Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method

Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method

2005, Vol. 1(1), p. 42-45 | Denis Cousineau
The article discusses the challenges and limitations of using standard error bars or confidence intervals in within-subject designs, particularly in the context of ANOVA. It highlights that traditional methods, such as those proposed by Loftus and Masson (1994), can be misleading and difficult to implement in commonly used graphing software. The authors propose a simpler alternative method that addresses these issues by adjusting the data to remove the variability associated with individual participants. This method involves creating a new variable \( Y \) by subtracting the mean of each participant's scores from their individual scores and then adding the group mean. This adjustment ensures that the error bars in the graph do not include between-subject variability, providing a clearer representation of the within-subject effects. The article also provides step-by-step instructions for implementing this method in SPSS 13, making it accessible to researchers.The article discusses the challenges and limitations of using standard error bars or confidence intervals in within-subject designs, particularly in the context of ANOVA. It highlights that traditional methods, such as those proposed by Loftus and Masson (1994), can be misleading and difficult to implement in commonly used graphing software. The authors propose a simpler alternative method that addresses these issues by adjusting the data to remove the variability associated with individual participants. This method involves creating a new variable \( Y \) by subtracting the mean of each participant's scores from their individual scores and then adding the group mean. This adjustment ensures that the error bars in the graph do not include between-subject variability, providing a clearer representation of the within-subject effects. The article also provides step-by-step instructions for implementing this method in SPSS 13, making it accessible to researchers.
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