Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
Health and Quality of Life Outcomes
Published in: Health and Quality of Life Outcomes 1/2018

Open Access 01-12-2018 | Research

Measuring subjective well-being from a multidimensional and temporal perspective: Italian adaptation of the I COPPE scale

Authors: Salvatore Di Martino, Immacolata Di Napoli, Ciro Esposito, Isaac Prilleltensky, Caterina Arcidiacono

Published in: Health and Quality of Life Outcomes | Issue 1/2018

Login to get access

Abstract

Background

The objective of this study is to present the psychometric and cultural adaptation of the I COPPE scale to the Italian context. The original 21-item I COPPE was developed by Isaac Prilleltensky and colleagues to integrate a multidimensional and temporal perspective into the quantitative assessment of people’s subjective well-being. The scale comprises seven domains (Overall, Interpersonal, Community, Occupation, Psychological, Physical, and Economic well-being), which tap into past, present, and future self-appraisals of well-being.

Methods

The Italian adapted version of the I COPPE scale underwent translation and backtranslation procedure. After a pilot study was conducted on a local sample of 683 university students, a national sample of 2432 Italian citizens responded to the final translated version of the I COPPE scale, 772 of whom re-completed the same survey after a period of four months. Respondents from both waves of the national sample were recruited partly through on-line social networks (i.e. Facebook, Twitter, and SurveyMonkey) and partly by university students who had been trained in Computer-Assisted Survey Information Collection.

Results

Data were first screened for non-valid cases and tested for multivariate normality and missing data. The correlation matrix revealed highly significant correlation values, ranging from medium to high for nearly all congeneric variables of the I COPPE scale. Results from a series of nested and non-nested model comparisons supported the 7-factor correlated-traits model originally hypothesised, with factor loadings and inter-item reliability ranging from medium to high. In addition, they revealed that the I COPPE scale has strong internal reliability, with composite reliability always higher than .7, satisfactory construct validity, with average variance extracted nearly always higher than .5, and and full strict invariance across time.

Conclusions

The Italian adaptation of the I COPPE scale presents appropriate psychometric properties in terms of both validity and reliability, and therefore can be applied to the Italian context. Some limitation and recommendations for future studies are discussed.
Appendix
Available only for authorised users
Footnotes
1
In the absence of clear guidelines in the literature, we opted for a time interval we believed to be sufficiently wide to avoid recollection biases.
 
2
All the analyses and results described in this and the next paragraphs refer to the national sample
 
3
All the congeneric variables composing the I COPPE are measured on a ratio scale ranging from 0 to 10.
 
4
These figures pertain to the 7-factors correlated-traits model, which was used as main explanatory model.
 
Literature
1.
Stoll L. A short history of wellbeing research. In: McDaid D, Cooper CL, editors. The economics of wellbeing: wellbeing a complete reference guide. Oxford: Wiley; 2014. p. 1–19.
2.
Dodge R, Daly AP, Huyton J, Sanders LD. The challenge of defining well-being. Int J Well-being. 2012;2(3):222–35.CrossRef
3.
Christopher J. Situating psychological well-being: exploring the cultural roots of its theory and research. J Couns Dev. 1999;77:141–52.CrossRef
4.
Pollard E, Lee P. Child well-being: a systematic review of the literature. Soc Indic Res. 2003;61(1):9–78.CrossRef
5.
Cooke PJ, Melchert TP, Connor K. Measuring well-being: a review of instruments. Couns Psychol. 2016;44(5):730–57.CrossRef
6.
Arcidiacono C, Di Martino S. A critical analysis of happiness and well-being. Where do we stand now, where do we want to go? Happiness and social well-being. Community Psychol Global Perspect. 2016;2(1):6–35.
7.
Ryan RM, Deci EL. On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Annu Rev Psychol. 2001;52(1):141–66.CrossRefPubMed
8.
Diener E. Subjective well-being: the science of happiness, and a proposal for a national index. Am Psychol. 2000;55:34–43.CrossRefPubMed
9.
Diener E, Lucas R, Schimmack U, Helliwell J. Well-being for public policy. Oxford: OUP; 2009.CrossRef
10.
Ryff C. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Pers Soc Pychol. 1989;57:1069–81.CrossRef
11.
Ryff CD, Keyes CLM. The structure of psychological well-being revisited. J Pers Soc Psychol. 1995;69(4):719–27.CrossRefPubMed
12.
Group TW. The World Health Organization quality of life assessment (WHOQOL): development and general psychometric properties. Soc Sci Med. 1998;46(12):1569–85.CrossRef
13.
Frisch MB, Cornell J, Villanueva M, Retzlaff PJ. Clinical validation of the quality of life inventory. A measure of life satisfaction for use in treatment planning and outcome assessment. Psychol Assessment. 1992;4(1):92–101.CrossRef
14.
15.
Dronavalli M, Thompson SC. A systematic review of measurement tools of health and well-being for evaluating community-based interventions. J Epidemiol Community Health. 2015;69(8):808–15.CrossRef
16.
Charlemagne-Badal SJ, Lee JW, Butler TL, Fraser GE. Conceptual domains included in wellbeing and life satisfaction instruments: a review. Appl Res Qual Life. 2015;10(2):305–28.CrossRef
17.
Di Martino S, Arcidiacono C, Eiroa-Orosa FJ. Happiness and well-being revisited: including the role of context, justice and values in our understanding of the good life. In: Brown NJL, Lomas T, Eiroa-Orosa FJ, editors. Handbook of critical positive psychology—a synthesis for social change. London: Routledge; 2017. p. 99–116.
18.
Prilleltensky I, Dietz S, Prilleltensky O, Myers N, Rubenstein C, Jin Y, McMahon A. Assessing multidimensional well-being: development and validation of the I COPPE scale. J Community Psychol. 2015;43:199–226.CrossRef
19.
Bronfenbrenner U. Interacting Systems in Human Development. Research paradigms: present and future. In: Bolger N, Caspi A, Downey G, Moorehouse M, editors. Persons in context: developmental processes. Cambridge: Cambridge University Press; 1988. p. 25–49.
20.
Bronfenbrenner U. Ecological models of human development. In: Husten T, Postlethwaite TN, editors. International encyclopedia of education, vol. 3. 2nd ed. New York: Elsevier Science; 1994. p. 1643–7.
21.
Zimbardo PG, Boyd JN. Putting time in perspective: a valid, reliable individual-difference metric. J Pers Soc Psychol. 1999;77:1271–88.CrossRef
22.
Zimbardo PG, Keough KA, Boyd JN. Present time perspective as a predictor of risky driving. Pers Individ Dif. 1997;23(6):1007–23.CrossRef
23.
Zimbardo PG, Boniwell I. Balancing one’s time perspective in pursuit of optimal functioning. Positive psychology in practice. Hoboken: Wiley; 2004.
24.
Drake L, Duncan E, Sutherland F, Abernethy C, Henry C. Time perspective and correlates of well-being. Time Soc. 2008;17(1):47–61.CrossRef
25.
Delle Fave A. Dimensions of well-being: research and intervention. Milano: Franco Angeli; 2006.
26.
Petrillo G, Capone V, Caso D, Keyes CLM. The mental health continuum–short form (MHC–SF) as a measure of well-being in the Italian context. Soc Indic Res. 2015;121(1):291–312.CrossRef
27.
Ruini C, Ottolini F, Rafanelli C, Ryff CD, Fava GA. La validazione italiana delle Psychological Well-being Scales (PWB). Riv Psichiatr. 2003;38(3):117–30.
28.
Di Fabio A, Busoni L. (2009). Proprietà psicometriche della versione italiana della Satisfaction With Life Scale (SWLS) con studenti universitari. Counseling. Giornale Italiano di Ricerca e Applicazioni. 2009;2:201–12.
29.
30.
Brislin RW. Back-translation for cross-cultural research. J Cross-Cult Psychol. 1970;1(3):185–216.CrossRef
31.
Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res. 2010;19(4):539–49.CrossRefPubMedPubMedCentral
32.
Douglas SP, Craig CS. Collaborative and iterative translation: an alternative approach to back translation. J Int Mark. 2007;15(1):30–43.CrossRef
33.
Couper MP, Baker RP, Bethlehem J, Clark CZF, Martin J, Nicholls WL, O’Reilly JM. Computer assisted survey information collection. New York: John Wiley & Sons; 1998.
34.
Weeks MF. Computer-assisted survey information collection: a review of CASIC methods and their implications for survey operations. J Off Stat. 1992;8(4):445–65.
35.
Mardia KV. Measures of multivariate skewness and kurtosis with applications. Biometrika. 1970;57(3):519–30.CrossRef
36.
Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55.CrossRef
37.
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;19:39–50.CrossRef
38.
Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull. 1980;88(3):588–606.CrossRef
39.
Saris WE, Aalberts C. Different explanations for correlated disturbance terms in MTMM studies. Struct Equ Modeling. 2003;10:193–213.CrossRef
40.
Kenny DA, Kashy D, Bolger N. Data analysis in social psychology. In: Gilbert D, Fiske S, Lindzey G, editors. Handbook of social psychology. 4th ed. New York: McGraw-Hill; 1998. p. 233–65.
41.
Hair JFJ, Black WC, Babin BJ, Anderson RE. Multivariate data analysis (7th edition). Pearson Education Limited: Harlow; 2014.
42.
Brown TA. Confirmatory factor analysis for applied research. New York: Guilford Publications, Incorporated; 2006. 
43.
Myers ND, Park SE, Lefevor GT, Dietz S, Prilleltensky I, Prado GJ. Measuring multidimensional subjective well-being with the I COPPE scale in a Hispanic sample. Meas Phys Educ Exerc Sci. 2016;20(4):230–43.CrossRef
44.
Satorra A, Bentler PM. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika. 2001;66(4):507–14.CrossRef
45.
Myers ND, Prilleltensky I, Jin Y, Dietz S, Rubenstein CL, Prilleltensky O, McMahon A. Empirical contributions of the past in assessing multidimensional well-being. J Community Psychol. 2014;42(7):789–98.CrossRef
46.
Raykov T. Estimation of composite reliability for congeneric measures. Appl Psychol Meas. 1997;21(2):173–84.CrossRef
47.
Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;56(2):81.CrossRefPubMed
48.
Ferketich SL, Figueredo AJ, Knapp TR. Focus on psychometrics. The multitrait–multimethod approach to construct validity. Res Nurs Health. 1991;14(4):315–20.CrossRefPubMed
49.
Byrne BM. Structural equation modeling with Mplus: basic concepts, applications, and programming. New York: Taylor & Francis; 2013.
50.
Byrne BM, Shavelson RJ, Muthén B. Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychol Bull. 1989;105(3):456–66.CrossRef
51.
Widaman KF, Reis SP. Exploring the measurement invariance of psychological instruments: applications in the substance use domain. In: Bryant MW, West SG, editors. The science of prevention: methodological advances from alcohol and substance abuse research. Washington, DC: American Psychological Association; 1997. p. 281–324.CrossRef
52.
Marsh HW. Confirmatory factor analysis models of factorial invariance: a multifaceted approach. Struct Equ Modeling. 1994;1(1):5–34.CrossRef
53.
Geiser C. Data analysis with Mplus. New York: Guilford Press; 2013.
54.
Farrell AM. Insufficient discriminant validity: a comment on Bove, Pervan, Beatty, and Shiu (2009). J Bus Res. 2010;63(3):324–7.CrossRef
55.
Li Z, Yin X, Jiang S, Wang M, Cai T. Psychological mechanism of subjective well-being: a stable trait or situational variability. Soc Indic Res. 2014;118(2):523–34.CrossRef
56.
Lucas RE, Donnellan MB. How stable is happiness? Using the STARTS model to estimate the stability of life satisfaction. J Res Pers. 2007;41(5):1091–8.CrossRefPubMedPubMedCentral
57.
Rubenstein CL, Duff J, Prilleltensky I, Jin Y, Dietz S, Myers ND, Prilleltensky O. Demographic group differences in domain-specific well-being. J Community Psychol. 2016;44(4):499–515.CrossRef
58.
Kagan C, Kilroy A. Psychology in the community. In: Haworth J, Hart G, editors. Well-being: individual, community and social perspectives. New York: Palgrave Macmillan; 2007. p. 97–113.
59.
Cummins RA. Objective and subjective quality of life: an interactive model. Soc Indic Res. 2000;52(1):55–72.CrossRef
60.
Diener E, Suh E. Measuring quality of life: economic, social, and subjective indicators. Soc Indic Res. 1997;40(1):189–216.CrossRef
Metadata
Title
Measuring subjective well-being from a multidimensional and temporal perspective: Italian adaptation of the I COPPE scale
Authors
Salvatore Di Martino
Immacolata Di Napoli
Ciro Esposito
Isaac Prilleltensky
Caterina Arcidiacono
Publication date
01-12-2018
Publisher
BioMed Central
Published in
Health and Quality of Life Outcomes / Issue 1/2018
Electronic ISSN: 1477-7525
DOI
https://doi.org/10.1186/s12955-018-0916-9

Other articles of this Issue 1/2018

Health and Quality of Life Outcomes 1/2018 Go to the issue

Research

Cross-cultural adaptation and psychometric properties of the Kessler Scale of Psychological Distress to a traumatic brain injury population in Swahili and the Tanzanian Setting

Research

What were they thinking when providing preference measurements for generic health states? The evidence for HUI3

Research

CHF-PROM: validation of a patient-reported outcome measure for patients with chronic heart failure

Research

Maternal predictors related to quality of life in pregnant women in the Northeast of Brazil

Research

Related factors of quality of life of type 2 diabetes patients: a systematic review and meta-analysis

Research

Adaption and validation of the nasal obstruction symptom evaluation scale in German language (D-NOSE)