Bfq-Rb(Russian Brief): Short Big-Five Questionnaireto Measure Facets And Factors Of Personality

Abstract

Several questionnaires exist for brief estimation of the Big Five personality factors. The majority of the short forms of the Big Five instruments aim to estimate the Big Five factors but not the facets within each factor. Assessing facets can be beneficial because facets may explain external behavior better than factors do. This paper presents a short form of the Big Five Questionnaire (BFQ) designed to assess both factors and facets, validated on a sample of Russian adolescents (14-18 years old). We created a short version (BFQ – Russian Brief; BFQ-RB), using data from a sample of 1128 adolescents (14-18 years) and then confirmed the factor structure on another subsample of 1087 adolescents. The psychometric properties of the newly created instrument – the BFQ-RB (Russian Brief) – were evaluated via item-level confirmatory factor analysis. We estimated three main models. In the first model, the selected items represented the Big Five factors. In the second model, the selected items represented ten correlated latent factors (facets). The third model was the second-order factor model fitted the data well, suggesting that the BFQ-RB enabled the estimation of both facets and factors. Our final instrument consists of 43 items, with each facet represented by 3-4 items and each second-order factor consisting of two facets, including the Lie scale.

Keywords: Big Fivepersonalityfacetsshort formconfirmatory factor analysis

Introduction

The Big Five model is one of the common models of personality. This model implies that the five factors of Extraversion, Openness, Agreeableness, Neuroticism (or Emotion Stability as a positive pole) and Conscientiousness are the basic dimensions of personality (e.g., Costa & McCrae, 1992). It has been postulated that these factors can identify major sources of individual differences in personality (McCrae & Costa, 1991). The Big Five model of personality has a hierarchical structure, with low-level traits (or facets) merging into high-level five factors. This model has been confirmed in cross-cultural studies on adult and children samples using a variety of questionnaires (e.g., De Fruyt, McCrae, Szirmák, & Nagy, 2004; Klimstra et al., 2011).

The majority of the existing Big Five instruments include a large number of items, requiring considerable time to complete. On the one hand, the large number of items allows assessing factors with greater accuracy. On the other hand, long questionnaires cannot be used when time or resources for assessment are limited. It is particularly difficult to use long instruments with children or adolescents.

To overcome the limitations of long instruments, several short versions of Big Five questionnaires for children and adults were created (e.g., for children – the Five Factor Personality Inventory – Children (FFPI-C; McGhee, Ehrler, & Buckhalt, 2007); the Big Five Questionnaire – Children (BFQ-C; Barbaranelli et al., 2003); for adults – Big Five Inventory (BFI; John & Srivastava, 1999); Mini-IPIP (Donnellan, Oswald, Baird, & Lucas, 2006). The majority of the short Big Five instruments aim to provide a brief estimation of the five factors, without assessing separate facets within each factor.

The Big Five model and its full instruments have focused on a two-level hierarchy. Assessing individual facets within each factor has benefits when examining relations between personality and external criteria (e.g., Paunonen et al., 2003). Using facets instead of factors can increase the predictive accuracy for various behavioral characteristics. Paunonen and Ashton (2001) compared the predictive ability of the five factors and the facets that constitute those factors for various behavior characteristics (Paunonen & Ashton, 2001). They concluded that facets could explain a substantial part of the variance of behavior characteristics better than factors could. These conclusions were confirmed in several studies (Paunonen et al., 2003; Ekehammar & Akrami, 2007).

Problem Statement

The current study was developed to create a short version of the Big Five instrument to assess facets and factors. Saving facets as a valid level of measurement in the short version of Big Five instruments can be useful from methodological perspective. When the full form implies facet and factor levels but the short form preserves the factor level only, the validity of the instrument may be compromised (Smith, McCarthy, & Anderson, 2000). Thus, saving the two hierarchical levels of measurement in the short form can improve the psychometric properties of the instrument.

Research Questions

We used the Big Five Questionnaire (BFQ; Caprara et al., 1993) as a basis for the development of the short version (BFQ-RB (Russian Brief). We have focused on the within-network construct validity approach (Marsh et al., 2005). The focus of this approach is the estimation of specific features of a construct – its components, structure, and dimensionality. We aimed to test the factor structure of the facets and factors using item-level confirmatory factor analysis.

Purpose of the Study

The main aim of the current study is to develop a short version of the Big Five instrument suitable for administration with Russian-speaking populations of adolescents to assess facets and factors. To achieve our goal, we used the Big Five Questionnaire (BFQ; Caprara et al., 1993) as a basis for the development of the short version (BFQ-RB (Russian Brief)). We used data from a survey of 2215 Russian adolescents (14-18 years) and applied confirmatory factor analysis to select items for the short version and assess the psychometric properties of the new instrument.

Research Methods

Findings

First, we tested two models (5-factor and 10-factor solutions) with 67 items. Both models had low goodness-of-fit (GOF) indices, although Model 2 fit the data better than did Model 1 (Table 01 ). The ten-factor model had some problems with the identification facet “Impulse Control” due to this facet having a high correlation with the facet “Emotion Control”.

After examining the factor loadings and modification indices, we excluded items that had higher modification indices and lower factor loadings. As a result, 36 items were retained. Then, we tested three models with the 36 selected items.

All models had satisfactory GOF indices (Table 02 ). Model 2 (36 items merged into ten latent facets) had better indices than did the other models. The model with the second-order factor solution fitted the data better than did the model with the five-factor solution. The model with the five-factor solution had the lowest GOF indices of the three models.

All standardized factor loadings in the model with the ten-factor solution were greater than 0.50 (Table 03 ), potentially confirming the good construct validity of the latent variables. In the model with the Big Five factor solution, the standardized factor loadings of some items for Extraversion and Openness factors were lower than 0.50.

In the model with the second-order factor solution, all factor loadings of the first-order factors were greater than 0.50 (Table 04 ). Moreover, the standardized factor loading of the facet “Emotion control” on the factor Emotion Stability was greater than one.

In the model with the ten-factor solution, the correlations between some facets that theoretically belong to one factor were lower than correlations between facets from different factors (Table 05 ). For example, “Dynamism”, which theoretically belongs to the Extraversion factor, had higher correlations with facets from other factors than it did with another facet from the Extraversion factor.

In the model with the Big Five factors solution, four factors (Agreeableness, Extraversion, Conscientiousness, and Openness) had high intercorrelations (0.68 - 0.76), while the factor Emotion Stability had low correlations with the other four factors (0.15 - 0.31) (Table 06 ).

In summary, our results demonstrated that the ten-factor solution and the second-order factors solution had better fit indices than did the Big Five factors solution.

Finally, we added seven items from the Lie scale (three items for the Egoistic subscale and four items for the Morality subscale) and tested three models: 1) the six-factor solution (Model 1); 2) the twelve-factor solution (Model 2); and 3) the second-order factor solution (Model 3). The fit indices for these models are presented in Table 07

According to these results, all the models with the Lie scale had satisfactory fit indices. The model with the six-factor solution had the worst fit indices of all the models. In summary, we can conclude that our short instrument (BFQ-RB) had satisfactory psychometric properties and was suitable to measure facets and factors.

To test the properties of the three models and replicate the results obtained on the first sample, we tested the three models without the Lie scale on the second subsample. All models had satisfactory fit indices. As in the first subsample, the model where each facet represented a separate latent construct had better fit indices than did the model with the Big Five factors solution. The five-factor model had the worst fit indices of the three models (Table 08 ).

All standardized factor loadings for the ten-factor model of the second subsample were greater than 0.50. In the model with the second-order factors solution, the standardized factor loadings of the first-order factors were greater than one for the Extraversion and Emotion Stability factors.

Summarized, we replicated the results obtained in the first sample and confirmed that the short version of the instrument fitted to measure both facets and factors.

Conclusion

The main aim of the study was to develop a short version of the Big Five instrument that could be useful for brief estimation of both the Big Five factors and their facets on a Russian language sample. To achieve this goal, we used the Big Five Questionnaire (BFQ; Caprara et al., 1993) as a basis for developing a short version. The BFQ was based on a model where each factor was represented by two facets, including the Lie scale.

We created a short version (BFQ – Russian Brief; BFQ-RB) using data from a sample of 1128 adolescents (14-18 years) and then confirmed the factor structure on another subsample of 1087 adolescents. We estimated three main models. In the first model, the selected items represented the Big Five factors. In the second model, the selected items represented ten correlated latent factors (facets). The third model was the second-order factor model. The third model fitted the data well, suggesting that the BFQ-RB enabled the estimation of both facets and factors.

Our final instrument consists of 43 items, with each facet represented by 3-4 items and each second-order factor consisting of two facets, including the Lie scale. All latent factors in the BFQ-RB had good indicators of internal consistency (Cronbach’s alpha) and overcame passed the threshold of .7, suggested as acceptable by Smith and colleagues (2000).

Previous researchers often used Cronbach’s alpha to confirm the good psychometric properties of facets. A high value of the alpha was supposed to demonstrate high reliability. However, a high value of Cronbach’s alpha does not necessarily indicate a high degree of internal consistency. First, Cronbach’s alpha does not confirm the unidimensionality of a scale. If a test has more than one concept or construct, reporting the alpha can be misleading (Tavakol &Dennick, 2011). Second, the length of the test affects the alpha. If the test length is too short, the value of the alpha is reduced (Tavakol&Dennick, 2011).

We used item-level confirmatory factor analysis, which confirmed the two-dimensionality of the factors and the unidimensionality of the facets. Although McCrae and co-authors (1996) pointed out that using CFA to investigate personality structure in the study of the Big Five had some restrictions (McCrae et al., 1996), their position was later criticized (e.g., Borsboom, 2006). Particularly, Borsboom pointed out that estimating personality traits and testing the hypothesis about the causal relations between latent traits and items required the specification of a reflective latent variable model similar to CFA (Borsboom, 2006).

All items had positive direction except the items of the Emotion Stability factor. Some authors recommended that scales include a roughly equal number of positively directed and negatively directed items to reduce the effect of potential acquiescence response bias (e.g., Furr, 2011; Wolfe, 1993). However, there is evidence that the negative items tend to form a different dimension (e.g., Wong, Rindfleisch & Burroughs, 2003). Consequently, some authors recommended using only positively directed items (e.g., Salazar, 2015). We decided not to include positively and negatively keyed items in the same factor or facet to improve the internal consistency of the scales.

The newly created instrument requires further validation and development. Although the model where each facet was considered as a separate latent construct fitted the data better than did the Big Five factors solution, a closer look at the results indicated that some factors were unidimensional rather than two-dimensional. Specifically, the facets “Impulse Control” and “Emotion Control” correlated highly in both subsamples (.88 and .86, respectively). This problem was also identified for facets within the Conscientiousness factor. In the second-order factors solution, the standardized factor loading of the “Emotion Control” facet was greater than one. Although standardized factor loadings are allowed to be greater than one, it may indicate on a high degree of multicollinearity in the data (Jöreskog, 1999). This problem may be solved by changing some items to make the two facets within the Emotion Stability and Conscientiousness factors more distinct.

Our analyses demonstrated that the Extraversion and Openness factors were two-dimensional. Moreover, some facets within these factors had greater correlations with facets from other factors. For example, in the first subsample, the facet “Dynamism” had a greater correlation with “Politeness” (.74), “Perseverance” (.72) and “Openness to Experience” (.77) than with the “Dominance” facet (.62). The same pattern was observed in the second subsample. Some revision of items from the “Dynamism” and “Openness to Experience” facets should be undertaken to achieve greater correlations of the facets within factors than between factors. It should be noted that a previous item-level study of the Big Five instrument also identified some problems with the Extraversion and Openness factors (Egan, Deary & Austin, 2000).

The “Dynamism” facet correlated highly with other facets, with the exception of facets from the Emotion Stability factor. It is possible that “Dynamism” not only is a subscale of Extraversion but also reflects a more general activity and energy of a person, with wider biological underpinnings than those of the Extraversion factor. Further validation studies will show whether it is necessary to revise the existing operationalization of the “Dynamism” facet as a subscale of the Extraversion construct.

The “Impulse Control” facet had low or non-significant correlations with other facets. Items representing facets of the Emotion Stability factor were reversed. There is evidence that reversed items have lower correlations with positively directed items (e.g., Dunbar et al., 2000). It is also possible that “Impulse Control” truly has low correlations with other facets. To disentangle these potential explanations, further studies should measure all facets in the same (positively keyed) format.

In this study, we focused on the evaluation of the internal structure of the BFQ-RB. All participants in the study completed a short 80-item questionnaire. To test whether the short instrument measures the same factors as the full questionnaire, it is necessary to compare this short instrument with the full BFQ. Finally, further research is needed to examine the criterion validity of the BFQ-RB by testing its correlations with other Big Five inventories and behavioral outcomes. This can be done in future studies.

Acknowledgments

This study was supported by the grant from the Russian Science Foundation [grant RSF №17-78-30028

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