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discriminant analysis

Member Training: Reporting Structural Equation Modeling Results

by Jeff Meyer

The last, and sometimes hardest, step for running any statistical model is writing up results.

As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.

Any good statistical report includes enough information that someone else could replicate your results with your data.

[Read more…] about Member Training: Reporting Structural Equation Modeling Results

Tagged With: CFA, discriminant analysis, error term, factor loadings, Intercept, Latent Growth Curve Model, mean, mediation, parameter estimates, principal component analysis, reliability, reporting, SEM, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Latent Growth Curve Models
  • Member Training: Confirmatory Factor Analysis

Member Training: A Predictive Modeling Primer: Regression and Beyond

by guest contributer

Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! [Read more…] about Member Training: A Predictive Modeling Primer: Regression and Beyond

Tagged With: bagging, boosting, Bootstrap, cross-validation, decision trees, discriminant analysis, K-Nearest Neighbors, lasso, linear regression, logistic regression, predictive models, random forests, Regression, Resampling Techniques, ridge regression, shrinkage methods, subset selection, supervised learning, tree-based methods

Related Posts

  • Member Training: Generalized Linear Models
  • Member Training: Resampling Techniques
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: Goodness of Fit Statistics

Multiple Imputation of Categorical Variables

by Karen Grace-Martin 1 Comment

Most Multiple Imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables.

Paul Allison, one of my favorite authors of statistical information for researchers, did a study that showed that the most common method actually gives worse results that listwise deletion.  (Did I mention I’ve used it myself?) [Read more…] about Multiple Imputation of Categorical Variables

Tagged With: categorical variable, discriminant analysis, dummy coding, logistic regression, Multiple Imputation

Related Posts

  • Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
  • Multiple Imputation in a Nutshell
  • How to Diagnose the Missing Data Mechanism
  • Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

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This Month’s Statistically Speaking Live Training

  • Member Training: Assumptions of Linear Models

Upcoming Free Webinars

The Pathway: Steps for Staying Out of the Weeds in any Data Analysis

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

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