Concomitant with the increasing complexity of many research designs has been the development of statistical software that is able to investigate and test such models. The breadth of these analytical tools is in evidence by the myriad disciplines that have employed such modeling approaches (e.g., education, psychology, health care, marketing, etc.). An example of the types of statistical modeling that are offered include:

• Structural Equation Modeling, including confirmatory factor analysis, latent growth curve modeling, multi-sample analysis, etc.
• Multilevel Modeling for testing nested structures
• Categorical and ordered categorical modeling including loglinear and logistic (binary and multinomial) models for assessing categorical variables and regression models for ordinal outcomes; latent class analysis for uncovering latent group membership
• Time Series Analysis including ARIMA models, spectral analysis, etc.
• Uncover unknown groups via finite mixture modeling (latent class analysis, etc.) or cluster analysis.