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These variables were included in all longitudinal analyses.
Both cross-sectional and longitudinal analyses were performed.
In longitudinal analyses subjects are repeatedly measured along time.
In general, longitudinal analyses can be more appropriate in confirming spillover and causal relationships.
Moreover, to fill in some gaps regarding causal directions, longitudinal analyses are needed.
With more than 20 years of data, the NIS is ideal for longitudinal analyses.
Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses.
Cross-lagged longitudinal analyses revealed an asymmetric developmental relation between ToM and working memory.
Furthermore, correlations between these parameters are better in longitudinal analyses than in cross-sectional analyses.
Analyses included chi-square tests for bivariate analyses and hierarchical linear modeling for longitudinal analyses.
Our longitudinal analyses demonstrate that PD and parkinsonism are modestly heritable.
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