Skip to main content
SpringerLink
Log in

Constructing an Index for the Subjective Well-being Under Neuroleptics Scale (SWN), short form: Applying structural equation modeling for testing reliability and validity of the index

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Structural equation modeling (SEM) has been widely used in psychology and sociology for testing validity of measurement instruments. However, this statistical technique has so far played minor role in quality-of-life research. The main objective of this paper is to demonstrate the potential of SEM for constructing and testing the validity of a Subjective Well-being under Neuroleptics (SWN) index for patients with schizophrenia. For these purposes, data from the GEO study (Gesundheitsökonomische Evaluation von Olanzapin in Deutschland; Health economics study of olanzapine in the treatment of schizophrenia in Germany) were used. The GEO is a prospective, comparative, noninterventional, observational study. A total of 646 participants treated with either olanzapine (n = 416) or haloperidol (n = 230) were enrolled in the study; 360 patients were available for factor analyses. The short (20-item) form of the SWN scale was administered to assess patients’ perspectives on their quality of life. The structural equation models (SEMs) were then applied to construct 5- and 10-item indexes based on SWN. The data indicate that the 5-item index is the most time-saving approach for evaluating perceptions of well-being (and thus, quality of life) among patients with schizophrenia. The application of SEM showed no appreciable loss of validity of this index.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

“p-value”:

p is the probability of getting as large a discrepancy as occurred with the present sample

AGFI:

Adjusted Goodness-of-Fit Index

AIC:

Akaike information criterion

BCC:

Brown–Cudeck criterion

BIC:

Bayes information criterion

CAIC:

Consistent Akaike information criterion

CFA:

confirmatory factor analysis

CMIN/DF:

Minimum value of the discrepancy function divided by degrees of freedom

Er:

emotional regulation

Err:

error

EuroQol:

European Quality of Life [questionnaire]

GEO:

Gesundheitsökonomische Evaluation von Olanzapin in Deutschland (Health economics study of olanzapine in the treatment of schizophrenia in Germany)

GFI:

goodness-of-fit index

ICD-10:

International Classification of Diseases, Tenth Edition

Mf:

mental functioning

MI:

modification index/indices

MLDL:

Munich Quality-of-Life Dimensions List

PCLOSE:

“p value” for testing the null hypothesis of the close fit

Pf:

physical functioning

QoL:

quality of life

res:

residual

RMS:

root mean square error of approximation (Steiger and Lind)

RMSEA:

root mean squared error of approximation (Browne and Cudeck)

Sc:

self-control

SEM:

structural equation model(s)

SF-36:

Medical Outcomes Study 36-Item Short-Form Health Survey

Si:

social integration

SRM:

Social Research Methodology [database]

SWN:

Subjective Well-being under Neuroleptics

ULS:

unweighted least squares

VAR:

variable

VAS:

Visual Analogue Scale

References

  1. Social Research Methodology (SRM) Database. http://www. niwi.knaw.nl/srmonline/index.htm. Last accessed May 15, 2003, 5:30 pm.

  2. Keller SD, Bentler PM and Ware JE (1998). Use of structural equation modeling to test the construct validity of the SF-36 Health Survey in 10 countries: Results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol 51(11): 1179–1188

    Article  PubMed  CAS  Google Scholar 

  3. Carlsson M and Hamrin E (2002). Evaluation of the life satisfaction questionnaire (LSQ) using structural equation modeling (SEM). Qual Life Res 11(5): 415–425

    Article  PubMed  Google Scholar 

  4. Saris WE, Scherpenzeel A and Veenhoven R (1996). A Comparative Study of Satisfaction with Life in Europe. Eötvös University Press, Budapest

    Google Scholar 

  5. Payck TR. Checkliste Psychiatrie (In German). Stuttgart: Thieme, 1988

  6. NIMH (National Institute of Mental Health) http://www. nimh.nih.gov/publicat/schizoph.htm. Last accessed February 12, 2003, 4:34 pm

  7. Foster RH and Goa KL (1999). Olanzapine: A pharmacoeconomic review of its use in schizophrenia. Pharmacoeconomics 15(6): 611–640

    Article  PubMed  CAS  Google Scholar 

  8. Goldner EM, Hsu L, Waraich P and Somers JM (2002). Prevalence and incidence studies of schizophrenic disorders: A systematic review of the literature. Can J Psychiatry 47(9): 833–843

    PubMed  Google Scholar 

  9. Karow A and Naber D (2002). Subjective well-being and quality of life under atypical antipsychotic treatment. Psychopharmacology (Berl) 162(1): 3–10

    Article  CAS  Google Scholar 

  10. Naber D, Moritz S and Lambert M (2001). Improvement of schizophrenic patients subjective well-being under atypical antipsychotic drugs. Schizophr Res 50(1–2): 79–88

    Article  PubMed  CAS  Google Scholar 

  11. Weisfelt M, Dingemans PM and Haan L (2002). Psychometrics properties of the Subjective Well-Being Under Neuroleptics scale and the Subjective Deficit Syndrome Scale. Psychopharmacology (Berl) 162(1): 24–28

    Article  CAS  Google Scholar 

  12. Bollen KA (1989). Structural Equations with Latent Variables. Wiley-Interscience, New York

    Google Scholar 

  13. Jöreskog KG (1978). Structural analysis of covariance and correlation matrices. Psychometrica 43: 443–472

    Article  Google Scholar 

  14. Byrne BM (2001). Structural Equation Modeling with AMOS. Basic Concepts, Applications and Programming (Multivariate Applications Series). Lawrence Erlbaum Associates, Hillsdale, New Jersey

    Google Scholar 

  15. Arbuckle J (ed.). Amos 4.0 User’s Guide. Chicago: SPPS, 1999

  16. Hu L and Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Eq Model 6: 1–55

    Article  Google Scholar 

  17. Marsh HW, Hau KT and Wen Z (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralising Hu and Bentler’s (1999) findings. Struct Eq Model 11: 320–341

    Article  Google Scholar 

  18. Harman HH (1976). Modern Factor Analysis. University of Chicago Press, Chicago

    Google Scholar 

  19. Hoogland JJ and Boomsma A (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociol Meth Res 26: 329–367

    Article  Google Scholar 

  20. Cronbach LJ and Meehl PE (1955). Construct validity in psychological tests. Psychol Bull 52: 281–302

    Article  PubMed  CAS  Google Scholar 

  21. Raykov T (1997). Estimation of composite reliability for congeneric measures. Appl Psychol Measure 21: 173–184

    Article  Google Scholar 

  22. Browne MW and Cudeck R (1993). Alternative ways of assessing model fit. In: Bollen, KA and Long, JS (eds) Testing Structural Equation Models, pp 136–162. Sage, Newburry Park, California

    Google Scholar 

  23. Akaike H (1973). Information theory and an extension of the maximum likelihood principle. In: Petrov, BN and Csaki, F (eds) Proceedings of the 2nd International Symposium on Information Theory, pp 267–281. Akademiai Kiado, Budapest

    Google Scholar 

  24. Akaike H (1987). Factor analysis and AIC. Psychometrica 52: 317–332

    Article  Google Scholar 

  25. Jöreskog KG and Sörbom D (2001). LISREL 8 User’s Reference Guide. Scientific Software, Lincolnwood, IL

    Google Scholar 

  26. Tanaka JS and Huba GJ (1985). A fit index for covariance structure models under arbitrary GLS estimation. Br J Math Stat Psychol 38: 197–201

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Politikwissenschaften, FB 03 Sozial- und Kulturwissenschaften, , Justus Liebig Universität Gießen, Karl-Glöckner-Straße 21 E, 35394, Gießen, Germany

    Peter Schmidt & Lars Haggenmüller

  2. Lilly Deutschland GmbH, Bad Homburg, Germany

    Johannes Clouth

  3. Hospital and Health Center for Psychiatry and Psychotherapy, Hamburg Eppendorf University Clinic, Hamburg, Germany

    Dieter Naber

  4. Kendle GmbH & Co. GMI KG, München, Germany

    Ursula Reitberger

Authors
  1. Peter Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes Clouth
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lars Haggenmüller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dieter Naber
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ursula Reitberger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Schmidt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schmidt, P., Clouth, J., Haggenmüller, L. et al. Constructing an Index for the Subjective Well-being Under Neuroleptics Scale (SWN), short form: Applying structural equation modeling for testing reliability and validity of the index. Qual Life Res 15, 1191–1202 (2006). https://doi.org/10.1007/s11136-006-0069-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-006-0069-x

Keywords

Search

Navigation