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Stage 3: Extensions of Linear Models

Stage 2 of the Four Stages of Statistical Skill was all about linear models and required a lot of honing of data analysis and software skills. Once you’re comfortable there, you’re ready to move onto Stage 3.

Here we move beyond linear models into statistical models and analyses that extend or are based on linear models.

Once again, you need all three skills, but at this stage, you should have down the data analysis skills and software skills that apply to any modeling.

So when you learn a new type of modeling at Stage 3, your emphasis will be on the statistical knowledge, and you’ll only need to learn any new data analysis skills or software skills necessary for this topic.

For example, if you’re working on logistic regression, many of the data analysis skills are the same as in linear regression—data cleaning and set up, model building and assessing fit, checking assumptions, etc. But there are a few, often minor differences. None are exactly the same as in a linear regression, but they are not a big leap, either.

Likewise, if you’re familiar and comfortable with using your statistical software for linear regression, it’s only a small step to learning how to use it for logistic. Yes, there are differences, but all you need to learn are those differences.

So at this stage, you’re going to learn all three components together for each new topic:

 

 1. Statistical Knowledge

2. Data Analysis Skills
3. Software Skills

Stage 3 Topics


With the methods at this stage, the only prerequisite is Linear Models. You don’t need to know any of these to learn any of the others.

Unless your job requires statistical analysis every day, you probably need half a dozen of these at most. Two or three is probably more reasonable.

Each of these links will take you to a Resource Page or Webinar on that topic where you can learn a lot more.

  • Logistic Regression

  • Mediation

  • Count Models including Poisson and Negative Binomial Regression

  • Multiple Imputation for Missing Data

  • Principal Component and Factor Analysis

  • MANOVA

  • Linear Mixed Models

  • Reliability and Validity Measures

  • Time Series

  • Cluster Analysis

  • Classification and Regression Trees

  • Survival Analysis

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This Month’s Statistically Speaking Live Training

  • February Member Training: Choosing the Best Statistical Analysis

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

Read Our Book



Data Analysis with SPSS
(4th Edition)

by Stephen Sweet and
Karen Grace-Martin

Statistical Resources by Topic

  • Fundamental Statistics
  • Effect Size Statistics, Power, and Sample Size Calculations
  • Analysis of Variance and Covariance
  • Linear Regression
  • Complex Surveys & Sampling
  • Count Regression Models
  • Logistic Regression
  • Missing Data
  • Mixed and Multilevel Models
  • Principal Component Analysis and Factor Analysis
  • Structural Equation Modeling
  • Survival Analysis and Event History Analysis
  • Data Analysis Practice and Skills
  • R
  • SPSS
  • Stata

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