Quality of Well-Being Scale

1. Abstract

The Quality of Well-Being Scale (QWB), alongside its self-administered adaptation, the Quality of Well-Being Scale Self-Administered (QWB-SA), is a landmark preference-weighted, multi-attribute utility instrument engineered to assess comprehensive health-related quality of life (HRQoL) and generate utility values for the computation of Quality-Adjusted Life Years (QALYs). Originating within the General Health Policy Model developed by Robert M. Kaplan and J.W. Bush at the University of California, San Diego, the instrument conceptualizes health status along a continuum anchored between optimal well-being (1.0) and death (0.0). The measurement model combines functional capacity across three distinct behavioral dimensions—Mobility (MOB), Physical Activity (PAC), and Social Activity (SAC)—with an exhaustive classification of acute and chronic physical and mental symptoms known as the Symptom/Problem Complexes (CPX). In the interviewer-administered system, 27 primary CPX complexes are evaluated, while the self-administered variant expands these into multi-item symptom inventories spanning acute ailments, chronic morbidities, sensory deficits, and affective states. Empirical utility weights, derived via consumer preference methodologies such as category rating and magnitude estimation from representative general community samples, are subtracted from unity (1.0) to produce a single, continuous, cardinal health utility score. The instrument exhibits exemplary psychometric properties, including robust construct validity, high interday reliability ($r > .90$), solid test-retest reliability across clinical trials, and acute sensitivity to small, clinically meaningful changes across diverse therapeutic areas such as chronic obstructive pulmonary disease, cystic fibrosis, human immunodeficiency virus, arthritis, and major depressive disorder. This article provides a comprehensive academic review of the scale’s historical foundations, theoretical and mathematical underpinnings, psychometric validation evidence, structural composition, scoring algorithms, and administrative guidelines.

2. Keywords

Quality of Well-Being Scale, QWB, QWB-SA, Health-Related Quality of Life, Health Utility, Quality-Adjusted Life Years, General Health Policy Model, Functional Status, Psychometrics, Health Economics

3. Authors

The original conceptualization and empirical development of the Quality of Well-Being Scale and its overarching conceptual framework, the General Health Policy Model, were spearheaded by:

• Robert M. Kaplan, Ph.D. — Distinguished Professor Emeritus of Health Policy and Management at the University of California, Los Angeles (UCLA) Fielding School of Public Health, and Distinguished Professor of Medicine at the University of California, San Diego (UCSD) School of Medicine. Dr. Kaplan is a foremost authority in behavioral medicine, health services research, and medical decision analysis.
• J.W. Bush, M.D., M.P.H. (Deceased) — Formerly Professor and Chief of the Division of Health Policy and Health Services Research, Department of Family and Preventive Medicine, University of California, San Diego. Dr. Bush was an early pioneer in the conceptual formulation of health index models and multi-attribute health state measurement.
• Charles C. Berry, Ph.D. — Professor Emeritus in the Department of Family Medicine and Public Health, Division of Biostatistics and Bioinformatics, University of California, San Diego. Dr. Berry contributed extensively to the mathematical modeling, metric properties, and utility weight estimation algorithms.
• William J. Sieber, Ph.D. — Clinical Professor, Department of Family Medicine and Public Health, and Director of the Health Outcomes Assessment Program at UC San Diego. Dr. Sieber co-led the development and validation of the self-administered version (QWB-SA).
• Theodore G. Ganiats, M.D. — Professor Emeritus of Family Medicine and Community Health, University of California, San Diego School of Medicine. Dr. Ganiats directed large-scale health outcomes studies, clinical trials, and comparative utility analyses using the QWB and QWB-SA instruments.

Institutional affiliations historically responsible for the stewardship of the scale include the Health Outcomes Assessment Program and the Health Services Research Center at the University of California, San Diego (9500 Gilman Drive, La Jolla, CA 92093).

4. Purpose

The primary purpose of the Quality of Well-Being Scale is to provide an exact, mathematically rigorous, and comprehensive quantification of human health status on a cardinal, interval-level utility scale. Unlike disease-specific assessment batteries that quantify narrow symptom profiles (such as isolated joint stiffness in rheumatoid arthritis or forced expiratory volume in respiratory illness), the QWB was engineered as a generic health status measure. This generic architecture allows researchers, clinicians, and health economists to compare outcomes across entirely disparate diseases, medical procedures, psychiatric conditions, and public health interventions on a common metric.

In clinical research and randomized controlled trials, the QWB captures the net impact of therapeutic regimens by integrating adverse side effects, therapeutic benefits, and functional repercussions into a single composite score. For example, a pharmacological agent might effectively reduce blood pressure but induce fatigue, sexual dysfunction, and cognitive clouding. Disease-specific instruments focused purely on cardiovascular parameters risk overlooking these collateral burdens. The QWB systematically documents functional and symptomatic detriments, thereby evaluating whether an intervention produces an overall net improvement in patient well-being.

Beyond individual-level clinical outcomes, the theoretical rationale for the QWB is intimately tied to health economic evaluation and health policy analysis. In cost-utility analysis (CUA), healthcare resource allocation decisions require measuring the output of care in terms of Quality-Adjusted Life Years (QALYs). A QALY combines the quantity of life (survival duration) with the quality of life experienced during those years. The QWB operationalizes the quality component by assigning each day lived a utility coefficient ranging from 0.0 (equivalent to death) to 1.0 (equivalent to completely asymptomatic, unconstrained optimal health). By multiplying the duration of a health state by its corresponding QWB utility score, investigators derive the exact number of QALYs produced or preserved by a given clinical intervention, facilitating transparent cost-effectiveness comparisons across health policy domains.

5. Psychological Construct

The core psychological and behavioral construct measured by the Quality of Well-Being Scale is functional health-related quality of life. Health, within this operational paradigm, is not merely the absence of organic pathology; rather, it represents the degree to which an individual can enact desired social, physical, and personal roles without impairment or somatic and psychological distress. The QWB model decomposes this construct into four fundamental, interdependent dimensions:

Mobility (MOB)

Mobility refers to an individual’s behavioral capacity to navigate through physical and geographic space, utilizing community infrastructure, private transportation, or public transit. It reflects spatial autonomy versus institutional or domestic confinement. The scale categorizes this capacity across discrete behavioral states:

• Level 5: Complete behavioral autonomy without health-related travel limitations.
• Level 4: Impaired spatial independence, such as being unable to drive, requiring assistance to use public transportation, or failing to travel as expected for chronological age due to health limitations.
• Level 2: Severe spatial confinement, operationalized as being confined as an inpatient within a hospital, rehabilitation facility, or nursing home.

Physical Activity (PAC)

Physical Activity measures biomechanical functionality and gross motor ambulation. This subscale assesses the respondent’s capacity to ambulate, ascend stairs, lift objects, stoop, bend, and maintain upright posturing without human assistance or assistive devices:

• Level 4: Fully autonomous physical activity and unrestricted ambulation.
• Level 3: Moderate mechanical limitation, including moving in a wheelchair independently, walking more slowly than peers, experiencing difficulty negotiating stairs or inclines, or struggling to stoop, bend, or lift household objects.
• Level 1: Extreme functional restriction, characterized by being confined to a bed, chair, or couch for most or all of the day, or requiring another individual to propel or maneuver one’s wheelchair.

Social Activity (SAC)

Social Activity captures the sociopsychological and instrumental enactment of normative developmental and occupational roles, including self-care autonomy, major role activities (such as gainful employment, educational attendance, or home management), and secondary discretionary activities (such as recreational pursuits, social engagement, and civic participation):

• Level 5: Unrestricted role enactment and self-care.
• Level 4: Selective limitation in recreational or discretionary leisure pursuits.
• Level 3: Measurable limitation in primary, major role functioning (e.g., modified work schedules, reduced school participation).
• Level 2: Total inability to engage in major role performance, although basic personal self-care (bathing, feeding, dressing) remains intact.
• Level 1: Complete functional dependency, involving both total cessation of major role functioning and the requirement of physical assistance to perform basic self-care tasks.

Symptom/Problem Complexes (CPX)

The Symptom/Problem Complexes represent a broad taxonomy of acute and chronic physical, affective, sensory, and cognitive disturbances. While MOB, PAC, and SAC quantify observable behavioral performance, the CPX dimension captures internal subjective distress and somatic pathology that diminish quality of life even when overt behavioral roles appear preserved. The original QWB delineates 27 discrete complexes (ranging from loss of consciousness, burns, limb paralysis, chronic articular pain, and gastrointestinal distress, to affective episodes of depression, crying spells, and excessive anxiety). In the QWB-SA, this classification is operationalized as an exhaustive checklist covering sensory impairments, localized bodily discomfort, cardiopulmonary signs, systemic fatigue, cognitive confusion, and emotional distress. Crucially, the CPX subscale ensures that sub-clinical or masked pathologies that burden patients psychological well-being are systematically captured.

6. Theoretical Framework

The theoretical architecture of the Quality of Well-Being Scale is anchored in the General Health Policy Model (GHPM), formulated by Kaplan and Bush. The GHPM synthesizes principles from medical sociology, psychophysics, expected utility theory, and epidemiological modeling. The model rests upon four central foundational postulates:

1. The Health Continuum and Mortality Anchor

The GHPM posits that at any discrete point in time, human health status can be represented as an exact location along an interval continuum anchored at two definitive theoretical boundaries: optimum health (assigned a value of 1.0) and death (assigned a value of 0.0). Unlike multidimensional profile instruments (such as the Medical Outcomes Study 36-Item Short Form Survey, SF-36) which provide unintegrated scores across diverse sub-domains, the GHPM asserts that health is fundamentally a unified, scalar phenomenon when evaluated through societal and personal preferences.

2. Preference-Based Social Utility

The assigning of values to health states within the QWB is grounded in psychophysical scaling and societal utility assessment. Kaplan, Bush, and Berry conducted extensive psychometric scaling experiments involving thousands of general population raters in San Diego, California. Utilizing category rating (e.g., visual analog or equal-appearing interval scales) and magnitude estimation, community judges evaluated descriptions of multi-attribute health states formed by the cross-classification of MOB, PAC, SAC, and CPX levels.

Through empirical mathematical modeling, these investigations verified that community preferences exhibit strong interval-scale properties and high structural consensus across diverse socioeconomic, ethnic, and age cohorts. The resulting regression-based weights reflect the societal “disutility” or net loss of well-being associated with each specific functional deficit and symptom complex.

3. The Vector Space Model of Health States

Mathematically, any observable health state on a given day $t$ is expressed as a multidimensional vector combining one level from each behavioral dimension plus the single most severe symptom/problem complex reported. The daily well-being score ($W_t$) is calculated via an additive multi-attribute utility function:

$$W_t = 1.0 – (\text{MOB disutility}) – (\text{PAC disutility}) – (\text{SAC disutility}) – (\text{CPX disutility})$$

In this formulation, perfect function with no symptoms incurs zero disutility, yielding a maximum score of 1.000. Conversely, severe functional limitations and debilitating symptoms accumulate additive decrements, driving the score downward toward zero or, in states deemed worse than death, negative values.

4. Prognosis and Well-Life Expectancy

The GHPM extends cross-sectional utility measurement into longitudinal policy modeling by conceptualizing true health outcomes as the integral of well-being over time. By tracking an individual’s or population’s $W_t$ across the lifespan, the model computes Well-Life Expectancy or Quality-Adjusted Life Years:

$$\text{QALYs} = \int_{0}^{T} W(t),dt$$

Where $T$ represents biological survival duration and $W(t)$ is the preference-weighted health utility at time $t$. Thus, the QWB operationalizes the theoretical bridge between day-to-day psychophysical health experiences and macroeconomic health decision analysis.

7. Validity

The psychometric validity of the QWB and QWB-SA has been substantiated across hundreds of clinical and epidemiological studies spanning nearly five decades.

Construct and Convergent Validity

Construct validity is evidenced by strong correlations between QWB utility scores and established physiological, functional, and self-report health parameters. In studies evaluating patients with chronic obstructive pulmonary disease (COPD) conducted by Kaplan, Atkins, and Timms (1984), QWB scores demonstrated statistically significant correlations with objective physiological metrics, including forced expiratory volume in one second ($FEV_1$, $r = .33$ to $.45$), maximum oxygen consumption ($VO_2\text{\max}$), and exercise endurance measured via treadmill testing. Similarly, in cystic fibrosis cohorts, QWB indices correlated significantly with pulmonary function indices ($FEV_1$ percent predicted, $r = .49, p < .001$) and Shwachman clinical severity scores.

In cross-instrument comparisons, Kaplan, Ganiats, Sieber, et al. (1998) demonstrated moderate-to-high convergent validity between the QWB and the SF-36. QWB scores correlated robustly with the SF-36 Physical Functioning scale ($r = .65$ to $.73$), Role Physical ($r = .55$), and the Physical Component Summary (PCS) score ($r = .68$). Moderate correlations with the SF-36 Mental Health and Vitality scales ($r = .42$ to $.51$) confirmed that while the QWB is heavily sensitive to physical functional capacity, its CPX dimension successfully captures psychological morbidity.

Discriminant and Known-Groups Validity

The QWB displays discriminative validity across clinical strata differing in disease stage, comorbidity, and biological severity. In investigations of individuals with HIV infection and AIDS (Kaplan et al., 1989), the QWB accurately differentiated between asymptomatic seropositive individuals (mean QWB $\approx .75$), patients with symptomatic AIDS-Related Complex (ARC, mean QWB $\approx .66$), and patients with overt clinical AIDS (mean QWB $\approx .58$). Significant gradient separations have likewise been observed across stages of rheumatoid arthritis (stratified by ACR functional class), chronic heart failure (NYHA Functional Classes I through IV), and diabetic peripheral neuropathy.

Predictive and Evaluative Validity

The evaluative validity (responsiveness to change) of the QWB has been verified in longitudinal clinical trials. In pharmacotherapeutic and behavioral rehabilitation trials, the QWB reliably detects therapeutic improvements and deteriorations that align with independent physician assessments and laboratory biomarkers. In a landmark clinical trial of auranofin in rheumatoid arthritis, the QWB recorded statistically significant gains corresponding with reductions in swollen joint counts and erythrocyte sedimentation rates. Furthermore, baseline QWB scores demonstrate longitudinal predictive validity, serving as an independent statistical predictor of one-year, five-year, and ten-year mortality in frail elderly populations and end-stage renal disease patients, outperforming several traditional biochemical markers.

8. Reliability

Because the QWB is a multi-attribute utility measure based on complex branching logic and additive combinations rather than an unweighted summative Likert scale measuring a single latent trait, standard psychometric internal consistency metrics (such as Cronbach’s coefficient alpha) are conceptually and mathematically inappropriate. The items within the symptom complex list do not represent parallel indicators of a uniform trait; a patient experiencing an earache is not inherently expected to report chest pain or shortness of breath. Consequently, reliability in the QWB literature is primarily established via inter-rater agreement, interday stability, and test-retest reliability.

Interday and Test-Retest Stability

In a seminal methodological investigation, Anderson, Kaplan, Berry, Bush, and Rumbaut (1989) examined the interday reliability of functional assessments using the QWB across diverse patient populations. Because daily health status fluctuates naturally due to transient acute symptoms (such as mild headaches or common colds), reliability was evaluated by comparing repeated assessments across consecutive days. The intra-class correlation coefficient (ICC) for stable chronic disease patients assessed over a 24- to 48-hour window consistently ranged between $.90$ and $.94$, indicating high stability in capturing core functional capacity.

In test-retest investigations using the self-administered QWB-SA over a 1- to 2-week interval among clinically stable outpatients, test-retest correlations have ranged from $r = .79$ to $.86$. In longitudinal community health studies, where health status was expected to remain constant over one month, Pearson correlation coefficients remained solid ($r = .72$ to $.80$), with variation primarily driven by documented, real-world incident acute health events rather than measurement error.

Inter-Interviewer and Cross-Modal Reliability

For the interviewer-administered QWB, studies evaluating inter-rater agreement between independent, masked interviewers conducting simultaneous or sequential administrations reported concordance rates exceeding $95%$, with inter-rater correlation coefficients across composite utility scores exceeding $.95$. Furthermore, Kaplan, Sieber, and Ganiats (1997) assessed the equivalence between the interviewer-administered QWB and the self-administered QWB-SA. Intraclass correlations between the administration formats ranged from $.82$ to $.89$, confirming that transitioning to a self-administered questionnaire does not compromise psychometric precision.

9. Factor Analysis

Classical psychometric factor analysis—whether exploratory factor analysis (EFA) or confirmatory factor analysis (CFA)—operates under the reflective measurement framework, which assumes that latent psychological traits cause observable item responses. In contrast, the Quality of Well-Being Scale is constructed as a formative (causal indicator) index. Functional limitations in mobility, physical movement, and social roles, coupled with somatic symptoms, do not reflect an underlying latent entity; rather, they collectively form and constitute the patient’s health status.

Nonetheless, numerous empirical factor-analytic investigations have examined the dimensionality of the symptom and functional components of the QWB and QWB-SA to verify structural integrity and identify underlying operational domains:

Structural Dimensionality and Factor Extractions

Exploratory factor analyses using principal axis factoring with promax or varimax rotation across large clinical and community cohorts typically reveal a distinct four-factor to six-factor latent architecture:

• Factor 1: Gross Biomechanical and Ambulation Function — Marked by high loadings ($> .70$) from Physical Activity items (climbing stairs, walking speed, stooping, lifting) and Mobility items (driving restrictions, public transport usage).
• Factor 2: Affective and Neuropsychological Distress — Capturing variance from CPX items assessing depressive affect, crying spells, excessive worry, cognitive confusion, and subjective loss of control (loadings ranging from $.60$ to $.82$).
• Factor 3: Somatosensory and Musculoskeletal Discomfort — Characterized by high loadings from joint pain, neck/back stiffness, muscle cramps, and localized physical discomfort ($loadings > .65$).
• Factor 4: Systemic and Cardiorespiratory Morbidity — Encompassing fatigue, shortness of breath, coughing, chest pain, and generalized weakness ($loadings = .55$ to $.78$).
• Factor 5: Instrumental Social Role Function — Comprising self-care capability and participation in primary work, educational, and domestic responsibilities.

Model Fit and Confirmatory Structural Equation Modeling

Confirmatory factor analyses testing a multidimensional hierarchical structure—where first-order factors for Physical Functioning, Somatic Symptoms, and Psychological Well-Being load onto a second-order global Health Status construct—have demonstrated adequate goodness-of-fit indices across diverse population samples:

• Comparative Fit Index (CFI): Values consistently range between $.92$ and $.96$.
• Tucker-Lewis Index (TLI): Values typically fall between $.91$ and $.95$.
• Root Mean Square Error of Approximation (RMSEA): Values range from $.042$ to $.058$, signifying good model fit.
• Standardized Root Mean Square Residual (SRMR): Values routinely remain below $.050$.

These findings substantiate that although the scale is scored via an additive utility algorithm, its underlying constituents reflect structurally distinct, clinically meaningful dimensions of functional health.

10. Instrument / Measurement Tool

The Quality of Well-Being assessment battery exists in two primary formats: the original clinician/interviewer-administered QWB and the patient-reported QWB-SA (Self-Administered). Both variants capture functional capacity over the preceding three to six days (classically evaluated over the prior 3 days in the standard protocol, assessing “yesterday,” “two days ago,” and “three days ago”).

Structure of the Assessment Tool

• Test Type: Multi-attribute health utility questionnaire and generic health-related quality of life instrument.
• Administration Format: Clinician-administered structured interview, paper-and-pencil self-report, or computer-assisted personal interview (CAPI/web-based).
• Item Inventory:
  • Mobility Scale (MOB): Evaluates spatial movement, travel limitations, and institutionalization across hierarchical levels.
  • Physical Activity Scale (PAC): Evaluates ambulation, biomechanical execution, and bed/chair confinement across hierarchical levels.
  • Social Activity Scale (SAC): Evaluates occupational, civic, recreational, and personal self-care role performance across hierarchical levels.
  • Symptom/Problem Complexes (CPX): In the classical QWB, 27 detailed diagnostic problem complexes. In the QWB-SA, an expanded inventory of chronic, acute, mental, and physical symptoms presented in a binary (Yes/No) endorsement grid.
• Response Format:
  • Behavioral scales (MOB, PAC, SAC) are scored by identifying the single highest-severity (lowest-performing) functional level endorsed on each target day.
  • CPX symptoms are endorsed as present (Yes) or absent (No) on each target day.
• Scoring and Preference Weighting Algorithm:
  • Step 1: For each of the three preceding days, determine the patient’s discrete functional level for MOB, PAC, and SAC.
  • Step 2: Identify all endorsed symptoms for that day and select the single symptom complex that carries the highest empirical disutility weight (the most severe symptom rule).
  • Step 3: Retrieve the empirically derived community weights for the identified MOB, PAC, SAC, and CPX categories.
  • Step 4: Subtract the sum of the four weights from 1.000 to compute the daily health utility score: $W_{\text{day}} = 1.0 – (W_{\text{MOB}} + W_{\text{PAC}} + W_{\text{SAC}} + W_{\text{CPX}})$.
  • Step 5: Average the daily well-being scores across the multi-day assessment window (typically 3 days) to yield the final composite QWB score.
  • Metric Range: Continuous scale from 0.000 (Death) to 1.000 (Optimal Asymptomatic Health). States with catastrophic dysfunction and agonizing distress can mathematically yield negative values (states considered worse than death).

11. Permissions & Fee and Test Year

The Quality of Well-Being Scale was originally formulated in the 1970s, with foundational validation studies published by Kaplan, Bush, and Berry in 1976 and 1979. The self-administered version, QWB-SA, was formalized, validated, and published in the late 1990s (Kaplan, Sieber, & Ganiats, 1997; Sieber et al., 2008).

The instrument is copyrighted by Robert M. Kaplan and the Regents of the University of California. The University of California, San Diego (UCSD) Health Services Research Center oversees licensing, scoring algorithms, and standardized administration manuals. The scale is widely accessible for academic, clinical, and non-commercial research purposes, typically without licensing fees upon completion of an official user agreement or academic registration. However, commercial entities, including pharmaceutical manufacturers and for-profit clinical research organizations (CROs), are required to obtain formal licensing and pay appropriate user fees. Researchers wishing to deploy the paper-and-pencil or computerized versions must contact the UCSD Health Outcomes Assessment Program or corresponding scale authors to secure the official scoring manual, conversion syntax, and permissions.

12. References

• Anderson, J. P., Kaplan, R. M., Berry, C. C., Bush, J. W., & Rumbaut, R. G. (1989). Interday reliability of function assessment for a health status measure: The Quality of Well-being Scale. Medical Care, 27(11), 1076–1084. https://doi.org/10.1097/00005650-198911000-00007
• Bowling, A. (2005). Measuring health: A review of quality of life measurement scales (3rd ed.). Open University Press.
• Kaplan, R. M. (1988). New health promotion indicators: The General Health Policy Model. Health Promotion International, 3(1), 35–49. https://doi.org/10.1093/heapro/3.1.35
• Kaplan, R. M., Anderson, J. P., Wu, A. W., Mathews, W. C., Kozin, F., & Orenstein, D. (1989). The Quality of Well-being Scale: Applications in AIDS, cystic fibrosis, and arthritis. Medical Care, 27(3), S27–S43. https://doi.org/10.1097/00005650-198903001-00003
• Kaplan, R. M., Atkins, C. J., & Timms, R. (1984). Validity of a quality of well-being scale as an outcome measure in chronic obstructive pulmonary disease. Journal of Chronic Diseases, 37(2), 85–95. https://doi.org/10.1016/0021-9681(84)90050-x
• Kaplan, R. M., & Bush, J. W. (1982). Health-related quality of life measurement for evaluation research and policy analysis. Health Psychology, 1(1), 61–80. https://doi.org/10.1037/0278-6133.1.1.61
• Kaplan, R. M., Bush, J. W., & Berry, C. C. (1976). Health status: Types of validity and the index of well-being. Health Services Research, 11(4), 478–507.
• Kaplan, R. M., Bush, J. W., & Berry, C. C. (1979). Health status index: Category rating versus magnitude estimation for measuring levels of well-being. Medical Care, 17(5), 501–525. https://doi.org/10.1097/00005650-197905000-00005
• Kaplan, R. M., Ganiats, T. G., Sieber, W. J., & Anderson, J. P. (1998). The Quality of Well-Being Scale: Critical similarities and differences with SF-36. International Journal for Quality in Health Care, 10(6), 509–520. https://doi.org/10.1093/intqhc/10.6.509
• Kaplan, R. M., Sieber, W. J., & Ganiats, T. G. (1997). The quality of well-being scale: Comparison of the interviewer-administered version with a self-administered questionnaire. Psychology & Health, 12(6), 783–791. https://doi.org/10.1080/08870449708406739
• McDowell, I. (2006). Measuring health: A guide to rating scales and questionnaires (3rd ed.). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195165678.001.0001
• Sieber, W. J., Groessl, E. J., David, K. M., Ganiats, T. G., & Kaplan, R. M. (2008). Quality of Well Being Self-Administered (QWB-SA) scale: User’s manual. Health Services Research Center, University of California, San Diego.

13. Items of the Scale (Questionnaire)