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Development and validation of a 6‐item Revised UCLA Loneliness Scale (RULS‐6) using Rasch analysis

Wongpakaran, Nahathai, Wongpakaran, Tinakon, Pinyopornpanish, Manee, Simcharoen, Sutapat, Suradom, Chawisa, Varnado, Pairada, Kuntawong, Pimolpun·2020·British Journal of Health Psychology

Abstract

Objective: The UCLA Loneliness Scale, containing 20 items, is one of the commonly used loneliness scales. Some shorter versions have been developed using factor analysis. The study aimed to shorten the UCLA Loneliness Scale using Rasch and factor analysis methods and test the psychometric properties of the new scale. Methods: The full sample of the study included 719 respondents, divided into three subsamples (205, 324, and 190 for samples 1–3, respectively). The original, 20‐item Revised UCLA Loneliness Scale (R‐ULS) was shortened using 205 students (sample 1); the shortened scale was then validated for construct and concurrent validity with 324 students (sample 2) and 190 clinical participants (sample 3). Confirmatory factor analysis and Rasch analysis were used for construct validity. Convergent, discriminant, and concurrent validity were assessed by exploring the correlation with other psychological measurements. Results: In sample 1, the R‐ULS was shortened to a 6‐item scale (RULS‐6) that fits the Rasch model. The RULS‐6 met the criteria of unidimensionality and local independence without differential item functioning due to age and sex, and good targeting the clinical sample. Person Separation Index (PSI) reflected that reliability from the Rasch perspective was acceptable. However, collapsing categories 2 (sometime) and 3 (rarely) may be required in a clinical sample. When tested in samples 2 and 3, the RULS‐6 fits the Rasch measurement model. Convergent and discriminant validity were demonstrated with interpersonal problems and attachment scales. As expected, a positive correlation was found between RULS‐6 and anxiety, depression subscale, interpersonal difficulties, and somatization subscales denoting concurrent validity. Cronbach’s alpha of the RULS‐6 was good (.83). Conclusion: Using Rasch analysis, the proposed RULS‐6 constituted a 70% reduction of the number of original items, yet preserved the psychometric properties in independent samples of students and psychiatric outpatients. Statement of contribution: What is already known on this subject? The UCLA Loneliness Scale is a common instrument used to gauge loneliness levels. The 20‐item revised scale (R‐ULS) has acceptable psychometric properties but its construct varies. Due to the length of the questionnaire, administration of R‐ULS is not always practical. Short versions vary in items and were developed with classic test theory (e.g., factor analysis). Rasch analysis – providing more accuracy based on measurement theory – could be used instead. What does this study add? Using a Rasch analysis approach, a 6‐item scale of loneliness (RULS‐6) was created. The RULS‐6 was tested in student and clinical samples, meeting Rasch measurement model criteria. The RULS‐6 showed promising psychometrics to be used in both non‐clinical and clinical samples.

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Cites

Research works printed but not linked: 59

  1. 10.1177/0013164411432166 · DOI
  2. 10.1097/01.mlr.0000103528.48582.7c · DOI
  3. 10.1177/0013164412450877 · DOI
  4. Andrich D., 2004, Final report on the psychometric analysis of the Early Development Instrument (EDI) using the Rasch model: A technical paper commissioned for the development of the Australian Early Development Instrument (AEDI)
  5. 10.4324/9781315814698 · DOI
  6. 10.1177/0049124192021002005 · DOI · Paper page
  7. 10.1016/j.erap.2008.09.006 · DOI
  8. 10.1089/cpb.2004.7.559 · DOI
  9. 10.1089/jpm.2013.0555 · DOI
  10. Cho Y. R., 2004, A study on a model for internet addiction of adolescents, Taehan Kanho Hakhoe Chi, 34, 541
  11. 10.1002/9781118574454.ch3 · DOI
  12. 10.1037/0033-2909.112.1.155 · DOI · Paper page
  13. COSMIN, 2005, COnsensus‐based Standards for the selection of health Measurement INstruments
  14. 10.1080/03601271003756628 · DOI
  15. 10.1037/10519-153 · DOI · Paper page
  16. 10.5334/pb.ae · DOI
  17. 10.1097/OPX.0b013e3181bab10c · DOI
  18. 10.1016/j.ijnurstu.2008.10.007 · DOI
  19. 10.1186/1752-4458-8-34 · DOI
  20. Hinz A., 2007, The acquiescence effect in responding to a questionnaire, GMS Psycho‐Social Medicine, 4, Doc07
  21. 10.1037/0022-006X.47.4.762 · DOI
  22. 10.1080/10705519909540118 · DOI · Paper page
  23. 10.1080/00223980.2013.825229 · DOI
  24. 10.1186/s40359-019-0285-0 · DOI
  25. 10.1016/j.anr.2017.05.002 · DOI · Paper page
  26. 10.1016/j.paid.2006.10.013 · DOI
  27. 10.1089/cpb.2004.7.379 · DOI
  28. 10.1016/j.jagp.2017.06.004 · DOI
  29. 10.1002/1097-4679(196701)23:1<84::AID-JCLP2270230123>3.0.CO;2-2 · DOI
  30. 10.1016/j.psychres.2016.02.020 · DOI
  31. Linacre J., 1994, Sample size and item calibration [or Person Measure] stability
  32. Linacre J. M., 1999, Category disordering vs. step disordering, Rasch Measurement Transactions, 13, 675
  33. Linacre J. M., 2006, Data variance explained by measures, Rasch Measurement Transactions, 20, 1045
  34. Linacre J. M., 2017, Winsteps® Rasch measurement computer program User's Guide
  35. Lotrakul M., 1999, Development of the Thai depression inventory, Journal of the Medical Association of Thailand, 82, 1200
  36. 10.1177/0164027504268574 · DOI · Paper page
  37. 10.1007/BF02296272 · DOI · Paper page
  38. 10.2522/ptj.20150423 · DOI
  39. Muthén L. K., 2015, Mplus User’s Guide
  40. 10.1207/s15327841mpee1004_1 · DOI
  41. 10.1007/s10433-014-0312-1 · DOI
  42. Nunnally J. C., 1994, Psychometric theory
  43. 10.1159/000441651 · DOI · Paper page
  44. 10.1177/1745691615568999 · DOI · Paper page
  45. 10.1016/j.paid.2006.10.035 · DOI
  46. 10.2466/pr0.1993.72.3c.1379 · DOI
  47. Schumacker R., 1996, Disattenuating correlation coefficients. Rasch Measurement Transactions, Rasch Measurement Transactions, 10, 479
  48. 10.4103/0972-6748.57861 · DOI
  49. Smith E. V., 2002, Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals, Journal of Applied Measurement, 3, 205
  50. 10.1002/art.23108 · DOI
  51. 10.1097/PSY.0b013e3181b40efc · DOI · Paper page
  52. 10.1007/s10433-017-0417-4 · DOI
  53. 10.1186/1751-0759-4-6 · DOI
  54. 10.5209/rev_SJOP.2012.v15.n3.39434 · DOI
  55. 10.4306/pi.2012.9.1.54 · DOI
  56. Wongpakaran T., 2012, Interpersonal problems among psychiatric outpatients and non‐clinical samples, Singapore Medical Journal, 53, 481
  57. Wright B. D., 1996, Reliability and separation, Rasch Measurement Transactions, 9, 472
  58. 10.1016/j.paid.2008.02.003 · DOI
  59. 10.1371/journal.pone.0157795 · DOI

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Metadata

Authors
Wongpakaran, N., Wongpakaran, T., Pinyopornpanish, M., Simcharoen, S., Suradom, C., Varnado, P., & Kuntawong, P.
Affiliations
  • Department of Psychiatry Faculty of Medicine Chiang Mai University Thailand
  • Jittavej Nakhon Sawan Ratchanakarin Hospital Nakhon Sawan Thailand
Venue
British Journal of Health Psychology
Publisher
Wiley
Published
2020-01-30
Volume
25
Issue
2
Pages
233-256
ISSN
  • 1359-107X
  • 2044-8287
Cited by
151
References
12
Language
en
Funders
Faculty of Medicine, Chiang Mai University

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