Abstract
In the present paper we investigate weekly fluctuations in the working memory capacity (WMC) assessed over a period of 2 years. We use dynamical system analysis, specifically a second order linear differential equation, to model weekly variability in WMC in a sample of 112 9th graders. In our longitudinal data we use a B-spline imputation method to deal with missing data. The results show a significant negative frequency parameter in the data, indicating a cyclical pattern in weekly memory updating performance across time. We use a multilevel modeling approach to capture individual differences in model parameters and find that a higher initial performance level and a slower improvement at the MU task is associated with a slower frequency of oscillation. Additionally, we conduct a simulation study examining the analysis procedure's performance using different numbers of B-spline knots and values of time delay embedding dimensions. Results show that the number of knots in the B-spline imputation influence accuracy more than the number of embedding dimensions.
Highlights
The aim of the present study is to investigate intraindividual variability in a measure of cognitive performance using the dynamical systems modeling approach. Nesselroade (1990a) distinguishes between intraindividual changes, meaning long-term change that occurs on macro time scales, and intraindividual variability, meaning short-term change that occurs on micro scales. Ram and Gerstorf (2009) conceptualize two types of intraindividual variability
We first introduce the methodological perspective of week to week withinperson variability in cognitive performance over 2 years, where we focus on dealing with missing data while applying dynamical systems analysis
OF THE SIMULATION STUDY Results presented in Table 5 contain the true value η, bias, Root mean square error (RMSE) and means of estimates from 1000 data points, with the estimation procedure applied 10 times repeatedly to the data due to the imputation of missing values
Summary
The aim of the present study is to investigate intraindividual variability in a measure of cognitive performance using the dynamical systems modeling approach. Nesselroade (1990a) distinguishes between intraindividual changes, meaning long-term change that occurs on macro time scales, and intraindividual variability, meaning short-term change that occurs on micro scales. Ram and Gerstorf (2009) conceptualize two types of intraindividual variability. The aim of the present study is to investigate intraindividual variability in a measure of cognitive performance using the dynamical systems modeling approach. Salthouse et al (2006) reported results of the within-person variability in cognitive performance. Nesselroade and Salthouse (2004) investigated the relationship between age and short-term intraindividual variability using perceptual motor performance. In the present study we focus on intraindividual variability in weekly achievement in a memory updating task (Oberauer et al, 2000, 2003; Schmiedek et al, 2009) in 9th graders over a period of 2 years. We first introduce the methodological perspective of week to week withinperson variability in cognitive performance over 2 years, where we focus on dealing with missing data while applying dynamical systems analysis. The second aspect describes how the week to week within-person structured variability can be predicted by covariates
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