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The NovoPsych Five Factor Personality Scale – 30 is a brief self-report personality inventory measuring the well established five factor model of personality (a.k.a OCEAN). It is for use by older adolescents (ages 16+) and adults, where personality characteristics are compared to age and gender norms on the five factors of Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism.
The NovoPsych Five Factor Personality Scale – 30 (NFFPS-30; Buchanan & Hegarty, 2023) assesses personality characteristics for the following factors:
The scale can be useful in understanding broad traits of clients, students, patients and colleagues, in mental health settings or in non-clinical environments. The results can be provided directly to the respondent and can help provide feedback and self-exploration or be an aid in clinical formulations.
Personality traits are important for many life outcomes, and have demonstrated predictive validity in subjective outcomes such as relationships and well-being (Roberts et al., 2007). Traits also relate to a variety of objective life-outcomes, such as annual income and educational attainment in nation-wide samples (e.g., Kajonius & Carlander, 2017). Personality traits furthermore seem to be growing in importance with the contexts of individualism in modern society (Skirbekk & Blekesaune, 2014) and personality traits are fairly stable and develop predictably throughout life (Briley & Tucker-Drob, 2014).
Personality is most frequently measured with the five factor model (FFM; McCrae, 2010). This represents regularities of thoughts, feelings, and behaviours in individuals expressed in five broad trait factors: : (1) Openness, (2) Conscientiousness, (3) Extraversion, (4) Agreeableness, and (5) Neuroticism. These traits are often known by the acronym, OCEAN.
A spotlight on FFM in clinical settings has been of particular focus since the use of personality traits in the DSM-5 (American Psychiatric Association, 2013; Strus, Cieciuch, & Rowiński, 2014). Many psychologists today agree that the FFM framework can be used as a foundation for integrating common and abnormal personality traits (Markon, Krueger, & Watson, 2005).
Organising personality into five trait factors is often too general for certain purposes. Therefore, the NFFPS-30 separates each of the five factors into six underlying and more specific personality facets. Facets enable higher precision of analysis (see Ziegler & Bäckström, 2016) and describe dispositions towards certain behaviours, affects, and cognitions within each factor domain (see Zillig, Hemenover, & Dienstbier, 2002).
For example, a broad personality descriptor like Extraversion could indicate that the person is sociable, happy, energetic, or dominant, or all of these. In other words, the scope and meaning of the term Extraversion does not have a high level of specificity. Another example of a multifaceted definitions is Openness. There are several specific lower order facet traits which could be more informative, such as Adventurousness in predicting tendency to travel, or Intellect in predicting choice of education.
The NFFPS-30 assesses an individual’s personality across five major factors, which are further divided into facets:
Openness to Experience: Measures receptiveness to new ideas, imagination, and intellectual curiosity.
Conscientiousness: Reflects how organised, responsible, and goal-oriented a person is.
Extraversion: Measures the extent to which a person is outgoing, energetic, and sociable.
Agreeableness: Assesses the level of compassion, cooperativeness, and kindness in an individual.
Neuroticism: Evaluates emotional stability, anxiety levels, and sensitivity to stress.
These factors and facets provide a comprehensive assessment of an individual’s personality traits and help practitioners gain insights into various aspects of an individual’s behaviour and preferences.
Percentiles are presented for each of the trait factors and facets, comparing the respondent’s scores to those of an age and gender related Australian sample (NovoPsych’s analysis of Johnson 2020 data). A percentile of 50 represents typical patterns of responding compared to peers. Descriptors for each factor and facet are also presented where it is considered High if the score is in the top 30% compares to peers, Low if in the bottom 30% or Average if in the middle 40% (i.e., High if the percentile is 70 or above, Low if the percentile is 30 or below, or Average if the percentile is between 30 and 70). Percentiles are based upon gender and age, which was categorised into the seven age groups.
On a facet level, percentiles may be presented with > or < symbols. This indicates that the top (>) or bottom (<) percentile rank has been reached due to ceiling or floor effects for that facet. Given that facets are derived from single items, caution is recommended when interpreting facet scores.
In the narrative report, ‘pattern types’ may also be presented (if there are high and low scores on personality factors). These ‘pattern types’ are based on the Abridged Big Five-Dimensional Circumplex (AB5C; Hofstee, de Raad, & Goldberg, 1992) model of personality. These descriptions are based upon those provided by Johnson (n.d.).
The NFFPS-30 is a shortened version of the IPIP-NEO-120. The IPIP-NEO-120 is a product of the International Personality Item Pool collaboration project (IPIP; Goldberg et al., 2006) and is a publicly available representation of the five factor measurement model (Johnson, 2014), drawing 120 items from the International Personality Item Pool (IPIP; Goldberg et al., 2006).
The NFFPS-30 was created to optimise length whilst still correlating strongly with the IPIP-NEO-120 in five factor model (FFM) measurement. To shorten the IPIP-NEO-120, NovoPsych performed a CFA using Johnson’s IPIP-NEO data repository (Johnson, 2020). The fit statistics for the CFA are presented on the NovoPsych website (see here). As a result of the CFA, the item that loaded highest onto each facet was kept, with one item per facet and still keeping the same six facets per factor structure as the IPIP-NEO-120. The correlations between NFFPS-30 and IPIP-NEO-120 factors (and their corresponding facets) were as follows:
Normative data was gathered from Johnson’s IPIP-NEO data repository (Johnson, 2020) to enable the calculation of percentiles. This data was analysed by NovoPsych to determine appropriate norms using Australian respondents. Initial data from the repository (N = 619,150) was first filtered for some data errors where responses were 0 for some questions (given questions responses need to be 1 to 5) and if any rows contained a 0 in a response, the whole row was removed (resultant n = 410,376). The age of clients was then used to remove data for clients who were below the age of 16 (resultant n = 385,902) and then data was filtered to only include data where the respondent was in Australia (resultant n = 14,163).
These respondents were made up of 5,252 males (between the age of 16 – 95) and 8,911 females (between the age of 16 – 88). Given that the respondent age was skewed positively, with a mean age of 26.9, the data was categorised into age groups to allow approximately equal sized groups (n ~ 2,000) for comparison. The resultant age groups were 16-17 year old (n = 2,509), 18-19 year olds (n = 2,279), 20-21 year olds (n = 1,624), 22-25 year olds (n = 1,742), 26-30 year olds (n = 2,032), 31-39 year olds (n = 2,128), and 40 year olds plus (n = 1,849).
Percentiles for each factor and facet, based upon gender and age, were then created in the R statistical program (Version 4.2.0; R Core Team, 2022) using the cNORM package (Version 3.0.2; Lenhard & Lenhard, 2021). This method of norming estimates percentiles on the basis of the raw data without requiring assumptions about the distribution of the raw data. This method minimises bias arising from sampling and measurement error, while handling marked deviations from normality, addressing bottom or ceiling effects and capturing almost all of the variance in the original norm data sample (Lenhard & Lenhard, 2021).
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Briley, D. A., & Tucker-Drob, E. M. (2014). Genetic and environmental continuity in personality development: A metaanalysis. Psychological Bulletin, 140(5), 1303-1331. https://doi.org/10.1037/a0037091
Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40(1), 84-96. https://doi.org/10.1016/j.jrp.2005.08.007
Hofstee, W. K., de Raad, B., & Goldberg, L. R. (1992). Integration of the Big Five and circumplex approaches to trait structure. Journal of Personality and Social Psychology, 63(1), 146–163. https://doi.org/10.1037/0022-3514.63.1.146
Johnson, J. A. (2014). Measuring thirty facets of the five factor model with a 120-item public domain inventory: Development of the IPIP-NEO-120. Journal of Research in Personality, 51, 78–89. https://doi.org/10.1016/j.jrp.2014.05.003
Johnson, J. A. (2020). Johnson’s IPIP-NEO data repository. Accessed at: https://osf.io/tbmh5/
Johnson, J. A. (n.d.). Descriptions used in IPIP-NEO Narrative Report. Accessed at: https://www.personal.psu.edu/faculty/j/5/j5j/IPIPNEOdescriptions.html
Kajonius, P. J., & Carlander, A. (2017). Who gets ahead in life? Personality traits and childhood background in economic success. Journal of Economic Psychology, 59, 164-170. https://doi.org/10.1016/j.joep.2017.03.004
Lenhard, W., & Lenhard, A. (2021). Improvement of Norm Score Quality via Regression-Based Continuous Norming. Educational and Psychological Measurement, 81(2), 229–261. https://doi.org/10.1177/0013164420928457
Markon, K. E., Krueger, R. F., & Watson, D. (2005). Delineating the structure of normal and abnormal personality: An integrative hierarchical approach. Journal of Personality and Social Psychology, 88(1), 139-157. https://doi.org/10.1037/0022-3514.88.1.139
McCrae, R. R. (2010). The place of the FFM in personality psychology. Psychological Inquiry, 21(1), 57-64. https://doi.org/10.1080/10478401003648773
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313-345. https://doi.org/10.1111/j.1745-6916.2007.00047.x
Skirbekk, V., & Blekesaune, M. (2014). Personality traits increasingly important for male fertility: Evidence from Norway. European Journal of Personality, 28(6), 521-529. https://doi.org/10.1002/per.1936
Strus, W., Cieciuch, J., & Rowiński, T. (2014). The circumplex of personality metatraits: A synthesizing model of personality based on the big five. Journal of Personality and Social Psychology, 18(4), 273-286. https://doi.org/10.1037/gpr0000017
Ziegler, M., & Bäckström, M. (2016). 50 facets of a trait—50 ways to mess up? European Journal of Psychological Assessment, 32(2), 105-110. https://doi.org/10.1027/1015-5759/a000372
Zillig, L. M. P., Hemenover, S. H., & Dienstbier, R. A. (2002). What do we assess when we assess a big 5 trait? A content analysis of the affective, behavioral, and cognitive processes represented in big 5 personality inventories. Personality and Social Psychology Bulletin, 28(6), 847-858. https://doi.org/10.1177/0146167202289013
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