Why Do Personality Models Use Different Names for Similar Traits?

The short answer: related patterns, different maps

Big Five and HEXACO labels can differ because the frameworks organize related personality patterns with different vocabularies, levels of detail, and boundaries around what a measure includes. Their overlap is substantial, but a familiar word is not enough to show that two scales mean exactly the same thing. For a careful comparison, check each definition and compare terms at the same level before translating them. That lets you describe a specific behavior as a close match, partial overlap, or still uncertain, without treating either model as a complete description of a person. A broad domain, a narrower facet, and a questionnaire scale may describe related territory while answering different questions about it. The practical distinction is between resemblance in a headline and equivalence in the specific behavior or content being compared. Start with the behavior you mean, then see whether each model uses its term at a comparable level and for comparable content; where those details diverge, keep the relationship qualified.

How can words become evidence for a model?

One route researchers use to study personality is the lexical approach: they examine words people use to describe personality and ask whether ratings of those descriptors form recurring patterns. This treats language as material for investigation, not as a ready-made scientific taxonomy. Researchers select descriptors, collect judgments or ratings, and analyze how the selected items relate. The resulting pattern is evidence about that set of words and measurements; it does not by itself establish that every everyday use of a word has one fixed technical meaning.

The primary study “The HEXACO personality factors in the indigenous personality lexicons of English and 11 other languages” reports two related but distinct designs. In its English study, researchers selected 449 adjectives rated as frequent in personality description, then examined their factor structure in relation to HEXACO questionnaire markers. The abstract also reports a peer-rating recovery. In a second study, lay judges rated the conceptual similarity between HEXACO descriptions and summaries of indigenous personality factors across 12 languages. The authors report convergent correspondence, alongside weaker discriminant correspondence. These are comparisons among selected descriptors, questionnaire markers, and judges’ ratings; they are not a dictionary proving that a term carries an identical meaning across languages or settings.

That distinction separates observation from organization. The observed evidence consists of descriptor choices, ratings, and their statistical or judged relationships. A factor solution is researchers’ way of organizing the observed pattern; a scale label is the name attached to a proposed grouping. Data constrain those choices, so the label is not arbitrary, but the analysis still depends on what was selected and how relationships were evaluated. Convergence across the study’s language comparisons supports the usefulness of this research route without settling every ordinary-language meaning.

The account in “The HEXACO Personality Inventory-Revised: History of the Inventory” adds a different kind of evidence: the developers describe how lexical research informed HEXACO and how they made decisions about factors, facets, interstitial scales, and revisions. This is a first-party history of one inventory’s development, not an independent study of all its design decisions. It shows that empirical inputs can be organized through deliberate model and scale choices, but it cannot serve as an origin story for every personality framework. Read the study as evidence about selected language patterns and the developer history as an account of HEXACO’s construction; neither makes ordinary words a complete or automatic translation key.

For a reader comparing labels, this matters because a term can begin as a recognizable description yet acquire a narrower role inside a questionnaire. The same word may be used casually for several related behaviors, while a scale groups particular descriptors according to a specified analysis. Before treating two labels as equivalents, ask what source material and rating process stand behind each and whether the model name refers to the same kind of grouping. If that information is unavailable, the everyday resemblance remains a useful starting point, not evidence that the technical categories match.

Sources: The HEXACO personality factors in the indigenous personality lexicons of English and 11 other languages; The HEXACO Personality Inventory-Revised: History of the Inventory

Why do models keep different numbers of levels?

Models use different numbers of levels because they solve different description problems. A domain is a broad organizing dimension that gathers related tendencies. An aspect is a narrower, internally coherent part of a domain; a facet is a more specific cluster of behaviors or questionnaire items. These words describe levels of organization, not three kinds of person. A framework with only broad domains compresses information: it is easier to summarize and compare, but distinct patterns can disappear inside one heading. A framework that names more aspects or facets preserves distinctions that may matter for a question, while demanding clearer definitions and enough evidence to support each distinction. More labels therefore mean more resolution only if the categories are meaningfully distinguished and measured; detail by itself does not establish accuracy or usefulness.

The primary study “Between facets and domains: 10 aspects of the Big Five” illustrates an intermediate level as a research proposal tested against data. The researchers factor-analyzed 75 facet scales drawn from two major inventories in the Eugene-Springfield community sample (N=481). Their analysis yielded two correlated aspects within each of the five broad Big Five domains. They then constructed a 100-item Big Five Aspect Scales measure and tested it in a second sample (N=480). The sequence matters: the aspects were not simply extra synonyms inserted between familiar domain and facet names. They were a proposed organization derived from relationships among existing facet scales, followed by a separate-sample validation of a measure intended to represent that organization. This makes the intermediate layer a deliberate empirical model choice.

That result does not settle how many layers every useful framework ought to have. The BFAS study supports this particular hierarchy—five domains, two aspects within each, and narrower facets beneath them—under its samples, instruments, and analytic choices. It cannot show that all personality content naturally divides into exactly ten aspects, or that another model must copy the same arrangement. A researcher might retain a broad domain where the goal is a compact overview, or examine lower-level distinctions where the question concerns a particular kind of behavior. The tradeoff is not simply simplicity against scientific quality: compression can make a pattern legible, while detail can make a relevant difference visible. Either level can mislead if readers treat its labels as exhaustive descriptions.

Imagine, hypothetically, someone who often starts conversations at a gathering but prefers listening in a small planning meeting. A broad social-engagement description could summarize a recurring tendency across both settings. A more detailed account might separate social initiative from preference for group size or conversational role. That illustration is not a BFAS finding, and it does not establish anyone’s trait from two moments; it shows what a choice of level can hide or reveal. When comparing frameworks, put like levels beside one another where possible: a domain against a domain, or a facet against a facet. If one framework offers only a broad domain and another a narrow facet, say that the comparison crosses levels. A broad term can contain the narrower behavior without being its exact counterpart, just as the narrower term may capture only one part of the broad description.

Sources: Between facets and domains: 10 aspects of the Big Five

What does cross-model evidence let us say?

The Big Five and HEXACO overlap substantially, but their correspondence is not a one-to-one translation. The meta-analysis “Using the Big Five Aspect Scales to translate between the HEXACO and Big Five personality models” combined four samples from three countries, with 1,586 participants in total. It reported that every HEXACO domain correlated at least |.51| with one or more Big Five Aspect Scales (BFAS) aspects. At the same time, half of the BFAS aspects were more strongly associated with HEXACO facets than with whole HEXACO domains. Those findings answer two different parts of the question: broad cross-model association is common in these data, while the closest counterpart can sit at a narrower level than a whole domain. The evidence therefore supports related structure, not identical maps.

A correlation describes how two measured variables vary together in the samples and instruments studied. A substantial coefficient can show that people scoring higher on one measured scale tended, within those data, to score in a related way on another. It does not show that the scales contain exactly the same items, define the same behavioral boundary, or can substitute for one another in every use. Nor does a coefficient between group-level measures tell an individual how to convert a personal result from one framework into a score in another. The meta-analysis establishes an empirical bridge between model measures; it does not provide a universal conversion table for readers’ profiles.

The finding that half the aspects related more strongly to HEXACO facets than to domains is particularly useful for interpreting apparent naming mismatches. If someone expects every Big Five domain to pair neatly with one HEXACO domain, the comparison can look confusing: the relationship may be distributed across narrower components, or a particular aspect may align more closely with a facet. This is not evidence that one framework is secretly a more detailed version of the other. It indicates that the answer depends partly on the level selected for comparison. A broad summary can preserve a meaningful common direction while overlooking where the frameworks divide that territory differently.

The opposite conclusion—that the frameworks are wholly unrelated—also fails to fit the reported pattern. Every HEXACO domain had at least one association of |.51| or greater with a BFAS aspect in the pooled analysis, so correspondence was frequent rather than absent. Yet frequency and strength of association do not erase differences in content or prove interchangeability. The study’s results concern selected measures and samples from three countries; the abstract does not establish that all labels, versions, populations, or everyday uses will map in the same way. This is why a coefficient is evidence for a relationship between measured scales, not a final verdict on the meaning of two words.

For a careful reader, the practical judgment is bounded: related labels may point toward overlapping measured tendencies, and a closer comparison may require moving from broad domains to aspects or facets. A cross-model claim should name the actual level and measure being compared, rather than letting similar wording imply equivalence. Where the levels differ, the comparison should say so; where only a domain-level association is available, it should not be sharpened into a claim about identical narrower behaviors. The meta-analysis supports neither forced separation nor automatic translation. It shows that overlap and structural difference can coexist in the same evidence, with the best description depending on what was measured and at which level.

Sources: Using the Big Five Aspect Scales to translate between the HEXACO and Big Five personality models

Why do narrower facets cross broad boundaries?

A narrower facet can relate to more than one broad domain because the scale used to represent it may contain behaviors that sit near several parts of the measured map. A statistical loading is an association between a scale and a factor in a particular analysis: it indicates how strongly the measured scale varies with that factor under the study’s model. It does not mean that a person contains discrete pieces of multiple traits, or that the facet has been proven to belong naturally to several domains.

In “A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure,” the researchers examined 268 facet scales drawn from 13 inventories. Under their operational rule, 157 scales—58.5%—had loadings above |.25| on more than one Big Five domain. This is a count of scales crossing an analytic threshold in that atlas, not a universal percentage of facets or people. The result makes the measurement map visible: many scale descriptions did not sit exclusively under one broad domain when evaluated with the selected instruments and cutoff.

The threshold matters because a loading just below the chosen boundary is treated differently from one just above it, even if their values are close. A stricter or looser cutoff could change which scales qualify as crossing; it changes the classification rule, not the underlying person's personality. The instrument set matters too. A facet scale is built from particular item content, and those items may combine behaviors that broader models organize separately. Thus a cross-loading can reflect blended scale content, correlations among domains, or both; the atlas's count alone does not distinguish a single explanation for every scale.

For illustration, imagine a questionnaire facet that asks about quickly taking charge in a group. That behavior could be relevant to a broad description of social assertiveness and also to one concerning initiative or goal pursuit. This is a hypothetical example, not a case from the study. Whether it loads across domains depends on the exact items, comparison scales, sample, factor analysis, and cutoff. Another inventory might define the facet more narrowly and yield a different pattern.

The study's result therefore supports a limited conclusion: broad domains can be connected by narrower measured scales, and a map of those connections depends partly on what the inventories measure and how the analyst counts a loading. It does not show that every facet is blended; some scales may have a clearer association with one domain. Nor does a cross-loading establish that the domains are interchangeable. When comparing two labels, inspect the scale definitions and ask what the reported association actually connects, rather than treating a line across a map as proof of identical meaning.

Sources: A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure

What exactly did the impulsivity example show?

The impulsivity example showed that scales carrying related names can have quite different relationships with one another and with the broader factor map. In Table 3 of “A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure,” the four scales are AB5C Impulse Control, HPI Impulse Control, NEO PI-R Impulsiveness, and TCI Impulsiveness. Their reported coefficients are not all positive, and the table distinguishes two kinds of value: raw correlations appear below the diagonal, while values corrected for attenuation using alpha appear above it.

Across the raw, below-diagonal entries, coefficients range from −.44 to .43. Across the alpha-corrected, above-diagonal entries, they range from −.65 to .22. One particularly clear contrast is between AB5C Impulse Control and NEO PI-R Impulsiveness: their raw correlation is −.44, while the corrected value is −.65. The correction changes the magnitude in this pair but leaves it negative. It would therefore be inaccurate to describe the four scales as correlating only from .22 to .44 or to collapse the two sets of coefficients into a single positive range.

The same table also prevents the opposite overstatement. There are positive associations among some of the scales, and all the reported Table 3 values are statistically significant. The differing signs and magnitudes do not mean the measures share no content; rather, their relationships vary across pairs. Alpha correction estimates what a correlation might look like after accounting for measurement unreliability under the correction procedure. It is not a second raw observation, and the corrected estimate should not be presented as if it were directly observed or as a universal property of the named trait.

The atlas reports another relevant distinction: the four scales showed different factor-loading patterns, and their network neighborhoods included different nearby content. In other words, the scales did not occupy one identical position simply because each label referred to impulsivity or impulse control. The authors' interpretation concerns these operational measures—the questionnaire scales as defined and analyzed—not every casual use of “impulsive,” and not a diagnosis or description of any individual. A label is a useful pointer to a topic, but it does not disclose which items, boundaries, or neighboring measured tendencies a particular scale includes.

The practical lesson is narrow. When two frameworks use similar names, compare the facet or item definitions and the surrounding correlation pattern before calling them equivalent. A reader can ask: what actions does each scale actually cover, at what level is it defined, and how does it relate to neighboring scales in the study? If only the name is available, the safest statement is that the terms sound related; the evidence here does not justify a stronger translation. If definitions and measured relationships are available, they may support a closer comparison, while still leaving room for differences in content and purpose.

Sources: A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure

Why can one everyday judgment bundle different behaviors?

A favorable everyday judgment such as “a good person” can cover actions that a personality model treats as different kinds of content. Here, content means the behavior or tendency a scale is intended to describe; a construct is the broader idea the scale is designed to represent. The distinction matters because the same warm impression can arise from honesty in a transaction, patience in conflict, or both. Naming the impression alone does not tell us which action prompted it.

The official “HEXACO-PI-R Scale Descriptions” make this distinction visible within one model. Fairness and sincerity are described under Honesty-Humility: the descriptions concern, among other things, avoiding manipulation for personal gain, not exploiting others, and being genuine. Forgiveness, compromise, and temper control are instead facets of Agreeableness (versus Anger), whose descriptions concern responses to interpersonal conflict, including willingness to forgive, cooperate or compromise, and remain even-tempered. These are neighboring parts of a personality vocabulary, but the instrument assigns them to different intended content. This source establishes how HEXACO defines its scales; it does not establish that a particular act reveals a person's motive or stable trait.

Consider a hypothetical case: a cashier accidentally gives someone too much change, and the person returns the extra money. That observable action concerns what they did in a transaction. It may be consistent with fair or sincere dealing, but one event cannot establish why they acted, how they usually behave, or whether the same description fits in other situations. Now imagine a different hypothetical moment: during a disagreement, the person listens without escalating, accepts a compromise, or later forgives an irritating remark. Those actions concern handling conflict. They might also contribute to a broad positive impression, yet they are not the same behavior as returning an overpayment. Neither example is a study case; both are illustrations of how one adjective can gather distinct observations under a single social impression.

Everyday shorthand remains useful: people often need a quick way to describe someone, and one act may plausibly matter to more than one account. The useful move is not to ban words such as “kind,” “decent,” or “easygoing,” but to unpack what they mean in the question at hand. Ask whether you mean truthful conduct, generosity, patience under disagreement, willingness to yield, or another action. Then keep the conclusion at that level. “They returned the extra change” reports an observation; “they are honest” interprets it as a tendency; “they are patient in disagreements” points to different evidence. Separating these statements preserves the behavior being discussed while leaving motive and broader pattern open for more evidence.

Sources: HEXACO-PI-R Scale Descriptions

How can a reader compare labels without overclaiming?

Use three possible outcomes for a comparison: close match for this purpose, overlap, or unresolved. This is a practical editorial rule derived from the distinctions already discussed, not a validated crosswalk between personality models. “Close match for this purpose” is reasonable when the definitions cover the behavior you care about at comparable levels and preserve the boundaries that matter to your question. It does not mean the labels are identical in every use. “Overlap” fits when the terms share some behavior but one is broader, or when a neighboring behavior sits elsewhere in one model. “Unresolved” fits when definitions, item content, or measurement details are not available at a comparable level. Each outcome describes what the comparison can support, rather than rating either model.

Try the rule on a hypothetical behavior: someone regularly returns borrowed tools on the date promised. First write the action plainly, without a trait label. Then add the setting and pattern: perhaps this happens with friends and coworkers, though there are only a few observed occasions. Next inspect the two model definitions you want to compare. If both describe dependable follow-through at a similar level, and neither includes a relevant extra behavior that changes your question, the match may be close for this purpose. If one term also covers planning or orderliness while the other focuses narrowly on keeping commitments, shared content is present but the labels overlap rather than match exactly. If one definition is unavailable, or only a broad domain name is given beside a narrow behavior, leave the comparison unresolved until the missing detail is clear.

Keep three layers separate as you write: the observation, the setting and frequency, and the trait interpretation. The observation is “they returned the tools on the promised date.” The context might be “in several recent exchanges with people at work and at home.” The interpretation might be “this could fit a tendency toward dependable follow-through.” Moving from one layer to the next involves inference: a remembered action is not itself a trait, and a small set of examples may not represent other settings. This separation also makes disagreement useful. If someone recalls repeated late returns, the evidence changes and the interpretation can be narrowed or revised without disputing what happened in the original instance.

A cautious comparison sentence can therefore preserve both what is known and what remains open: “In these examples, returning things when promised looks related to both labels; one definition also includes behaviors I have not observed, so I would call them overlapping for this question.” If later definitions show that both scales cover the same specific behavior at the same level, the wording can move toward a close match. If the behavior turns out to be rare, strongly tied to one setting, or described differently by additional examples, keep the interpretation qualified. Published correlations can inform comparisons between measured scales, but they cannot settle every pair of ordinary labels or tell a reader how to classify a single action. A defensible outcome may be partial similarity or uncertainty; the point is to name the boundary rather than force a neat translation.

When does a cautious crosswalk need to stay unresolved?

Leave the crosswalk unresolved when the available comparison cannot answer the behavior-level question you actually have. A broad domain beside a narrow facet is a mismatch in level: they may be related, but the broad label cannot establish that the specific behavior is what connects them. The same is true when a definition names neighboring tendencies but leaves out the action you are trying to understand. Similar wording cannot fill that gap. In either case, the honest note is about the comparison's limits, not about a person being inconsistent or either model being defective.

Keep it open when the scales contain different behavior, or when the evidence connects measures only in a particular sample and does not offer comparable details for the pair in front of you. “A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure” is an example of empirical mapping among operational scales; its associations describe those measures in the study context, not a ready-made translation for every reader's everyday label. A map can show where measured scales relate while leaving unanswered whether the reader's recurring action belongs under either term. That second question calls for a behavior description and observations, rather than converting a scale-level association into a personal classification.

An unresolved note can still be precise: “I often take time to think before answering in unfamiliar group discussions; I am not yet sure whether either model's term captures that pattern.” The sentence identifies what happens and where, while leaving the model language provisional. It does not infer a motive such as shyness, disinterest, or carefulness from response timing alone. A different setting may reveal a different pattern, and the same outward pause can have more than one explanation.

The view could become clearer if the two definitions specify the relevant behavior at comparable levels, or if the same pattern appears repeatedly across settings that matter to the question. Those observations can support a more useful description of recurrence; they still do not settle why the person acts that way. More examples can also complicate the first impression, so the wording should remain revisable. An unresolved crosswalk does not make either framework useless: each may still organize the particular pattern for its own purpose, and a direct account can be informative without forcing equivalence. If the question grows from this one behavior to how several tendencies combine, the private Context Profile at /assessment can be an optional reflection aid, not a validated crosswalk, diagnosis, or career recommendation. For a work decision, compare tendencies with role conditions rather than treating the profile as a prediction.

Sources: A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure

Choose the next description you can support

Write one action and its setting in ordinary words, such as “I pause before answering in a new group.” Then look at the two model definitions at comparable levels and ask whether each actually covers that behavior. Keep the narrowest wording the comparison supports: a shared action may be related to both terms without making the terms interchangeable. If the definitions leave the action out or the available evidence does not match the comparison you need, leave the labels tentative and keep the behavioral description. This is a useful result; you do not need a settled translation before you can notice a pattern. When you reflect on what you observed, separate how often it happens from what you think it means. A few instances can guide what to notice next, while leaving broader interpretation open. If later examples show a different action or setting, revise the description before reconsidering either label.

Explore /topics to learn how different personality models define and organize traits. If your question has widened to how tendencies combine in your own life, the private Context Profile at /assessment offers a reflection aid; it is not a validated crosswalk, a diagnosis, or a career recommendation. For work questions, compare tendencies with the conditions a role asks of you. Which behavior, in which situation, are you trying to understand?