Growth curve modeling is a statistical method used to analyze repeated measures data, particularly in developmental psychology. It allows researchers to examine individual differences in within-person change over time. This method has become increasingly popular in the social and statistical sciences due to its flexibility in handling complex data structures, including partially missing data, unequally spaced time points, and nonlinear trajectories. Growth models differ from traditional longitudinal methods in their ability to estimate inter-individual variability in intra-individual change patterns. They can be fitted using either multilevel modeling or structural equation modeling frameworks. Growth models require at least three repeated measures per individual and assume that data are continuous and normally distributed, though alternative methods can handle non-normal or discrete data. Growth models can be estimated with partially missing data, but assumptions about the mechanism of missingness must be met for valid results. Different growth curve shapes, such as linear, quadratic, or piecewise linear, can be modeled to capture various patterns of change. Predictors can be incorporated into growth models to examine their effects on growth factors. Growth models can also be used to simultaneously model the growth of two or more constructs over time, and to analyze differences between groups. Additionally, growth models can be used to identify latent subgroups within a sample, even when the grouping variable is not directly observed. The adequacy of fit for growth models is assessed using various statistical indices, and models are compared based on these indices. Growth models offer a powerful tool for testing theoretically derived hypotheses and understanding individual differences in developmental change. However, careful consideration must be given to ensure that the statistical model aligns with the underlying developmental theory. The authors provide a comprehensive overview of growth curve modeling, addressing key questions and offering recommendations for further reading.Growth curve modeling is a statistical method used to analyze repeated measures data, particularly in developmental psychology. It allows researchers to examine individual differences in within-person change over time. This method has become increasingly popular in the social and statistical sciences due to its flexibility in handling complex data structures, including partially missing data, unequally spaced time points, and nonlinear trajectories. Growth models differ from traditional longitudinal methods in their ability to estimate inter-individual variability in intra-individual change patterns. They can be fitted using either multilevel modeling or structural equation modeling frameworks. Growth models require at least three repeated measures per individual and assume that data are continuous and normally distributed, though alternative methods can handle non-normal or discrete data. Growth models can be estimated with partially missing data, but assumptions about the mechanism of missingness must be met for valid results. Different growth curve shapes, such as linear, quadratic, or piecewise linear, can be modeled to capture various patterns of change. Predictors can be incorporated into growth models to examine their effects on growth factors. Growth models can also be used to simultaneously model the growth of two or more constructs over time, and to analyze differences between groups. Additionally, growth models can be used to identify latent subgroups within a sample, even when the grouping variable is not directly observed. The adequacy of fit for growth models is assessed using various statistical indices, and models are compared based on these indices. Growth models offer a powerful tool for testing theoretically derived hypotheses and understanding individual differences in developmental change. However, careful consideration must be given to ensure that the statistical model aligns with the underlying developmental theory. The authors provide a comprehensive overview of growth curve modeling, addressing key questions and offering recommendations for further reading.