Kashdan and colleagues (2020) further refined the 5DC inventory (Kashdan et al., 2018) by splitting the social curiosity facet into two separate facets: General and Specific Social Curiosity. The resulting revised inventory (5DCR) describes a six-dimensional, non-hierarchical curiosity structure. By distinguishing between general and specific social curiosity, the authors wanted to account for findings suggesting that social curiosity encompasses multiple dimensions (Litman & Pezzo, 2007; Renner, 2006). Furthermore, they suspected that this lack of differentiation in the original 5DC (Kashdan et al., 2018) was responsible for the low criterion validity of the Social Curiosity facet. By distinguishing two types of social curiosity, the revised inventory can better distinguish between a person's motivation to understand other people's behavior, thoughts, and feelings (general social curiosity) and motivation to discover details about other people in "indirect, surreptitious, [and] secretive ways" (specific social curiosity, p. 2) (Kashdan et al., 2020).

In addition, the revised version includes several technical improvements: The 5DCR presents each of the six facets in a shorter format with 4 items instead of 5 (i.e., 24 items measuring 6 facets compared to 25 items measuring only 5 facets in the 5DC). The authors removed the item with the lowest loading from each facet, resulting in a 20% reduction in response time per facet while maintaining sufficiently high internal consistency (.80 to .90 in Kashdan et al., 2020).

The nomological network created thus far for the 5DCR is particularly aligned with the Big Five personality traits and Schwartz's ten basic human values (Kashdan et al., 2020). Interestingly, all curiosity facets show relevant correlations with the five personality dimensions, with Openness and Extraversion showing particularly pronounced correlations with the curiosity facets of the 5DCR (Kashdan et al., 2020; Grüning & Lechner, 2022). The Covert Social Curiosity facet is the only one of the six curiosity facets to correlate to a lesser degree with the Big Five Inventory. This overarching pattern of substantial relationships between the 5DCR and the Big Five personality traits suggests that curiosity is an interstitial construct in its own right, rather than a subfacet of the Big Five personality traits. Kashdan et al. (2020) find the same pattern for correlational analyses with Schwartz scores. The curiosity facets of the 5DCR show relevant associations with almost all ten variables of the construct. The Schwartz values of self-determination and stimulation show particularly strong associations with all 5DCR curiosity dimensions. This observation is consistent with the notion of curiosity trait and its process to actively explore new aspects (see, e.g., joyous exploration), engage in boundary experiences (e.g., thrill seeking and stress tolerance), and learn new content (e.g., deprivation sensitivity and social curiosity).

Item generation and selection

The English version of the 5DCR was developed by Todd Kashdan and colleagues (2020) and represents a further development of the 5DC (Kashdan et al., 2018). The goal of the new development was to create an instrument that splits one of the five previous facets (social curiosity) into two independent facets (general and covert social curiosity). The development of the English version is described in detail in Kashdan et al. (2020).

Samples

Validation of the English-source version of 5DCR was based on an online quota sample of adults aged 18 to 69 years living in the UK (M = 44 years; SD = 15 years), of whom 48.8% were male (51.2% female) and 23.7% had a general university entrance qualification. The data collection used a quote for age, sex, and education strata. Only native English speakers were recruited for the sample to avoid linguistic errors. Data collection was conducted in January 2020 via web-based surveys (Computer Assisted Self-Administered Interviewing, CASI) by the online access panel provider respondi AG.

Item analyses

All analyses were performed using the R statistical software (R Core Team, 2014). There were no missing values for the individuals who were not excluded after the data processing (Grüning & Lechner, 2022). The factorial structure of the items and the factorial structure of the facet scores were examined with exploratory (EFA) and confirmatory factor analyses (CFA). Table 2 indicates a well-interpretable six-factorial structure of the items in the EFA. The same picture emerges from the results of seven CFAs of the whole instrument and the individual facets in Table 3.

Table 2

Exploratory factor analysis of the German translation of the six 5DCR factors

  Note. Exploratory factor analysis, oblimin rotated, N = 483.

Table 3

Confirmatory factor analyses for the complete 5DCR in English and each of its six facet

Item parameters

Table 4 presents the mean, standard deviation, skewness, and excess of all 24 items. In addition, the selectivity of the items is reported.

Table 4

Means, standard deviations, skewness, kurtosis, and selectivity of manifest items

Objectivity

Because 5DCR contains fixed written instructions, a standardized order of items, and a fixed number of labeled categories, it can be applied objectively. 5DCR data can be scored objectively because the scale is accompanied by clear rules indicating how to construct sum scores and mean scores for the six individual facets. 5DCR can be interpreted objectively because reference values (i.e., descriptive statistics of a quota sample) are available.

Reliability

Table 5 shows the reliabilities of the six curiosity facets in the English sample (from the UK). The facets internal consistency ranged from good (ω = .88) to excellent (ω = .92). In addition, test-retest reliability of scale scores was estimated over a two-week period with a subsample of 182 respondents. This reliability was acceptable for all facets, too, ranging from .64 to .76.

Table 5

Internal consistency as McDonald's Omega and the retest reliability of the six facets of the 5DCR 

Validity

Table 6 shows the correlations between the Big Five personality traits (Soto & John, 2017), Locus of Control (Nießen et al., 2021), digital self-concept (Schauffel et al., 2021), social desirability (Nießen et al., 2019), and four additional correlates (i.e., the single-item measures of life satisfaction, political orientation, religiosity, and physical health) with the six curiosity facets of the 5DCR. The nomenclature of Gignac and Szodorai (2016) is used to interpret the correlations.

In close agreement with recent correlational analyses by Kashdan et al. (2020), the 5DCR facets show relevant medium to large associations with all Big Five personality traits, especially extraversion (.19 ≤ r ≤ .37) and openness (.13 ≤ r ≤ .45). The facet of specific social curiosity showed significantly lower correlations with the Big Five than the other five facets aligning with the original results of Kashdan et al. (2020).

In addition, we find small to moderate associations between the 5DCR and self-rated physical health and life satisfaction. The associations suggest that life satisfaction is higher for respondents with higher curiosity for all facets (.04 ≤ r ≤ .16). In addition, self-rated health was lower for those with high levels of joyous exploration (r = -.07), social curiosity general (r = -.10) and covert (r = -.10), and thrill seeking (r = -.06). In addition, 5DCR showed moderate correlations with social desirability (-.25 ≤ r ≤ .24), suggesting that social desirability is particularly relevant to responses to the Stress Tolerance facet. Finally, the consistently large correlations of the curiosity facets with different domains of a person's digital self-concept, particularly for joyous exploration (.49 ≤ r ≤ .56) and social curiosity general (.29 ≤ r ≤ .40), indicate that curiosity is important in different ways for digital exploration and online interaction.

Table 6

Correlations of the six curiosity facets with other individual personality constructs

Note. The correlation coefficients’ interpretation is based on effect size guidelines proposed by Gignac and Szodorai (2016): relatively small effects (r = .10), typical (medium) effects (r = .20), and relatively large effects (r = .30). According to these authors, a correlation of .20 corresponds to the 50th percentile of a meta-analytical distribution of correlations in individual differences research. Therefore, in the table, medium to large effects are highlighted in bold.

Measurement invariance

The measurement invariance of the German translation of the 5DCR and its English source version was tested for the entire curiosity model and for each of the six facets separately. The results of all seven analyses are presented by Grüning and Lechner (2022). According to the criteria of Chen (2007), Rutkowski and Svetina (2014), and Putnick and Bornstein (2016), all seven tested models achieved at least partial scalar measurement invariance between the German and the UK samples. The same was true for measurement invariance across age groups, sex, and education levels (Grüning & Lechner, 2022).

Descriptive statistics

Reference values in the form of group means for 5DCR are shown in Table 7. The reference values allow the user to compare the 5DCR values from own studies with those of relevant subgroups for persons of different ages, gender, and persons with different levels of education.

Table 7

Mean values of 5DCR facets in different age groups, for different gender and different education levels

 Note. GMS = General matriculation standard.

Further literature

The scale was first published in the Journal of Personality Assessment.

Data sources

The dataset supporting the conclusions of this article available in the Open Science Framework (https://osf.io/vkfa3/).

• David J. Grüning, GESIS & Heidelberg University; david.gruening@gesis.org
• Clemens M. Lechner, GESIS; clemens.lechner@gesis.org