Sample size recommendations for continuous-time models: compensating shorter time series with larger numbers of persons and vice versa
Martin Hecht,Steffen Zitzmann +1 more
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The N/T compensation effect is illustrated: with an increasing number of persons N at constant T, the model estimation performance increases, and vice versa, with an increase number of time points T at constant N, the performance increases as well.Abstract:
Autoregressive modeling has traditionally been concerned with time-series data from one unit (N = 1). For short time series (T < 50), estimation performance problems are well studied and documented...read more
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How Human–Chatbot Interaction Impairs Charitable Giving: The Role of Moral Judgment
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Continuous-Time Modeling of the Bidirectional Relationship Between Incidental Affect and Physical Activity.
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Thomas J. DiCiccio,Bradley Efron +1 more
TL;DR: Bootstrap methods for estimating confidence intervals have been surveyed in this article, with a focus on improving the accuracy of the standard confidence intervals in a way that allows routine application even to very complicated problems.
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How To Use A Monte Carlo Study To Decide On Sample Size and Determine Power
Linda K. Muthén,Bengt Muthén +1 more
TL;DR: In this paper, the authors demonstrate how substantive researchers can use a Monte Carlo study to decide on sample size and determine power, using two models, a confirmatory factor analysis (CFA) model and a growth model.
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A critique of the cross-lagged panel model.
TL;DR: This article presents an alternative model that separates the within-person process from stable between-person differences through the inclusion of random intercepts, and discusses how this model is related to existing structural equation models that include cross-lagged relationships.
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Bootstrap confidence intervals : when, which, what? A practical guide for medical statisticians
TL;DR: This article reviews the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses.