The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates

63 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

How Low Should You Go? Low Response Rates and the Validity of Inference in IS Questionnaire Research (2006, background): “Research using questionnaires often involves the collection of data on multiple occasions raising a number of concerns pertaining not only to external validity but also statistical conclusion validity (see Sivo and Willson, 1998; Sivo and Willson, 2000; Sivo, 2001; Sivo, Fan, and Witta, 2005).”
Residual Structures in Latent Growth Curve Modeling (2010, methodology): “Simulation studies using Monte Carlo methods have shown that different specifications of residual structures can have a large impact on variance and covariance estimates (see Ferron, Dailey, & Yi, 2002 ; Kwok, West, & Green, 2007 ; Sivo, Fan, & Witta, 2005 ), especially when an autocorrelated str”
Do graduated university incubator firms benefit from their relationship with university incubators? (2016, methodology): “To correct for stochastic effects well known to inhabit longitudinal data [see Sivo et al. (2005)], we evaluated the viability of autoregressive-moving average (ARMA) processes, and as such processes have the potential to distort parameter estimates.”