Table of Contents Introduction Discrete and Continuous Time (flow) in Dynamical Systems Discrete Time: Autoregressive models Discrete Time: Difference models Continuous Time: Differential Estimation Approaches Continuous Time: Unobserved Latent Variable Approach using SEM Summary References
Introduction The page was originally made for Jonathan Butner’s Dynamical Systems Modeling for the Social Sciences class, Spring 2018, University of Utah. This webpage will serve as an introduction to handling of time in dynamical systems models.

Table of Contents Data Input Stacked Models in Lavaan Model Comparison Using lavaan
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout will focus on implementing stacked models in lavaan, which allow us to test a model for two different groups (for example, control vs. intervention). This syntax imports the X variable, 192 person dataset called HW9 2017.

Table of Contents Data Input Structural Equation Modeling Using lavaan: Measurement Model Structural Equation Modeling Using lavaan: Full Model Model Comparison Using lavaan Interpreting and Writing Up Your Model
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we will overview how to establish a measurement model in R using the lavaan package.

Table of Contents Data Input Confirmatory Factor Analysis Using lavaan: Factor variance identification Model Comparison Using lavaan Calculating Cronbach’s Alpha Using psych
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we will overview how to complete a confirmatory factor analysis in R using the lavaan package.

Table of Contents Data Input Scree Plot and Parallel Analysis Minimum Average Partial Factor Extraction Orthogonal Rotation Using Varimax Oblique Rotation Using Direct Oblimin
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we will overview how to determine number of factors, or dimensions, to extract from your data.

Table of Contents Data Input Introduction to Lavaan Inspecting matrices when things go wrong Modeling in Lavaan Using a Covariance Matrix
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout will serve as an introduction to the lavaan package in R, which can be used for structural equation modeling. Mainly, we will focus on how path models can be conducted simply as a series of regressions in the R package lavaan, including estimation of indirect effects with bootstrapping.

Table of Contents Data Input Path Models Models Using Q and W Indices
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we will overview how path models can be conducted simply as a series of regressions. such as transposing and inversing matrices. This syntax imports the 4 variable dataset from datafile pathmodel example 3.

Table of Contents Data Input Creating Matrices and Vectors Operations Using Matrices
Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we go through how to create matrices and vectors in R as well as perform a few matrix algebra operations, such as transposing and inversing matrices.

Table of Contents Data Input and Cleaning Create and Export a Correlation Matrix Multiple Regression Using Multiple Regression to show how coefficients are a function of residuals
Made for Jonathan Butner’s Structural Equation Modeling (SEM) Class, Fall 2017, University of Utah. This handout begins by showing how to import data into R. Then, correlation matrices are generated, followed by a two predictor regression analysis.

Table of Contents Introduction Major assumptions of regression Checking the assumption of linearity Checking the assumption of constant variance of residuals (Homoscedasticity) Checking the assumption of normality of residuals Checking for multicollinearity Checking the data for outliers Quickly and effortlessly checking many assumptions at once References
Introduction This tutorial will help you test major assumptions of linear regression using R. The tutorial assumes that you have some familiarity understanding and interpreting basic linear regression models already.

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