3.14: Key Takeaways and References

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Key Takeaways

There are many steps between putting electrodes on the participant’s head prior to the recording session and seeing the p value from the statistical analysis after the whole study is complete. As a result, there are many opportunity for some kind of problem or error that can lead you to draw the wrong conclusion from the study. As a result, it’s crucially important that you constantly check for problems and errors.
Some problems can be detected during preprocessing and averaging. For example, the response button may have been broken, leading to no event codes for responses. Or the participant’s blinks may have been smaller than typical, causing many blinks to escape the artifact detection procedure. You can often catch these errors by checking the number of rejected and accepted trials and by examining the continuous and epoched EEG data.
The process of scoring the amplitude or latency of an ERP component can also go awry for some participants, so you should use the Viewer tool to examine the scores alongside the waveforms for each participant.
Once you’ve entered the scores into a statistical analysis, you should compare the descriptive statistics from your statistical package with the grand average waveforms to make sure that the analysis was performed correctly. It’s really easy to accidentally put the scores in the wrong order.

References

Cousineau, D. (2005). Confidence intervals in within-subjects designs: A simpler solution to Loftus and Masson’s method. Tutorials in Quantitative Methods for Psychology, 1, 42–45.

Cramer, A. O. J., van Ravenzwaaij, D., Matzke, D., Steingroever, H., Wetzels, R., Grasman, R. P. P. P., Waldorp, L. J., & Wagenmakers, E.-J. (2015). Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies. Psychonomic Bulletin & Review, 23, 640–647.

Farrens, J. L., Simmons, A. M., Luck, S. J., & Kappenman, E. S. (2019). Electroencephalogram (EEG) Recording Protocol for Cognitive and Affective Human Neuroscience Research. Protocol Exchange. https://doi.org/10.21203/rs.2.18328/v3

Frane, A. V. (2021). Experiment-Wise Type I Error Control: A Focus on 2 × 2 Designs. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920985137. https://doi.org/10.1177/2515245920985137

Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, Second Edition. MIT Press.

Luck, S. J., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn’t). Psychophysiology, 54, 146–157.

Luck, S. J., Stewart, A. X., Simmons, A. M., & Rhemtulla, M. (2021). Standardized Measurement Error: A Universal Measure of Data Quality for Averaged Event-Related Potentials. Psychophysiology.

Morey, R. D. (2008). Confidence intervals from normalized data: A correction to Cousineau (2005). Tutorials in Quantitative Methods for Psychology, 4, 61–64.