Assessing Multidimensional Well-Being: Development and Validation of the I Coppe Scale

Prilleltensky, Isaac; Dietz, Samantha; Prilleltensky, Ora; Myers, Nicholas D.; Rubenstein, Carolyn L.; Jin, Ying; McMahon, Adam

doi:10.1002/jcop.21674

Cited by 87 publications

(88 citation statements)

References 55 publications

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“…Rather than examine well‐being through an omnibus lens, as is the approach used in the majority of research studies, we use this domain‐specific tool to provide a more accurate and informed depiction of well‐being. The value of these specific domains for well‐being has been previously documented (Prilleltensky et al., ). Using this tool, we examine both OWB and DSWB among diverse groups (see Table for participant demographics).…”

Section: Demographic Group Differences In Domain‐specific Well‐beingmentioning

confidence: 81%

“…While many empirical studies link OWB to demographic variables such as income, gender, and age (Diener & Ryan, ), not much is known about group differences in domain‐specific well‐being (DSWB). Our study contributes to this research by using the I COPPE Scale, designed to measure self‐report scores of DSWB in the following domains: I nterpersonal, C ommunity, O ccupational, P hysical, P sychological, and E conomic (Myers et al., ; Prilleltensky et al., ).…”

Section: Demographic Group Differences In Domain‐specific Well‐beingmentioning

confidence: 99%

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Demographic Group Differences in Domain‐specific Well‐being

Rubenstein

Duff

Prilleltensky

et al. 2016

Journal Community Psychology

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Although research is available on group differences for overall well‐being, little research has explored group differences for domain‐specific well‐being. Therefore, we examined differences in overall and domain‐specific well‐being across several demographic variables: gender, income, marital status, age, ethnicity, education level, employment status, occupation, and housing tenure. We analyzed data from 1,087 participants on the I COPPE Scale, which provides scores for overall, interpersonal, community, occupational, physical, psychological, and economic well‐being. Group differences were found across multiple domains with small to large effect sizes. While there were no gender differences, compared with those in the same demographic variable, higher income earners, married, elderly, Hispanic, educated, white‐collar professionals, and homeowners reported the highest levels of well‐being. The unemployed reported the lowest level of well‐being on all but one of the domains–the interpersonal domain. Findings suggest people report different levels of well‐being based on their unique demographic and life circumstances.

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Section: Demographic Group Differences In Domain‐specific Well‐beingmentioning

confidence: 81%

Section: Demographic Group Differences In Domain‐specific Well‐beingmentioning

confidence: 99%

Demographic Group Differences in Domain‐specific Well‐being

Rubenstein

Duff

Prilleltensky

et al. 2016

Journal Community Psychology

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“…Researchers have proposed methods to optimize reliability when shorter tests were desirable (e.g., saving administration time, Raykov, 2014) or inevitable (e.g., long tests consisting of several sub-domains of the latent trait). Prilleltensky et al (2015) recently developed a 21-item scale measuring the overall well-being. Seven sub-domains (e.g., psychological well-being) were included in the scale and each sub-domain was measured by only 3 items.…”

Section: Discussionmentioning

confidence: 99%

DIF Analysis with Multilevel Data: A Simulation Study Using the Latent Variable Approach

Jin

Eason

2016

JEI

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The effects of mean ability difference (MAD) and short tests on the performance of various DIF methods have been studied extensively in previous simulation studies. Their effects, however, have not been studied under multilevel data structure. MAD was frequently observed in large-scale cross-country comparison studies where the primary sampling units were more likely to be clusters (e.g., schools). With short tests, regular DIF methods under MAD-present conditions might suffer from inflated type I error rate due to low reliability of test scores, which would adversely impact the matching ability of the covariate (i.e., the total score) in DIF analysis. The current study compared the performance of three DIF methods: logistic regression (LR), hierarchical logistic regression (HLR) taking multilevel structure into account, and hierarchical logistic regression with latent covariate (HLR-LC) taking multilevel structure into account as well as accounting for low reliability and MAD. The results indicated that HLR-LC outperformed both LR and HLR under most simulated conditions, especially under the MAD-present conditions when tests were short. Practical implications of the implementation of HLR-LC were also discussed.

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“…We used an adapted shortened version of the interpersonal, community, occupational, physical, psychological, and economic scale (I COPPE; Prilleltensky et al, 2015) to measure self-reported well-being in the following domains: (1) overall life situation, (2) relationships, (3) community, (4) occupation, (5) physical health and (6) psychological health. For each domain participants were asked to rate on a scale from 0 ¼ worst possible to 10 ¼ best possible) their current situation regarding each specific domain (e.g.…”

Section: Mwbmentioning

confidence: 99%

“…When it comes to relationships with important people in your life, on which number do you stand now?'). The I COPPE scale taps more dimensions of well-being than other widely adopted standardized measures in the field, such as for example the Gallup Corporation (Rath and Harter, 2010) and the International Wellbeing Group (2006) and it has previously shown convergent validity in its full version on a sample of U.S. adults (Prilleltensky et al, 2015). As described in the results section, in the current study exploratory and confirmatory techniques were adopted to analyse the factor structure of this instrument in its shortened version adopted here on our Italian and Serbian sample groups.…”

Section: Mwbmentioning

confidence: 99%

Testing multidimensional well-being among university community samples in Italy and Serbia

Lietz

Piumatti

Mosso

et al. 2016

Health Promot. Int.

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SummaryToday well-being attracts the attention of public health professionals who are looking to explore life satisfaction as a whole and its specific domains. In order to contribute in moving the measurement of subjective well-being from a primarily academic activity to the sphere of intervention, we need to assess tools to measure multidimensional well-being (MWB) adopting state-of-the-art statistical techniques. Through structural equation modelling our goal was to test a MWB model among Italian and Serbian university students and to further observe its relationships with measures of life goals' pursuing. This crosssectional pilot study was conducted on a consecutive sample of 86 Italian (45% female; M age ¼ 24.20, SD ¼ 2.02) and 83 Serbian (55% female; M age ¼ 23.52, SD ¼ 2.48) university students. Participants filled in an anonymous questionnaire investigating: self-perceived MWB, standardized control measures of wellbeing (life satisfaction and eudaimonic well-being), and commitment and stress regarding personal goal pursuing. Results evidenced how Serbians reported higher scores on MWB and on control measures than Italians. Moreover, the most frequently reported goals were to complete studies, to obtain job position and to be healthy. Exploratory and multi-group confirmatory factor analyses yielded a one-factor solution of MWB across Italian and Serbian sub-groups. MWB resulted positively associated with standardized control measures in both national groups. The results support the strength of our MWB model applied to samples of young university students in Italy and Serbia. Based on such findings, future studies may adopt this instrument in larger populations of university students in these two countries.

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Assessing Multidimensional Well-Being: Development and Validation of the I Coppe Scale

Cited by 87 publications

References 55 publications

Demographic Group Differences in Domain‐specific Well‐being

Demographic Group Differences in Domain‐specific Well‐being

DIF Analysis with Multilevel Data: A Simulation Study Using the Latent Variable Approach

Testing multidimensional well-being among university community samples in Italy and Serbia

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