensure it was received at least in one of the
123
114
L. E. Go´mez et al.
ways. The research team then phoned each one of the centres selected with a view to: (a) confirming it had received the letter from ICASS; (b) confirming its postal address for sending it the scales via an express courier service; (c) informing it in greater detail about the research project and the number of users it was to evaluate; (d) confirming its involvement in the research; and (e) being at its disposal by phone or e-mail for any queries, comments or suggestions it might have regarding the scale. Once the participation of all the centres included in the sample had been confirmed, the next step involved using an express courier service to send each one a package containing: (a) a manual with the necessary instructions for completing the scale; and (b) the specific number of scales to be filled in by each centre plus five more to ensure reaching the total number of participants considered ideal for validating the scale (N = 2,210). In total, 4,500 scales were sent out and 3,029 were returned (67.31%). At this point, we should like to stress the vital importance contact by phone had regarding our study’s high response rate. Although calling by phone requires much more time and effort than other methods, we positively recommend it as it increases the chances of a high response rate.
2 Results In order to conduct the analyses, we used the statistical package LISREL 8.8 (Scientific Software International 2006) for Windows. Confirmatory factor analysis (CFA) is one of the techniques most widely used accordingly when a researcher has hypotheses on the structure of the latent variables, their inter-relationships and their relationships with the variables observed (conceptual model) (Batista-Foguet and Coenders 2000; Bollen 1989; Byrne 1998; Kaplan 2000; Kline 2005; Loehlin 2004; Marcoulides et al. 2001). 2.1 Preparing the Data Firstly, the data were screened and we noted the absence of univariate and multivariate normality, the low percentage of lost cases (random and no higher than 2.20% in any item), the nature of the outliers, the scarce linearity of the data and the absence of multicollinearity. Given these conditions and the high number of items, all the CFAs were undertaken on 32 parcels by means of the DWLS (Diagonal Weighted Least Squares) estimation method and on the matrix of polychoric variances-covariances and the estimation of asymptotic covariances. 2.2 Estimating the Models’ Parameters In the first solution, corresponding to the unidimensional model (Fig. 1), although all the coefficients were significant (‘t-values’ significantly different from zero) we noticed that the prediction error (h) ranged between .36 (p6_2) and .96 (p3_1). It is thereby deduced that the squared coefficient of multiple correlation (R2) for each indicator fell within a range of between .04 and .64. Moreover, only four of these coefficients are above .50. This means that the proportion of variance in the variables observed that can be explained by the latent factor (quality of life) is far from suitable. In turn, the factorial saturations (k) range between .27 (p3_1) and .80 (p6_2), with 14 out of the 32 factor loadings being lower than .50. In short, a single dimension is not enough to reproduce the original covariance matrix, and so we tested a multifactorial design.
123
A Comparison of Alternative Models
e11 e12 e13 e14 e21 e22
.60 .55 .54 .56 .64 .93
P1_1 P1_2 P1_3
115
.63
.29
.67
.46
.68
P1_4
.66
P2_1
.60
.27 .27 .72
P2_2
.26
P2_3
.60
.73
P2_4
.55
.75
.80
e23
.64
e24
.70
e31
.96
e32
.87
e33
.93
P3_3
.27
e34
.96
P3_4
.20
e41
.67
P4_1
.57
e42
.51
P4_2
.70
.64
e43
.51
p4_3
.70
.41
e44
.60
P4_4
.63
.49
P3_1 P3_2
QOL .21 .37
.41 .44 .56
P5_1 P5_2 P5_3 P5_4 P6_1 P6_2 P6_3 P6_4 P7_1 P7_2 P7_3
.91
e51
.79
e52
.93
e53
.93
e54
.48
e61
.36
e62
.46
e63
.44
e64
.83
e71
.81
e72
.69
e73
.84
e74
.41
P7_4
.51
P8_1
.74
e81
P8_2
.59
e82
P8_3
.83
e83
P8_4
.76
e84
Fig. 1 Standardised parameters for model 1 (unidimensional). Note. QOL quality of life
The second model tested was the one by Schalock and Verdugo (2002): quality of life comprises eight dimensions that correlate with each other. The factorial design of this model (Fig. 2) shows that the prediction errors (h) range between .20 (p6_2) and .86 (p2_2). Therefore, the coefficients of determination (R2) range between .14 and .80. Half these values are above .50. Regarding factor loadings (k), they fall within the range of between .38 (p2_2) and .89 (p6_2). 81.3% of the factorial saturations exceed the value of .50 and they were all statistically significant with t values of more than 2.58 (p \ .01). Furthermore, all the coefficients were significant. Insofar as the third model is concerned, proposed by Wang et al. (in press), in which quality of life is understood to be a hierarchical structure in which the eight basic dimensions are grouped into a single higher-order factor (quality of life), we found that the data did not fit the model. So much so, in fact, that the software program could not reach a standardised solution and, therefore, it was not possible to obtain standardised parameters (Fig. 3). It was not even possible to obtain them by means of other much less restrictive estimation methods such as ULS (Unweighted Least Square). Accordingly, it can be concluded that the theoretical model in no way fits the empirical data in our study, and therefore does not constitute a plausible approach to the empirical data used. An initial examination of the fourth model, the so-called ‘Salamanca model’ (Fig. 4), reveals several offending estimates. Thus, saturation k of the endogenous latent variable Physical wellbeing and the correlation A between the higher-order factors Empowerment and Personal wellbeing adopt values higher than 1. Along these same lines, a variance of zero (fPW) is obtained. Accordingly, although the model has been theoretically identified, there are problems regarding empirical identification.
123
L. E. Go´mez et al.
116
θδ1
.39
θδ2
.34
θδ3
.33
θδ4
.34
θδ5
.44
θδ6
.86
θδ7
.49
θδ8
.55
θδ9
.73
θδ10
.31
θδ11
.52
θδ12
.76
θδ13
.59
θδ14
.40
θδ15
.40
p4_3
θδ16
.51
P4_4
P1_1 P1_2 P1_3
.78
.49
.63
.81 .82
.73
.40
EW
PW
.50
.75
P2_2
.38
P2_3
.71
P3_2 P3_3
.34
IR
.83
.31
.77
SI
.53 .54
.78
.51 .50
.07
MW
.49
P4_2
.77
θδ19
P5_4
.82
θδ20
P6_1
.32
θδ21
P6_2
.20
θδ22
P6_3
.30
θδ23
P6_4
.28
θδ24
P7_1
.74
θδ25
P7_2
.75
θδ26
P7_3
.57
θδ27
P7_4
.79
θδ28
P8_1
.55
θδ29
P8_2
.28
θδ30
P8_3
.70
θδ31
P8_4
.55
θδ32
.66 .46
.38 .82
.25
.59
.75
.67
.77
.85
PD
.70
θδ18
P5_3
.84 .85
.31
.70
P3_4
P5_2
.37
.68
.52
.64
SD .56
.35
P4_1
.89
.66
.67
.69
.24
.93
.83
θδ17
.46
.29 63
P2_4 P3_1
.60
.77
P2_1
.76
.48 .43
.81
P1_4
P5_1
RI .64
.54 .67
Fig. 2 Standardised parameters for model 2 (Schalock and Verdugo 2002). Note. EW emotional wellbeing, PW physical wellbeing, IR interpersonal relations, SD self-determination, MW material wellbeing, SI social inclusion, PD personal development, RI Rights
Finally, the last model subjected to confirmation was the so-called Schalock model. On this occasion, the standardised solution (Fig. 5) gave rise to prediction errors (h) of between .15 and .80 for the parcels p6_2 and p3_1, respectively (therefore, the coefficients of determination range between .20 and .85). Regarding the factor loadings of the endogenous variables on the variables observed (k), there are fairly high values that range between .45 and .92. The same circumstance, albeit much more pronounced (i.e., excessively high values), occurs in the factor loading of the endogenous variables on the exogenous ones (.74 B c B .97) and in the correlations between the exogenous variables (A C .90). 2.3 Goodness of fit of the Models Once the parameters have been estimated, the final step in the CFA consists in evaluating the fitting of the theoretical models to the study data. Table 1 therefore presents some of the more common goodness of fit indices. The overall or absolute fit index traditionally used for verifying the null hypothesis (i.e., the model fits the population data perfectly) is the Satorra-Bentler Chi-Square Index (Satorra and Bentler 1994). When analysing the values returned by all the models, we would have to reject the null hypothesis in all cases (p = .000). Nonetheless, from a more pragmatic and less restrictive perspective, it is advisable to examine not so much the level of statistical significance but rather the magnitude of v2 (Arias 2008): high values would correspond to a deficient fit and low values to a better fit. Therefore, the second model (i.e., quality of life is a construct made up of eight inter-related factors) is by far the one recording
123
A Comparison of Alternative Models
e11 e12 e13 e14 e21 e22
.60
P1_1
.55
P1_2
.54 .56 .64 .93
117
ζ1
ζ5
P1_3
ζ6 IR
e23 e24
.70
e31
.96
P3_1
e32
.87
P3_2
e33
.93
P3_3
e34
.96
P3_4
e41
.67
P4_1
e42
.51
P4_2
e43
.51
p4_3
e44
.60
P4_4
e52
.93
e53
.93
e54
.48
e61
.36
e62
.46
e63
.44
e64
.83
e71
.81
e72
.69
e73
.84
e74
P8_1
.74
e81
P8_2
.59
e82
P8_3
.83
e83
P8_4
.76
e84
P5_4
ζ2
P2_2
.64
.79
P5_3
P1_4 P2_1
e51
P5_2
PW
EW
.91
P5_1
SD
P2_3
P6_1 P6_2 P6_3
P2_4
P6_4
ζ3
QOL MW
ζ7
P7_1 P7_2
SI
P7_3 P7_4
ζ4
ζ8 PD
RI
Fig. 3 Standardised parameters for model 3 (Wang et al. in press). Note. QOL quality of life, EW emotional wellbeing, PW physical wellbeing, IR interpersonal relations, SD self-determination, MW material wellbeing, SI social inclusion, PD personal development, RI Rights
the lowest value (v2S–B = 1251.16); although all the values returned are fairly high given that this index is strongly influenced by the sample size. Concerning the ratio of v2/gl, the second model (Schalock and Verdugo 2002) is the only one attaining an acceptable value. Precisely for the above reason and with a view to obtaining a more reliable representation of the true goodness of fit of the models, it is advisable to consider another type of indices (Cea 2002; Roussel et al. 2002). In order to overcome the drawbacks posed by the overall fit index, a raft of partial fit indices have been developed. Amongst these are the fit indices of an absolute, parsimonious and incremental nature. Accordingly, the partial fit indices of an absolute nature GFI (Goodness-of-Fit Index) and AGFI (Adjusted Goodness-of-Fit Index) assess the degree to which the model’s variances and covariances correctly reproduce the original matrices (the former does so in an absolute manner whilst the latter do so in keeping with the degrees of freedom). Both should exceed the value of .90; a condition fulfilled only by the second, fourth and fifth models, albeit the second with greater excellence (.96). The SRMR (Standardized Root Mean Square), for its part, records no more than an acceptable value in the case of the second model. Regarding the parsimonious fit indices, only the second model records a value of RMSEA (Root Mean Square Error of Approximation) that can be considered good (RMSEA = .058). The fourth and fifth models, however, record reasonable values (RMSEA = .073), whereby the unidimensional model and the one considering a first-order factor attain values that lead to the rejection of both models (above .10). On the other hand, the probability of the RMSEA value being lower than .05 is \ 1% in all cases (P-Value for Test of Close Fit = .000).
123
L. E. Go´mez et al.
118
θε1
.40
θε2
.34
θε3
.32
θε4
.33
θε5
.46
θε6 θε7
.46
θε8
.54
.59
θε14
.40
θε15
.40
θε16
.51
θε21
.30
θε22
.20
θε23
.31
θε24
.49
.27
ζ7 P7_1
.77
P1_2 P1_3
.49
.81
.51
SI
EW
.83
.66
.85
.82
.89
θε13
ζ1 P1_1
ζ2
.71
P2_3
P7_4
Empower ment
.33
ζ8
P8_1
.51
.74
P2_2
.67
.70 1.05
.33
IR
.86
RI
.55
.74 .72
.82
.68
Personal Wellbeing
ζ4
.93 .78
PW
PD
.78
.56
.74
P5_2
.48
P5_3
1.00 .42
P4_4
P5_4
.73
ζ6 .46
P6_1 .83 .88 .83 .85
.28
θε26
.57
θε27
.78
θε28
.55
θε29
.25
θε30
.70
θε31
.58
θε32
Physical & Material Wellbeing
ζ3 .60
SD
P3_1 .52
.63
MW
.76
θε17
.45
θε18
.77
θε19
.83
θε20
P5_1 .49
.70
P6_3
.00
ζ5
.13
.64
P6_2
P8_3
θε25
.74
P8_4
P4_1
p4_3
P8_2
.65
P2_4
P4_2
P7_3
.47
P1_4
P2_1
P7_2
.76
.82 .69
P3_2 P3_3
.50
P6_4
P4_3
.73
θε9
.33
θε10
.52
θε11
.75
θε12
Fig. 4 Standardised parameters for model 4 (Salamanca model). Note. EW emotional wellbeing, PW physical wellbeing, IR interpersonal relations, SD self-determination, MW material wellbeing, SI social inclusion, PD personal development, RI Rights
Finally, the incremental fit indices: NFI (Normed Fit Index), TLI (Tucker-Lewis Index), CFI (Comparative Fit Index), IFI (Incremental Fit Index) and RFI (Relative Fit Index), assess the extent to which one model is better than the others. According to these, the first and third model should be rejected (in fact, it should be remembered that the third model did not reach a standardised solution, which makes it somewhat meaningless to interpret the fit indices) by recording values &.70. In the rest of the models, the values show a good fit (&.90), although the second model is the one returning the manifestly best coefficients (.94–.97). Consequently, the results obtained clearly show that the second model (Schalock and Verdugo 2002), in which quality of life is understood to consist of eight basic inter-related dimensions, is by far the one that best fits the data and, therefore, the one that will be used for the subsequent analyses. 2.4 Reliability and Validity of the Model by Schalock and Verdugo Regarding the indices of reliability and validity of the final model (model 2: Schalock and Verdugo 2002), and in addition to the reliability of the individual indicators (see R2 in Fig. 2), computation was made of the composite reliability of each latent variable (i.e., the internal consistency of the eight constructs or reliability of the constructs) and of the
123
A Comparison of Alternative Models
θε13
.42
θε14
.19
θε15
.23
θε16
.33
θε21
.19
θε22
.15
θε23
.25
θε24
.22
θε5
.20
θε6
.66
ζ4
P4_1
p4_3
.90
θε7 θε8
.43
θε25
.45
θε26
.48
θε27
.36
θε28
.64
θε29
.39
PD
.88
.97
.82
P4_4
.07
Independ ence
ζ6 .90
P6_2 P6_3
.32
θε31
.58
θε32
.56
P1_1 .89
EW
.89
.93
.92
P1_2 P1_3
.22
θε1
.20
θε2
.21
θε3
.23
θε4
.88
SD
P1_4
.97
.87
.96
.88
ζ2
.97
Personal wellbeing
.06
P2_1 .90
P2_2 P2_3
.98
.58
.30
IR
.80 .75
P2_4
.90
ζ7
P7_1
ζ5
.70
P5_1
.74
.55
PW
P5_2
.53
P5_3 P5_4
.74
P7_2 P7_3
.72
ζ3
.94
.60
Social integration
ζ8 .11 .78
P8_2 P8_3
.94 .83 .65
RI
θε17 θε18
.72
θε19
.74
θε20
.84
SI
.80
P8_1
.28
.85
.51
.11
P7_4
θε30
ζ1 .88
.13
P6_1
P6_4
.36
.06
.76
P4_2
119
.45
P3_1 .45
MW
.85 .68
P3_2 P3_3
.48
P4_3
.80
θε9
.28
θε10
.54
θε11
.77
θε12
.67
P8_4
Fig. 5 Standardised parameters for model 5 (Schalock model). Note. EW emotional wellbeing, PW physical wellbeing, IR interpersonal relations, SD self-determination, MW material wellbeing, SI social inclusion, PD personal development, RI Rights
model, and the average variance extracted for each one of the latent constructs (i.e., validity or degree to which the indicators accurately measure the corresponding construct) and for the model. All the values mentioned are included in Table 2. As can be seen in Table 2, the reliabilities of four of the listed constructs exceed the threshold of .75; whist the average variance extracted is over 50% in three cases. This means that the values obtained in Self-determination, Emotional wellbeing, Personal development and Rights reflect positively on the validity and reliability of the indicators used for the empirical explanation of the latent constructs. The dimensions that come out worse in the model are Material wellbeing, Interpersonal relations, Physical wellbeing and, especially, Social inclusion. Regarding the model’s indicators of reliability and validity, the indices obtained were more than satisfactory ([.93).
3 Discussion In social and behavioral research, is not infrequent that an initially considered model does not fit an analyzed data set well and require changes and adjustments. There are two
123
L. E. Go´mez et al.
120 Table 1 Fit indices for the various models
Absolute
Fit indices
Model 1
Model 2
Model 3
Model 4
Model 5
v2S–B
5974.82
1251.16
5572.16
1792.28
1779.39
gl p v2/gl
464
436
.000 12.88
.000 2.87
456
453
.000
453 .000
12.22
3.96
.000 3.93
Partial Absolute
Parsimonious Incremental
SRMR
.13
.076
.18
.097
.096
GFI
.89
.96
.77
.94
.94
AGFI
.78
.96
.73
.93
.92
RMSEA
.15
.058
.19
.073
.073
Pclose
.000
.000
.000
.000
.000
NFI
.76
.95
.77
.93
.93
TLI
.75
.96
.77
.94
.94
CFI
.77
.97
.79
.94
.94
IFI
.77
.97
.79
.94
.94
RFI
.74
.94
.75
.92
.92
Table 2 Composite reliability (qc) and average variance extracted (qv)
Domains
qc
qv
Emotional wellbeing
.881
.648
Interpersonal relations
.729
.415
Material wellbeing
.734
.419
Personal development
.815
.525
Physical wellbeing
.618
.297
Self-determination
.914
.727
Social inclusion
.614
.289
Rights
.782
.478
Model 2
.965
.938
possible main sources of model misfit (Raykov and Markoulides 2008): (a) the analyzed data are not of good quality (e.g., the measures used have low validity or reliability), and (b) the model is deficient in some sense (that is, misspecified). Given the difficulties deriving from the validation of the Schalock and Verdugo model and the alternative solutions suggested recently in other studies even when instruments had been developed on the base of this model, this paper had the goal of demonstrating that the eight-correlated factors proposed fit well to the data when instrument is valid and reliable, and so the problem does not lie in the model misfit but in the quality of the used data. For that reason, we have compared the fit of those competing models of individual quality of life—derived from the Schalock and Verdugo theory—that have been found in other studies in order to determine which model best fits and the suitability of Schalock and Verdugo model in comparison to less parsimonious and hierarchical solutions. The results of the analysis of the various models fit led us to the following conclusions: (1) There is no suitable data fit to the model in which quality of life is understood to be a unidimensional concept; (2) There is no suitable data fit to the model by Wang, in which
123
A Comparison of Alternative Models
121
quality of life consists of 8 first-order factors and 1 s-order one (‘quality of life’) (Model by Wang et al. in press); (3) There is a fit that could be considered acceptable to the Salamanca model, in which quality of life consists of 8 first-order factors and 3 s-order ones (Personal Wellbeing’, ‘Empowerment’ and ‘Physical and Material Wellbeing’);(4) There is a fit that could be considered suitable to the Schalock model, in which quality of life consists of 8 first-order factors and 3 s-order ones (‘Independence’, ‘Social Integration’ and ‘Personal Wellbeing’); (5) The best data fit by far is to the multidimensional model by Schalock and Verdugo (2002), in which quality of life is composed of eight-first-order correlated factors. It is therefore concluded that the eight-dimension model considered by Schalock and Verdugo (2002) presents a suitable portrayal of the quality of life and so it is demonstrated that model is not deficient and hence is not in need of modification to improve its degree of consistency with the analyzed data. If we make a careful study of the variance of the variables observed that explain the latent variables (R2), Self-determination is remarkable for the reliability of its indicators, followed by Emotional wellbeing and Personal development. The less reliable indicators in general terms, however, are included in Social inclusion. The remaining dimensions are characterised by combining indicators with low and moderate reliability. These results have important implications for current models, development of instruments and application of adequate interventions in order to improve individual quality of life in social service recipients. We have shown that it is not needed to resort to hierarchical solutions since the eight correlated first-order factors is the one that may best fit if the used instrument is valid and reliable. Therefore, having an increasingly sound and internationally accepted model and suitable instruments for its valid and reliable evaluation has meant that today we are now one step on from the conceptualisation and evaluation of quality of life. We can say that we are now at just the right moment to undertake its evaluation in the applied field and in several spheres. Accordingly, we now see the main line of research to be its application in social services with a view to helping to develop and evaluate programmes designed to improve personal results and quality of life. Since we have used an objective measurement of quality of life (in which professional evaluate their clients quality of life), we provide evidences about the adequateness of eightdomains model for objective assessment in social service recipients. However, we have tried to make a comparison of models that are not specific to objective assessment. On the contrary, the eight-domains model was proposed and formulated for both evaluations (subjective and objective). In this case, we have find evidences about its adequateness using an objective assessment, but this does not mean that it would not be adequate if a subjective assessment were applied. Nevertheless, evidences about the adequateness of this model in subjective assessments is still needed, since these results could be reflecting that professionals may have a certain structure in their evaluations of social service recipients’ quality of life. We should keep in mind that quality of life is a psychological phenomenon that is strongly linked to the meanings of issues. Anyway, we are reasonably convinced about this structure may work using subjective assessment if we take into account that several focus groups (Verdugo et al. 2010) including social service recipients were asked to develop the Gencat Scale and they confirmed the importance of the eight domains. Even so, empirical evidence using subjective instruments is still needed to confirm the proposed structure. We therefore consider the GENCAT Scale to be a significant contribution in this sense. We can use it to address what up until now has been the foremost difficulty arising in our field, namely, the real application of the concept in social services. Thus, we now have an instrument sensitive to changes that will enable us to make a valid evaluation of personal
123
L. E. Go´mez et al.
122
results as a criterion for identifying needs and designing programmes, at the same time as we also monitor progress in the inclusive process and in the planning of supports focusing on the individual (Schalock et al. 2007; Verdugo 2009). We therefore look upon the GENCAT Scale as a unique and extraordinary instrument that is extremely useful for driving change in professional practices and in the personal results obtained by people at greater risk of social exclusion (microsystem), in organisations (mesosystem) and in social policies (macrosystem). Acknowledgments Work on this research was partially supported by a grant (EDU/894/2009)given to the Excellence Group of Research (GR197) by the Junta de Castilla y Leo´n (Spain). It was also founded by the Catalan Welfare and Social Services Institute (‘Instituto Catala´n de Asistencia y Servicios Sociales’)(ICASS, Generalitat of Catalonia) and the Science and Technology Ministry (R&D Projects, 2009)(PSI2009-10953).
Appendix A See Table 3. Table 3 The gencat scale (Verdugo et al. 2008, 2009) Items and domains Emotional wellbeing 1
He/she is satisfied with their present life
2
He/she shows symptoms of depression
3
He/she is happy and in a good mood
4
He/she expresses feelings of helplessness or insecurity
5
He/she shows symptoms of anxiety
6
He/she is satisfied with themselves
7
He/she has problems of conduct
8
He/she is motivated when performing some kind of activity
Interpersonal relations 1
He/she does things they enjoy with other people
2
The relations with his/her family are as they would like them to be
3
He/she complains about a lack of close friends
4
He/she has a negative view of their friendships
5
He/she says they feel undervalued by their family
6
He/she finds it difficult to start up a relationship with a potential partner
7
He/she gets on well with their colleagues at work
8
He/she says they feel loved by the people who are important to them
9
Most of the people with whom they interact are in a similar situation to their own
10 He/she has a satisfactory sex life Material wellbeing 1
Where he/she lives stops them from leading a healthy life (noise, fumes, odours, gloom, lack of ventilation, damage, inaccessibility…)
2
His/her workplace complies with rules on health and safety
3
He/she has the material possessions they need
4
He/she is unhappy with where they live
5
Where he/she lives is clean
123
A Comparison of Alternative Models
123
Table 3 continued Items and domains 6
He/she has enough money to cover their basic needs
7
He/she does not earn enough to be able to afford luxuries
8
Where he/she lives has been adapted to their needs
Personal development 1
He/she finds it difficult to cope with everyday situations
2
He/she has access to new technologies (Internet, mobile phone, etc.)
3
The work they do enables them to learn new skills
4
He/she finds it difficult to effectively deal with the problems they have to face
5
He/she does their work competently and responsibly
6
The service he/she attends caters for their personal development and the learning of new skills
7
He/she is involved in the drafting of their own individual programme
8
He/she lacks motivation at work
Physical wellbeing 1
He/she finds it difficult to sleep
2
Technical aids are available if he/she needs them
3
He/she has healthy eating habits
4
His/her state of health allows them to lead a normal life
5
He/she maintains good personal hygiene
6
The service he/she attends supervises the medication they take
7
His/her health problems cause them pain and discomfort
8
He/she finds it difficult to access healthcare resources (preventive care, GP, at home, in hospital, etc.)
Self-determination 1
He/she has personal targets, goals and interests
2
He/she decides how to spend their free time
3
The service he/she attends caters for their preferences
4
He/she defends their ideas and opinions
5
Other people decide upon his/her personal life
6
Other people decide how he/she spends their money
7
Other people decide what time he/she goes to bed
8
He/she organises their own life
9
He/she chooses who they live with
Social Inclusion 1
He/she frequents communal areas (public swimming pools, cinemas, theatres, museums, libraries‌)
2
His/her family provides support whenever needed
3
There are physical, cultural or social barriers that hinder his/her social inclusion
4
He/she lacks the necessary support for taking an active part in everyday life in their community
5
His/her friends provide support whenever it is needed
6
The service he/she attends encourages them to take part in community activities
7
The only friends he/she has are the ones who attend the same service
8
He/she is rejected or discriminated against by others
Rights 4
He/she finds it difficult to defend their rights when these are violated
3
He/she has information on their basic rights as a citizen
123
L. E. Go´mez et al.
124 Table 3 continued Items and domains 7
One or more of his/her legal rights have been impaired (citizenship, vote, legal processes, respect for their beliefs, values, etc.)
1
His/her family violates their privacy (reading their letters, entering without knocking…)
5
The service he/she attends respects their privacy
2
He/she is treated with respect in their environment
6
The service he/she attends respects their possessions and their ownership rights
8
The service he/she attends respects and defends their rights (confidentiality, information on their rights as a user…)
9
The service respects the privacy of his/her information
10
He/she is exposed to exploitation, violence or abuse
References Arias, B. (2008). Desarrollo de un ejemplo de ana´lisis factorial confirmatorio con LISREL, AMOS y SAS. In M. A. Verdugo, M. Crespo, M. Badı´a, & B. Arias (Eds.), Metodologı´a en la investigacio´n sobre discapacidad. Introduccio´n al uso de las ecuaciones estructurales. Salamanca, Spain: INICO. Aznar, A. S., & Castan˜o´n, G. G. (2005). Quality of life from the point of view of Latin American families: A participative research study. Journal of Intellectual Disability Research, 49, 784–788. Batista-Foguet, J. M., & Coenders, G. (2000). Modelos de ecuaciones estructurales. Madrid: Hespe´rides. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Bonham, G. S., Basehart, S., & Marchand, C. B. (2003). Ask me! FY2003 report: The quality of life of Marylanders with developmental disabilities receiving DDA funded support. Baltimore, Maryland: The ARC of Maryland. http://www.bonhamresearch.com/PDF/2003TBasehart%20Ask%20Me%20 FY003Sum.pdf. Retrieved Oct 26 2006. Bonham, G. S., Basehart, S., Schalock, R. L., Marchand, C. G., Kirchner, N., & Rumenap, J. M. (2004). Consumer-based quality of life assessment: The Maryland ask me! project. Mental Retardation, 42, 338–355. doi:10.1352/0047-6765(2004)42\338:CQOLAT[2.0.CO;2. Byrne, B. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS. New Jersey: Earlbaum. Cea, M. A. (2002). Ana´lisis multivariable. Teorı´a y pra´ctica en la investigacio´n social. Madrid: Sı´ntesis. Chou, Y. C., & Schalock, R. L. (2009). Survey outcomes and cross-national comparisons of quality of life with respect to people with intellectual disabilities in Taiwan. Journal of Policy and Practice in Intellectual Disabilities, 6, 7–10. doi:10.1111/j.1741-1130.2008.00200.x. Claes, C., van Hove, G., van Loon, J., Vandevelde, S., & Schalock, R. L. (2009). Quality of life measurement in the field of intellectual disabilities: Eight principles for assessing quality of life-related personal outcomes. Social Indicator Research. doi:10.1007/s11205-009-9517-7. Cummins, R. A. (1996). Quality of life and disability. In P. O0 Brien & R. Murray (Eds.), Human services: Towards partnership and support (pp. 255–268). Auckland, NZ: Dunmore. Cummins, R. A. (1997). Assessing quality of life. In I. Brown (Ed.), Quality of life for people with disabilities (2nd ed., pp. 116–150). Cheltenham, UK: Stanley Thorne. Cummins, R. A. (2000). Objective and subjective quality of life: An interactive model. Social Indicators Research, 52, 55–72. doi:10.1023/A:1007027822521. Cummins, R. A. (2005). Moving from the quality of life concept to a theory. Journal of Intellectual Disability Research, 49, 699–706. doi:10.1111/j.1365-2788.2005.00738.x. Felce, D., & Perry, J. (1995). Quality of life: Its definition and measurement. Research in Developmental Disabilities, 16, 51–74. doi:10.1016/0891-4222(94)00028-8. Felce, D., & Perry, J. (1996). Assessment of quality of life. In R. L. Schalock (Ed.), Quality of life, vol. I: Conceptualization and measurement (pp. 63–73). Washington, DC: American Association on Mental Retardation. Felce, D., & Perry, J. (1997). Quality of life: The scope of the term and its breadth of measurement. In R. I. Brown (Ed.), Quality of life for persons with disabilities: Models, research and practice (2nd ed., pp. 56–71). Cheltenham, UK: Stanley Thornes.
123
A Comparison of Alternative Models
125
Gardner, J. F., & Carran, D. (2005). Attainment of personal outcomes by people with developmental disabilities. Mental Retardation, 43, 157–174. doi:10.1352/0047-6765(2005)43[157:AOPOBP]2.0. CO;2. Scientific Software International. (2006). LISREL, v.8.8 (computer software). Lincolnwood, IL: Scientific Software International. Jenaro, C., Verdugo, M. A., Caballo, C., Balboni, G., Lachapelle, Y., Otbrebski, W., et al. (2005). Crosscultural study of person-centered quality of life domains and indicators: A replication. Journal of Intellectual Disability Research, 49, 734–739. doi:10.1111/j.1365-2788.2005.00742.x. Kaplan, D. (2000). Structural equation modeling: foundations and extensions. Psychometrika, 54, 681–697. Keith, K. D. (2007). Quality of life. In A. Carr, G. O’Reilly, P. N. Walsh, & J. M. Cevoy (Eds.), The handbook of intellectual disability and clinical psychology practice (pp. 143–168). London, UK: Routledge. Keith, K. D., & Schalock, R. L. (2000). Cross-cultural perspectives on quality of life. Washington, DC: American Association on Mental Retardation. Keith, K. D., Heal, L. W., & Schalock, R. L. (1996). Cross-cultural measurement of critical quality of life concepts. Journal of Intellectual and Developmental Disability, 21, 273–293. doi:10.1080/136682596 00033201. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press. Kober, R., & Eggleton, I. R. C. (2002). Factor stability of the Schalock and Keith (1993) quality of life questionnaire. Mental Retardation, 40, 157–165. doi:10.1352/0047-6765(2002)040\0157:FSOTSA[ 2.0.CO;2. Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. Mahwah, NJ: Lawrence Erlbaum Associates. Marcoulides, G. A., & Schumacker, R. E. (Eds.). (2001). New developments and techniques in structural equation modeling. Mahwah, NJ: Lawrence Erlbaum Associates. Petry, K., Maes, B., & Vlaskamp, C. (2005). Domains of quality of life of people with profound multiple disabilities: The perspective of parents and direct support staff. Journal of Applied Research in Intellectual Disabilities, 18, 35–46. doi:10.1111/j.1468-3148.2004.00209.x. Petry, K., Maes, B., & Vlaskamp, C. (2007). Support characteristics associated with the quality of life of people with profound intellectual and multiple disabilities: The perspective of parents and direct support staff. Journal of Policy and Practice in Intellectual Disabilities, 4, 104–110. doi:10.1111/ j.1741-1130.2007.00107.x. Raykov, T., & Markoulides, G. (2008). An introduction to applied multivariate analyses. New York: Taylor & Francis Group. Roussel, P., Durieu, F., Campoy, E., & El Akremi, A. (2002). Me´thodes d’e´quations structurelles: Recherche et applications en gestion. Paris: Economica. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. Von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA: Sage. Schalock, R. L., & Bonham, G. S. (2003). Measuring outcomes and managing for results. Evaluation & Program Planning, 26, 229–235. doi:10.1016/S0149-7189(03)00027-2. Schalock, R. L., & Keith, K. D. (1993). Quality of life questionnaire. Worthington, OH: IDS Publishing. Schalock, R. L., & Verdugo, M. A. (2002). Quality of life for human service practitioners. Washington, DC: American Association on Mental Retardation. Schalock, R. L., & Verdugo, M. A. (2007). El concepto de calidad de vida en los servicios y apoyos para personas con discapacidad intelectual. Siglo Cero, 38, 21–36. Schalock, R. L., Verdugo, M. A., Jenaro, C., Wang, M., Wehmeyer, M., Xu, J., et al. (2005). A crosscultural study of quality of life indicators. American Journal on Mental Retardation, 110, 298–311. doi:10.1352/0895-8017(2005)110[298:CSOQOL]2.0.CO;2. Schalock, R. L., Gardner, J. F., & Bradley, V. J. (2007). Quality of life for people with intellectual and other developmental disabilities. Applications across individuals, organizations, communities, and systems. Washington, DC: American Association on Intellectual and Developmental Disabilities. Schalock, R. L., Keith, K. D., Verdugo, M. A., & Gomez, L. E. (in press). Quality of life model development in the field of intellectual disability. In R. Kober (Ed.), Quality of life for people with intellectual disability. New York: Springer. Skevington, S. (2002). Advancing cross-cultural research on quality of life: Observations drawn from the WHOQOL development. Quality of Life Research, 11, 135–144. doi:10.1023/A:1015013312456. Verdugo, M. A. (2006). Co´mo mejorar la calidad de vida de las personas con discapacidad. Salamanca: Amaru´. Verdugo, M. A. (2009). Calidad de vida, I ? D ? i y polı´ticas sociales. Siglo Cero, 40, 5–21.
123
126
L. E. Go´mez et al.
Verdugo, M. A., Prieto, G., Caballo, C., & Pela´ez, A. (2005). Factorial structure of the quality of life questionnaire in a Spanish sample of visually disabled adults. European Journal of Psychological Assessment, 21, 44–55. doi:10.1111/j.1365-2788.2005.00750.x. Verdugo, M. A., Schalock, R. L., Go´mez, L. E., & Arias, B. (2007). Construccio´n de escalas de calidad de vida multidimensionales centradas en el contexto: la Escala GENCAT. Siglo Cero, 38, 57–72. Verdugo, M. A., Arias, B., Go´mez, L. E., & Schalock, R. L. (2008a). Formulari de l’Escala Gencat de Qualitat de vida. Manual d’aplicacio´ de l’Escala Gencat de Qualitat de vida. Barcelona: Departamento de Accio´n Social y Ciudadanı´a, Generalitat de Catalun˜a. Verdugo, M. A., Arias, B., Go´mez, L. E., & Schalock, R. L. (2008b). Escala GENCAT. Informe sobre la creacio´ d’una escala multidimensional per avaluar la qualitat de vida de les persones usua`ries dels serveis socials a Catalunya. Barcelona: Departamento de Accio´n Social y Ciudadanı´a, Generalitat de Catalun˜a. Verdugo, M. A., Arias, B., Go´mez, L. E., & Schalock, R. L. (2009). Formulario de la Escala GENCAT de Calidad de vida. Manual de aplicacio´n de la Escala GENCAT de Calidad de vida. Barcelona: Departamento de Accio´n Social y Ciudadanı´a, Generalitat de Catalun˜a. Verdugo, M. A., Arias, B., Go´mez, L. E., & Schalock, R. L. (2010). Development of an objective instrument to assess quality of life in social services: Reliability and validity in Spain. International Journal of Clinical and Health Psychology, 10(1), 105–123. Wang, M., Schalock, R.L., Verdugo, M.A., & Jenaro, C. (in press). Examining the factor structure and hierarchical nature of the quality of life construct. American Journal on Intellectual and Developmental Disabilities. Xu, J., Wang, M., Xiang, Y., & Hu, X. (2005). Quality of life for people with intellectual disabilities in China: A cross-culture perspectives study. Journal of Intellectual Disabilities Research, 49, 745–749. doi:10.1111/j.1365-2788.2005.00744.x.
123