The BEIS-10 is based on Salovey and Mayer’s (1990) theoretical framework which posits that emotionally intelligent individuals can accurately perceive emotions (both in themselves and others), use emotions to facilitate thinking and problem solving, understand the meaning of emotions, and manage emotions effectively. EI develops over time and represents a distinct type of intelligence that contributes to more adaptive psychological functioning. 

The BEIS-10 measures five distinct EI dimensions:

1. Appraisal of Own Emotions – assessing an individual’s ability to recognise and identify their own emotional states
2. Appraisal of Others’ Emotions – measuring the capacity to interpret and understand emotions in others through verbal and nonverbal cues
3. Regulation of Own Emotions – evaluating an individual’s perceived ability to control and manage their emotional responses
4. Regulation of Others’ Emotions – assessing perceived ability to influence and manage the emotional states of others
5. Utilisation of Emotions – measuring how effectively individuals can use emotional states to facilitate problem-solving and creativity

For clinicians, assessing EI can provide valuable insights into client outcomes across multiple domains. Meta-analytic studies have demonstrated the predictive utility of EI across both health-related outcomes (including physical and mental health; Schutte et al., 2007) and performance-related variables (such as academic achievement and occupational performance; Van Rooy & Viswesvaran, 2004). This evidence suggests that understanding a client’s EI profile can help inform interventions targeting both wellbeing and performance enhancement.

The BEIS-10’s development addresses a significant need in clinical practice for a brief, theoretically-sound measure of EI that can inform case conceptualisation, treatment planning and approach to building the therapeutic relationship. Its structure permits examination of both overall EI and specific EI components, enabling clinicians to develop more targeted and effective interventions based on their clients’ specific emotional processing strengths and challenges. The BEIS-10 highlights both areas of challenge that may become specific treatment targets as well as areas of strength considered protective or to be leveraged to support the therapeutic process.

BEIS-10 scores consist of a total raw score (range from 10 to 50) and five sub-scale scores, with higher scores indicating greater self-perceived emotional intelligence capabilities. These scores are converted into percentiles based on a large combined normative sample (N = 2,770) drawn from multiple studies across different populations and countries. 

Sub-scales are presented for the BEIS-10:

• Appraisal of Own Emotions (items 1, 2; range 2-10): Assesses an individual’s capacity to identify and understand their own emotional states. This includes awareness of mood changes, recognition of physiological responses to emotions, and the ability to label emotional experiences accurately.
• Appraisal of Others’ Emotions (items 3, 4; range 2-10): Measures one’s ability to accurately perceive and interpret others’ emotional states through both verbal and non-verbal cues. This includes recognition of facial expressions, tone of voice, and body language.
• Regulation of Own Emotions (items 5, 6; range 2-10): Evaluates an individual’s ability to manage and modify their emotional responses. This includes skills in emotional self-control, ability to calm oneself when upset, and capacity to maintain emotional balance.
• Regulation of Others’ Emotions (items 7, 8; range 2-10): Assesses capability to influence and manage others’ emotional states. This includes helping others feel better when down, providing emotional support, and facilitating positive emotional states in others.
• Utilisation of Emotions (items 9, 10; range 2-10): Measures how effectively individuals harness emotions to enhance thinking, problem-solving, and creativity. This includes using emotions to guide decision-making and leveraging emotional states for improved performance.

A percentile score interpretation framework provides qualitative descriptors ranging from Low to High emotional intelligence:

• Low: 5th percentile and below
• Below Average: 6th to 24th percentile
• Average: 25th to 75th percentile
• Above Average: 76th to 94th percentile
• High: 95th percentile and above

On first administration, a stacked bar graph is presented showing the percentiles for the total score and subscales with the descriptors in the background of the plot. If the scale is administered on multiple occasions, a graph is produced to track emotional intelligence development over time for both the total and the subscale percentiles.

The BEIS-10 was developed by Davies et al. (2010) as a shortened version of the 33-item Emotional Intelligence Scale (EIS; Schutte et al., 1998). Internal consistency reliability for the BEIS-10 has been demonstrated across multiple studies, with Cronbach’s alpha coefficients ranging from .74 to .91 for the total scale (Balakrishnan & Saklofske, 2015; Davies et al., 2010; Howell & Miller-Graff, 2014). At the subscale level, reliability coefficients are .60 – .84 for Appraisal of Own Emotions, .67 – .89 for Appraisal of Others’ Emotions, .48 – .84 for Regulation of Own Emotions, .57 – .88 for Regulation of Others’ Emotions, and .67 – .86 for Utilisation of Emotions.

The five-factor structure of the BEIS-10 has been supported through confirmatory factor analysis across multiple studies. Davies et al. (2010) found good model fit for the five-factor solution (CFI = .97, NNFI = .94, RMSEA = .06). This structure was later replicated by Balakrishnan and Saklofske (2015) who reported strong fit indices (CFI = .98, TLI = .96, RMSEA = .08, SRMR = .04) for the five-factor model.

Research has demonstrated expected relationships between the BEIS-10 and theoretically related constructs. Studies have found moderate negative correlations with neuroticism (r = -.18 to -.32), weak to moderate positive correlations with conscientiousness (r = .15 to .18) and agreeableness (r = .12 to .29), and mixed or non-significant correlations with extraversion and openness to experience. These correlations align with theoretical expectations. 

Norms were created for the BEIS-10 total score so that client’s results could be contextualised compared to a community sample. For the normative data a variety of samples were sourced:

• Davies et al. (2010): 955 student athletes in the UK (M = 36.84; SD = 4.43)
• Rizzo & Schwartz (2021): 148 chronic pain patients in the USA (M = 37.10; SD = 7.10)
• Hatamnejad et al. (2023): 405 medical students in Iran (M = 34.60; SD = 6.16)
• Sharma (2024): 120 young adults (18 – 26) in India (M = 36.66; SD = 3.37)
• Coyne (2020): 142 nurses in the USA (M = 40.85; SD = 4.24)
• Balakrishnan & Sakofske (2015): 269 university students in Canada (M = 37.89; SD = 7.74)
• Testa & Sangganjanavanich (2015): 451 counsellors in the USA (M = 41.97; SD = 3.83)
• Moussa & Abdelrehim (2024): 280 students in Egypt (M = 38.41; SD = 4.84)

These were combined to provide an overall community sample of 2,770 where the mean was 37.82 and standard deviation was 5.19. The mean and standard deviation are used by NovoPsych to compute community percentiles for overall responses. Some of the aforementioned studies also provided means and standard deviations at the subscale level (Balakrishnan & Saklofske, 2015; Davies et al., 2010; Hatamnejad et al., 2023; Moussa & Abdelrehim, 2024) and these were combined (n = 1,909) to provide percentiles for subscales:

• Appraisal of Own Emotions: M = 7.18; SD = 1.36
• Appraisal of Others’ Emotions: M = 7.42; SD = 1.42
• Regulation of Own Emotions: M = 7.29; SD = 1.42
• Regulation of Others’ Emotions: M = 7.31; SD = 1.36
• Utilisation of Emotions: M = 7.57; SD = 1.39

Percentiles were then used to create descriptors for the total score and subscales of the BEIS-10 as follows:

• 5th percentile and below: Low
• 6th – 24th percentile: Below Average
• 25th – 75th percentile: Average
• 76th – 94th percentile: Above Average
• 95th percentile and above: High

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The items on the GBI are distributed across two subscales:

• Depression Subscale: Focuses on capturing symptoms associated with depressive episodes, such as fatigue, sleep disturbances, and feelings of hopelessness.
• Hypomania/Biphasic Subscale: Evaluates the presence of hypomanic and manic symptoms, including elevated mood, increased energy, and impulsivity, along with shifts between depressive and hypomanic states. This subscale effectively captures the fluctuating nature of bipolar.

The GBI is useful for assessing bipolar disorder risk and monitoring symptoms in clinical practice. It has been validated across diverse demographics, including adolescents (13+), adults, in community health and psychiatric settings (Danielson et al., 2003).

The inventory is useful for screening for subtypes of bipolar disorders:

• Bipolar I Disorder: Characterised by one or more manic episodes, which are often severe and can be accompanied by depressive episodes. A manic episode typically involves elevated or irritable mood, increased energy, and risky behavior. Depressive episodes can also occur, though not necessarily.
• Bipolar II Disorder: Involves one or more major depressive episodes and at least one hypomanic episode. Hypomania is a milder form of mania with less severe symptoms that do not cause significant impairment. Individuals with Bipolar II do not experience full manic episodes.
• Cyclothymic Disorder (Cyclothymia): Features periods of hypomanic symptoms and periods of depressive symptoms lasting for at least two years (one year in children and adolescents). These symptoms do not meet the criteria for hypomanic or major depressive episodes but do cause distress or impairment.

The GBI total score ranges from 0 to 219. The GBI total score reflects the frequency and intensity of symptoms related to bipolar disorder.

Score is also expressed as the “percent endorsed”, which uses a binary scoring where “Never or Hardly Ever” and “Sometimes” are not endorsed, while responses “Often” and “Very Often or Almost Constantly” are considered endorsed. Percent endorsed convert scores to 0% to 100%.

Total scores are classified into three risk categories:

• Unlikely to have Bipolar Disorder (Percent endorsed between 0 and 26%): Individuals are likely to not have bipolar classified as likely non-cases, meaning they are unlikely to have bipolar. About 80% of the population typically falls within this category, indicating that their mood variability falls within the normal range.
• Indeterminate (Percent endorsed between 27% and 36%): Scores in this range indicate respondents may exhibit mild or atypical mood disturbances that are consistent with bipolar symptoms, but not to the extent that they clearly indicate bipolar disorder. This score range represents 12% of individuals among members of the community.
• Likely to have Bipolar Disorder (Percent endorsed 37% and above): Individuals within this range are classified as likely cases of bipolar disorder. Scores in this range reflect meaningful syndromal behavior consistent with bipolar types I, II, or Cyclothymia (Klein et al., 1986).

The subscale scores are also computed using the Binary scoring system, where scores are expressed as the percent of items endorsed as occurring “Often” and “Very Often or Almost Constantly”:

• Depression Subscale
  • (items 3, 23, 45, 63, 47, 56, 62, 73, 9, 10, 13, 70, 21, 33, 49, 59, 1, 12, 41, 5, 25, 37, 52, 14, 39, 55, 29, 36, 50, 71, 16, 60, 65, 67, 20, 32, 34, 72, 18, 26, 58, 68, 6, 28, 69)
  • A higher score on the Depression subscale indicates higher depressive symptomatology. The percent of symptoms endorsed are classified into three categories (Pendergast et al., 2014):
    • Low – 0 to 4% – Unlikely to have any mood disorder
    • Moderate – 5% to 31% – May have a mood disorder of some kind
    • High – 32% to 100% – Likely to have a mood disorder of some kind (4.75 times more likely than the general public)
• Hypomania/Biphasic Subscale
  • (items 2, 24, 35, 48, 19, 40, 53, 4, 7, 15, 22, 30, 31, 66, 11, 17, 42, 51, 27, 44, 54, 8, 57, 64, 38, 43, 46, 61)
  • A higher score on the Hypomania/Biphasic subscale suggests mood variability, impulsivity, or energy dysregulation consistent with hypomanic or manic episodes. The percent of symptoms endorsed are classified into three categories (Pendergast et al., 2014):
    • Low – 0 to 18% – Unlikely to have bipolar disorder
    • Moderate – 19% to 68% – 4.21 times more likely to have bipolar disorder than the general public.
    • High – 69% to 100% – 6.49 times more likely to have bipolar disorder than the general public.

A percentile is used to contextualise a respondent’s score compared to the normative community sample. The community sample represents the typical level of mood-related symptoms found in the general population (Bullock et al., 2011; Pendergast et al., 2014). A percentile of 50 suggests typical (and relatively healthy) experience of psychomotor energy, whereas a percentile of 80 indicates that the respondent scores higher than 80 percent of individuals, indicating some symptoms consistent with bipolar disorder.

The horizontal graphs presents the total score (0 to 219, scored using the likert method) and percent endorsed score for the subscales (from 0 to 100%). These graphs shows scores in comparison to the normative community and clinical distributions, with shaded areas around the two middle quartiles (between the 25th and 75th percentile). The clinical distribution represents individuals diagnosed with bipolar disorder (Bullock et al., 2011; Pendergast et al., 2014). This graph helps contextualise patterns of responding in comparison to the distribution of responses among community samples and those with a diagnosis of bipolar disorder (Pendergast et al., 2014).

For the total score, when 37% of symptoms are endorsed the scale has a sensitivity of .90, meaning it correctly identifies 90% of true cases (Klein et al., 1986). At this level, its specificity is .98, indicating it correctly identifies 98% of non-cases.

Higher scores on the GBI may suggest the presence of bipolar disorder, which frequently coexists with other psychiatric conditions. For instance, research indicates that one in six adults with bipolar disorder also has ADHD (Schiweck et al., 2021). People with ADHD also score higher on the GBI than the general public. Other commonly associated disorders include autism spectrum disorder, borderline personality disorder, and substance use disorders (Menezes et al., 2019; Skokauskas & Frodl, 2015). Recent evidence highlights the GBI’s effectiveness in differentiating bipolar disorder from conditions such as unipolar depression and ADHD, making it a valuable tool for addressing comorbidity challenges in psychiatric assessments (Pendergast et al., 2014).

The GBI’s psychometric properties have been evaluated extensively, demonstrating it as a reliable and valid tool for assessing symptoms of bipolar disorder. The GBI captures both the core behavioural symptoms and dimensions like intensity, duration, frequency, and rapid shifts in mood. The items on the GBI were developed based on several sources:

1. symptom literature related to adult and adolescent depression, hypomania, and mania;
2. symptoms observed in bipolar patients during depressive phases;
3. behavioural characteristics associated with cyclothymia; and
4. symptoms outlined in the Research Diagnostic Criteria (RDC) manual (Depue et al., 1981).

During its development, the GBI was evaluated against a binomial classification of bipolar “cases” and “non-cases,” making it effective for identifying individuals at risk of bipolar disorder versus those with subclinical mood variations or other mood disorders. Recent research suggests that the GBI’s effectiveness could be improved by reorganising its items into more homogeneous subscales (Youngstrom et al., 2001). While earlier studies pointed to a single general factor accounting for most variance (Depue et al., 1981), newer analyses (Danielson et al., 2003; Depue et al., 1989; Pendergast et al., 2015; Youngstrom et al., 2001, 2013) support a two-factor structure, comprising of the following subscales:

• Depression and
• Hypomania/Biphasic

In the original study (Depue et al., 1981), analysis was performed on a non-clinical sample of 850 undergraduate students (51.5% female) with an age range of 18 to 32 years old. The majority of participants (90%) were 22 years of age or younger. The GBI exhibited strong construct validity, as evidenced by its significant correlation with clinician-rated assessments of mood disturbance (Depue et al., 1981). The scale showed strong overall internal consistency (alpha = .94) (Depue et al., 1981; 1989) and test-retest reliability over 15 weeks was reported to be 0.73 (Depue et al., 1981).

The Hypomania and Depressive scale scores demonstrate strong psychometric properties across various samples, including internal reliability alphas above .90, test-retest reliabilities over .70, robust predictive validity, and satisfactory convergent and discriminant validity (Youngstrom, 2008; Youngstrom et al., 2013).

The GBI uses two methods of scoring: Binary and Likert.

Using the binary scoring method the responses “Never or Hardly Ever” and “Sometimes” are scored as 0, and responses “Often” and “Very Often or Almost Constantly” are scored as 1. In NovoPsych, subscales are scored using the binary method, yielding a “percent endorsed” metric.

Alternatively, using the Likert scoring method, the response “Never or Hardly Ever” is scored as 0, “Sometimes” is scored as 1, “Often” is scored as 2, and “Very Often or Almost Constantly” is scored as 3. NovoPsych uses both the likert method (score range = 0 to 219) and binary method (percent endorsed) for the total score.

When using the Binary scoring, the GBI uses a two-threshold system, with one threshold for likely cases and another for non-cases, to classify individuals based on their total scores. This two-cut-off score system divides scores into three categories:

• Total scores of 19 or lower (0% to 26% endorsement) are classified as likely non-cases.
• Total scores between 20 and 26 (27% to 36% endorsement) are classified as indeterminate.
• Total scores of 27 to 73 (more than 37%+ endorsement) indicate likely cases of bipolar disorder. (Depue et al., 1981; Klein et al., 1986).

With the primary cut-off of 27 (37% endorsement), for bipolar conditions, the scale has a sensitivity of .90, meaning it correctly identifies 90% of true cases. Its specificity was .98 indicating it correctly identifies 98% of non-cases (Klein et al., 1986). In the study by Depue et al. (1981), a cut score of 27 secured a Positive Predictive Value (PPV) of 0.98, indicating that 98% of individuals who receive a positive result on the scale (indicating the presence of a condition) truly have bipolar disorder. Finally, the Negative Predictive Value (NPV) of the scale is .87, indicating that 87% of the individuals who receive a negative test result (indicating they do not have the condition) are correctly identified as not having bipolar disorder (Waugh et al., 2014). However, the false negative rate, where actual cases are missed, was 9% for scores below 19 and increased for scores in the 20-26 range (Depue et al., 1981).

For the depression subscale (range 0 to 46 as per Binary scoring method), a range of scores from 0 to 2 (0% to 4%) are categorised as “Low”, 3 to 14 (5% to 31%) as “Moderate”, and 15 to 46 (32% to 100%) as “High”.

For the hypomania/biphasia subscale (range 0 to 28 as per Binary scoring method), scores from 0 to 5 (0% to 18%) are categorised as “Low”, 6 to 19 (19% to 68%) are categorised as “Moderate”, and 20 to 28 (69% to 100%) are categorised as “High” (Pendergast et al., 2014).

Although the study by Pendergast et al., (2014) does not provide sensitivity and specificity data directly, it measured related constructs through Diagnostic Likelihood Ratios (DLRs). Diagnostic Likelihood Ratios (DLRs) are tools that examine how much a test result changes the chances that someone has a specific condition (Straus et al., 2011). A DLR of 1 is neutral, DLR < 1 decreases the likelihood of the condition, and DLR > 1 increases the likelihood (Straus et al., 2011). For the hypomania/biphasia scale, the following DLRs were reported for the severity categories when comparing individuals with and without bipolar disorder:

• Low (0–5): DLR = 0.09. This means that if a person scores in the low range, they have a very low probability of having bipolar disorder.
• Moderate (6–19): DLR = 4.21. A score in this range makes bipolar disorder 4.21 times more likely compared to the general public.
• High (20+): DLR = 6.49. A score in this high range increases the odds of bipolar disorder by 6.49 times compared to the general public, indicating a strong likelihood of BD when scores are 20 or above.

A similar analysis was conducted on the depression subscale, however, the comparison was made between individuals with and without any mood disorder:

• Low (0–2): DLR = 0.14. A score in the low range means it is unlikely the person has any mood disorder. A DLR of 0.14 greatly decreases the likelihood of a mood disorder.
• Moderate (3–14): DLR = 0.78. This value is close to 1, which means it doesn’t significantly change the odds of having or not having a mood disorder.
• High (15+): DLR = 4.75. A score in this high range increases the likelihood of having a mood disorder by about 4.75 times, making it a strong indicator of the presence of a mood disorder.

The sample used to compute normative percentiles for the total score consisted of 87 Australian participants (68% female) with a mean age of 26.3. (Bullock et al., 2011). The study used the Likert scoring method. The GBI scores were divided between low and high scorers, so a pooled average was calculated to represent the typical score of an individual in the normative sample. For the total score,

• Normative sample: M = 59.16, SD = 19.18
• Individuals with Bipolar Disorder, M = 112.67, SD = 52.85

Two other samples were used for comparison purposes at the subscale level. In Sample 1, a total of 359 adolescents (69.6% female), aged 14 to 19 years (M = 18.35, SD = 1.52), participated in the Teen Emotion and Motivation (TEAM) project (Alloy et al., 2012a). Sample 2 consisted of 614 university students (33.2% female), aged 18 to 24 years (M = 19.63, SD = 1.76), who were part of the Longitudinal Investigation of Bipolar Spectrum (LIBS) project (Alloy et al., 2008; 2012b). The above samples were used in the study by Pendergast et al., (2014), which analysed both individuals from the general public sample and those with bipolar disorder. This study used the binary scoring method, meaning that responses “Never or Hardly Ever” and “Sometimes” were scored as 0, and responses “Often” and “Very Often or Almost Constantly” were scored as 1 (Pendergast et al., 2014). The means and standard deviations for both the subscales in Pendergast et al. (2014) are as follows:

• For Depression subscale:
  • Normative Sample: M = 7.24, SD = 9.43
  • Individuals with Bipolar Disorder: M = 20.76, SD = 12.01 (Pendergast et al., 2014)
• For Hypomania/ Biphasic subscale:
  • Normative Sample: M = 5.66, SD = 9.43
  • Individuals with Bipolar Disorder: M = 14.22, SD = 6.65 (Pendergast et al., 2014)

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Based on attachment theory’s fundamental premise that humans possess an innate socio-biological need to form and maintain strong affectional bonds, the ASQ-SF evaluates how individuals approach and manage relationships across their lifespan (Bowlby, 1988; Mikulincer & Shaver, 2016). This attachment drive serves multiple adaptive functions, including enhancing survival through proximity-seeking behaviours during periods of distress or threat, facilitating emotion regulation, and providing a secure base for exploration and personal growth (Karantzas et al., 2010; Fraley & Shaver, 2000). 

The scale is structured around two primary dimensions of adult attachment, representing the major patterns of attachment-related anxiety and avoidance:

1. Anxious Attachment (e.g., “I worry a lot about my relationships”) – characterised by hyperactivating strategies, excessive need for approval, and fear of abandonment
2. Avoidant Attachment (e.g., “I find it difficult to depend on other people”) – characterised by deactivating strategies, discomfort with closeness, and defensive self-reliance

These main dimensions are further refined through five specific subscales that provide detailed insight into interpersonal relationship patterns:

• Discomfort with Closeness (emotional and psychological intimacy avoidance)
• Relationships as Secondary (diminished prioritisation of relationships)
• Preoccupation with Relationships (excessive focus on relationship dynamics)
• Need for Approval (dependency on others’ validation)
• Confidence in Interpersonal Interactions (secure relationship engagement)

A distinctive strength of the ASQ-SF lies in its deliberately broad conceptualisation of attachment relationships, utilising language that extends beyond romantic partnerships (Karantzas et al., 2010). This broader focus enables assessment across diverse populations, including adolescents and individuals with limited romantic relationship experience. The measure’s versatility makes it particularly valuable for understanding developmental trajectories in attachment patterns and their influence on various interpersonal contexts, including therapeutic relationships, family dynamics, and peer interactions (Karantzas et al., 2010; Mikulincer & Shaver, 2016).

The ASQ-SF is a contemporary attachment measure; its dimensional and subscale structure provides a nuanced understanding of attachment patterns, thus addressing limitations of existing attachment measures which conceptualise attachment as a categorical construct. In their comprehensive systematic review, Pollard et al. (2023) identified the ASQ-SF as one of the most robust and psychometrically sound measures available for attachment assessment.

The assessment of attachment patterns through the ASQ-SF provides insights that inform case conceptualisation, treatment planning, and therapeutic process (Levy et al., 2018). Understanding a client’s attachment style helps predict potential challenges in the therapeutic alliance, anticipate transference patterns, and guide interventions that promote secure attachment development (Daniel, 2006). Research indicates that attachment patterns are significantly associated with various psychopathological presentations, including depression, anxiety, personality disorders, and interpersonal difficulties (Dozier et al., 2008). Additionally, attachment assessment can help identify specific mechanisms maintaining psychological distress, such as maladaptive emotion regulation strategies, interpersonal schemas, and patterns of seeking or avoiding support (Malik et al., 2015). The detailed subscale structure of the ASQ-SF allows clinicians to target interventions more precisely, addressing specific aspects of attachment insecurity while building on existing strengths in interpersonal functioning (Wallin, 2007). Furthermore, attachment patterns have been shown to influence treatment outcomes across various therapeutic modalities, making attachment assessment valuable for treatment selection and modification (Mikulincer et al., 2013).

Scores consist of two main attachment styles, Avoidant Attachment and Anxious Attachment alongside subscales. The scoring system is based on a combination of direct subscale scores and adjustments using the Confidence subscale items to provide a more nuanced measure of attachment security:

• Avoidant Attachment (score range -5 to 75): This scale reflects the extent to which an individual avoids intimacy and is distrusting of others. It is computed by summing Discomfort with Closeness and Relationships as Secondary scores and then subtracting items 1, 14 & 28 from the Confidence subscale
• Anxious Attachment (score range -18 to 57): This scale reflects excessive need for reassurance, fear of rejection, and desire to merge with relationship partners. It is computed by summing Preoccupation and Need for Approval scores and subtracting items 24, 26 & 29 from the Confidence subscale.

The five subscales provide detailed information about specific aspects of attachment and interpersonal functioning:

• Discomfort with Closeness (Items 2, 3, 11, 12, 15, 16, 18, 20, 27; score range: 9 to 54):
  This subscale measures an individual’s tendency to maintain emotional and psychological distance in relationships. It reflects fundamental difficulties with intimacy and vulnerability, including discomfort with emotional closeness and reluctance to share personal feelings or rely on others. High scores suggest a pronounced tendency to maintain rigid emotional boundaries and may indicate defensive strategies developed to protect against perceived relationship threats or emotional pain. This discomfort often manifests in behaviours such as emotional withdrawal, difficulty trusting others, and resistance to deepening relationships beyond a superficial level.
• Relationships as Secondary (Items 4, 5, 6, 9; score range: 4 to 24):
  This subscale assesses the extent to which an individual prioritises relationships relative to other aspects of life, such as work, personal achievements, or independent pursuits. It reflects the degree of investment in interpersonal connections and willingness to acknowledge and attend to relationship needs. High scores indicate a tendency to devalue the importance of relationships, often manifesting as excessive self-reliance and a strong preference for maintaining independence from others. This pattern may represent either a defensive strategy or a genuine preference for autonomy, with implications for how individuals balance personal and relational needs.
• Preoccupation with Relationships (Items 13, 17, 22, 23, 25; score range: 5 to 30):
  This subscale measures the extent of cognitive and emotional investment in relationships, particularly concerning worries about relationship status and stability. It reflects the intensity of an individual’s focus on relationship dynamics and their tendency to monitor, analyse, and worry about their relationships. High scores indicate significant hypervigilance to relationship cues and excessive concern about relationship status, often leading to difficulties maintaining perspective or engaging in non-relationship activities. This preoccupation can manifest as constant relationship monitoring, difficulty concentrating on other tasks due to relationship concerns, and a tendency to overanalyse relationship interactions.
• Need for Approval (Items 7, 8, 10, 19, 21; score range: 5 to 30):
  This subscale assesses the degree to which an individual depends on others’ validation and acceptance for maintaining self-worth and emotional stability. It reflects sensitivity to rejection and criticism, as well as the strength of need for others’ approval and acceptance. High scores indicate excessive dependency on external validation and heightened sensitivity to others’ opinions, often manifesting as people-pleasing behaviours, difficulty making independent decisions, and intense fear of rejection. This need for approval can significantly impact relationship dynamics, decision-making processes, and overall emotional well-being.
• Confidence in Interpersonal Interactions (Items 1, 14, 28, 24, 26, 29; score range: 6 to 36):
  This subscale measures the degree of security and comfort an individual experiences in relationship contexts. Unlike the other subscales, higher scores on this dimension indicate more secure attachment patterns. It reflects an individual’s capacity for balanced relationship engagement, including comfort with both intimacy and autonomy. High scores suggest healthy self-reliance combined with the ability to form and maintain meaningful relationships, seek support when needed, and navigate relationship challenges effectively. This subscale plays a unique role in the scoring system, as items from it are subtracted from both attachment dimensions to adjust for security, reflecting the buffering effect of interpersonal confidence against both anxious and avoidant tendencies.

Each score is converted to a percentile based on normative data, and descriptors (Low, Average, High) are assigned based on percentile ranges (Hegarty et al., 2024):

• Low: 25th percentile or below
• Average: 26th to 74th percentile
• High: 75th percentile or above

Percentiles are computed based on an adult community sample, where a percentile of 50 represents typical patterns of responding. Participants in the community sample (n = 3,576) ranged in age from 15 to 73 years and were predominantly (90%) of Anglo-Australian background.

The interpretative text includes the Primary Attachment Pattern Analysis (Hegarty et al., 2024). This pattern analysis is based upon the specific combination of score descriptors (Low, Average, High) across all dimensions. These patterns range from secure styles (e.g., “Secure Style”) to various insecure patterns (e.g., “Fearful-Avoidant Style”, “Dismissive-Avoidant Style”). Each pattern provides specific insights into attachment organisation and relationship dynamics, along with associated therapeutic implications and approaches. The interpretation considers interactions between the avoidant and anxious scales to provide a comprehensive understanding of the person’s attachment style and its implications for relationships and therapeutic work. The specificity of the overall Attachment Pattern allows for highly tailored therapeutic recommendations and insights into potential treatment challenges and opportunities.

The 29-item ASQ-SF by Alexander et al. (2001) was derived from the 40-item Attachment Style Questionnaire by Feeney et al. (1994). Karantzas et al. (2010) demonstrated that of the two scales (ASQ and ASQ-SF), the ASQ-SF is the superior, more parsimonious attachment measure. While both versions showed similar, satisfying values of subscale internal consistency (α = 0.72 – 0.85), the best factor model fit was found in the short form (Karantzas et al., 2010). Results suggest that the multidimensional structure of the ASQ-SF allows for important distinctions in people’s attachment cognitions and behaviours (Karantzas et al., 2010). In doing so, these measures capture key elements of attachment styles that are important in understanding the nature of human attachment.

Normative data from adults collected by Karantzas and colleagues (2010) has been synthesised to produce means and standard deviations for scores and subscales (see Table 6, Karantzas et al., 2010). This data is used to compute percentiles (Hegarty et al., 2024). The sample consisted of 3,576 participants selected from the wider community in eastern Australian states and territories. Participants ranged in age from 15 to 73 years (M = 25.67 years, SD = 11.20) and were predominantly (90%) of Anglo-Australian background.

• Avoidant Attachment (M = 27.6, SD = 11.46)
• Discomfort with closeness (M = 30.88, SD = 9.61)
• Relationships as secondary (M = 9.05, SD = 4.80)
• Anxious attachment (M = 21.15, SD = 10.04)
• Preoccupation (M = 16.30, SD = 5.79)
• Need for approval (M = 16.48, SD = 7.11)
• Confidence (M = 23.95, SD = 6.72)

NovoPsych determined descriptors for each of the attachment scores and subscale scores that are determined by percentiles (Hegarty et al., 2024). The percentile ranges chosen were consistent with previous attachment researchers (e.g., Mayseless & Scher, 2000; Kaitz et al., 2004; Rain et al., 2016):

• ‘High’ scores: 75th percentile or more
• ‘Low’ scores: 25th percentile or less
• ‘Average’ scores: 26th-74th percentile

The NovoPsych version of the ASQ-SF employs a sophisticated pattern analysis system that examines the interplay between avoidant and anxious attachment dimensions. These attachment patterns were developed by three PhD level psychologists with extensive clinical experience (Hegarty et al., 2024). The analysis generates attachment pattern interpretations based on specific combinations of scores across dimensions. Patterns focus on the fundamental interaction between anxious and avoidant attachment dimensions, categorising individuals into broader attachment styles. For example:

• Secure Style (low avoidance, low anxiety: both below 25th percentile)
• Fearful-Avoidant Style (high avoidance, high anxiety: both above 75th percentile)
• Predominantly Avoidant or Anxious Styles (elevation in one dimension above 75th percentile)

This dual-level pattern analysis system, supported by empirical attachment research (Bartholomew & Horowitz, 1991; Brennan et al., 1998), provides both the specificity needed for clinical intervention and the broader categorical framework useful for research and general understanding of attachment organisation.

Assess your patients with the Attachment Style Questionnaire – Short Form (ASQ-SF) using a NovoPsych account

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