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
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