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A new paper by Speelman, Parker, Rapley, and McGann document the unfortunate bad habit among modern psychologists of analyzing aggregates and then making unjustified conclusive statements about individuals. As has now been documented in numerous papers, results based on between-person averages do not tell us how many individuals in a study actually behaved in a manner consistent with expectation. In fact, with the small effect size magnitudes of most psychological studies, it is likely that MOST participants in a given study behaved in ways contrary to expectation. Only by conducting person-centered analyses can the number of hypothesis-consistent individuals be determined.

I have uploaded an updated version of the software (October 1st, 2023). Three significant changes have been made. First, a new suite of tools has been added for importing and saving to CSV (comma separated values) format. This is a generic format compatible with Excel, R, SPSS, and many other programs. The user can now save and import data definitions along with the data themselves. This suite of methods makes moving data to and from Excel a breeze. Seond, because of these new generically functional methods, the procedures for importing data from SPSS and SAS have been deleted. Third, for the complex Efficient Cause procedures, the user can now save and retrieve the setup for the analyses. Selecting the orderings and choosing the options for these analyses can be time consuming, especially if multiple analyses are required. The new save option should save a lot of time! Finally, a number of minor bugs have been removed from the program.

Please contact me ASAP if you encounter any issues with the new version.

Psychology Is a Property of Persons, Not Averages or Distributions: Confronting the Group-to-Person Generalizability Problem in Experimental Psychology by Ryan M. McManus, Liane Young, and Joseph Sweetman of Boston College. The authors use OOM-like methods, including the c-value, in their analyses. They show that while a result might be statistically significant, most participants in a study are likely to respond in ways contrary to expectation. The authors also document that majorities of psychologists and laypeople erroneously believe a statistically significant finding means that most participants in a study have behaved in ways consistent with expectation (theoretical prediction). Reanalyzing data from over a dozen published studies, however, the authors demonstrate this belief to be erroneous. Once again, the p-value is shown to create more confusion than clarity. Will psychologists ever let it go or at least relegate it to its proper, limited role in a tiny subset of studies?

A new paper titled Ordinal Pattern Analysis: A Tutorial on Assessing the Fit of Hypotheses to Individual Repeated Measures Data by Timothy Beechey has been published in the Journal of Speech, Language, and Hearing Research. The paper is provides an excellent introduction to OPA but is unfortunately behind a paywall. An erratum was published in March of 2023. It is certainly great to see these simple, intuitive methods gaining traction. The Ordinal Pattern Analysis has also made its way into R. Dr. Beechey’s R code can be found online here.

Figure 1 in this interesting article shows clearly how “effects” can vary greatly from person-to-person. Such studies are becoming more common. I only wish the world of academia weren’t so big, as then it would be less likely that I would miss such interesting work.

Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10, 10763 (2020).

I have been adding content to the Youtube channel. Eventually, I will have a complete set of videos describing the features of the software. You can find an index of the videos here along with links to websites that provide various analysis-specific resources (e.g., definitions, APA style write-ups, methods for determining sample size). Please visit and subscribe!

Here is an interesting paper by Sayette and colleagues titled “A Person-Centered Analysis of Craving in Smoking-Cue-Exposure Research” published in Clinical Psychological Science. While its appears they did not use the OOM software, they did follow the approach to focusing on individual responses in our Persons as Effect Sizes paper.

I have published a paper titled Drawing Inferences from Randomization Tests. It is published here in Personality and Individual Differences. My hope in writing this paper was to describe the types of inferences one can draw from the randomization tests used in the OOM software. Here is the abstract:

Randomization tests grew out of permutation tests that were developed in the 1930s. Since then statisticians have expounded upon their nature as well as their various strengths and weaknesses. Uncertainty remains, however, with regard to the types of inferences that can be drawn from randomization tests, if indeed any type of inference can be drawn at all. In this paper we propose that randomization tests can play a role in drawing what are known as abductive inferences and inferences to best explanation from empirical research. Contemporary philosophers of science hold that such inferences are central to scientific reasoning; hence, randomization tests may serve as an effective bridge between the specific realm of statistical inference and the more general realm of scientific inference.

Speelman and McGann have published a paper in Frontiers titled “Statements About the Pervasiveness of Behavior Require Data About the Pervasiveness of Behavior.” This is a nice companion piece to our Persons as Effect Sizes paper. Generally, the argument we are all making is that one must be careful to focus on the individuals in one’s study. Aggregate statistics do not tell the entire story of one’s data. OOM can be used to analyze the data presented by Speelman and McGann, as their pervasiveness index is equivalent to the Percent Correct Classifications (PCC) index. They also discuss setting up thresholds for determining the number of people classified correctly according to expectation. In OOM this goal is accomplished with the Classification Imprecision option available in most analyses.

James Lamiell and Kate Slaney (Eds.) have published their new book, Problematic Research Practices and Inertia in Scientific Psychology: History, Sources, and Recommended Solutions. There are chapters on statistics, measurement, psychologists’ distaste for criticism, and the struggle to understand persons using aggregate methods. We have a chapter in which we use OOM to analyse data from a study on Dissociative Identity Disorder. We also address strategies to help connect mainstream researchers to OOM the ideas expressed in Lamiell and Slaney’s book.