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- Working memory (1)
- complex span (1)
- latent factors (1)
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Background: Recent studies have suggested substantial fluctuations of cognitive performance in adults both across and within days, but very little is known about such fluctuations in children. Children's sleep behavior might have an important influence on their daily cognitive resources, but so far this has not been investigated in terms of naturally occurring within-person variations in children's everyday lives.
Methods: In an ambulatory assessment study, 110 elementary school children (8–11 years old) completed sleep items and working memory tasks on smartphones several times per day in school and at home for 4 weeks. Parents provided general information about the children and their sleep habits.
Results: We identified substantial fluctuations in the children's daily cognitive performance, self-reported nightly sleep quality, time in bed, and daytime tiredness. All three facets were predictive of performance fluctuations in children's school and daily life. Sleep quality and time in bed were predictive of performance in the morning, and afternoon performance was related to current tiredness. The children with a lower average performance level showed a higher within-person coupling between morning performance and sleep quality.
Conclusions: Our findings contribute important insights regarding a potential source of performance fluctuations in children. The effect of varying cognitive resources should be investigated further because it might impact children's daily social, emotional, and learning-related functioning. Theories about children's cognitive and educational development should consider fluctuations on micro-longitudinal scales (e.g., day-to-day) to identify possible mechanisms behind long-term changes.
Based on a meta-analysis, Redick and Lindsey (2013) found that complex span and n-back tasks show an average correlation of r = 0.20, and concluded that "complex span and n-back tasks cannot be used interchangeably as working memory measures in research applications" (p. 1102). Here, we comment on this conclusion from a psychometric perspective. In addition to construct variance, performance on a test contains measurement error, task-specific variance, and paradigm-specific variance. Hence, low correlations among dissimilar indicators do not provide strong evidence for the existence, or absence, of a construct common to both indicators. One way to arrive at such evidence is to fit hierarchical latent factors that model task-specific, paradigm-specific, and construct variance. We report analyses for 101 younger and 103 older adults who worked on nine different working memory tasks. The data are consistent with a hierarchical model of working memory, according to which both complex span and n-back tasks are valid indicators of working memory. The working memory factor predicts 71% of the variance in a factor of reasoning among younger adults (83% for among older adults). When the working memory factor was restricted to any possible triplet of working memory tasks, the correlation between working memory and reasoning was inversely related to the average magnitude of the correlations among the indicators, indicating that more highly intercorrelated indicators may provide poorer coverage of the construct space. We stress the need to go beyond specific tasks and paradigms when studying higher-order cognitive constructs, such as working memory.