The International Personality Item Pool – Neuroticism, Extraversion, Openness – 120 item version (IPIP-NEO-120) is a 120-item self-report personality inventory for use by older adolescents and adults (ages 16+). The IPIP-NEO-120 measures the well established five factor model of personality (a.k.a OCEAN) and their associated facets:
The scale can be useful in understanding broad traits of patients, clients 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 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 focus 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, such as when differentiating job candidates for a specific task or individualising clinical diagnoses. For instance, recognising that someone is high on trait factor 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 is not universally defined. 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. Research and practice is therefore better served by using narrower and more specific traits, described as facets.
Facets should enable higher precision of analysis (see Ziegler & Bäckström, 2016). The IPIP-NEO instrument makes use of this by including a number of facet traits, consisting of dispositions towards certain behaviours, affects, and cognitions within each factor domain (see Zillig, Hemenover, & Dienstbier, 2002). The IPIP-NEO-120 consists of 6 facet traits for each one of the 5 trait factors.
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). IPIP-NEO was built on open-source items correlating with the original NEO-PI-R (Costa & McCrae, 1995).
IPIP-NEO-120 was created seeking to optimise length, reliability, and validity in five factor model (FFM) measurement, and even surpassed the original IPIP-NEO-300 in mean facet reliability (alpha > .80) (Johnson, 2014). The internal reliability (Cronbach’s alpha) for the factors (and their corresponding facets) were as follows (Johnson, 2014):
The original IPIP-NEO was designed to measure constructs similar to those in the NEO PI-R (Costa & McCrae, 1995). Therefore, the primary validity of the IPIP-NEO inventories is represented by the correlations between its scales and the corresponding scales of the NEO PI-R. Those correlations average .66 (.91 corrected for attenuation due to unreliability) for the 4-item scales from the IPIP-NEO-120 (Johnson, 2014).
The five factor structure of the IPIP-NEO-120 has been confirmed in a large US public sample (Kajonius & Johnson, 2019). It was also clear that the five trait factors were supported by a substructure made up of facet traits, thus supporting a more nuanced facet structure. Openness was the one factor in the IPIP-NEO-120 that was more loosely structured, being composed of items constituting various facets such as Imagination, Liberalism, and Intellect (Kajonius & Johnson, 2019). Kajonius & Johnson (2019) found that there may be both independent facet traits (e.g., Modesty) as well as perhaps domain-convergent facet traits (e.g., Self-discipline and Friendliness) in each of the FFM trait factors. One example is that the facet traits Imagination, Emotionality, and Liberalism were weakly related to the general Openness factor. Another example is the Activity and Assertiveness facets in the Extraversion factor. In the IPIP-NEO-120, Openness seems to be more characterised by artistic (aesthetic) interests and intellectual endeavours, rather than emotions and politics, and Extraversion seems better characterised by social energy and positive temperaments, than being busy and assertive (which tended to sort under Conscientiousness; Kajonius & Johnson, 2019).
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 Australian norms. 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 binned 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).
The IPIP-NEO-120 assesses an individual’s personality across five major factors, which are further divided into facets:
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 also presented for each of the trait factors and facets that were calculated by NovoPsych based upon Australian data from 5,252 males (between the age of 16 – 95) and 8,911 females (between the age of 16 – 88) that was derived from data provided by Johnson (2020). Descriptors for each factor and facet are also presented where it is considered High if it is in the top 30% of scores, Low if in the bottom 30% of scores or Average if in the middle 40% of scores (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.
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.).
A socially desirable responding (SDR) scale is also presented (Items 39, 41R, 45, 51R, 75, 81R, 101R, 109; where R indicates reverse scoring) where a higher score (and percentile) may be indicative of impression management and/or self-deception. However, it is important for the clinician to look at these SDR results, especially in relation to other factors and facets in the assessment, to determine whether this is a type of response bias (where there is a tendency to give *overly* positive self-descriptions (Paulhus, 2002)) or if other factors and facets may indicate that self-descriptions aren’t *overly* positive. So, although a higher score may be indicative of impression management and/or self-deception, it is important to be used in conjunction with clinical judgement. These results are considered High if at or above the 90th percentile, Low if at or below the 10th percentile, or otherwise are considered Average.
Plots are displayed for each of the factors and their associated facets, with a dotted line at the 50th percentile (which is the average level for the relevant comparison group).
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