Coffee or Tea? Examining Cross-Cultural Differences in Personality Nuances Across Former Colonies of the British Empire

Achaa-Amankwaa, Priscilla; Olaru, Gabriel; Schroeders, Ulrich

doi:10.31234/osf.io/dpqrx

Cited by 6 publications

(9 citation statements)

References 41 publications

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“…But as was discussed before, items often have retest reliabilities of .65 or higher (Lowman, Wood, Armstrong, Harms, & Watson, 2018;Wood, Nye, & Saucier, 2010;Henry & Mõttus, 2020), which may be higher than many intuitively expect. Higher-than-assumed single item reliability is also consistent with findings that items out-predict scales for outcomes and other variables (Achaa-Amankwaa, Olaru, & Schroeders, 2020;Elleman, McDougald, Condon, & Revelle, 2020;Mõttus & Rozgonjuk, 2019;Seeboth & Mõttus, 2018;Vainik, Mõttus, Allik, Esko, & Realo, 2015). Therefore, the allegedly low reliability of items should not be a reason for not reporting item-level findings.…”

Section: Better Use Of Already Existing Datasupporting

confidence: 76%

“…Such examples abound (Mõttus et al, 2015); for example, Mõttus and Rozgonjuk (2019) reported that items within half of the personality facets varied in the directions of their age differences, leading items to contain over 40% more age-sensitive information than facets and over twice as much as the Big Five domains. More nuanced investigations into how personality is linked with various life outcomes or vary across cultures have led to similar conclusions (Achaa-Amankwaa, Olaru, & Schroeders, 2020;Elleman, McDougald, Condon, & Revelle, 2020;Seeboth & Mõttus, 2018;.…”

Section: What Makes Good Descriptive Research?mentioning

confidence: 88%

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Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

et al. 2020

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We argue that it is useful to distinguish between three key goals of personality science—description, prediction and explanation—and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximizing out–of–sample predictive accuracy. We argue that naturally occurring variance in many decontextualized and multidetermined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human–interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause–effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon–specific and person–specific than anticipated thus far. Progress in all three areas—description, prediction and explanation—requires higher dimensional models than the currently dominant ‘Big Few’ and supplementing subjective trait–ratings with alternative sources of information such as informant–reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities. © 2020 European Association of Personality Psychology

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Section: Better Use Of Already Existing Datasupporting

confidence: 76%

Section: What Makes Good Descriptive Research?mentioning

confidence: 88%

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Such examples abound (Mõttus et al, 2015); for example, Mõttus and Rozgonjuk (2019) reported that items within half of the personality facets varied in the directions of their age differences, leading items to contain over 40% more age-sensitive information than facets and over twice as much as the Big Five domains. More nuanced investigations into how personality is linked with various life outcomes or vary across cultures have led to similar conclusions (Achaa-Amankwaa, Olaru, & Schroeders, 2020;Elleman et al, 2020;Seeboth & Mõttus, 2018;. At which level of a personality hierarchy should descriptive findings stop?…”

Section: What Makes Good Descriptive Research?mentioning

confidence: 90%

“…Patterns in how personality differences relate to the variables of interest can also be explored atheoretically. For example, item-or facet-level associations can be organized in the descending order of effect size to highlight the strongest associations and find commonalities in them (e.g., Achaa-Amankwaa et al, 2020;Elleman et al, 2020;Bem & Funder, 1978;Block, Block, & Gjerde, 1986;Block, Gjerde, & Block, 1991). In some fields such as genetics, recent progress has almost entirely resulted from atheoretically scanning association patterns rather than imposing theoretical constraints on the findings (e.g., Nagel et al, 2018;Plomin & von Stumm, 2018) and there is no reason why following suit could not help personality scientists.…”

Section: What Makes Good Descriptive Research?mentioning

confidence: 99%

Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the Big Few traits

Mõttus¹,

Wood²,

Condon³

et al. 2020

Preprint

106

186

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We argue that it is useful to distinguish between three key goals of personality science – description, prediction and explanation – and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximising out-of-sample predictive accuracy. We argue that naturally-occurring variance in many decontextualized and multi-determined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause-effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and person-specific than anticipated thus far. Progress in all three areas – description, prediction, and explanation – requires higher-dimensional models than the currently-dominant “Big Few” and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities.

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“…Another study found that, across 40 outcomes, the specific variance of nuances accounted for a substantial portion of the predictive power of domain-level models, suggesting that the idiosyncrasies of individual items are more responsible for domain-level relationships than is commonly understood (Seeboth & Mõttus, 2018). Lastly, one recent study found that a participant's country of origin was better predicted with personality items than facets or domains (Achaa-Amankwaa, Olaru, & Schroeders, 2020). Taken together, this body of research indicates that examining personality-outcome relationships at a level higher than nuances "entails a substantial loss of information" (Mõttus et al, 2015, p. 25) and risks misattributing nuance-level personality-outcome relationships to broader traits.…”

Section: Us Zip Codes May Better Aggregate Personality Than Statesmentioning

confidence: 99%

Smaller is better: Associations between personality and demographics are improved by examining narrower traits and regions

Elleman¹,

Condon²,

Holtzman³

et al. 2020

Preprint

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The personality of individuals is clustered by geographic regions; a resident of a region is more similar to another resident than to a random non-resident. Research in geographical psychology often has focused on this clustering effect in broad regions, such as countries and states, using broad domains of personality, such as the Big Five. We examined the extent to which (a) a narrower geographic unit, the U.S. ZIP Code, accounted for more variance explained in aggregating personality than a broader region, the U.S. state; and (b) progressively narrower personality traits (domains, facets, and nuances, respectively) provided more information in describing personality-demographic relationships. Results from this study (n_participants = 39,886, n_zipcodes = 2,074) indicated that the variance explained by aggregating personality was multiple times as large for U.S. ZIP Codes than for states (median = 4.4). At the level of personality domains, ZIP Code population density and income disparity were positively correlated with Openness and negatively correlated with Conscientiousness and Agreeableness. Facets within each domain were differentially correlated with each demographic, which demonstrated that facets added information to the personality-demographic relationships beyond that of domains. Item-level analysis revealed the most informative finding: population density and income disparity were positively associated with personality nuances related to politically liberal beliefs, anti-authoritarian attitudes, lower concern for abiding by rules and laws, and greater beliefs of self-exceptionalism. Findings suggest that future studies in geographical and personality psychology could benefit from using the narrowest feasible unit of analysis.

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Coffee or Tea? Examining Cross-Cultural Differences in Personality Nuances Across Former Colonies of the British Empire

Cited by 6 publications

References 41 publications

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

Descriptive, predictive and explanatory personality research: Different goals, different approaches, but a shared need to move beyond the Big Few traits

Smaller is better: Associations between personality and demographics are improved by examining narrower traits and regions

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