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+). Developed by Johnson (2014), this assessment provides a shorter alternative to the original 300-item IPIP-NEO while maintaining strong psychometric properties.
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.
The IPIP-NEO-120 measures the well-established FFM of personality and its associated facets:
Example IPIP-NEO-120 Item
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 the 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 definition is Openness. There are several specific lower-order facet traits which could be more informative, such as Adventurousness in predicting the tendency to travel, or Intellect in predicting choice of education. Research and practice are 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.
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 nationwide samples (e.g., Kajonius & Carlander, 2017). Personality traits furthermore seem to be growing in importance in the context of individualism in modern society (Skirbekk & Blekesaune, 2014), and they are fairly stable and develop predictably throughout life (Briley & Tucker-Drob, 2014).
The scale can be useful for 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.
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 serve as a foundation for integrating common and abnormal personality traits (Markon, Krueger, & Watson, 2005).
The IPIP-NEO-120 assesses an individual’s personality across five major factors, each comprising 24 items that are further divided into 6 facets (4 items per facet):
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 ages of 16 – 95) and 8,911 females (between the ages of 16 – 88) that were derived from data provided by Johnson (2020). Descriptors for each factor and facet are assigned based on percentile scores:
Percentiles are based upon gender and age, which were categorised into 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, 41, 45, 51, 75, 81, 101, 109), 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 use SDR result in conjunction with clinical judgement. The SDR results are classified as follows:
The IPIP-NEO-120 generates a comprehensive interpretive report designed for ease of clinical interpretation. The report is organised to present results systematically, moving from broad personality factors to specific facets, with visual and narrative components at each level.
The report begins with a summary table displaying results for the five major personality factors. For each factor, the table shows the raw score, community percentile, and descriptor (based on the client’s age and gender, if provided). Percentiles falling outside the Average range (i.e., High or Low) are highlighted in blue to draw attention to clinically relevant information.
Following the summary table, each factor is represented on a graph as a continuum with behavioural anchors at both extremes (e.g., for Extraversion: “Reflective, independent, self-sufficient” versus “Outgoing, warm, seeks adventure”), allowing clinicians to immediately contextualise what a client’s score means in terms of real-world behaviour and functioning. The client’s percentile score is plotted on each continuum to make it easy to identify which traits may be contributing to presenting concerns, which traits may serve as strengths or resources in treatment, and which personality domains warrant deeper exploration through the facet-level results.
After the Big Five overview, the report presents five tables with detailed facet-level information for each factor (raw score, community percentile, descriptor). Following the facet tables, a series of corresponding horizontal bar graphs, each consisting of seven horizontal bars reflecting the percentile scores, are presented: The top bar shows the overall factor score (demonstrating how the six facets each contribute), and the six bars below show each facet score.
Finally, a standalone table presents the SDR results showing the raw score, community percentile, and descriptor (Very Candid / Self-Critical, Valid Response Profile, or Review Response Validity). Following the tables and bar graph visualisations, the narrative report provides detailed written interpretations:
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 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) was 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) within 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 question 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 ages of 16 – 95) and 8,911 females (between the ages 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 equally 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 measures stable personality traits – characteristic patterns of thinking, feeling, and behaving that remain relatively consistent over time. However, clinicians often need to assess current symptom severity, which requires state-based measures that capture how someone is functioning right now rather than their typical patterns. For example, whilst the Neuroticism factor and its facets (Anxiety, Depression, Anger, Vulnerability) indicate a general proneness to negative emotions, they do not measure whether a client is currently experiencing a depressive episode or anxiety disorder. When assessing current symptom levels, administer the Depression Anxiety Stress Scales (DASS-21) or similar symptom-specific measures alongside the IPIP-NEO-120. This combination allows clinicians to distinguish between trait vulnerability (e.g., high Neuroticism suggesting predisposition to emotional difficulties) and current clinical state (e.g., elevated DASS-21 Depression scores indicating active depressive symptoms requiring immediate intervention).
When time constraints limit comprehensive personality assessment, the NovoPsych Five Factor Personality Scale – 30 item version (NFFPS-30) offers a brief alternative that captures the same Big Five factors with single-item facet indicators. The NFFPS-30 is suitable for screening, whilst the IPIP-NEO-120 is preferred when detailed facet-level analysis is needed for formulation – for instance, distinguishing whether high Neuroticism is primarily driven by Anxiety, Depression, or Vulnerability facets, which may suggest different treatment targets.
When personality pathology is suspected, such as pervasive interpersonal difficulties, identity disturbance, or patterns consistent with DSM-5 personality disorders, consider administering the Personality Inventory for DSM-5 – Short Form (PID-5-SF), which assesses maladaptive trait variants rather than normal-range personality. The IPIP-NEO-120 and PID-5-SF can be used together to provide a comprehensive picture: the IPIP-NEO-120 identifies where the client sits on normal trait dimensions, whilst the PID-5-SF highlights whether traits have reached maladaptive levels that significantly impair functioning. Additionally, the Maladaptive Schema Scale (MSS) can complement this assessment battery by revealing the underlying early maladaptive schemas that may drive both normal and pathological trait expressions. Together, these three assessments offer a multi-level view of personality: what traits look like (IPIP-NEO-120), whether they have reached maladaptive levels (PID-5-SF), and the deeper cognitive-emotional structures maintaining these patterns (MSS).
The IPIP-NEO-120 supports clinical formulation and treatment planning by providing a comprehensive profile of personality traits that may contribute to a client’s presenting concerns or serve as resources in treatment. Clinicians can use the results to identify traits that may be maintaining difficulties. For example, high Neuroticism facets such as Vulnerability or Anxiety may indicate heightened sensitivity to stress that warrants therapeutic attention. Equally important, the assessment highlights potential strengths; a client with high Conscientiousness facets such as Self-Discipline may benefit from structured goal-setting interventions, whilst high Agreeableness may predict strong engagement in therapeutic relationships. The facet-level detail allows clinicians to move beyond broad trait descriptions to understand the specific behavioural, cognitive, and emotional patterns that characterise each individual, enabling more tailored and effective intervention strategies.
The IPIP-NEO-120 report may include personality pattern types when a client scores in the extreme range (high or low) on two or more of the Big Five factors. These pattern types, based on the Abridged Big Five-Dimensional Circumplex (AB5C) model, describe how combinations of traits interact to create recognisable personality styles. For example, a Personable Type (high Extraversion, high Agreeableness) describes someone who enjoys social interaction and is typically well-liked, whilst a Principled Leader Type (low Agreeableness, high Conscientiousness) describes someone who is task-focused and maintains high standards. Pattern types can provide a useful starting point for discussing personality with clients in accessible language, helping them recognise familiar patterns in their own behaviour. However, clinicians should use these descriptions as conversation starters rather than rigid categories, exploring with clients how well the description fits their experience and in what contexts these patterns are most evident.
The SDR scale provides important information about response validity, but requires careful clinical interpretation rather than automatic profile invalidation. Elevated SDR scores (above the 90th percentile) may indicate impression management or self-deception, particularly if accompanied by unusually low Neuroticism scores and elevated Agreeableness and Conscientiousness. However, some individuals genuinely possess these characteristics without response bias. The key is to examine whether the overall profile shows typical variation or appears uniformly positive. Conversely, very low SDR scores (below the 10th percentile) may reflect unusually candid responding or a tendency toward self-critical evaluation; examine whether Neuroticism facets such as Depression and Self-Consciousness are also elevated, which would suggest the latter interpretation. In either case, the SDR result should inform clinical judgement rather than replace it, prompting deeper exploration of how the client approached the assessment and whether results align with other clinical observations.
Personality traits are relatively stable over the lifespan, but research demonstrates they can and do change, typically showing gradual maturation patterns (such as increased Conscientiousness and decreased Neuroticism with age) as well as change in response to significant life experiences or therapeutic intervention. The IPIP-NEO-120 is best suited for baseline assessment and formulation rather than routine progress monitoring, as meaningful trait-level changes generally occur over months or years rather than weeks. Re-administration may be clinically useful at extended intervals (such as annually, or following significant life transitions) to track broader patterns of personality development, particularly when treatment goals include shifting trait-related patterns. For more frequent monitoring of treatment-sensitive symptoms, symptom-specific measures are typically more responsive and appropriate. When the IPIP-NEO-120 is re-administered, age- and gender-specific norms ensure that any observed changes reflect genuine shifts in relative standing rather than normative developmental patterns.
Sharing personality assessment results can be a powerful therapeutic intervention when approached thoughtfully. The IPIP-NEO-120 report includes visual displays with behavioural anchors at both ends of each trait continuum, which can help clients understand that all positions on the spectrum have adaptive value – there is no “good” or “bad” profile. Begin by exploring traits in the Average range to establish that the assessment captures a nuanced picture, then discuss extreme scores in terms of both potential strengths and challenges they may create in specific contexts. For example, low Extraversion is not a deficit but reflects a preference for quieter, more reflective engagement with the world; however, it may create challenges in contexts that demand extensive social energy. Encourage clients to reflect on whether the results resonate with their self-perception and in which situations various traits are most evident. This collaborative exploration promotes self-understanding and can help clients recognise how their characteristic patterns contribute to both their presenting concerns and their resources for change.
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
Briley, D. A., & Tucker-Drob, E. M. (2014). Genetic and environmental continuity in personality development: A metaanalysis. Psychological Bulletin, 140(5), 1303-1331. https://psycnet.apa.org/doi/10.1037/a0037091
Costa, P. T., & McCrae, R. R. (1995). Domains and facets: Hierarchical personality assessment using the revised NEO personality inventory. Journal of Personality Assessment, 64(1), 21-50. https://doi.org/10.1207/s15327752jpa6401_2
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. (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
Paulhus, D. L. (2002). Socially desirable responding: The evolution of a construct. In H. I. Braun, D. N. Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and educational measurement (pp. 49–69). Lawrence Erlbaum Associates Publishers.
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
