Modeling GHQ Total and SDQ Difficulty Score to Extract Incomplete Information on One Using the Other

Abstract

Background: A natural approach to analyze multidimensional data is use of multivariate statistical analysis. If the number of dimensions/variables is small and variables are correlated, Multivariate Normal Distribution (MVN) is applied frequently. If the distribution of underlying variables is not normal, transformations are applied to convert them to normal variables.

Objective: Many a times, information may be missing on one or more dimensions of the data. The study intended to estimate the missing information through the available information.

Method: In a series of three independent surveys to examine the psychological health of young adults during COVID-19 period, enrolled in higher educational institutions in India, Strength and Difficulty Questionnaire (SDQ) 17+ extended version was used. In addition, General Health Questionnaire (GHQ) was used in third survey. The data was divided into two datasets; based on third survey and based on first two surveys. MVN was used to estimate GHQ scores through difficulty score dimension of SDQ and vice versa. The model was applied to data of first two surveys to estimate GHQ scores at the time of these surveys. The model was applied on 162 respondents who were common in all the three surveys.

Result: The estimated values for the third survey data were consistent with the observed and simulated values. Further it was found that out of 64 respondents with high GHQ scores in third survey, 55 had it during first two surveys also.

Conclusion: The results can be extended to estimate any missing information whenever variables are correlated.