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An evaluation of the factor structure of the Self-Assessed Wisdom Scale (SAWS) and the creation of the SAWS-15 as a short measure for personal wisdom

Published online by Cambridge University Press:  22 February 2021

Trilas M. Leeman ,
Bob G. Knight ,
Erich C. Fein ,
Sonya Winterbotham  and
Jeffrey Dean Webster
Trilas M. Leeman*
Affiliation:
School of Psychology and Counselling, University of Southern Queensland, Toowoomba, Australia
Bob G. Knight
Affiliation:
School of Psychology and Counselling, University of Southern Queensland, Toowoomba, Australia
Erich C. Fein
Affiliation:
School of Psychology and Counselling, University of Southern Queensland, Toowoomba, Australia
Sonya Winterbotham
Affiliation:
School of Psychology and Counselling, University of Southern Queensland, Toowoomba, Australia
Jeffrey Dean Webster
Affiliation:
Department of Psychology, Langara College, Vancouver, Canada
*
Correspondence should be addressed to: Trilas M. Leeman, School of Psychology and Counselling, University of Southern Queensland, West Street, Darling Heights, Queensland, Australia, 4350. Phone: +61746312638; Fax: +61 7 4631 2721. Email: Trilas.Leeman@usq.edu.au.
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Abstract

Objectives:

Although wisdom is a desirable life span developmental goal, researchers have often lacked brief and reliable construct measures. We examined whether an abbreviated set of items could be empirically derived from the popular 40-item five-factor Self-Assessed Wisdom Scale (SAWS).

Design:

Survey data from 709 respondents were randomly split into two and analyzed using confirmatory factor analysis (CFA).

Setting:

The survey was conducted online in Australia.

Participants:

The total sample consisted of 709 participants (Mage = 35.67 years; age range = 15–92 years) of whom 22% were male, and 78% female.

Measurement:

The study analyzed the 40-item SAWS.

Results:

Sample 1 showed the traditional five-factor structure for the 40-item SAWS did not fit the data. Exploratory factor analysis (EFA) on Sample 2 offered an alternative model based on a 15-item, five-factor solution with the latent variables Reminiscence/Reflection, Humor, Emotional Regulation, Experience, and Openness. This model, which replicates the factor structure of the original 40-item SAWS with a short form of 15 items, was then confirmed on Sample 1 using a CFA that produced acceptable fit and measurement invariance across age groups.

Conclusions:

We suggest the abbreviated SAWS-15 can be useful as a measure of individual differences in wisdom, and we highlight areas for future research.

Keywords

SAWSwisdomSAWS-15measurementreliabilityage groupinvariance
Type
Original Research Article
Information
International Psychogeriatrics , Volume 34 , Special Issue 3: Issue Theme: Positive Psychiatry Factors and Health , March 2022 , pp. 241 - 251
Copyright
© International Psychogeriatric Association 2021

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References

Alquraan, M., Alshraideh, M. and Bsharah, M. (2010). Psychometric properties and differential item function (DIF) analyses of Jordanian version of self-assessed wisdom scale (SAWS-Jo). International Journal of Applied Educational Studies, 9, 52–66.Google Scholar
Ardelt, M. (2003). Empirical assessment of a three-dimensional wisdom scale. Research on Aging, 25, 275–324. doi: 10.1177/0164027503025003004 CrossRefGoogle Scholar
Ardelt, M., Pridgen, S. and Nutter-Pridgen, K. L. (2018). The relation between age and three-dimensional wisdom: variations by wisdom dimensions and education. The Journals of Gerontology: Series B, 73, 1339–1349. doi: 10.1093/geronb/gbx182 CrossRefGoogle ScholarPubMed
Assmann, A. (1994). Wholesome knowledge: Concepts of wisdom in a historical and cross-cultural perspective. In: Featherman, D. L., Lerner, R. M. and Perlmuter, M. (Eds.), Lifespan Development and Behaviour (pp. 187–224). Hillsdale, NJ: Erlbaum.Google Scholar
Bangen, K. J., Meeks, T. W. and Jeste, D. V. (2013). Defining and assessing wisdom: a review of the literature. American Journal of Geriatric Psychiatry, 21, 1254–1266. doi: 10.1016/j.jagp.2012.11.020 CrossRefGoogle ScholarPubMed
Brienza, J. P., Kung, F. Y. H., Santos, H. C., Bobocel, D. R. and Grossmann, I. (2018). Wisdom, bias, and balance: toward a process-sensitive measurement of wisdom-related cognition. Journal of Personality and Social Psychology, 115, 1093–1126. doi: 10.1037/pspp0000171 CrossRefGoogle Scholar
Byrne, B. M. (2016). Structural Equation Modelling with Amos: Basic Concepts, Applications, and Programming (3rd Ed.). New York, NY: Routledge.CrossRefGoogle Scholar
Carmines, E. G. and Zeller, R. A. (1979). Reliability and Validity Assessment. London: Sage.10.4135/9781412985642CrossRefGoogle Scholar
Cheung, G. W. and Rensvold, R. B. (2002). Evaluating goodness-of-fit indices for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi: 10.1207/S15328007SEM0902_5 CrossRefGoogle Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Comfrey, A. L. and Lee, H. B. (1992). A First Course in Factor Analysis (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage.Google Scholar
Erikson, E. H. (1959). Identity and the Life Cycle. New York, NY: Norton.Google Scholar
Field, A. (2017). Discovering Statistics using IBM SPSS Statistics (5th ed.). London: SAGE.Google Scholar
Flora, D. B. and Flake, J. K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: decisions for scale development and validation. Canadian Journal of Behavioural Science, 49, 78–88. doi: 10.1037/cbs0000069 CrossRefGoogle Scholar
Fung, S., Chow, E. O. and Cheung, C. (2020). Development and validation of a brief self-assessed wisdom scale. BMC Geriatrics, 20. Retrieved from: https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-020-1456-9 10.1186/s12877-020-1456-9CrossRefGoogle ScholarPubMed
Glück, J. and Bluck, S. (2013). The MORE life experience model: A theory of the development of personal wisdom. In: Ferrari, M. and Weststrate, N. M. (Eds.), The Scientific Study of Personal Wisdom (pp. 75–97). Dordrecht, Netherlands: Springer.Google Scholar
Glück, J., König, S., Naschenweng, K., Redzanowski, U., Dorner, L., Straßer, I. and Wiedermann, W. (2013). How to measure wisdom: content, reliability, and validity of five measures. Frontiers in Psychology, 4, 1–13. doi: 10.3389/fpsyg.2013.00405 CrossRefGoogle ScholarPubMed
Gorsuch, R. L. (1983). Factor Analysis (2nd ed.). Hillsdale, MI: Erlbaum.Google Scholar
Greene, J. A. and Brown, S. C. (2009). The Wisdom Development Scale: further validity investigations. International Journal of Aging and Human Development, 68, 289–320. doi: 10.2190/AG.68.4.b CrossRefGoogle ScholarPubMed
Grossmann, I. and Kung, F. Y. H. (2019). Wisdom and culture. In: Kitayama, S. and Cohen, D. (Eds.), Handbook of Cultural Psychology (2nd ed., pp. 343–364). New York, NY: Guilford Press.Google Scholar
Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: what we are doing and how can we improve? International Journal of Human–Computer Interaction, 32, 51–62. doi: 10.1080/10447318.2015.1087664 CrossRefGoogle Scholar
Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In Hoyle, R. H. (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 1–15). Thousand Oaks, CA: Sage.Google Scholar
Hu, L.-T. and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. doi: 10.1080/10705519909540118 CrossRefGoogle Scholar
Kahn, J. H. (2006). Factor analysis in counseling psychology research, training, and practice: principles, advances, and applications. The Counseling Psychologist, 34, 684–718. doi: 10.1177/0011000006286347 CrossRefGoogle Scholar
Krafcik, D. (2015) Words from the wise: exploring the lives, qualities, and opinions of exemplars of the wise. Integral Review, 11, 7–35.Google Scholar
Levenson, M. R., Jennings, P. A., Aldwin, C. M. and Shiraishi, R. W. (2005). Self-Transcendence: conceptualization and measurement. The International Journal of Aging and Human Development, 60, 127–143.CrossRefGoogle ScholarPubMed
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198–1202. doi: 10.2307/220157 CrossRefGoogle Scholar
Meacham, J. (1990). The loss of wisdom. In: Sternberg, R. J. (Ed.), Wisdom: Its Nature, Origins, and Development (pp. 181–211). Cambridge, UK: Cambridge University Press.10.1017/CBO9781139173704.010CrossRefGoogle Scholar
Meeks, T. W. and Jeste, D. V. (2009). Neurobiology of wisdom: a literature overview. Archives of General Psychiatry, 66, 355–365. doi.10.1001/archgenpsychiatry.2009.8 CrossRefGoogle ScholarPubMed
Meyers, L.S., Gamst, G. and Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation. Thousand Oaks, CA: Sage.Google Scholar
Moberg, D. (2008). Mentoring and practical wisdom: are mentors wiser or just more politically skilled? Journal of Business Ethics, 83, 835–843. doi: 10.1007/s10551-008-9668-5 CrossRefGoogle Scholar
Pasupathi, M., Staudinger, U. M. and Baltes, P. B. (2001). Seeds of wisdom: adolescents’ knowledge and judgment about difficult life problems. Developmental Psychology, 37, 351–361. doi: 10.1037/0012-1649.37.3.351 CrossRefGoogle ScholarPubMed
Shaughnessy, J. J., Zechmeister, E. B. and Zechmeister, J. S. (2012). Research methods in psychology (9th. ed.). New York, NY: McGraw–Hill.Google Scholar
Smith, G. T., McCarthy, D. M. and Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12, 102–111. doi: 10.1037/1040-3590.12.1.102 CrossRefGoogle ScholarPubMed
Sternberg, R. J. (2005). Older but not wiser? The relationship between age and wisdom. Ageing International, 30, 5–26. doi: 10.100/BF02681005 CrossRefGoogle Scholar
Stevens, J. P. (2002). Applied Multivariate Statistics for the Social Sciences (4th ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Tabachnick, B. G. and Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Harlow, Essex: England.Google Scholar
Taylor, M., Bates, G. and Webster, J. D. (2011). Comparing the psychometric properties of two measures of wisdom: predicting forgiveness and psychological well-being with the Self-Assessed Wisdom Scale (SAWS) and the Three-Dimensional Wisdom Scale (3D-WS). Experimental Aging Research, 37, 129–141. doi: 10.1080/0361073X.2011.554508 CrossRefGoogle Scholar
Thomas, M. L. et al. (2019). A new scale for assessing wisdom based on common domains and a neurobiological model: The San Diego Wisdom Scale (SD–WISE). Journal of Psychiatric Research, 108, 40–47. doi: 10.1016/jpsychires.2017.09.005 CrossRefGoogle Scholar
Thomas, M. L., Bangen, K. J., Ardelt, M. and Jeste, D. V. (2017). Development of a 12-item abbreviated Three-Dimensional Wisdom Scale (3D–WS–12): Item Selection and Psychometric Properties. Assessment, 24, 71–82. doi: 10.1177/1073191115595714 CrossRefGoogle ScholarPubMed
Webster, J. D. (2003). An exploratory analysis of a self-assessed wisdom scale. Journal of Adult Development, 10, 13–22. doi: 10.1023/A:1020782619051 CrossRefGoogle Scholar
Webster, J. D. (2007). Measuring the character strength of wisdom. The International Journal of Aging & Human Development, 65, 163–183. doi: 10.2190/AG.65.2.d CrossRefGoogle ScholarPubMed
Webster, J. D. (2010). Wisdom and positive psychosocial values in young adulthood. Journal of Adult Development, 17, 70–80. doi: 10.1007/s10804-009-9081-z CrossRefGoogle Scholar
Webster, J. D. (2019). Self-report wisdom measures: Strengths, limitations, and future directions. In: Sternberg, R. J. and Glück, J. (Eds.), The Cambridge Handbook of Wisdom (pp. 297–320). Cambridge, UK: Cambridge University Press.10.1017/9781108568272.015CrossRefGoogle Scholar
Webster, J. D., Westerhof, G. J. and Bohlmeijer, E. T. (2014). Wisdom and mental health across the lifespan. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69, 209–218. doi: 10.1093/geronb/gbs121 CrossRefGoogle ScholarPubMed
Webster, J. D., Weststrate, N. M., Ferrari, M., Munroe, M. and Pierce, T. W. (2018). Wisdom and meaning in emerging adulthood. Emerging Adulthood, 6, 118–136. doi: 10.1177/2167696817707662 CrossRefGoogle Scholar
Worthington, R. and Whittaker, T. (2006). Scale development research: a content analysis and recommendations for best practices. Counseling Psychologist, 34, 806–838. doi: 10.1177/0011000006288127 CrossRefGoogle Scholar
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