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Part of
Handbook of Pragmatics: 24th Annual Installment
Edited by Jan-Ola Östman and Jef Verschueren
[Handbook of Pragmatics 24] 2022
► pp. 3–31

Regression analysis

| University of Turku
Article outline
  • 1.Introduction
  • 2.Building blocks
  • 3.Model 0: Modeling the numeric response variable as a function of one numeric predicting variable
  • 4.Model 1: Modeling the numeric response variable as a function of one categorical predicting variable
  • 5.Model 2: Modeling the numeric response variable as a function of two categorical predicting variables
  • 6.Model 3: Modeling the numeric response variable as a function of two categorical predicting variables that interact
  • 7.Logistic regression
  • 8.Model 4: Modeling the binary response variable as a function of one categorical predicting variable
  • 9.Model 5: Modeling the binary response variable as a function of two categorical predicting variables that interact
  • 10.Model 6: Modeling the binary response variable as a function of two categorical predicting variables that interact with a third categorical variable
  • 11.Independence assumption and mixed effects models
  • 12.Model 8: Modeling the binary response variable as a function of two categorical predicting variables that interact with a third categorical variable and two nested random predicting variables
  • 13.Statistical significance, model planning, and effect size
  • 14.Conclusion
  • Notes
  • References
Published online: 6 April 2022
DOI logo
https://doi.org/10.1075/hop.24.reg2
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