Abstract
This simulation study compares the person-specific (PS) and multilevel modeling (MLM) approaches in the accuracy and reliability of autoregressive (AR) parameter estimates when data are generated from a first-order AR model and the functional form of the analytic model is correctly specified. Influences of a variety of factors on accuracy and reliability are examined, including time series length, sample size, the distribution of the AR coefficients, and the variability of the AR coefficients. Neither sample size nor distribution has an effect on accuracy or reliability. MLM generally has better accuracy than PS at both the population level and the individual level. However, in MLM, individuals who deviate farther from the sample mean are modeled less accurately than individuals who are closer to the sample mean. The two approaches do not differ in the reliability of the AR estimates. For both approaches, higher variability in the AR coefficients is associated with higher reliability. Implications on modeling practices are discussed.
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Notes
- 1.
I also simulated data following a highly skewed distribution, which was not used in Liu (2017). It did not show any large effect on the results. Hence, it is excluded in this paper to facilitate comparison between the two studies.
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Liu, S. (2018). Accuracy and Reliability of Autoregressive Parameter Estimates: A Comparison Between Person-Specific and Multilevel Modeling Approaches. In: Wiberg, M., Culpepper, S., Janssen, R., González, J., Molenaar, D. (eds) Quantitative Psychology. IMPS 2017. Springer Proceedings in Mathematics & Statistics, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-77249-3_32
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