Several theoretical models describing how stressor-strain relationships unfold in time (e.g., M. Frese & D. Zapf, 1988) were tested with a longitudinal study, with 6 measurement waves, using multivariate latent growth curve models. The latent growth curve model made it possible to decompose trait and state components of strains and to show that both trait and state components are affected by wo...

A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many models are used to fit microbial count data, the modified Gompertz and Baranyi equation being two of the most widely used. Rapid, automated methods such as turbidimetry have been widely used to obtain growth parameters, but do not directly give the microbial growth curve. Optical density (OD) dat...

By far, different models for determining the relationship between the flow rate  and amount of precipitation have been developed. many models are based on regression models with limited assumptions. one of the most common methods for estimating  sediment of rivers is sediment rating curve. for better estimation of the amount of sediment based of sediment curve  rating equation, it is possible t...

Latent Growth Curve Models (LGCM) are discussed as a general data-analytic approach to the analysis of change. Conventional, but popular, methods of analyzing change over time, such as the paired t-test, repeated measures ANOVA, or MANOVA, have a tradition, which is quite different from the more recently developed latent growth curve models. While the former originated from the idea of variance...