Zajenkowski, M., van der Linden, D., & Rogoza, R. (2022). Self-assessed intelligence, objective intelligence and the higher-order structure of personality. Personality and Individual Differences, 190, 111553. https://doi.org/10.1016/j.paid.2022.111553

What happens when an AI can read a published article, identify a methodological problem, and independently reanalyze the data?

From Chess Computers to Scientific Research

When the first chess computers appeared, they were not particularly good. They could beat beginners, but experienced players had little reason to fear them. Over time, however, chess programs improved. Eventually, they became competitive with grandmasters. Today, even the world’s best human chess players cannot consistently compete with the strongest chess engines.

Chess computers did not merely become better opponents. They also changed how humans play chess. Professional players now use computer analysis to evaluate positions, identify mistakes, and prepare strategies. Even the best chess players can learn from their computers.

A similar transformation is underway in scientific research.

Artificial intelligence is already contributing to research in fields such as molecular biology, chemistry, physics, and mathematics. AI systems have predicted the structures of hundreds of millions of proteins, identified promising new materials, and contributed to mathematical discoveries.

These are technically demanding fields in which scientific progress requires sophisticated theories, complex methods, and specialized knowledge.

Psychology should not expect to be immune to this transformation.

Indeed, much psychological research involves comparatively simple statistical analyses of correlations among questionnaire responses. If AI can assist with problems in molecular biology and mathematics, it should hardly be surprising that it can also evaluate a personality psychology article.

I recently put this possibility to the test.

An AI Reviews a Published Personality Psychology Article

I have been critically examining research on the General Factor of Personality (GFP), a proposed broad personality disposition that supposedly explains why socially desirable characteristics tend to be positively correlated.

The GFP is typically extracted from correlations among Big Five personality measures. Researchers who believe that it is a substantive personality factor interpret it as a general disposition toward social effectiveness or adaptability.

An alternative explanation is that the correlations partly reflect halo: people who evaluate themselves or others favorably on one characteristic tend to provide favorable ratings on other characteristics.

These explanations are fundamentally different. The first proposes an actual personality disposition. The second concerns the measurement process.

One way to distinguish them is to examine relationships between questionnaire-based personality ratings and characteristics measured independently of those ratings.

I therefore asked ChatGPT to evaluate an article by Zajenkowski, van der Linden, and Rogoza (2022), titled Self-assessed intelligence, objective intelligence and the higher-order structure of personality, published in Personality and Individual Differences.

The article was particularly interesting because the authors measured intelligence in two ways. Participants rated their own intelligence, but they also completed objective cognitive ability tests.

This provides an opportunity to examine whether the GFP is associated with actual intelligence or merely with people’s beliefs about their intelligence.

The article had undergone the usual scientific publication process. Researchers conducted the studies, wrote the manuscript, submitted it to a scientific journal, received peer review, and obtained editorial acceptance.

I wanted to know whether AI could identify problems that this process had apparently failed to resolve.

The First Step: Reading the Article Critically

I uploaded the published article to ChatGPT and asked whether it provided evidence for the GFP.

ChatGPT examined the study’s hypotheses, measurements, methods, results, and statistical tables.

It quickly identified an important pattern.

Across two studies, the GFP was positively associated with self-assessed intelligence, but its correlations with objectively measured intelligence were much weaker.

	Study 1	Study 2
GFP and self-assessed intelligence	.29	.35
GFP and objective intelligence	.15	.05

People who described their personalities favorably also tended to describe themselves as intelligent. However, their actual performance on intelligence tests showed little relationship with the GFP.

This pattern is consistent with a general positive self-evaluation factor.

But ChatGPT also noticed something more important.

Objective intelligence was primarily associated with one specific Big Five domain: Openness/Intellect.

The correlations were .34 in Study 1 and .23 in Study 2. Correlations with the other four domains were substantially smaller, and some were negative.

This raises an obvious question.

Does objective intelligence have anything to do with a general personality factor, or is its association with the GFP merely a consequence of including Openness/Intellect in the GFP composite?

The published analyses did not answer this question.

The Second Step: Identifying the Statistical Problem

The authors constructed their higher-order personality factors through a series of factor analyses.

First, they extracted Plasticity from Extraversion and Openness/Intellect. They extracted Stability from Emotional Stability, Agreeableness, and Conscientiousness.

They then conducted another factor analysis to extract a GFP from Plasticity and Stability.

This is a questionable way to test a hierarchical theory.

A one-factor solution involving only two variables does not provide a meaningful test of whether a general latent personality factor explains the observed correlations. The general factor is essentially imposed rather than evaluated against alternative models.

More importantly, the procedure does not distinguish variance that is common to different personality traits from variance that is specific to individual traits.

Imagine that intelligence is positively correlated with Openness/Intellect but unrelated to the common variance among the Big Five.

Because Openness/Intellect contributes to the GFP score, the GFP may still correlate with intelligence.

Researchers could then report a relationship between intelligence and the GFP even though the relationship is entirely attributable to a specific personality characteristic.

This is precisely the distinction that a hierarchical structural equation model can examine.

The obvious next step was to fit such a model.

The Third Step: AI Conducts Its Own Statistical Analysis

Here is where the experience became particularly interesting.

I pointed out that the article published the complete correlation matrices for both studies. Consequently, we did not need access to the individual participants’ responses. We could fit covariance-structure models directly to the published correlations.

I asked ChatGPT to do so.

It wrote and executed Python code that reconstructed the correlation matrices, specified alternative latent-variable models, estimated their parameters using maximum likelihood, and calculated model-fit statistics.

It then compared three explanations:

1. General-factor model: Objective intelligence is associated with a common factor underlying the Big Five.
2. Specific-trait model: Intelligence is associated with variance specific to Openness/Intellect rather than with the general factor.
3. Combined model: Intelligence is allowed to relate to both the general factor and the specific Openness/Intellect component.

The comparison is straightforward. If a substantive GFP explains the association with intelligence, the general-factor relationship should be necessary to account for the data. If intelligence is related specifically to Openness/Intellect, the specific-trait model should account for the association without requiring a relationship with the GFP.

ChatGPT fitted these models to the first study.

The results were striking.

The Results: The Alternative Model Fits Better

The first model, in which intelligence was associated only with the general personality factor, fit poorly:

χ²(9) = 38.44, RMSEA = .119.

The alternative model, allowing intelligence to relate specifically to Openness/Intellect, fit substantially better:

χ²(9) = 13.63, RMSEA = .047.

The difference becomes even clearer when the competing paths are tested within the combined model.

Adding the Openness-specific relationship to the general-factor model produced a substantial improvement:

Δχ²(1) = 25.04, p < .000001.

In contrast, adding a GFP–intelligence relationship to the specific-trait model produced virtually no improvement:

Δχ²(1) = 0.24, p = .627.

In other words, the specific association with Openness/Intellect was needed to explain the results. An additional relationship with the general factor was not.

This is exactly the kind of theoretically informative analysis that the original article could have conducted.

The second study presented an additional problem.

A single general-factor model of the five personality domains failed to fit the correlations adequately:

χ²(5) = 30.20, RMSEA = .146.

The fitted solution also approached a boundary, making the general-versus-specific decomposition unreliable in that sample.

Nevertheless, a conventional multiple regression using all five personality domains again showed that objective intelligence was primarily associated with Openness/Intellect. Controlling for the other four domains, its standardized regression coefficient was approximately .24.

The results therefore did not provide a convincing replication of a general-factor explanation for intelligence.

What Did AI Actually Demonstrate?

There are two important qualifications.

First, the analysis does not prove that the GFP is entirely a halo artifact. Establishing that conclusion would require a measurement model capable of separating evaluative bias from substantive personality variance, ideally using multiple methods and informants.

Second, the models were fitted to Big Five domain scores rather than individual questionnaire items. The analysis therefore tests a simplified hierarchical structure rather than a complete measurement model.

Nevertheless, the reanalysis establishes something important.

The original article’s small correlations between objective intelligence and the GFP do not require a substantive GFP–intelligence relationship. In Study 1, a model involving specific Openness/Intellect variance fits much better. In Study 2, the proposed general-factor measurement structure itself is not adequately supported.

The broader pattern is consistent with an evaluative interpretation: favorable self-assessments of personality correlate strongly with favorable self-assessments of intelligence, whereas objective intelligence is primarily related to a specific personality domain.

That is a more informative interpretation than simply reporting correlations between constructed GFP scores and intelligence.

Why Didn’t Peer Review Catch This?

The most interesting question is not whether ChatGPT could perform these analyses.

It could.

The interesting question is why a published article did not already contain them.

The authors had access to the complete data. They were familiar with the GFP literature and its competing interpretations. They had the statistical tools needed to fit hierarchical models.

The reviewers and editor also had an opportunity to evaluate whether the analyses adequately tested the theoretical claims.

Yet the manuscript was published without this critical comparison.

We cannot know what the reviewers noticed, requested, or discussed. Peer-review reports are generally not publicly available. But we can evaluate the published product.

And the published product leaves an important theoretical question unanswered even though the necessary data were available.

This illustrates a broader limitation of human peer review.

Reviewers are often selected because they are knowledgeable about a particular literature. They may share the conceptual assumptions and methodological conventions of the researchers whose work they evaluate.

Within an established research paradigm, reviewers may focus on whether a study follows accepted procedures rather than whether those procedures actually distinguish competing explanations.

An article can therefore receive favorable reviews while making little progress toward resolving the theoretical questions that supposedly motivate the research.

Publication is evidence that a manuscript passed a particular editorial process. It is not evidence that its statistical analyses were the most informative analyses available.

AI Does Not Have to Respect Established Paradigms

An AI assistant has a potentially important advantage.

It does not have to build an academic career within the GFP paradigm. It does not need publications, grants, citations, or approval from established researchers in the field.

Of course, AI can reproduce prevailing scientific assumptions because it has learned from the existing literature. It can also make mistakes, including statistical mistakes.

But it can be asked to examine competing explanations directly, without regard to whether a particular interpretation is fashionable or supported by influential researchers.

In this case, the AI was initially asked to evaluate a published article. It identified a measurement problem, examined the correlation tables, and implemented a more informative statistical analysis.

This is not the same as replacing the entire scientific process with an autonomous machine. I supplied the scientific question and prompted the alternative analysis.

Nevertheless, the AI carried out substantial technical work independently, including writing and executing the statistical analysis and evaluating the resulting model comparisons.

Only a few years ago, many psychologists would have regarded AI primarily as a tool for improving writing, summarizing literature, or answering factual questions.

The ability to move from a published article to a new statistical analysis is a much more consequential development.

Is AI Already a Better Reviewer?

Chess provides a useful analogy, but there is an important difference.

In chess, there are unambiguous rules and an objective criterion for success. A computer can evaluate positions and test moves against clear outcomes.

Scientific research is more complicated. Researchers must decide which questions matter, which constructs are meaningful, whether measurements are valid, and what conclusions follow from imperfect evidence.

No statistical model can resolve all these questions automatically.

However, many components of scientific research are sufficiently formalized to permit computational evaluation.

An AI can examine whether a statistical model is identified, whether its restrictions are theoretically justified, whether alternative models fit better, and whether the conclusions follow from the reported results.

It can also reconstruct analyses from published summary statistics, provided the necessary information is available.

These are precisely the kinds of technical issues that peer reviewers are expected to evaluate.

The chess analogy therefore suggests a plausible future in which AI is not merely a convenient research assistant but an increasingly capable methodological critic.

Human researchers may continue to provide important theoretical insights and scientific judgment. But they should not assume that they will remain superior at statistical analysis or at detecting weaknesses in published research.

The present example demonstrates that AI can already perform some of these tasks remarkably well.

The Future of Psychological Science

The implications extend well beyond the GFP debate.

Thousands of psychological articles are published every year. Many rely on standard statistical procedures, including correlations, regressions, factor analyses, and structural equation models.

Published articles often contain enough statistical information to evaluate alternative explanations without collecting new data.

AI makes such evaluations dramatically more accessible.

Researchers no longer need to spend hours manually reconstructing covariance matrices, programming models, and calculating fit statistics simply to investigate whether a published conclusion survives a reasonable alternative analysis.

More ambitious projects could systematically examine entire research literatures, identifying articles that rely on poorly fitting models, questionable measurement assumptions, or interpretations that are not uniquely supported by the evidence.

This could change the incentives of psychological research.

At present, publication remains the primary currency of academic success. Researchers are rewarded for producing publishable findings, and journals generally rely on a small number of human reviewers to evaluate manuscripts.

But if independent AI analyses routinely identify serious weaknesses in published research, the distinction between publishable research and scientifically convincing research may become increasingly visible.

In that environment, journals could improve quality control by requiring computational checks and systematic comparisons with plausible alternative models.

AI would not eliminate the need for human judgment. It could, however, make it much harder for weak statistical arguments to survive merely because they conform to established research practices.

Conclusion

The example discussed here is not a demonstration that AI is infallible or that human scientists are obsolete.

It demonstrates something more concrete.

A published article used correlations between intelligence and a constructed General Factor of Personality to investigate the higher-order structure of personality.

An AI assistant examined the article, identified a missing theoretical comparison, reconstructed the published correlation matrices, and fitted alternative structural equation models.

The alternative model accounted for the findings better than the general-factor explanation.

The original authors, reviewers, and editor did not provide this comparison in the published article.

That is a significant result, not because it settles the GFP debate, but because it illustrates the changing relationship between human expertise and artificial intelligence.

The question is no longer whether AI can help psychologists write their articles. It is whether AI can evaluate psychological research more critically than the humans who write, review, and publish it.

This case suggests that, for at least some important methodological tasks, the answer is already yes.

---

References

Zajenkowski, M., van der Linden, D., & Rogoza, R. (2022). Self-assessed intelligence, objective intelligence and the higher-order structure of personality. Personality and Individual Differences, 190, 111553. https://doi.org/10.1016/j.paid.2022.111553

Wang, H., et al. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620, 47–60. https://doi.org/10.1038/s41586-023-06221-2

Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2

Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362, 1140–1144. https://doi.org/10.1126/science.aar6404

Reanalysis note

The analyses described above were generated using ChatGPT from the correlation matrices published in Tables 1 and 2 of Zajenkowski et al. (2022). Models were estimated using Python and maximum likelihood. Correlations were published to two decimal places, so reported fit statistics are approximate. The code and reconstructed matrices should be independently checked before treating the findings as a definitive published reanalysis.

To understand psychology, it is necessary to understand how academia works. Research at universities is organized around paradigms (Kuhn, 1962). A paradigm is a set of shared ideas, assumptions, methods, and practices—something like a scientific culture.

The overarching paradigm in academic psychology is that scientific claims should be supported by empirical evidence, often obtained through quantitative analysis. In some areas of psychology, data are relatively inexpensive to collect, making it easy to conduct studies and produce results.

The harder part is getting the results published in a peer-reviewed journal. This is where belonging to an established research paradigm becomes advantageous. Researchers working within a paradigm share assumptions about which questions are important, which methods are appropriate, and which findings constitute progress. They also serve as reviewers and editors for journals in their field.

Consequently, research that conforms to an established paradigm has a natural audience and an established route to publication. Research that challenges its fundamental assumptions faces greater obstacles. Reviewers may be less receptive to findings that undermine the theoretical foundations of their own research programs.

These dynamics can allow paradigms to survive long after their scientific usefulness has diminished. In psychology, several competing research programs may coexist for decades, investigating similar phenomena with different theoretical interpretations while paying remarkably little attention to contradictory findings.

The resulting publications generate citations, support academic careers, and demonstrate research productivity. However, the production of publications is not the same as the production of knowledge. A research program can remain productive in terms of publications without making much progress toward answering its central scientific questions.

Kuhn (1962) described how established paradigms resist challenges and how scientific revolutions can involve generational changes in the scientific community. However, the conditions under which science operates are changing.

The internet has made it possible to distribute scientific findings and criticism without relying exclusively on peer-reviewed journals. Artificial intelligence has further accelerated access to information, allowing researchers to examine publications, compare competing claims, and identify inconsistencies more efficiently.

In my scientific utopia, weak paradigms would lose their influence more quickly because their shortcomings could be exposed and discussed almost immediately after publication. Researchers would have less opportunity to sustain an unproductive research program by repeatedly publishing variations on the same empirical finding.

This brings me to the subject of this blog post: the General Factor of Personality (GFP) paradigm.

A phenomenon discovered over 100 years ago

The General Factor of Personality is a relatively recent name for a phenomenon recognized more than a century ago.

Thorndike (1920) observed that when people evaluate other individuals, their ratings of different desirable characteristics tend to correlate more strongly than expected. People judged favorably on one characteristic also tend to receive favorable ratings on others.

For example, individuals rated as intelligent may also be rated as attractive, conscientious, and likable. Conversely, individuals who receive unfavorable ratings on one characteristic tend to receive unfavorable ratings on others.

Thorndike interpreted these correlations as evidence of a halo effect: a general impression of a person influences judgments of specific attributes.

The phenomenon is robust and highly replicable. Nobody seriously needs another demonstration that favorable personality ratings tend to correlate.

The scientific question is what produces these correlations.

Two competing explanations

The first explanation is evaluative bias.

When we like someone, we tend to exaggerate their positive characteristics and minimize their negative ones. When we dislike someone, we tend to do the opposite.

This produces correlations between ratings of attributes that may be only weakly related—or even unrelated—in reality.

For example, cognitive ability, physical attractiveness, and happiness are distinct characteristics. Yet people’s subjective evaluations of these attributes may be positively correlated partly because they share a general tendency toward favorable or unfavorable self-evaluation.

The second explanation is that desirable attributes genuinely cluster together in individuals.

According to proponents of the GFP, some people tend to possess more desirable characteristics across multiple personality domains. This tendency may reflect a broad psychological disposition, sometimes interpreted as social effectiveness. Some researchers have also proposed evolutionary or genetic explanations for the GFP.

These hypotheses are fundamentally different.

The halo hypothesis explains correlations among ratings.

The substantive GFP hypothesis proposes a common source of variation in people’s actual characteristics.

To illustrate the distinction, consider intelligence and attractiveness. If the two attributes were independent, approximately half of the people above average in intelligence would also be above average in attractiveness, simply by chance.

If the correlation were (r = .30), approximately 60% of those above the median on one attribute would also be above the median on the other, assuming normally distributed attributes.

Thus, even a moderate positive correlation would leave considerable room for people who are highly intelligent but unattractive, or attractive but less intelligent.

The existence of these combinations is not the central issue. The question is whether there is a genuine common factor that causes desirable attributes to covary.

What have we learned in 100 years?

After a century of research, one might reasonably expect scientists to have made substantial progress in distinguishing these explanations.

Fortunately, there are ways to do so.

One approach is to compare subjective ratings with more objective indicators. If correlations among favorable ratings are substantially stronger than correlations among independently measured attributes, this suggests that evaluative bias contributes to the observed covariance.

Another, particularly informative approach is to compare ratings obtained from different informants.

If a substantive personality trait causes people to possess multiple desirable characteristics, the common factor should produce convergent evidence across independent observers.

If the factor primarily reflects evaluative halo, much of its variance should be specific to individual raters.

Biesanz and West (2004) found that Big Five ratings were correlated when obtained from the same informant, whereas the structure was much closer to orthogonal when different informants were used.

Anusic et al. (2009) extended this approach using multitrait–multimethod models. The results showed that the general evaluative halo was largely specific to individual raters, whereas broader substantive personality factors demonstrated greater cross-rater convergence.

Moreover, the halo factor was strongly related to independent measures of evaluative biases in self-perception.

These findings do not imply that all correlations among personality traits are artifacts. Nor do they rule out genuine higher-order personality factors.

However, they provide direct evidence that much of the general evaluative covariance in personality ratings reflects the way people evaluate themselves and others rather than a single underlying personality disposition.

This is precisely the kind of evidence that should inform attempts to interpret the GFP as a substantive trait.

One might think that such findings would have shifted the research agenda away from repeatedly demonstrating the existence of a general factor toward investigating its remaining substantive variance and incremental validity.

But the GFP paradigm continues to produce studies demonstrating that the factor can be extracted from different datasets.

A new study using artificial intelligence

In 2025, Dimitri van der Linden and colleagues published a paper titled The General Factor of Personality (GFP) in Natural Language: A Deep Learning Approach in the Journal of Research in Personality.

The authors used a large language model to examine associations among personality-descriptive adjectives in natural language.

They found that the first principal component distinguished desirable from undesirable personality attributes. Moreover, loadings on this component correlated highly ((r = .86)) with loadings from an earlier study based on human personality ratings.

The authors interpreted this finding as further evidence for the generality of the GFP.

However, why should this finding be surprising?

It has long been recognized that personality adjectives have both descriptive and evaluative meanings.

The descriptive, or denotative, meaning concerns what a word says about a person’s characteristics or behavior. The evaluative, or connotative, meaning concerns whether the attribute is considered good or bad.

Consider the difference between describing someone as relaxed and describing the same person as lazy.

Both terms may refer to a tendency toward low activity, but they convey very different evaluations.

Similarly, intelligent and kind describe different attributes, but both have positive evaluative meanings.

A language model trained on human language can learn these evaluative associations. Finding a general evaluative dimension in its representation of personality adjectives therefore tells us something about how personality descriptions are organized in language.

It does not establish that desirable attributes genuinely cluster together in individuals because of a common personality trait.

Indeed, the emergence of a general evaluative factor in linguistic representations is entirely compatible with the halo hypothesis.

The study reports that its first component accounted for 22.7% of the variance in the language-model data, compared with 7.11% in the original human-rating data. Although these percentages are not directly comparable because the datasets have different structures, the findings clearly demonstrate the prominence of evaluation in linguistic representations.

But the paper offers no independent behavioral evidence that this factor reflects social effectiveness or another substantive personality disposition.

What did the new study teach us?

The authors’ discussion acknowledges the unresolved question of what the general factor represents.

They consider the possibility that it reflects methodological artifacts or evaluative biases, as well as the alternative that it reflects a substantive psychological phenomenon.

That is essentially where the debate stood before the study.

The paper does acknowledge halo bias and cites Anusic et al. (2009). However, it does not provide a new empirical test capable of distinguishing the bias explanation from the substantive GFP explanation.

This is the central problem.

The study replicates the phenomenon that needs explaining but contributes little toward explaining it.

After more than 100 years of research on evaluative correlations in personality judgments, another demonstration of a general evaluative factor is not a major theoretical advance.

The more interesting questions concern how much of the covariance is genuine, how much is produced by evaluative bias, and whether any remaining substantive factor has meaningful explanatory or predictive value beyond specific personality traits.

These are questions that require appropriate measurement models, independent informants, and external validation.

Extracting a first principal component from another dataset does not answer them.

When does a research paradigm stop producing knowledge?

The GFP illustrates a broader problem in academic psychology.

A research program can continue to generate publications because its central empirical phenomenon is easy to reproduce.

The repeated appearance of the same finding creates an impression of cumulative scientific progress. Yet the underlying theoretical question may remain unanswered.

The GFP paradigm has repeatedly demonstrated that favorable personality ratings tend to correlate. The phenomenon is real. The disagreement concerns its interpretation.

The evidence from multitrait–multimethod studies indicates that a substantial part of this general evaluative covariance is attributable to rater-specific influences.

This creates a serious challenge for theories that interpret the first general factor in personality ratings as a broad substantive disposition.

It is still possible that some residual covariance reflects genuine common causes. However, proponents need to demonstrate that such a factor can be measured independently of evaluative biases, identify the psychological processes that produce it, and establish its incremental validity.

Merely extracting another general factor does not satisfy these requirements.

Even if a small substantive GFP exists, it would not follow that it is more useful than the specific attributes it is supposed to explain.

A person’s intelligence, honesty, conscientiousness, sociability, and emotional stability can have important consequences. A weak general tendency toward desirable characteristics would not eliminate the need to measure these attributes separately.

The purpose of personality science should be to explain individual differences, not merely to identify increasingly broad statistical summaries of personality ratings.

The van der Linden et al. (2025) study demonstrates that a general evaluative factor can be recovered from natural language using artificial intelligence.

That is a methodological replication of a familiar result.

What it does not demonstrate is that the General Factor of Personality is a meaningful psychological trait.

A more important application of research on evaluative bias concerns the growing use of artificial intelligence to evaluate people (Kim et al., 2025). Recent studies show that AI models reproduce human halo effects. For example, models infer intelligence, competence, and trustworthiness from facial appearance, even though these judgments lack adequate validity. These biases can also influence simulated hiring decisions (Kim et al., 2025; Lehr et al., 2026). Thus, while researchers continue to debate whether the General Factor of Personality reflects a substantive disposition, artificial intelligence is already reproducing the evaluative biases that contribute to this factor.

Conclusion

Rather than demonstrating once again that desirable attributes are associated in language, personality researchers could make a more useful contribution by investigating how these associations create biased evaluations of people and how such biases can be prevented.

References

Anusic, I., Schimmack, U., Pinkus, R. T., & Lockwood, P. (2009). The nature and structure of correlations among Big Five ratings: The halo–alpha–beta model. Journal of Personality and Social Psychology, 97, 1142–1156. https://doi.org/10.1037/a0017159

Biesanz, J. C., & West, S. G. (2004). Towards understanding assessments of the Big Five: Multitrait–multimethod analyses of convergent and discriminant validity across measurement occasion and type of observer. Journal of Personality, 72, 845–876. https://doi.org/10.1111/j.0022-3506.2004.00282.x

DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant sample. Journal of Personality and Social Psychology, 91, 1138–1151. https://doi.org/10.1037/0022-3514.91.6.1138

Kim, K., Ryu, J., Jeon, H., & Suh, B. (2025). Blinded by context: Unveiling the halo effect of MLLM in AI hiring. Findings of the Association for Computational Linguistics: ACL 2025, 26067–26113. https://doi.org/10.18653/v1/2025.findings-acl.1338

Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.

Thorndike, E. L. (1920). A constant error in psychological ratings. Journal of Applied Psychology, 4, 25–29. https://doi.org/10.1037/h0071663

van der Linden, D., Cutler, A., van der Linden, P. A., & Dunkel, C. S. (2025). The general factor of personality (GFP) in natural language: A deep learning approach. Journal of Research in Personality, 117, 104635. https://doi.org/10.1016/j.jrp.2025.104635

Reference: Lykken, D. T. (1971). Multiple factor analysis and personality research. Journal of Experimental Research in Personality, 5, 161–170.

In 1971, David T. Lykken published a remarkable article titled Multiple Factor Analysis and Personality Research in the Journal of Experimental Research in Personality (now Journal of Research in Personality). More than half a century later, the article remains highly relevant to one of the fundamental problems in personality psychology:

What can we learn about the structure of personality from analyzing correlations among psychological measures with multiple factor analysis, now commonly called exploratory factor analysis (EFA)?

Factor analysis has played an enormous role in personality psychology. The Big Five, HEXACO, and various higher-order personality models are all closely connected to exploratory analyses of correlations among personality items, adjectives, or scales. Most instruments used to measure broad personality traits have been influenced by EFA results. The widespread use of these measures encourages the assumption that they measure specific traits. That is, scale scores are treated as measures of distinct underlying personality dispositions.

The basic procedure is familiar. Researchers collect a large number of personality measures, calculate their correlations, extract a smaller number of factors, rotate the solution, and assign names to the resulting dimensions. These factors often appear psychologically meaningful. They may be recognizable across datasets and may even predict important outcomes. But does this establish that the factors correspond to fundamental psychological dispositions?

Lykken thought not.

His article was written at the invitation of the journal’s editors to stimulate debate about the scientific role of factor analysis. He acknowledged Paul Meehl and Auke Tellegen for helpful comments, although he emphasized that they did not necessarily share his conclusions.

Indeed, Tellegen subsequently became an influential advocate of using EFA to identify the structure of affective experiences. Watson and Tellegen (1985) proposed that Positive Affect (PA) and Negative Affect (NA) are two largely independent dimensions of affect. Their model was based on the consistent appearance of two factors in orthogonally rotated exploratory factor analyses. This model subsequently guided the development of the widely used Positive and Negative Affect Schedule (PANAS; Watson et al., 1988).

This provides a particularly revealing example of Lykken’s concern. The independence of the two rotated factors is imposed by the orthogonal rotation procedure. It is not an empirical discovery that positive and negative emotional experiences are independent. Moreover, the PANAS scales were deliberately constructed using items with minimal secondary loadings, helping produce relatively independent scale scores.

The resulting scales may be useful measures of positive and negative activation, but their relatively low correlation does not establish that positive and negative emotional experiences are independent. The controversy illustrates how a statistical representation of a covariance matrix can be transformed into a substantive claim about psychological structure.

Thus, even within Lykken’s immediate intellectual circle, the fundamental issue he raised in 1971 remained unresolved: Does exploratory factor analysis reveal the underlying structure of psychological phenomena, or does it merely provide a convenient statistical representation of their correlations?

Lykken was not opposed to quantitative psychology or to the study of personality differences. Quite the contrary. He was an influential University of Minnesota psychologist whose research included temperament, psychophysiology, and behavioral genetics. He even coauthored a study with Tellegen showing that individual differences in subjective well-being are substantially influenced by genetic factors (Lykken & Tellegen, 1996).

Thus, Lykken’s criticism of EFA did not come from an outsider who questioned the existence or importance of personality traits. It came from a personality psychologist who regarded stable individual differences as important phenomena requiring scientific explanation but questioned whether EFA was an appropriate tool for discovering their underlying structure.

Three Uses of Factor Analysis

Lykken began by distinguishing three applications of factor analysis.

The first is data reduction. When researchers have hundreds of correlated measures, a smaller number of statistical dimensions may summarize much of the information. This can be useful, especially when the goal is to simplify a dataset without losing too much information.

The second is hypothesis testing. If a theory predicts that several measures reflect a common underlying disposition, researchers can examine whether their correlations are consistent with that prediction.

The third application is dimensional discovery: using exploratory factor analysis to discover the fundamental psychological variables responsible for individual differences.

Lykken acknowledged that factor analysis is useful for the first two aims, but he was deeply skeptical about its ability to discover dimensions that correspond to actual psychological dispositions responsible for differences in behavior and life outcomes.

In other words, EFA can help describe individual differences, but identifying statistical dimensions is not the same as discovering the psychological mechanisms that explain those differences. A factor that summarizes correlations among several measures does not necessarily represent a common cause of those correlations.

Lykken began by distinguishing three applications of factor analysis.

The first is data reduction. When researchers have hundreds of correlated measures, a smaller number of statistical dimensions may summarize much of the information. This can be useful, especially when the goal is to simplify a dataset without losing too much information.

The second is hypothesis testing. If a theory predicts that several measures reflect a common underlying disposition, researchers can examine whether their correlations are consistent with that prediction.

The third application is dimensional discovery: using exploratory factor analysis to discover the fundamental psychological variables responsible for individual differences.

Lykken acknowledged that EFA is useful for the first two aims, but he was deeply skeptical about its ability to discover dimensions that map on actual personality dispositions that explain differences in behavior and life-outcomes. In other words, EFA can help with the description of personality differences, but it cannot be used to explain behavior.

The distinction is easy to overlook because the statistical computations are similar. However, the scientific claims are radically different. A statistical factor may efficiently summarize several correlated measures without representing a distinct psychological mechanism. Conversely, a psychological mechanism may be causally important without producing a strong factor in the available measurements.

Lykken’s central criticism concerned the unwarranted movement from the first claim to the second.

The Problem With Assuming That Nature Has a Simple Factor Structure

Traditional factor analysis is often motivated by the assumption that correlations among observed variables result from a relatively small number of underlying common factors.

A classic representation is a hierarchy in which narrow characteristics reflect broader dispositions, which may themselves reflect still more general dispositions.

This arrangement has an appealing simplicity. It suggests that a complex psychological system can be understood by identifying a relatively small number of fundamental dimensions.

Lykken questioned whether biological and psychological systems actually have this organization.

He compared personality research with the study of biological organs and complex machines.

An engineer trying to understand an unfamiliar computer would not simply measure its outputs, extract factors, and assume that the resulting dimensions correspond to its internal components.

Instead, the engineer would investigate how the system operates, identify its functional subsystems, and determine how those subsystems interact.

Similarly, a biologist studying the kidney would seek to understand its anatomical and physiological organization.

Lykken emphasized that the structure of biological systems is often hierarchical, but not necessarily a neat hierarchy in which a small number of broad causes produce progressively narrower effects.

Furthermore, important biological mechanisms do not necessarily generate the largest individual differences.

A process can be essential to survival and still show little variability across healthy individuals.

Consequently, extracting the dimensions that account for the most variance does not necessarily identify the mechanisms that are most important for understanding the system.

This is a fundamental limitation of using correlations among people to discover the organization of psychological processes.

The Problem of Mixing Different Levels of Abstraction

One of Lykken’s most insightful observations concerned the levels of abstraction represented by factors.

He described unpublished analyses of anxiety items that produced factors corresponding to several different kinds of fear.

Some concerned highly specific situations, such as roller coasters. Others concerned broader categories, such as heights or physical danger. Still another represented general fearfulness.

But these characteristics do not occupy the same conceptual level.

Fear of roller coasters is a special instance of fear of heights, which overlaps with a broader fear of physical danger, which may itself reflect general fearfulness.

A single exploratory analysis had placed these dimensions alongside one another as though they represented comparable factors.

This is not merely an issue of choosing better labels.

The factor analysis did not distinguish the hierarchy of psychological characteristics that the researcher was trying to discover.

Lykken pointed out that researchers might obtain a more orderly solution if they already understood the relevant hierarchy and selected their items accordingly.

But that creates an obvious problem: if the structure is already known, what exactly has the exploratory analysis discovered?

This observation is particularly relevant to contemporary personality research.

Personality questionnaires often combine broad domain indicators, narrow facet indicators, and highly specific behavioral descriptions.

There is no guarantee that an exploratory factor analysis will identify these levels correctly.

A prominent factor might represent a broad disposition, a particularly well-represented narrow facet, or some mixture of characteristics at different levels.

The factor’s position in an exploratory solution does not settle the question.

Five Electrical Circuits: An Ingenious Demonstration

Lykken’s most convincing argument involved artificial systems whose actual structure was known.

He constructed five different electrical circuits containing resistors and capacitors.

The circuits differed in the number of components, their arrangement, and the ways in which the components were connected.

For each circuit, he generated 50 simulated individuals by varying component values. He then obtained ten measurements describing the circuit’s response to inputs at different frequencies.

These measurements were subjected to factor analysis.

If factor analysis could discover the underlying structure of such systems, the different circuits should have produced solutions revealing their different internal components.

That did not happen.

All five circuits produced two-factor solutions, despite their different internal structures.

The extracted factors were not recognizable as the actual resistors or capacitors.

An engineer familiar with the circuits could have identified their internal structure from the original measurements more directly.

The factor solutions were orderly statistical descriptions, but they did not reveal how the circuits worked.

This demonstration remains powerful because it avoids the familiar difficulty of evaluating personality research: we do not know the true psychological mechanisms in advance.

With the electrical circuits, the mechanisms were known.

The results showed that a successful factor analysis could fail to recover them.

One can object that the circuits were not designed to satisfy the assumptions of factor analysis. But that is precisely the issue.

Why should we assume that psychological systems are organized in ways that satisfy those assumptions?

A factor-analytic method may perform very well when data are generated by a factor model. That does not establish that it can discover the structure of systems generated by other kinds of mechanisms.

Factor Analysis of Automobiles: Plausible Factors Without Useful Explanations

Lykken’s second example is equally illuminating.

He collected 17 measurements from road tests of approximately 100 automobiles. The variables included engine characteristics, vehicle dimensions, acceleration, fuel economy, and price.

Four factors accounted for approximately 86% of the common variance.

The rotated factors were readily interpretable.

One factor was associated with acceleration and engine size. Another was associated with braking and price. A third characterized smaller, economical automobiles. The fourth involved measures such as hill-climbing performance, price, and oil capacity.

A researcher could assign plausible labels to these dimensions.

But would the factors help an automotive engineer understand what determines automobile performance?

Lykken argued that they would not.

Consider the association between automobile weight and acceleration.

Across the sample, heavier vehicles tended to have more powerful engines and therefore could accelerate faster.

Yet the physical relationship within an automobile is different. Holding engine power constant, increasing vehicle weight reduces acceleration.

The observed correlation across automobiles reflected how manufacturers combined different components in particular vehicle designs.

It did not reveal the causal effect of vehicle weight.

This distinction is extremely important for psychology.

A correlation across individuals does not necessarily reveal how the same variables are causally related within an individual.

For example, people who report high motivation may also report high stress. But increasing motivation within a particular individual may not increase stress in the same way.

The between-person correlation can reflect many different combinations of underlying processes.

Lykken was warning against interpreting correlations among people as though they directly revealed the organization of the mechanisms operating within people.

That warning remains relevant to modern personality theories that interpret higher-order factors as underlying regulatory or motivational systems.

Why Interpretability Is Not Enough

The electrical-circuit and automobile examples illustrate another central problem.

Factor analysis almost always produces patterns that researchers can interpret.

Once a factor is defined by its strongest loadings, psychologists are skilled at finding a plausible common theme.

A factor involving kindness, generosity, sincerity, and courtesy might be called Agreeableness.

A factor involving energy, assertiveness, and sociability might be called Extraversion.

These interpretations may be useful for describing the measures.

But assigning a meaningful label is not evidence that the factor corresponds to a distinct causal process.

This is particularly problematic when the factors are subsequently reified.

A statistical dimension called Extraversion becomes a psychological disposition called Extraversion. Researchers then explain correlated behaviors by claiming that they are caused by Extraversion.

The explanatory argument risks becoming circular.

We observe that people who describe themselves as sociable also tend to describe themselves as outgoing. We extract a common factor called Extraversion. We then explain the correlation between sociability and outgoingness by invoking Extraversion.

That explanation becomes scientifically informative only when the latent disposition has implications beyond the correlations used to define it.

This is why independent validation matters.

A personality construct becomes more credible when it predicts new observations, relates systematically to independently measured biological or behavioral processes, and survives demanding tests of competing explanations.

Factor analysis can contribute to that process, but it cannot substitute for it.

Discovery Versus Verification

Lykken’s distinction between the context of discovery and the context of verification is one of the most enduring lessons of the article.

Exploratory factor analysis can be useful for generating hypotheses.

Researchers can inspect different dimensional solutions, try alternative rotations, examine clusters of variables, and search for patterns worth investigating.

There is nothing wrong with using statistical methods creatively to identify potentially interesting relationships.

The problem arises when the exploratory procedure is treated as though it had already verified the resulting psychological theory.

Lykken described a study by Meehl, Lykken, Schofield, and Tellegen that used factor analysis to identify patterns among indicators of schizophrenia-related characteristics.

They also conducted a separate exercise in which judges attempted to reproduce factor labels from subsets of high-loading items.

This demonstrated that the factor interpretations could be reproduced without simply relying on the original researcher’s intuition.

But Lykken emphasized that such agreement did not establish that the factors represented distinct psychological mechanisms.

A cluster of related clinical ratings might reflect observer expectations, a genuine common cause, or several processes producing similar symptoms.

The labels could be reliable without the proposed explanation being correct.

This distinction is as relevant today as it was in 1971.

Replicating an exploratory factor structure across datasets is valuable evidence of a stable statistical pattern.

But it is not necessarily a decisive test of the substantive theory used to interpret that pattern.

Common Method Variance: An Especially Modern Problem

Another remarkable passage concerns a study of stress tolerance using several measurement methods.

The researchers included rating scales, perceptual-motor tests, MMPI scores, and physiological measures.

Factor analysis identified five major factors.

However, these factors were largely associated with the different methods used to collect the data.

In other words, the statistical analysis primarily recovered shared method variance rather than the psychological constructs that the researchers hoped to identify.

Lykken recognized that this was a problem for interpreting the factors.

The broader lesson is that observed covariance can arise from several sources.

Measures may correlate because they assess the same substantive characteristic. But they may also correlate because they share a method, response process, item format, or source of systematic error.

A factor analysis does not automatically distinguish these possibilities.

This problem is especially important in personality psychology, where much research relies on self-report questionnaires.

People differ not only in their substantive personality characteristics but also in how they describe and evaluate themselves.

A generally favorable self-description can produce positive correlations among desirable characteristics from different personality domains.

A corresponding tendency to reject undesirable descriptions can create additional covariance.

Such correlations may be reproduced across samples without establishing a substantive general personality disposition.

The relevant scientific task is therefore not simply to recover the covariance matrix. It is to determine which processes generate its different components.

What Lykken’s Critique Means for the Big Five

Lykken published his critique before the Big Five became the dominant taxonomy in personality psychology.

Nevertheless, his argument applies directly to contemporary lexical factor analyses.

There is substantial evidence that familiar broad dimensions recur in personality ratings. The Big Five are useful descriptive summaries, and measures based on them predict many important outcomes.

These accomplishments should not be dismissed.

But they establish less than is sometimes claimed.

The existence of five prominent dimensions in a correlation matrix does not establish that personality consists of five fundamental causal dispositions.

A five-factor solution may reflect a useful compression of a much more complicated structure.

The distinction becomes particularly important when moving from broad factors to higher-order constructs such as Stability, Plasticity, or a General Factor of Personality.

If the first-order factors are themselves mixtures of substantive dispositions and systematic measurement variance, their correlations cannot be interpreted straightforwardly as evidence for additional substantive common causes.

Extracting another factor from those correlations does not resolve the underlying measurement problem.

It simply summarizes the correlations at another level.

Lykken’s objection to interpreting statistical hierarchies as biological hierarchies is therefore especially relevant to claims about fundamental personality organization.

What Has Changed Since 1971?

Modern structural equation modeling (SEM) allows researchers to specify and compare more differentiated measurement models.

Instead of assuming that five common factors explain all shared covariance among hundreds of personality items, researchers can distinguish multiple sources of variation.

For example, a hierarchical measurement model can include:

• Narrow personality facets measured by multiple items.
• Broader factors representing covariance among facets.
• Secondary loadings where items reflect more than one disposition.
• Facet-specific residual covariance.
• Systematic measurement factors, such as acquiescence or general evaluative halo.

This makes it possible to examine whether a proposed personality structure reproduces the observed covariance matrix after accounting for narrower substantive dimensions and measurement effects.

Structural equation modeling does not change the fact that patterns of correlations alone cannot reveal the causal mechanisms that produce them. However, it offers important advantages over conventional EFA.

EFA is essentially a restrictive measurement model that relates observed variables to unobserved factors. It separates common variance from item-specific variance, including random measurement error. However, it generally assumes that residual covariances are zero and does not distinguish substantive common variance from systematic measurement variance. Consequently, an EFA factor may reflect a mixture of substantive personality characteristics and shared measurement biases.

SEM can be used to specify more differentiated measurement models that include method factors as well as substantive factors. This makes it possible to estimate substantive trait variance separately from identifiable sources of systematic measurement error and potentially obtain more valid measures of the intended constructs.

However, these factors are still statistical representations of covariance among the observed variables. Better measurement models do not automatically reveal the causal mechanisms underlying personality differences.

The most important additional advantage of SEM is that the correlations among factors in the measurement model can be examined using structural models derived from substantive theories. This makes it possible to test whether different theories predict the observed patterns of relationships among latent constructs.

SEM is sometimes conflated with confirmatory factor analysis (CFA), and CFA is often presented as a method for confirming factor structures previously identified by EFA. This is one possible application, particularly when the goal is to develop and validate a measure of a single construct.

However, SEM is not limited to confirming exploratory factor structures. When multiple constructs are modeled simultaneously, it offers opportunities to evaluate both measurement and substantive theories.

First, researchers can test whether a theoretically specified measurement model adequately reproduces the observed covariance matrix. A model that fails this test provides a questionable basis for interpreting its factors as measures of the intended constructs.

Second, researchers can examine whether a theoretically specified structural model accounts for the correlations among these factors without producing a substantial loss of model fit.

For example, a theory may predict that several personality facets are related because they share a broader underlying disposition. Another theory may predict that the correlations arise from multiple narrower dispositions or shared measurement biases. SEM allows researchers to specify these explanations and evaluate whether their implied covariance structures are consistent with the data.

Good model fit does not prove that a causal theory is correct, because different causal models can generate the same correlations. Nevertheless, poor fit can provide evidence against a theory’s statistical implications, and comparisons among competing models can help determine which explanations remain viable.

These applications of SEM go well beyond extracting exploratory factors or confirming simple factor structures. They provide an opportunity to move from describing correlations toward testing the empirical implications of explanatory theories.

One important development is the availability of structural equation modeling.

Modern SEM allows researchers to specify and compare more differentiated measurement models.

Instead of assuming that five common factors explain all shared covariance among hundreds of personality items, researchers can distinguish multiple sources of variation.

For example, a hierarchical measurement model can include:

• Narrow personality facets measured by multiple items.
• Broader factors representing covariance among facets.
• Secondary loadings where items reflect more than one disposition.
• Facet-specific residual covariance.
• Systematic measurement factors, such as acquiescence or general evaluative halo.

This makes it possible to examine whether a proposed personality structure reproduces the observed covariance matrix after accounting for narrower substantive dimensions and measurement effects.

Structural equation modeling does not change the fact that patterns of correlations do not reveal causal mechanisms. However, it offers important advantages over conventional EFA. EFA is essentially a restrictive measurement model that relates observed variables to unobserved factors. By separating common from unique variance, it can reduce the influence of random measurement error on estimates of common factors. However, common variance may also reflect systematic measurement error, which conventional EFA does not distinguish from substantive trait variance. SEM can be used to specify measurement models with method factors to obtain better measures of substantive factors. However, these factors are still descriptive and depend on the observed variables.

The real advantage of SEM is that the correlations among the factors in the measurement model can be modeled using structural theories that predict specific patterns of relationships among them. This makes it possible to test the empirical implications of competing causal theories.

SEM is sometimes equated with Confirmatory Factor Analysis (CFA), and it is often assumed that its only role is to confirm the results of an EFA. Confirming a previously identified factor structure is one application of CFA, but SEM can do considerably more, especially when multiple constructs are modeled simultaneously.

Here, SEM offers two important opportunities. First, it is possible to test whether a theoretically specified measurement model adequately fits the data. Second, it is possible to examine whether a structural model can explain the pattern of correlations among the latent factors without substantially reducing model fit.

These applications of SEM go well beyond EFA and the confirmation of simple factor structures. They offer an opportunity to move from description to testing the predictions of explanatory theories.

SEM was still developing as a statistical method when Lykken published his criticism of EFA in 1971. However, it subsequently became a highly influential tool in twin research for estimating genetic and environmental contributions to individual differences. Lykken used biometric structural models in his own twin research.

These analyses typically relied on personality scale scores rather than item-level measurement models that simultaneously distinguished substantive trait variance from systematic measurement variance. It would have been difficult, although not impossible, to conduct these studies using more elaborate measurement models.

For analyses of single personality traits, the additional complexity might not always have changed the main conclusions concerning genetic influences. However, accounting explicitly for measurement error and systematic method variance can change estimates of genetic and environmental contributions.

Multivariate twin models that simultaneously represented numerous personality facets, broad factors, and method effects would have been particularly challenging to estimate with the statistical software and computing resources available at the time. Advances in statistical software and computer hardware have substantially reduced these technical obstacles.

Given his skepticism about exploratory dimensional discovery in 1971, it is reasonable to ask whether Lykken would have supported contemporary efforts to replace EFA-derived sum scores with more comprehensive SEM measurement models that adequately fit the observed data.

Whether Lykken would have embraced modern SEM as a way to advance personality research remains speculative. However, his criticism of using EFA to discover the underlying structure of personality leaves little doubt that he would have been skeptical of subsequent attempts to interpret EFA-derived personality hierarchies as representations of biological systems. In particular, he explicitly rejected the assumption that biological systems have the simple hierarchical structure often imposed by factor-analytic models:

“Now the structure of organisms, while hierarchical, certainly does not have the simple-minded Christmas-tree shape envisaged by MFA” (Lykken, 1971, p. 163).

Conclusion

Why did Lykken’s criticism have so little impact on personality research, even among close colleagues with whom he collaborated?

One possible explanation is that identifying the limitations of an established method is not sufficient to change scientific practice. As Thomas Kuhn emphasized, scientific paradigms are rarely abandoned simply because their problems become apparent. Researchers also need a viable alternative that allows them to make progress.

In 1971, such an alternative was not readily available for investigating complex personality structures. Today, advances in structural equation modeling, statistical software, and computing power have made it possible to specify and test measurement models that would have been prohibitively difficult to estimate at the time.

Lykken’s criticism therefore deserves renewed attention, not because its arguments have suddenly become valid, but because researchers now have better tools to address the problems he identified. The opportunity is to move beyond extracting and naming factors toward developing and testing explicit theories of personality structure.

A Critical Review of the Cybernetic Theory of Meta-Traits

To understand psychological science, it is necessary to understand how academia works. Research at universities is organized around paradigms (Kuhn, 1962). A paradigm consists of shared ideas, assumptions, methods, and practices—something like a scientific culture.

Psychology is organized into broader research paradigms that roughly correspond to scientific disciplines. Developmental psychologists study children and developmental changes. Experimental cognitive psychologists study mental processes, often using computer-based tasks and reaction times. Personality psychologists typically collect data from moderately large samples (often N = 100 or more), measure individual differences on multiple variables, and examine the correlations among these measures. This approach is often described as multivariate or correlational, in contrast to experimental research that manipulates independent variables to study their effects on specific outcomes.

Within the personality paradigm, we can distinguish smaller paradigms, or mini-paradigms, organized around specific theories, constructs, or measurement instruments.

For example, the life-satisfaction paradigm, strongly associated with Ed Diener’s research, focuses on subjective evaluations of people’s lives rather than an objectively defined concept of happiness. Much of this research also relies on the Satisfaction With Life Scale developed by Diener and colleagues.

Other paradigms are organized around specific models and instruments for measuring personality. The Big Five paradigm can itself be divided into smaller research traditions that use different instruments to operationalize the five broad personality dimensions. Competing paradigms, such as HEXACO, propose alternative structures and corresponding measurement instruments.

One Big Five mini-paradigm is the Cybernetic Meta-Trait Paradigm, developed by Colin DeYoung in collaboration with Jordan Peterson and colleagues beginning in 2002.

The aim of this blog post is to examine what we have learned from more than 20 years of research in this paradigm. To do so, I conducted a literature review with the assistance of my AI collaborator, which can locate, read, and summarize scientific publications much faster than I can. The objective is not simply to count publications or summarize findings, but to evaluate what this research program has contributed to our scientific understanding of personality.

What Is the Cybernetic Metatrait Paradigm?

The empirical starting point was Digman’s (1997) observation that the Big Five personality dimensions are often systematically correlated.

Digman proposed that these correlations might reflect two broader personality dispositions, which he called Alpha and Beta. These names were descriptive placeholders rather than explanations of the psychological processes responsible for the correlations.

DeYoung, Peterson, and Higgins (2002) proposed the more theoretically meaningful labels Stability and Plasticity.

Stability refers to the common variance among Agreeableness, Conscientiousness, and Emotional Stability (low Neuroticism). Plasticity refers to the common variance between Extraversion and Openness to Experience.

However, giving statistical factors psychologically meaningful names does not establish what they are. Correlations among self-reported personality characteristics cannot, by themselves, reveal the psychological mechanisms responsible for those correlations.

DeYoung subsequently developed a more comprehensive theory, presented in Cybernetic Big Five Theory (2015). This theory conceptualizes personality as a system of goal-directed regulation and adaptation. Stability reflects the maintenance of goals and organized functioning in the face of disruption, whereas Plasticity reflects exploration, adaptation, and engagement with new possibilities.

The theory thus moves beyond describing correlations among personality measures toward proposing psychological functions that might explain them.

But proposing a theory and finding empirical observations consistent with it does not establish that the theory is correct. This is particularly important in correlational research, where different causal models can generate similar patterns of observed associations. Convincing evidence for a theory therefore requires also to present evidence against competing theories. However, this work is rarely conducted by researchers within a paradigm because it acknowledges competing paradigms and the possibility that the assumptions of ones own paradigm might be false.

The central question of this review is whether research within the cybernetic meta-trait paradigm has produced findings that answer broader questions in personality research outside the paradigm.

Do the Metatraits Even Exist?

Much of the research conducted over the past two decades has focused on a more fundamental question: Do Stability and Plasticity actually exist? Are the Big Five dimensions genuinely correlated because of broader personality dispositions, or do their observed correlations arise from the way personality is measured?

Ashton and Lee (2008) challenged the interpretation of higher-order factors by emphasizing the effects of measurement and the heterogeneity of the traits represented by broad personality scales. Most research on metatraits has relied on Big Five sum scores. However, these scores combine variance associated with broad personality dimensions with variance associated with narrower characteristics that may overlap across domains.

For example, an Extraversion scale emphasizing Excitement Seeking may correlate with an Openness scale emphasizing Novelty Seeking. Both measure aspects of an adventurous disposition. In contrast, an Extraversion scale emphasizing sociability and an Openness scale emphasizing enjoyment of intellectually demanding activities, such as solving puzzles, may show much weaker correlations. Consequently, correlations among Big Five sum scores do not necessarily imply the existence of higher-order personality dispositions. They may reflect the particular content of the instruments used to measure the Big Five.

An important empirical challenge is therefore to establish that the proposed higher-order structure is not an artifact of overlapping item content, secondary factor loadings, or evaluative response biases. Recently, DeYoung and colleagues (2026) published another article arguing that Stability and Plasticity are genuine higher-order personality traits. I have critically examined the methodological problems underlying this claim in several previous blog posts (Schimmack, 2019, 2020, 2022, 2026). The most recent analysis, Lost in Factor Space: The Search for Personality Meta-Traits (Schimmack, 2026), specifically challenges the use of estimated Big Five factor scores to test hypotheses about correlations among latent personality traits.

The present review addresses a different question. For the sake of argument, I will assume that Stability and Plasticity are genuine personality dispositions. What have more than two decades of research taught us about their psychological nature, and what contributions have these constructs made to broader theories of personality?

For the sake of argument, I will assume that Stability and Plasticity are genuine personality dispositions. I will also accept the proposed hierarchical organization in which these broad dispositions influence the Big Five and, through them, narrower personality traits such as helpfulness, courage, argumentativeness, and neatness.

The question is what follows from accepting these assumptions.

What have 20 years of research taught us about the psychological nature of Stability and Plasticity? What role do these metatraits play in explaining personality and behavior?

More specifically, has the Cybernetic Metatrait Paradigm produced findings that are important for personality psychology generally, or has it mainly generated research of interest to scientists already committed to the metatrait framework?

A productive scientific paradigm should do more than demonstrate the existence of the constructs around which it is organized. It should clarify their nature, identify the mechanisms responsible for them, and explain phenomena that competing theories cannot explain equally well.

What Do Metatraits Contribute to the Prediction of Behavior and Life Outcomes?

Personality psychology has three major aims: to identify and classify personality traits, to investigate their causes, and to understand how they influence behavior, feelings, and thoughts.

Personality psychology has arguably made the greatest progress toward the third aim. Personality measures predict a wide range of behaviors and consequential life outcomes, sometimes over remarkably long periods. For example, personality characteristics assessed around the time of marriage can predict the likelihood of divorce years later.

These findings demonstrate that relatively stable individual differences contain information about people’s future lives. The challenge is to understand which dispositions are responsible for these predictions and how they influence particular outcomes.

This is where the cybernetic metatraits face a fundamental problem.

Metatraits are, by definition, more remote from specific behaviors and outcomes than the narrower personality traits they are supposed to influence. The theory proposes that Stability and Plasticity contribute to individual differences in the Big Five, which in turn contribute to more specific personality dispositions.

Consequently, under a hierarchical common-cause model, the influences of metatraits on specific outcomes should be reflected in the narrower dispositions through which they operate.

An Example: Personality and Life Satisfaction

One of the most robust findings in life-satisfaction research is that Neuroticism is among the strongest Big Five predictors of life satisfaction. People who are prone to negative emotions, worry, and dissatisfaction tend to evaluate their lives less favorably.

This is not a particularly surprising finding. What makes it important is the stability of these individual differences and their predictive associations over long periods.

However, Neuroticism is a broad personality dimension that includes several distinguishable characteristics, such as anxiety, irritability, emotional vulnerability, and depressiveness.

These narrower traits need not have identical relationships with life satisfaction.

For example, irritability may create interpersonal problems without necessarily having the same relationship with a person’s own life satisfaction as depressiveness. Anxiety may produce unpleasant experiences, but it can also motivate vigilance and avoidance of threatening situations.

The Depression facet of Neuroticism is the strongest predictor of low life-satisfaction and it is stronger than the broad Neuroticism disposition (Anglim et al., 2020; Schimmack et al., 2004). This finding shows that moving down the hierarchy towards specific traits can increase prediction and clarify the meaning of findings. So what can the meta-trait of Stability add to this finding?

Stability is defined as a higher-order disposition contributing to individual differences in Emotional Stability, Agreeableness, and Conscientiousness. For the sake of argument, assume that Stability really exists as a common psychological cause of these three personality dimensions. How would this improve our prediction of life satisfaction? It doesn’t.

Suppose depressiveness is the strongest specific predictor of life satisfaction. If Stability influences depressiveness through Neuroticism, then some portion of depressiveness reflects the influence of Stability. But this influence is already represented in the depressiveness measure. Other components of Stability could predict life satisfaction through additional pathways. For example, Conscientiousness might contribute through goal achievement, and Agreeableness through interpersonal relationships. Yet these pathways can be investigated directly by measuring the relevant narrower traits. A broad Stability score does not explain which mechanisms matter. Indeed, it risks obscuring the different ways in which personality influences life satisfaction.

A Mathematical Problem for the Predictive Utility of Metatraits

The problem is not merely that metatraits are more abstract than the Big Five. It follows from a basic property of multiple regression. Adding predictors cannot reduce the proportion of variance explained by an ordinary least-squares regression model in the same sample.

Consider Plasticity, which is defined by the shared variance between Extraversion and Openness. If we measure Extraversion and Openness separately, we can use both variables to predict any outcome. Alternatively, we can combine them into a single Plasticity score.

But the second approach cannot explain more variance than a multiple regression using both Extraversion and Openness, provided Plasticity is computed as a linear combination of those two scores. The multiple regression is free to assign different weights to Extraversion and Openness. It can therefore reproduce the prediction obtained from the Plasticity composite or improve upon it.

More generally, a multiple regression using all five Big Five scores must explain at least as much variance as one using only Stability and Plasticity scores derived from those same variables.

This is not an empirical hypothesis about the relationships between personality and life outcomes. It is a mathematical property of regression. Thus, metatraits have no inherent advantage in predicting outcomes once their constituent traits have been measured.

Why the Big Five Are Useful but Metatraits May Not Be

There is an important reason why broad personality measures such as the Big Five have become so successful. Each Big Five domain represents a large number of more specific personality characteristics.

A comprehensive assessment of personality might require hundreds of items covering dozens of facets. However, administering such extensive measures is often impractical. Big Five instruments provide an economical alternative.

For example, Neuroticism measures tend to capture variance associated with anxiety, stress sensitivity, emotional vulnerability, irritability, and depressiveness. A brief Neuroticism scale may therefore predict a variety of outcomes associated with these characteristics, even when it does not measure the most relevant specific trait very precisely.

Consider life satisfaction again. A short Neuroticism scale containing anxiety-related items may still predict life satisfaction because anxiety and depressiveness share substantial variance. The measure indirectly captures some of the depressive disposition that is particularly relevant to the outcome.

The practical advantage of the Big Five is that a small number of measures can cover a broad range of personality characteristics and predict many different outcomes. However, this advantage is much less obvious for the metatraits.

Plasticity is defined as the shared variance between Extraversion and Openness. Yet there is no independently established measure of Plasticity that does not rely on indicators of its constituent personality dimensions. Consequently, researchers typically estimate Plasticity from measures of Extraversion and Openness. Once these dimensions have been measured, however, averaging the two measures destroys important information about Extraversion and Openness.

Take life-satisfaction as an example, again. Extraversion is a positive predictor of life-satisfaction, but Openness is not (Anglim et al., 2020). These findings are informative because they suggest that characteristics associated with Extraversion, especially positive emotionality, are more relevant to life satisfaction than many of the cognitive and experiential tendencies associated with Openness.

Now suppose we average Extraversion and Openness to create a Plasticity score. We have combined a useful predictor with another variable that contributes comparatively little. Under these circumstances, averaging the two typically dilutes the predictive association and obscures the difference between the domains.

Rather than improving our understanding of life satisfaction, the Plasticity composite discards information that is scientifically relevant to explaining it.

What about Understanding Personality Traits?

The limited usefulness of metatraits for predicting behavior and life outcomes leaves understanding the structure of personality as the central objective of the metatrait paradigm.

Why are some personality traits correlated with each other? What psychological mechanisms produce these correlations? And why do some traits share variance while others are largely independent?

These questions are at the heart of Cybernetic Big Five Theory. DeYoung proposes that Stability and Plasticity are not merely statistical summaries of correlations among the Big Five, but broader psychological dispositions that help explain why these correlations exist.

The important question is what answers the meta-trait paradigm has provided to these questions. The theoretical assumptions are spelled out in DeYoung’s Cybernetic Big Five Theory (2015).

Cybernetics is the study of goal-directed systems that regulate their behavior through feedback. A thermostat is a simple example. It monitors the temperature, compares it with a desired state, and adjusts its actions to maintain that state.

Human behavior is much more complicated, but the general principle is similar. People pursue goals, monitor their progress, respond to obstacles, and adjust their behavior when circumstances change.

DeYoung argues that individual differences in personality reflect differences in the functioning of these regulatory systems. Within this framework, the two metatraits represent two fundamental requirements of adaptive behavior.

Stability is the capacity to maintain goals and organized functioning despite disruptions. It supposedly explains why people who are emotionally stable also tend to be conscientious and agreeable.

Plasticity is the capacity to explore new possibilities and adapt to changing circumstances. It supposedly explains why people who are extraverted also tend to be open to new experiences and ideas.

The theory is appealing because successful adaptation requires both functions. An organism that cannot maintain its goals will have difficulty achieving them. An organism that cannot explore new possibilities will have difficulty adapting when existing strategies no longer work.

But this general observation does not explain individual differences in personality. Everybody needs some ability to maintain goals and explore alternatives. The question is whether people differ in two general psychological capacities that produce correlations among the Big Five.

More specifically, what makes emotionally stable people more conscientious and agreeable? And why should people who enjoy social interactions also enjoy intellectual exploration?

Can We Identify the Biological Causes of Metatraits?

It is difficult to study the causal mechanisms underlying personality traits. Personality traits are relatively stable individual differences, and their biological causes are likely to involve complex interactions among genes, brain systems, and environmental influences. The challenge becomes even greater when the aim is to explain abstract dispositions such as Stability and Plasticity, rather than narrower characteristics such as anxiety, sociability, or impulsivity.

Nevertheless, Cybernetic Big Five Theory makes some relatively straightforward predictions that can be tested empirically.

The theory proposes that dopamine contributes to Plasticity, whereas serotonin contributes to Stability. One way to test these hypotheses is to examine whether drugs that alter these neurotransmitter systems produce changes in personality traits.

For example, if serotonin is a common biological cause of Stability, drugs that alter serotonergic functioning should produce coordinated changes in the three constituent Big Five dimensions: Neuroticism, Agreeableness, and Conscientiousness. Similarly, if dopamine is a common cause of Plasticity, drugs that alter dopaminergic functioning should affect both Extraversion and Openness.

Of course, neurotransmitter systems are complicated. Dopamine and serotonin act through different receptors and pathways, and pharmacological interventions are not perfectly selective. Moreover, acute changes in behavior are not necessarily equivalent to enduring changes in personality.

However, these complications do not make pharmacological studies irrelevant. They make it necessary to specify which biological mechanisms are hypothesized to influence the meta-traits and what pattern of personality changes should follow from manipulating them.

What Have Pharmacological Studies Found?

There is evidence that drugs affecting serotonergic functioning can change personality scores.

For example, Knutson et al. (1998) administered the selective serotonin reuptake inhibitor (SSRI) paroxetine or a placebo to healthy volunteers for four weeks. The treatment reduced negative affect and hostility and increased affiliative behavior.

Tang et al. (2009) conducted a placebo-controlled study of paroxetine in patients with major depression. They found substantial reductions in Neuroticism and increases in Extraversion. These effects remained significant after statistically controlling for improvements in depressive symptoms.

These findings are important because they demonstrate that pharmacological interventions can produce changes in personality measures.

However, the observed pattern does not correspond to the predictions of a simple serotonin–Stability theory.

Lower Neuroticism is consistent with an increase in Stability. But the studies do not establish corresponding increases in Agreeableness and Conscientiousness. Moreover, paroxetine increased Extraversion, which belongs to Plasticity rather than Stability.

Thus, the existing findings suggest that serotonergic interventions influence particular personality characteristics rather than demonstrating coordinated changes in the traits defining Stability.

The evidence for dopamine and Plasticity is even more limited.

Researchers have manipulated dopaminergic functioning using drugs such as haloperidol and sulpiride. However, the studies reviewed here primarily examined whether personality moderated the effects of these drugs on cognitive or behavioral tasks.

Such findings do not demonstrate that the drugs change personality traits.

The relevant experiment would be to administer a drug that alters dopaminergic functioning and examine whether Extraversion and Openness change together, as predicted by a common Plasticity mechanism.

I did not identify a controlled pharmacological study that provides this test.

Why Has There Been So Little Direct Experimental Research?

This raises a question about the scientific progress of the metatrait paradigm.

If dopamine and serotonin are central to the proposed explanations of Plasticity and Stability, why have researchers not conducted more experiments directly testing their effects on the constituent personality traits?

The metatrait paradigm has existed for more than 20 years. During this period, researchers have repeatedly examined whether Stability and Plasticity can be identified in personality data and whether metatrait scores correlate with various psychological outcomes.

Yet the central biological hypotheses have received comparatively little direct empirical investigation. Of course, pharmacological studies are expensive, ethically constrained, and difficult to interpret. Demonstrating changes in enduring personality dispositions may also require longer treatment periods and repeated assessments.

Nevertheless, the relevant research designs are feasible. Studies of antidepressant medications have already demonstrated that personality scores can change following pharmacological interventions. A research program that proposes biological causes of personality differences should ultimately investigate whether manipulating these causes produces the predicted changes.

What Has the Meta-Trait Paradigm Explained?

In conclusion, the cybernetic metatrait paradigm has produced few findings that substantially advance our understanding of personality beyond the paradigm itself.

Without independent measures of Stability and Plasticity, metatraits cannot improve the prediction of behaviors and life outcomes beyond the lower-order traits from which they are constructed. The Big Five, and especially their narrower facets, provide more detailed information about the personality characteristics that matter for particular outcomes.

The metatraits’ potential contribution is therefore primarily explanatory. If Stability and Plasticity are genuine higher-order dispositions, they might explain why certain personality traits are correlated.

However, after more than 20 years of research, the proposed biological mechanisms remain poorly understood. Theories linking Stability to serotonin and Plasticity to dopamine have generated relatively little direct experimental evidence. Moreover, the available pharmacological findings do not provide the coordinated changes in personality traits that would support a straightforward interpretation of these biological mechanisms.

This raises a broader question about the importance of the phenomenon that originally motivated the paradigm.

Digman (1997) observed surprisingly strong correlations among Big Five measures, challenging the assumption that the five major personality dimensions are largely independent.

Subsequent research demonstrated that some of these correlations reflect measurement artifacts, including evaluative biases and overlapping item content. When these influences are taken into account, correlations among the substantive Big Five dimensions are often substantially weaker.

This leaves a more limited theoretical question: Do the remaining correlations reflect systematic influences of broader personality dispositions, or do they arise from several distinct mechanisms that produce modest associations among particular traits?

That is a legitimate scientific question. However, its importance for personality psychology should not be exaggerated.

Most individual differences in personality are not captured by the two metatraits. They are found at lower levels of the personality hierarchy, including the Big Five and the many narrower dispositions that contribute to them.

These narrower traits are more closely related to the behaviors, emotions, and thoughts that personality psychologists seek to understand. They can also be connected to more specific psychological and biological mechanisms.

Consequently, research programs that focus on these lower levels of the personality hierarchy appear more promising for advancing both the prediction and explanation of personality.

The central limitation of the meta-trait paradigm is not that Stability and Plasticity may fail to exist. It is that their existence may not be particularly important.

References

Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting psychological and subjective well-being from personality: A meta-analysis. Psychological Bulletin, 146(4), 279–323. https://doi.org/10.1037/bul0000226

DeYoung, C. G. (2013). The neuromodulator of exploration: A unifying theory of the role of dopamine in personality. Frontiers in Human Neuroscience, 7, Article 762. https://doi.org/10.3389/fnhum.2013.00762

DeYoung, C. G. (2015). Cybernetic Big Five Theory. Journal of Research in Personality, 56, 33–58. https://doi.org/10.1016/j.jrp.2014.07.004

Schimmack, U. (2019a, August 13). When personality psychologists are high. Replicability-Index. https://replicationindex.com/2019/08/13/when-personality-psychologists-are-high/

Schimmack, U. (2019b, September 7). Testing hierarchical models of personality with confirmatory factor analysis. Replicability-Index. https://replicationindex.com/2019/09/07/testing-hierarchical-models-of-personality-with-confirmatory-factor-analysis/

Schimmack, U. (2019c, September 13). Confirmation bias is everywhere: Serotonin and the meta-trait of stability. Replicability-Index. https://replicationindex.com/2019/09/13/stability-serotonin/

Schimmack, U. (2020, October 25). No evidence for a higher-oder plasticity factor. Replicability-Index. https://replicationindex.com/2020/10/25/no-evidence-for-a-higher-oder-plasticity-factor/

Schimmack, U. (2022, January 25). More madness than genius: Meta-traits of personality. Replicability-Index. https://replicationindex.com/2022/01/25/more-madness-than-genius-meta-traits-of-personality/

Schimmack, U. (2026, October 31). Lost in factor space: The search for personality meta-traits. Replicability-Index. https://replicationindex.com/2026/10/31/lost-in-factor-space-the-search-for-personality-meta-traits/

Schimmack, U., Oishi, S., Furr, R. M., & Funder, D. C. (2004). Personality and life satisfaction: A facet-level analysis. Personality and Social Psychology Bulletin, 30(8), 1062–1075. https://doi.org/10.1177/0146167204264292

I first learned structural equation modeling with Ralf Schwarzer at the Free University Berlin. Alexander Grob taught me how to achieve good model fit when I was a post-doctoral student at the University of Illinois. Since then I have learned by experience and with a few tips from Linda Muthen. In the beginning, I would often encounter convergence problems. That is, the software (usually MPLUS) terminated without results. Over time, I learned how to diagnose these problems and avoid them. However, knowledge about these solutions is not widely shared and I see published articles that report conversion failure as a result. However, convergence failures are not a result. They reveal a problem in testing a model that may occur for a simple reason (e.g., not specifying a negative starting value for a negative loading).

I hope that AI will make it easier for people to find problems in their code (or just ask AI to write the code), but here are some resources that I (well AI) found that may help to avoid these problems.

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Yes. There are several useful resources in the SEM community, including dedicated troubleshooting guides, tutorials with executable code, and methodological papers on model identification. Some align closely with what you are proposing for OPeRA.

The most relevant resources are these:

1. Tutorials specifically addressing convergence and identification

Where Did I Go Wrong with My Model? Ten Tips for Getting Results in SEM

Structural Equation Modeling: A Multidisciplinary Journal (2023).

A practical troubleshooting article covering identification problems, poorly fitting measurement models, convergence warnings, and stepwise model development. Probably the best general starting point. Get Access

The Empirical Check for Identification

From A lavaan Compendium for Structural Equation Modeling in Educational Research.

Particularly relevant to your concern about whether adding secondary loadings or correlated residuals makes a model unidentified. It demonstrates how to generate data from the specified model and investigate whether its parameters can be recovered. tdjorgensen.github.io

Mplus User’s Guide — Chapter 14: General Convergence Problems

Official Mplus guidance on diagnosing optimization failures, examining TECH5/TECH8 output, changing starting values, rescaling variables, and estimating parts of a model separately before assembling the full specification. This is remarkably close to your proposed diagnostic approach. statmodel.com

Video lessons on SEM convergence problems

A short instructional series shared by an SEM researcher in the lavaan discussion community. The examples use Stata, but the underlying diagnostics apply to other SEM programs. groups.google.com

2. Especially relevant to your personality models

Two additional tutorials address the kinds of complex factor structures you have been working with.

Marsh & Alamer (2024): When and How to Use Set-Exploratory Structural Equation Modelling

This open-access paper compares CFA with set-ESEM, which allows selected cross-loadings rather than requiring every secondary loading to be zero. It includes real-data examples and lavaan code.

The authors show that restrictive CFA specifications can distort factor correlations and structural parameters. This is directly relevant to your concern about the inflated HH–Agreeableness correlation in Blötner’s model. Wiley Online Library

Bifactor ESEM Tutorial

This provides executable specifications for general and specific factors. It is useful for understanding why bifactor models can become difficult to identify and what constraints different parameterizations impose. bpb-us-e1.wpmucdn.com

3. What seems to be missing

The existing resources tend to concentrate on either statistical identification or technical troubleshooting.

What I find less developed is the kind of case-based model-development tutorial you have in mind:

Start with a theoretically interesting model that does not converge. Fit simpler nested models, identify the specific component responsible for failure, investigate whether the problem is numerical or structural, impose defensible constraints, and then assess whether the resulting model provides a substantively interpretable account of the data.

Such a tutorial would also distinguish three outcomes that researchers sometimes conflate: convergence, acceptable fit, and theoretical validity.

That would be especially useful for personality researchers working with many correlated facets, secondary loadings, hierarchical structures, and method factors.

4. An opportunity for OPeRA

I would not present OPeRA as introducing convergence diagnostics to SEM. The expertise and instructional materials clearly exist.

Instead, OPeRA could bring established SEM expertise into personality-structure research, using actual personality datasets to illustrate problems that arise in complex measurement models.

It could also invite methodological specialists to contribute reproducible tutorials, with Mplus and lavaan scripts and example data.

The Jorgensen identification chapter and Marsh and Alamer’s set-ESEM paper would be particularly good foundations.

In The Structure of Scientific Revolutions, Thomas Kuhn (1962) described how scientific communities develop around a paradigm. A paradigm involves more than a theory. It includes assumptions about what exists, which questions are worth asking, which methods are appropriate, and what constitutes acceptable evidence. These shared assumptions make cumulative research possible. However, they can also prevent researchers from recognizing the limitations of established practices.

Paradigms are like cultures, and like cultures, they can contain subcultures. For example, academic psychologists generally favor quantitative methods to provide empirical evidence for theoretical claims. Within academic psychology, experimental psychologists emphasize the manipulation of independent variables to test causal hypotheses. Personality psychologists, in contrast, rely primarily on correlational evidence because personality traits are difficult or impossible to manipulate directly.

This does not make personality psychology inherently less scientific. Astronomy also relies extensively on observational data, yet few would question its status as a science.

However, there is an important difference between astronomy and mainstream personality psychology.

Astronomers distinguish between the objects they study and the instruments used to observe them. A star is not defined by the measurements obtained with a particular telescope. Different instruments provide different information about the same object, and astronomers develop theories that explain these observations, taking the properties of the instruments into account.

Personality psychologists are less consistent in maintaining this distinction. Personality inventories often serve simultaneously as measurement instruments and definitions of the constructs they supposedly measure.

Consider Extraversion. Personality psychologists have proposed several theories about its psychological nature, emphasizing sociability, positive affect, reward sensitivity, or approach motivation. However, proposing a theory is not the same as providing empirical support for it. The existence of competing theories does not tell us which, if any, identifies the psychological disposition responsible for individual differences in Extraversion.

Meanwhile, researchers continue to use questionnaire sum scores labeled Extraversion as if the theoretical construct were already established. Different inventories combine different behaviors and characteristics, producing positively correlated but nonidentical scores. Yet there is no generally accepted explanatory model that specifies precisely what Extraversion is, demonstrates how its underlying disposition produces the observed responses, and establishes which inventory provides the most valid measurement.

Extraversion is therefore an established label for a family of correlated questionnaire scores, but not an equally well-established explanatory construct.

Another way to understand this problem is to compare personality taxonomies with geographical maps.

Maps represent real properties of Earth, including the distribution of land and water, elevation, and the location of geographical features. We can evaluate their accuracy by comparing them with observations of the physical world.

However, maps also use coordinate systems whose reference points are conventional rather than natural. Longitude provides a familiar example.

Greenwich became the internationally accepted reference for longitude in 1884. Before that, different countries used different prime meridians. The choice of Greenwich did not reveal a fundamental property of Earth. It established a common reference system that made navigation and geographical communication easier.

Changing the prime meridian changes the longitude assigned to a location. It does not change the location itself.

Personality inventories are similar to coordinate systems. They organize individual differences within a multidimensional personality space. The Big Five, the Big Five Aspect Scales, and HEXACO provide different ways of representing that space.

The existence of parallel and competing paradigms has advantages and disadvantages. The advantage is that competing paradigms can lead to empirical tests of competing predictions and lead to new discoveries. The disadvantage is that it is difficult to integrate research findings from different paradigms. For descriptive paradigms, the later problem can be resolved by translating findings from one descriptive system into another. For example, we can translate 15.00 Berlin into the local time in Sydney just like we can convert inches into centimeters or Fahrenheit into Celsius.

In personality space, we can use the correlations among dimensions from different inventories to translate findings from one system into another. A recent article by Ashton and Lee, the inventors of the HEXACO paradigm, provides an example. The authors focused on the description of pro- and anti-social traits with the Big Five Aspect Scales (BFAS) and their own HEXACO dimensions.

The BFAS uses two dimensions, Compassion (COM) and Politeness (POL), to represent individual differences in prosocial and antisocial characteristics. Although these names reflect the content of the scales, they do not establish that the dimensions measure compassion or politeness as independently defined psychological constructs. To avoid confusing labels with theoretical constructs, I will refer to these dimensions as BFAS-COM and BFAS-POL.

HEXACO also uses two dimensions to organize much of this domain: Agreeableness and Honesty-Humility. Importantly, HEXACO Agreeableness differs from Big Five Agreeableness, and Honesty-Humility refers to a particular combination of characteristics represented by its questionnaire items. I will therefore refer to these dimensions as HEXACO-A and HEXACO-HH.

Both systems provide two-dimensional representations of interpersonal individual differences. Specific characteristics, such as kindness, sympathy, helpfulness, forgiveness, or fairness, can be located within either system based on their correlations with the respective dimensions.

We can examine how closely these two systems correspond by calculating correlations among their four sum scores.

The correlations show that the two coordinate systems are related but not aligned. HEXACO-A correlates .29 with BFAS-COM and .50 with BFAS-POL. Similarly, HEXACO-HH correlates .33 with BFAS-COM and .48 with BFAS-POL. Thus, both HEXACO dimensions are more strongly associated with BFAS-POL than with BFAS-COM.

More importantly, these are not simply two different orientations of the same two-dimensional space. If they were, we could translate scores from one system into the other without losing information. Instead, each system captures information that is not represented by the other.

Some of this difference may reflect measurement error, but it may also reflect the fact that interpersonal personality characteristics cannot be adequately represented in just two dimensions.

The important conclusion is that BFAS and HEXACO provide different descriptive representations of interpersonal personality characteristics. Neither system can be assumed to represent the fundamental psychological structure simply because its dimensions were obtained through factor analysis.

The Confusion of Objects and Instruments in the HEXACO Paradigm

The main problem with personality paradigms organized around specific inventories is that researchers may come to treat their preferred descriptive framework as a theoretically privileged representation of personality.

Lee and Ashton (2026) provide an example. They examined the relationships between BFAS-POL and the HEXACO dimensions and concluded that BFAS Politeness is best understood as roughly an equal blend of HEXACO Agreeableness and Honesty-Humility.

This interpretation goes beyond demonstrating correlations among questionnaire scores. It suggests that the HEXACO dimensions provide the appropriate framework for understanding BFAS Politeness.

But why should we privilege the HEXACO coordinate system?

We would not claim that a location northwest of Greenwich is best understood as a blend of North and West. North and West are coordinates, not fundamental ingredients of a geographical location. A different coordinate system could describe the same location differently.

Similarly, the observation that BFAS-POL correlates with both HEXACO-A and HEXACO-HH does not establish that these HEXACO dimensions are the fundamental psychological dispositions underlying BFAS-POL.

Lee and Ashton go beyond describing correlations. They argue that personality measures must distinguish the six HEXACO dimensions for personality phenomena to be properly understood (p. 7). This is a theoretical claim about the privileged status of the HEXACO framework.

But the analyses do not establish that these six dimensions represent fundamental personality dispositions rather than one useful organization of correlated characteristics.

Observations obtained with one instrument do not explain observations obtained with another. Measurements from one telescope do not explain measurements from a second telescope. Both are observations that require an explanatory theory of the objects and processes producing them.

The same principle applies to personality questionnaires. Correlations among sum scores describe relationships among observations. They do not identify the psychological dispositions responsible for those relationships.

The methods needed to investigate these questions already exist. Structural equation modeling makes it possible to specify latent personality dispositions, distinguish them from their observed indicators, and test whether proposed measurement and structural models adequately reproduce the observed data.

I applied this approach to the same data used by Lee and Ashton, specifying a simple model of the relationships among the 40 items used to construct BFAS-COM, BFAS-POL, HEXACO-A, and HEXACO-HH. The model reproduced the broad pattern of sum-score correlations but failed to adequately reproduce the complete item covariance structure.

The purpose here is not to present an alternative model or claim that a particular personality theory is correct. The point is that an explanatory interpretation should be formulated as a testable model rather than inferred from correlations among questionnaire scores.

There is also a broader problem with the competing descriptive frameworks.

Neither BFAS nor the two HEXACO dimensions provide a comprehensive representation of prosocial and antisocial personality characteristics. The HEXACO dimensions capture much more of the information represented by BFAS-POL than by BFAS-COM. Conversely, HEXACO-HH represents characteristics that are only partly captured by BFAS-POL.

These differences suggest that interpersonal personality characteristics may require more than two dimensions for an adequate representation. Measurement error and differences in item content may also contribute to the imperfect correspondence.

The fundamental question is not whether BFAS or HEXACO provides the better map of personality space. It is whether we can develop and empirically test theories of the psychological dispositions that these maps attempt to represent.

Until we distinguish theoretical constructs from the instruments used to measure them, personality psychology risks confusing its coordinate systems with the underlying structure of personality.

Shaky Scientific (Epistemiological) Foundations of Personality Psychology

Personality psychology lacks solid scientific foundations. Behaviorism was impoverished, but at least it was based on clear rules. Only the effects of situational manipulations on observable behavior were admissible empirical observations, and theories were not allowed to invoke metaphysical constructs like thoughts and feelings to explain behavior.

Modern empirical psychology has moved on from behaviorism, but behaviorism still influences the thinking of psychologists without their awareness. Personality psychology survived behaviorism, but it kept one of its rules: causal questions can only be answered with experiments. This is a problem for a research area that cannot run experiments with random assignment to study causality.

Are taller people more confident because of their height? We cannot simply assign people to a tall or short condition, alter their height, and see what happens. Behaviorism simply decided that such questions are not psychological questions. Nowadays, hundreds of articles report correlations of personality traits with each other or other variables, but personality psychologists have not revised the way they think about causality.

For the most part, they have accepted that observational research cannot test causal theories and frame their research in terms of prediction: can my personality item sum score predict something of interest? For example, does a Conscientiousness scale predict GPA? The answer is yes, the correlation is about r = .2, and no definition of conscientiousness or causal theory is needed to explain the correlation.

However, personality psychologists sometimes make causal assumptions, even if these assumptions are not spelled out explicitly to avoid criticism of causal claims based on correlational research.

The most common causal assumption in personality research is that responses to similar items correlate because they measure a common trait. For example, responses to the items “I am talkative” and “I am outgoing” are correlated, and this correlation is used as evidence that both items measure a common trait, a personality disposition. This justifies averaging the responses to create a personality item sum score and calling it sociability. Importantly, the name of a personality item sum score is arbitrary. It could also be called T346, or “that thing that is reflected in the shared variance between responses to items like talkative and outgoing.”

No theoretical definition of a construct and no theory of measurement is needed. Sociability is whatever a specific sociability scale measures. Here lies the contradiction: a causal assumption, a common trait, is used to justify summing the items, and then an operational definition is used to avoid ever testing that assumption.

The problem with this approach to measurement becomes more obvious when we go beyond correlations among items that are synonymous or semantically similar. For example, take the items “I am often in a good mood” and “I am talkative.” Responses to these items are also positively correlated, but the items have distinct meanings. While it may be easier to imagine that people in a good mood talk a lot, it is also easy to imagine people who are in a good mood while absorbed in a solitary activity (flow) without engaging in any social interaction.

Research on the relationships among specific traits like sociability, good humor, anxiety, and anger is nearly 100 years old and took off when Thurstone (1934) developed multiple-factor analysis to study these relationships systematically. In the 1980s, the Big Five model emerged, and it has dominated personality psychology for nearly half a century. However, a clear definition of the Big Five is still elusive.

The reason is that personality psychologists rarely study the Big Five factors. Instead, they create more and more Big Five scales that average a set of items and are given Big Five names. The problem is that these sum scores depend on the items that are summed, and different sum scores produce different results. Thus, Extraversion is not a clearly defined construct. It is a label for scales that tend to correlate moderate to highly with each other because they contain overlapping sets of related items.

These sum scores cannot be used to study the Big Five factors. For example, we can create an Extraversion sum score by averaging the items “I am often in a good mood” and “I talk a lot.” Now we can show that this sum score correlates with a Good Humor scale that contains the item “I am often in a good mood” and with a Sociability scale that contains the item “I talk a lot.” However, these correlations are no longer empirical observations of something interesting. Scales that share items will be correlated, just as 6 and 9 are both divisible by 3 because both are products of 3 and another number (3 × 2 = 6, 3 × 3 = 9). The shared items guarantee the correlation, just as the shared factor of 3 guarantees divisibility.

Unfortunately, many personality psychologists seem unable to realize that these correlations do not tell us anything theoretically interesting or are simply not interested in scientific theories of personality. To examine why Good Humor and Sociability are correlated we need distinct measures of each construct not sum scores that average across items from both measures.

But when faced with a correlation between two observed measures, the alarm bells go off because we are now asking a causal question without experimental evidence. As this is considered improper, the question is never asked.

The simple solution to this problem is to shed the experimental dogma and treat personality psychology like any other science that cannot manipulate the objects it studies. Take astronomy as an example. We resolved the question whether Earth revolves around the sun or vice versa without randomly assigning planets and stars to different experimental conditions. Welcome to personality science, where we test causal theories without random assignment and without directly observable phenomena.

In personality science, Big Five theory makes clear predictions. It assumes that correlations among specific traits are influenced by five broader dispositions. People who are more often in a good mood are also more likely to be talkative because both dispositions reflect a broader disposition called Extraversion. Importantly, Extraversion is just a label, and it is confusing to use the everyday meaning of the term as if we knew what this broader disposition is. A better name might be Factor I, following the numbering of the factors in the lexical tradition (Norman, 1963; Goldberg, 1990), which evokes no associations with bubbly conversation partners.

The nature of Factor I is unknown, but we infer that it exists from the observation that Good Humor and Sociability are correlated. This makes Factor I a causal theory, and the theory could be wrong. Maybe there is another reason why the two traits are correlated. Personality psychologists avoid testing this theory by using sum scores and giving them labels. A sum score is simply a fact about how people answered a set of questions; no theory is needed to call it Extraversion. To keep the two apart, we can refer to factors by number (Factor I) and to scale scores by their labels, together with the instrument used: BFI Extraversion, BFI-2 Extraversion, NEO-FFI Extraversion, and so on. A scale can be used without examining whether it is related to Factor I, or without knowing what Factor I is. How well BFI Extraversion measures Factor I is an empirical question, and the answer may differ for each instrument.

From Exploratory Factor Analysis to Confirmatory Factor Analysis

Thurstone’s original method was exploratory factor analysis (EFA). It did not impose constraints on the relationships between specific traits and broader factors. Researchers chose the number of factors, and the method produced the best possible loadings (the relationships between observed variables and factors) given the correlations among the measures. The model usually did not reproduce all of the correlations, but fit to the data was not a major concern. Often the goal was not to test a theory, but simply to reduce a large number of items to a few sum scores.

Since then, the measurement methods have been developed further in a field called psychometrics. In psychometric measurement models, correlations among items are also explained by unobserved factors (often called latent variables), but these models differ from EFA in two important ways. First, researchers can specify a predicted pattern of loadings and thereby test a theory about why items are correlated; this approach is called confirmatory factor analysis (CFA; Jöreskog, 1969). Second, a good theory should explain the full pattern of correlations, so model fit matters.

Personality psychologists tried these methods to find evidence for Big Five theory, but their models did not fit the data (McCrae et al., 1996). Instead of asking whether their theory might be at least partially wrong, they concluded that confirmatory factor analysis is not appropriate for personality questionnaires. To this day, many of them prefer exploratory methods that never put a specific structural hypothesis to the test.

This is akin to astronomers refusing to use more advanced telescopes and working with the one invented by Galileo. Not surprisingly, little progress has been made. The structural model established in the 1980s has barely been tested, let alone improved.

Using CFA with theoretically constrained loading patterns, it is actually possible to find evidence for the Big Five factors. They do not explain all of the relationships among specific traits, which explains earlier failures, but many traits are related to one or more of the Big Five factors.

This makes it possible to create measurement models of the Big Five without knowing what they are. The shared variance among a set of specific traits is attributed to the causal influence of a broader trait. This does not mean that the Big Five really exist; until more evidence emerges, we can study them as “hypothetical constructs.” For example, we can examine whether Factor I predicts outcomes like life satisfaction or whether Factor III predicts GPA.

Many psychologists, especially older ones socialized during the era of behaviorism, are reluctant to view factors as at least hypothetically real entities (Revelle, 1983, 2016) . However, astronomy shows how a science can make progress by studying hypothetical constructs. Black holes are a useful reminder that some things are real even though we can never see them. Nothing escapes a black hole, not even light, so no telescope will ever show us one. Astronomers know that black holes exist only through their effects on things we can observe, such as stars orbiting an invisible mass or light bending around empty space. No astronomer would say that black holes are unscientific because they are unobservable. Yet this is exactly the rule behaviorism imposed on psychology: if we cannot observe it, it does not belong in science. Personality traits are like black holes. We cannot see them, but we can infer them from their effects on what people say and do. The scientific question is not whether something is observable, but whether the indirect evidence is strong enough to justify the inference. For the Big Five, that evidence is modest, but the reason is that nobody has even tried to find evidence for or against them. If we never test a theory, it’s validit remains unknown. And we cannot test it by correlating more and more personality item sum scores with each other. At some point, we have to study variables that are not directly observable like the Big Five factors with measurement models that rely on the indirect evidence of correlations among specific traits that are easier to measure.

Do Meta-Traits Produce Correlations Among the Big Five?

Some personality psychologists have proposed that there is a set of even broader personality dispositions than the Big Five. This theory was originally proposed by Digman (1997) who found consistent patterns in correlations among Big Five scales. For example, Extraversion scales tended to correlate with Openness scales. These days, the strongest proponent of this theory is Colin DeYoung, who renamed the factors Plasticity and Stability.

The main problem for this theory is that it rests on correlations among Big Five scales, not Big Five factors. Even if BFI-Extraversion and BFI-Openness scales correlate positively, it does not provide evidence that the corresponding factors, Factor I and Factor IV, are correlated with each other. The reason is that scale scores also contain information about the specific traits that were used to create the sum scores. To study the relationship among Big Five factors, we would first need to create a measurement model of the Big Five factors and then examine whether a model with Meta-Traits fits the data and whether it fits the data better than alternative models.

Even the latest article claiming support for the metatraits (DeYoung et al., 2026) did not provide this crucial test. The authors used exploratory factor analysis to compute five factor scores and then treated these scores as measures of the Big Five. But factor scores are not factors. As Revelle (2024) put it, “factor score estimates are not latent variables, they are merely weighted sum scores.” So, it can be both true that Extraversion and Openness sum scores created by DeYoung are positively correlated, but Factor I and Factor V are not.

The fact that 30 years after Digman, an article that uses sum scores to test theories about the correlation among factors underscores my observation that personality psychology as a field is stuck in behavioristic thinking, lacks clearly defined constructs, and lacks measurement models for Big Five scales. My invitation to collaborate on a proper test of Meta-Trait theory with proper measurement models was rejected by DeYoung. Others who are more interested in their own sum scores (inventors of the BFI-2 or HEXACO inventory) similarly showed no interested in testing personality theories.

To turn personality psychology into personality science does require more than just a change of journal names like “Personality Science” or “Social and Personality Science.” It requires a psychometric revolution that takes measurement and model fit seriously. I had little hope that such a revolution would happen during my life time, but AI is revolutionizing sciences as I am typing. So there is some hope that it may also force personality psychologists to become a science with explanatory theories.

Scientific revolutions usually happen when they give new researchers something new to do. I think applying psychometrics to the measurement of personality is timely (or should I say long overdue) and an opportunity for young scientists ready to make a mark. The availability of free software and the new norm to share data provides a unique opportunity for graduate students to do original research and publish articles in journals that are not controlled by guardians of the sum score paradigm.

References

DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant sample. Journal of Personality and Social Psychology, 91, 1138–1151.

Digman, J. M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73, 1246–1256.

Goldberg, L. R. (1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59, 1216–1229.

McCrae, R. R., Zonderman, A. B., Costa, P. T., Jr., Bond, M. H., & Paunonen, S. V. (1996). Evaluating replicability of factors in the Revised NEO Personality Inventory: Confirmatory factor analysis versus Procrustes rotation. Journal of Personality and Social Psychology, 70, 552–566.

Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings. Journal of Abnormal and Social Psychology, 66, 574–583.

Revelle, W. (1983). Factors are fictions, and other comments on individuality theory. Journal of Personality, 51, 707–714.

Revelle, W. (2024). The seductive beauty of latent variable models: Or why I don’t believe in the Easter Bunny. Personality and Individual Differences, 221, 112552. https://doi.org/10.1016/j.paid.2024.112552

Thurstone, L. L. (1934). The vectors of mind. Psychological Review, 41, 1–32.

An open invitation to personality researchers

Personality psychology has produced numerous taxonomies of personality traits, but we still lack a widely accepted hierarchical measurement model that specifies how narrower traits relate to broader personality dimensions.

Although the Big Five are widely accepted as a descriptive framework, they lack a consensual measurement model. Moreover, other models like the HEXACO model exist without any attempt to create a unified model. This means that personality psychology still lacks a scientific taxonomy of personality traits. Instead, different researchers use different inventories, item selections, and statistical models, often producing different conclusions about personality structure.

How can we establish a common empirical foundation?

Like all other sciences, we have to start with a broad – not necessarily representative or exhaustive – list of objects that we want to classify. For personality psychologists these objects are specific personality traits. The key information for a classification system is how specific traits are related to each other. A key problem is that the standard method used by personality psychologists (EFA, PCA) is limited and produces new structures with every new set of traits. The solution to this problem is to use hierarchical structural equation modeling (or Hierarchical Factor Analysis, HFA) that imposes a theoretical structure on the data that does not change with the item set and correlations, but tests whether the data are consistent with the theoretical model.

I am inviting personality researchers, psychometricians, graduate students, and anyone interested in personality structure to participate in an open, collaborative many-analyst project.

This is a good time for such a project because data are openly available, software is free, and learning HFA is easier than ever with the help of AI. AI can even fit models for you. So, even personality scientists who are not familiar with structural equation models can formulate models and test them.

Participation does not require funding, new data collection, or specialized software. All you need is a computer. Call it theoretical or computational personality science.

The starting point for this project are data from the FacetMAP dataset (Irwing et al., 2024) that contains correlations for 77 facets, each measured with six items.

UlrichSchimmack/Personality-Trait-Taxonomy-PTT-: Developing Hierarchical SEM Models of Personality Traits

I created a GitHub repository that contains the data, syntax files for MPLUS and lavaan, and a model with five independent factors (the classic Big Five model). The model has a well fitting measurement model for the facets. Thus, researchers can focus on alternative models of the structural model that relates the facets to each other. However, modification to the measurement model are also possible. Starting with this model can save a lot of time and prevent convergence problems, but experienced users are free to start from scratch.

The immediate goal is to develop and evaluate measurement models that can help us answer several fundamental questions:

• Which facets can be measured reliably and distinctly?
• How are narrow personality traits related to one another?
• Can these relationships be explained by broader personality factors?
• How much of the shared variance reflects substantive personality characteristics versus method variance?
• Which structural conclusions remain robust across alternative, theoretically defensible measurement models?

Researchers are invited to contribute their own analytical approaches, challenge existing specifications, and propose alternative models.

Rather than trying to establish which researcher or existing taxonomy is correct, we want to identify results that withstand independent scrutiny and develop a measurement model that researchers with different theoretical perspectives can agree on.

All analyses, code, and findings can be shared openly, including results that challenge preferred theoretical models.

The FacetMAP dataset is only the beginning. Once we have established promising models, we can test whether the same structure emerges in other datasets and item pools, including large collections of personality-descriptive adjectives.

The ultimate goal is not another personality inventory. It is a cumulative, empirically grounded understanding of the hierarchical structure of personality traits.

Researchers interested in contributing are invited to explore the GitHub repository, run the existing models, and propose new analyses.

Ulrich Schimmack
University of Toronto Mississauga

Dozens of studies have correlated students’ personality with their GPA (Grade Point Average). The reason is simple. Students are easily accessible, and GPA is an easily measured objective outcome variable. The main finding of these studies is that personality predicts GPA. This is not very surprising to the average reader or students themselves, but in psychology, it was once common to dismiss personality as an explanation for behavior and behavioral consequences. Personality matters!

The more interesting question is which personality traits predict GPA and how much of the variance in GPA they explain. Study after study and meta-analyses of these studies show that Conscientiousness (being orderly, planning ahead, persistence, and low impulsivity and procrastination) is the strongest personality predictor of GPA (O’Connor & Paunonen, 2007; Poropat, 2009). Effort matters, and some students are more able and willing to work hard for a higher GPA. Nobody claimed that psychology is rocket science.

However, it may be surprising how weak the relationship between Conscientiousness and GPA really is. The typical meta-analytic estimate is a correlation of approximately r = .24 (O’Connor & Paunonen, 2007; Poropat, 2009). This means that Conscientiousness explains only about 6% of the variance in GPA.

To make this correlation more meaningful, we can use it to compute the probability that a student who is above average in Conscientiousness also has an above-average GPA. Assuming a bivariate normal distribution, the probability is only about 58%, compared with 50% if Conscientiousness and GPA were unrelated.

This blog post uses data from a published journal article (McCredie & Kurtz, 2020) to examine three additional questions. First, do studies that rely on self-ratings underestimate the true relationship between Conscientiousness and GPA because self-ratings are imperfect measures of personality? Second, are informants, such as parents and peers, better judges of students’ Conscientiousness than students themselves? Finally, do evaluative biases in self-ratings reflect a real personality characteristic that predicts higher GPA, or are they merely rating biases?

The use of multiple raters also makes it possible to examine another interesting question: Does having a positive view of oneself predict higher GPA?

This possibility was suggested by Biderman et al. (2019), who examined evaluative factors in Big Five and HEXACO personality questionnaires. They found that a general evaluative factor predicted GPA even after controlling for the individual personality domains.

[Insert image of Biderman et al. (2019)]

However, their analyses relied exclusively on self-ratings. This makes it difficult to determine whether the evaluative factor represents a substantive personality characteristic or whether it captures valid personality variance that is not adequately separated from evaluative bias.

A multi-informant design provides a stronger test of this question.

Results: How Valid Are Self- and Informant Ratings?

I used structural equation modeling to reanalyze the open data. The model distinguished common personality factors from evaluative factors that were specific to each rater. Here, I summarize the key findings.

All three raters contributed to the measurement of Conscientiousness. The standardized loadings on the common Conscientiousness factor were:

• Self-ratings: .79
• Parent ratings: .75
• Peer ratings: .64

These coefficients indicate how strongly each rating reflects the common Conscientiousness factor. Their squares suggest that the common factor accounted for approximately 62% of the variance in self-ratings, 56% in parent ratings, and 41% in peer ratings.

Thus, self-ratings were at least as strongly related to the common factor as parent ratings, whereas peer ratings showed somewhat weaker agreement.

These findings are broadly consistent with the literature on self–other agreement in personality judgments (Connelly & Ones, 2010). There is no compelling evidence that informant ratings are consistently and substantially more valid than self-ratings. Both methods provide useful information, and their relative validity may depend on the trait, the informant, and the circumstances.

How Strongly Does Conscientiousness Predict GPA?

The common Conscientiousness factor predicted GPA with a correlation of approximately r = .31 (SE = .09).

This estimate is somewhat higher than the typical meta-analytic correlation of r = .24 based on observed personality scores. The difference is consistent with the expectation that measurement error attenuates correlations between observed personality scores and external outcomes.

However, the difference is not dramatic. Even a correlation of .31 explains less than 10% of the variance in GPA.

Thus, correcting for measurement error strengthens the relationship between Conscientiousness and academic performance, but it does not fundamentally change the conclusion: Conscientiousness matters, but most of the variance in GPA remains unexplained.

Do Positive Self-Evaluations Predict GPA?

One of the advantages of the multi-informant model is that it can distinguish common personality variance from rater-specific evaluative variance.

Personality ratings are influenced by the desirability of traits. People generally prefer to describe themselves and others as conscientious, agreeable, extraverted, emotionally stable, and open to experience. As a result, ratings of different personality traits may be positively correlated simply because a rater has a generally favorable impression of the person being rated.

This phenomenon is commonly called a halo effect.

The model identified clear evaluative factors in all three rating sources: self-ratings, parent ratings, and peer ratings.

If these evaluative factors represented a substantive personality characteristic, we might expect them to show positive agreement across raters. For example, people who evaluate themselves especially favorably might also receive especially favorable evaluations from their parents and peers.

However, the correlations among the three evaluative factors ranged from −.45 to .09, and none was statistically significant.

There was no consistent positive agreement across raters. This pattern is consistent with the interpretation that the evaluative factors primarily reflect rater-specific tendencies rather than a common personality characteristic.

The second finding was equally interesting. The evaluative factors did not predict GPA.

In particular, the evaluative factor in self-ratings was not a significant predictor of GPA (β = −.12, SE = .14).

The sample size limits strong conclusions about small effects. Nevertheless, the results provide no evidence that students who evaluate themselves more favorably across personality traits achieve higher grades after controlling for their substantive personality characteristics.

This finding is relevant to Biderman et al. (2019), who reported positive relationships between evaluative factors and GPA in three large samples. Their standardized regression coefficients ranged from approximately .15 to .21.

The present results do not establish that those findings were statistical artifacts. However, they illustrate an important identification problem in single-rater studies. A general evaluative factor extracted from self-report items may contain substantive personality variance as well as evaluative bias. Predicting an external outcome does not, by itself, establish which component is responsible for the relationship.

The multi-informant model provides a way to separate these sources of variance. In the present data, the common Conscientiousness factor predicted GPA, whereas the self-specific evaluative factor did not.

What About the Other Big Five Traits?

Conscientiousness was not the only personality trait associated with GPA in this sample.

Neuroticism was a negative predictor (β = −.22), suggesting that students who were more emotionally unstable tended to have lower grades. Openness was a positive predictor (β = .15).

Extraversion (β = −.05) and Agreeableness (β = −.01) were practically unrelated to GPA.

These findings should not be interpreted as establishing a universal pattern. The effects of personality traits other than Conscientiousness tend to be smaller and less consistent across studies (O’Connor & Paunonen, 2007; Poropat, 2009). The present sample is too small to draw strong conclusions about these additional predictors.

Why Did Peer Ratings Predict GPA?

Perhaps the most interesting finding was that the unique variance in peer ratings of Conscientiousness was related to GPA.

At first glance, this might suggest that peers are better judges of Conscientiousness than students themselves or their parents. However, this interpretation is difficult to reconcile with the lower loading of peer ratings on the common Conscientiousness factor.

If peer ratings were generally more valid indicators of Conscientiousness, we would expect them to show stronger agreement with the other measures, assuming that the common factor captures the valid trait variance.

An alternative explanation is that peers rely heavily on academic performance when evaluating their friends’ Conscientiousness.

For example, students who receive high grades may be perceived as conscientious even if they are disorderly, impulsive, or unreliable in other areas of life. Conscientiousness is a broad personality trait, and students may be highly conscientious about their academic work without being equally conscientious in other domains.

This would make peer ratings useful predictors of GPA without necessarily making them better measures of general Conscientiousness.

Another possibility is that peers observe academically relevant behaviors that are less visible to parents and are not adequately captured by students’ self-ratings.

These explanations are not mutually exclusive. Facet-level data and more detailed information about what peers actually observe would be needed to distinguish them.

The broader lesson is that unique predictive validity does not necessarily imply superior construct validity. A measure can predict an outcome because it captures criterion-specific information rather than because it is a better measure of the intended broad personality trait.

Conclusions

Three results stand out.

1. Self-ratings and ratings by a single knowledgeable informant tend to be about equally valid. Some studies report differences in validity, but there is no consistent evidence that informant ratings are substantially superior to self-ratings. The present results are consistent with this broader conclusion.
2. Personality matters for real-world outcomes, but the effects are not huge. Conscientiousness is a reliable predictor of academic performance, and correcting for measurement error produces a somewhat stronger relationship. Nevertheless, most of the variance in GPA remains unexplained. Personality matters, but it does not determine destiny.
3. Evaluative factors in personality ratings appear to reflect primarily rater-specific bias. The lack of consistent positive agreement among evaluative factors across raters and their failure to predict GPA suggest that much of this variance reflects evaluative tendencies rather than a substantive personality characteristic. Even the evaluative component of self-ratings, which may reflect positive self-evaluation, provided no evidence of incremental validity for GPA.

As always, results from a single study are never conclusive. The sample is relatively small, and some estimates have substantial uncertainty. Nevertheless, the pattern of findings is broadly consistent with the existing literature on self–other agreement and the validity of personality ratings.

References

Biderman, M. D., McAbee, S. T., Hendy, N. T., & Chen, Z. J. (2019). Validity of evaluative factors from Big Five and HEXACO questionnaires. Journal of Research in Personality, 80, 84–96. https://doi.org/10.1016/j.jrp.2019.04.010

Connelly, B. S., & Ones, D. S. (2010). An other perspective on personality: Meta-analytic integration of observers’ accuracy and predictive validity. Psychological Bulletin, 136(6), 1092–1122. https://doi.org/10.1037/a0021212

O’Connor, M. C., & Paunonen, S. V. (2007). Big Five personality predictors of post-secondary academic performance. Personality and Individual Differences, 43(5), 971–990. https://doi.org/10.1016/j.paid.2007.03.017

Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996