Why to treat subjects as fixed effects.

Area of Science:

• Cognitive psychology
• Psycholinguistics
• Statistical modeling

Background:

• Previous research recommended R² targets account for subject idiosyncrasies as fixed effects.
• An interaction involving subjects led to the breakdown of data into individual subject analyses.
• A commentary questioned the reliability of single-subject data and the validity of random-effects models for general conclusions.

Purpose of the Study:

• To examine the consequences of employing a subjects-as-fixed-effects assumption in data analysis.
• To evaluate the benefits of fixed-effect analysis over random-effects analysis in psycholinguistic research.
• To clarify the implications of fixed-effect modeling for understanding reading mechanisms.

Main Methods:

• Analysis of word naming latencies from a dataset of 2,820 words read by 4 participants.
• Examination of the fixed-effect assumption in contrast to random-effects analysis.
• Evaluation of regression models and cognitive constraints.

Main Results:

• The fixed-effect assumption produces the correct target for by-items regression models.
• It more accurately constrains cognitive models and reveals general reading mechanisms.
• Fixed-effect analysis offers more powerful tests of effects and is often preferable.

Conclusions:

• The subjects-as-fixed-effects approach offers significant advantages in analyzing psycholinguistic data.
• This method enhances the accuracy of cognitive models and the identification of general reading mechanisms.
• Fixed-effect analysis is a valuable tool for researchers, even when individual differences are not the primary research question.