Dealing with Model Mis-Specification in the Analysis of Morale in Old Age


A major challenge for analysis of data from observational and survey studies is dealing with model mis-specification. A common reason for model mis-specification is the violation of the independence assumption. Model mis-specification is frequently due to the inclusion of variables that are correlated with the error terms (serial correlation) or due to variables omitted from the study. The application of standard regression models to such data could lead to over inflated results, i.e. erroneous results, and misleading conclusions. Longitudinally designed studies make substantial improvements and provide an additional handle to control omitted variables. However, even with longitudinal data, model mis-specification could occur because of the nature of observations, i.e. surveys often include objectively as well as subjectively measured variables. Subjective variables are responsible for model mis-specification, therefore, compounding the problem further. One solution to such problems is the application of instrumental variables. The instrumental variable method is seldom used with social survey data. The main criticism is the arbitrary selection of variables as instruments. Longitudinal data, because of its temporal structure, provide natural instruments. In this paper, a pragmatic strategy for analysis is proposed that utilises the nature of the data (subjective/objective) and a combination of methods within a longitudinal modelling framework to correct for model mis-specification. These applications are illustrated by using recurrent continuous morale in old age from a longitudinal survey of the elderly. The results suggest a strong presence of heterogeneity effect, i.e. current levels of morale appear to be individual-specific and independent of its previous levels


Morale; Old Age

Share and Cite:

Shahtahmasebi, S. (2014) Dealing with Model Mis-Specification in the Analysis of Morale in Old Age. Open Journal of Social Sciences, 2, 64-71. doi: 10.4236/jss.2014.21008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Moser, C., Spagnoli, J. and Santos-Eggimann, B. (2011) Self-perception of aging and vulnerability to adverse outcomes at the age of 65-70 years. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66, 675-680.
[2] Kotter-Grühnn, D. and Smith, J. (2011) When time is running out: Changes in positive future perception and their relationships to changes in well-being in old age. Psychology and Aging, 26, 381.
[3] Hassel, A.J., et al. (2011) Oral health-related quality of life is linked with subjective well-being and depression in early old age. Clinical Oral Investigations, 15, 691-697.
[4] Cho, J., et al. (2011) The relationship between physical health and psychological well-being among oldest-old adults. Journal of Aging Research, 2011, Article ID: 605041.
[5] Ailshire, J.A. and Crimmins, E.M. (2011) Psychosocial factors associated with longevity in the United States: Age differences between the old and oldest-old in the health and retirement study. Journal of Aging Research, 2011, Article ID: 530534.
[6] Deng, J., et al. (2010) Subjective well-being, social support, and age-related functioning among the very old in China. International Journal of Geriatric Psychiatry, 25, 697-703.
[7] Wenger, G.C., et al. (1996) Social isolation and loneliness in old age: Review and model refinement. Ageing and Society, 16, 333-358.
[8] Fernández-Ballesteros, R. (2011) Quality of life in old age: Problematic issues. Applied Research in Quality of Life, 6, 21-40.
[9] Shahtahmasebi, S. (2004) Quality of life: A longitudinal analysis of correlates of morale in old age. The Scientific World Journal, 4, 100-110.
[10] Wenger, G.C. (1992) Morale in old age: A review of the literature. International Journal of Geriatric Psychiatry, 7, 699-708.
[11] Wenger, G.C., Davies, R. and Shahtahmasebi, S. (1995) Morale in old age: Refining the model. International Journal of Geriatric Psychiatry, 10, 933-943.
[12] Davies, R.B. (1987) The limitation of cross-sectional analysis. In: Crouchley, R., Ed., Longitudinal Data Analysis, Sage, London.
[13] Gibbons, R.D., et al. (1993) Some conceptual and statistal issues in analysis of longitudinal psychiatric data: Application to the NIMH Treatment of Depression Collaborative Research Program dataset. Archives of General Psychiatry, 50, 739-750.
[14] Litwin, H. and Shiovitz-Ezra, S. (2011) Social network type and subjective well-being in a national sample of older Americans. The Gerontologist, 51, 379-388.
[15] Wenger, G.C. (1984) The supportive network—Coping with old age. George Allen and Unwin, London.
[16] Shahtahmasebi, S. and Berridge, D. (2010) Conceptualising behaviour in health and social research: A practical guide to data analysis, ed. J. Merrick. Nova Sci, New York.
[17] Anderson, T.W. and Hsiao, C. (1981) Estimation of dynamic models with error components. American Statistics Association, 76, 375.
[18] Chamberlain, G. (1980) Analysis of covariance with qualitative data review of economic studies. XLVII, 225-238.
[19] Davies, R., Martin, A.M. and Penn, R. (1988) Linear modelling with clustered observations an illustratative example of earnings in the engineering industry. Environment and Planning A, 20, 1069-1084.
[20] Wallace, D. and Silver, J.L. (1988) Econometrics—An introduction. Addison-Wesley, New York.
[21] Pickles, A.R. and Davies, R.B. (1989) Inference from cross-sectional and longitudinal data for dynamic behavioural processes. In: Hauer, J., Ed., Urban Dynamics and Spatial Choice Behaviour, Kluwer Academic, 49, 81-104.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.