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Longitudinal Data Analysis consists of tools and techniques used to analyze data collected from the same set of variables measures at multiple points of time. The variables that are measured are collected from a wide variety of subjects and studied to gain insights through repeated measurements. Researchers collect longitudinal data in order to draw causal links in the interventions and endpoints. It also helps in controlling the various outcomes by using explanatory variables across the statistical models. During repeated measurement analysis, the subjects are examined under pre-specified conditions and the differences in the results are noted. The results and outcomes are usually measurements and indicators that can vary on several occasions. With the repeated measures taken of the variables, there are various methods suited to the various combinations of these variables. The issue of omitted variables bias which is always present with the connections between the outcomes and explanatory variables, is also improved by longitudinal data analysis.
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Longitudinal data analysis can be complicated by theoretical and practical issues. The problems can be of missing data or data correlated and collected at irregularly spaced intervals. But modern statistical methods can tackle these issues, yet still have hindrances while using them, while verification of assumptions of correct outcome interpretations. Such confusion can lead to inaccurate analysis. Other challenges are:
Longitudinal data analysis is mostly used for:
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