The Impact of Sensitivity on Socially Withdrawn Behavior

The Impact of Sensitivity on Socially Withdrawn Behavior

Marit Bredesen

Submitted as a Master Thesis at the Department of Psychology, University of Oslo

12th term

2.11.2021

Abstract

Author: Marit Bredesen

Title: The Impact of Sensitivity on Socially Withdrawn Behavior Supervisor: Evalill Bølstad

Co-supervisor: Kristin Gustavson

Background. Sensory Processing Sensitivity (SPS) refers to inter-individual differences in sensitivity to both negative and positive environments, and individuals with SPS are assumed to have more awareness and responsivity to contextual stimuli. According to some neuro- cognitive evidence, individuals high in SPS are also expected to process stimuli and

information more strongly and deeply than others. A central assumption regarding SPS is that highly sensitive individuals, due to their sensitivity to sensory stimulation and the demands placed on them because of deeper and stronger processing of that stimuli, may need to withdraw from social stimuli more often than less sensitive individuals. Some see SPS as being higher in 20-30% of all individuals. The purpose of the current thesis was to explore the association of SPS with the different types of social preferences underlying social withdrawal (unsociability, avoidance, shyness, and isolation) in a Norwegian sample of young adults. A further purpose was to investigate whether SPS is associated with socially withdrawn behavior independently of other known determining factors of social withdrawal, such as the personality traits of low Extraversion and high Neuroticism, as well as depression and social anxiety, and as such investigate whether SPS is a unique and useful construct when viewed as a risk factor for social withdrawal. Finally, an additional goal was to investigate the factor structure of a scale used to assess SPS, the Highly Sensitive Person Scale (HSPS), as several international studies differ in the number of factors that underlie the trait of SPS.

Method. A sample of 244 Norwegian university students answered online questionnaires that included measures of SPS, social preferences, Big Five personality traits, depression, and social anxiety. Exploratory and confirmatory factor analyses were conducted to explore the factor structure of the HSPS and differentiate between the subtypes of SPS. Furthermore, Structural equation modelling (SEM) was used to explore the association between sensitivity subtypes and social preference subtypes, controlling for personality traits, social anxiety, and depression.

Results. The factor analyses indicated a three-factor solution of the HSPS, with the factors Ease of Excitation (EOE), Aesthetic Sensitivity (AES), and Low Sensory Threshold (LST).

This model showed good fit to the data. The SEM analyses revealed that SPS was a

significant predictor of the preference for solitude dimension (unsociability and avoidance) of preference for social withdrawal. The subcategory of EOE positively predicted unsociability, and LST positively predicted avoidance. AES negatively predicted avoidance. Furthermore, low Neuroticism and low Extraversion predicted high unsociability, whereas high

Neuroticism and low Extraversion predicted high shyness. Low Extraversion predicted high isolation. High depression positively predicted high shyness and high isolation. Results may indicate that there is an association between SPS and one subdimension of preference for social withdrawal - preference for solitude. This association is over and beyond the

association between preference for social withdrawal and personality traits, depression, and social anxiety in this study. This may have implications for how preference for solitude may be understood.

The present study is part of a larger study managed by Robert J. Coplan at the University of Ottawa, in collaboration with other universities around the world. However, data were

collected by the PI of the Norwegian sub-study and main supervisor, Evalill Bølstad, a fellow student and the author. The hypotheses were developed independently by the author, and analyses conducted by the author under supervision by co-supervisor Kristin Gustavson.

Acknowledgements

The execution of this thesis was made possible by Dr. Robert Coplan, who included Norway in his “Beliefs About Social Withdrawal” study. I would like to thank my supervisor,

associate professor, Evalill Bølstad for letting me be part of this project. I have gained a great degree of knowledge on how to execute a study, from choosing scales and translating and back-translating. Evalill has been a supportive and encouraging supervisor throughout the time we have worked together, and I thank her for her encouragement to follow my own ideas and her belief that I may succeed. I would also like to thank associate professor Kristin Gustavson for her patient and generous guidance during the statistical analysis process, and for introducing me to RStudio. Finally, I would like to thank my fellow student Nora Braathu for participating in the creation of the study and recruiting participants.

I would like to thank my parents, who both passed away during my psychology degree, as well as my other family and friends for being supportive and helpful throughout yet another higher education. I give a special thank you to my niece, who through her struggle with selective mutism, social anxiety, and social withdrawal, inspired the subject of this thesis.

Finally, I would like to thank my foster son, who rejuvenated my life during extended social isolation due to covid-19, and who will continue to do so for the rest of my life. I also thank my classmates of the last few years for a mutually supportive environment and dedication to our future profession.

Thank you.

Abstract ... II Acknowledgements ... IV

1. Introduction ... 1

1.1 Sensory Processing Sensitivity ... 2

1.2 Sensitivity and preference for social withdrawal ... 5

1.3 Sensitivity and personality ... 7

1.4 SPS, preference for social withdrawal and personality ... 9

1.6 The Present study ... 13

2. Methods... 15

2.1 Participants ... 16

2.2 Procedures ... 16

2.3 Ethics ... 17

2.4 Measures ... 17

2.4.1 Highly Sensitive Person Scale ... 18

2.4.2 The Social Preference Scale-Revised ... 18

2.4.3 The Big Five Inventory ... 19

2.4.4 Short Mood and Feeling Questionnaire ... 19

2.4.5 Depression, Anxiety, Stress Scale (DASS) ... 20

2.5 Statistical Analysis ... 20

2.5.1 Exploratory factor analysis ... 20

2.5.2 Confirmatory factor analysis ... 21

2.5.3 Structural equation modeling ... 22

3. Results ... 23

3.1 Descriptives ... 23

3.2 Factor analysis of the HSPS ... 26

3.3 Structural Equation Modeling with HSPS, Social Preference, Big Five, social anxiety and depression ... 30

4. Discussion ... 33

4.1 Main findings ... 33

4.2 The factor structure of the HSPS ... 33

4.3 The relations between SPS and preference for social withdrawal ... 36

4.6 Implications ... 42

4.7 Strengths and limitations ... 43

4.8 Future directions and conclusion ... 44

5. References ... 47 6. Appendix ... 63

1. Introduction

Sensory Processing Sensitivity (SPS) has received a great deal of attention since its conception in the late 1990s. SPS may be understood as a common trait describing

interindividual differences in sensitivity to both negative and positive environments, such as subtle social stimuli, expressions of art, caffeine, hunger, pain, loud noises, strong smells and tastes, and violent media images (Aron & Aron, 1997). Aron and Aron developed a theory suggesting that SPS is an underlying trait that differentiates individuals in how they process stimuli, and that involves greater arousability because of enhanced SPS. Individuals with SPS are seen as having more awareness and responsivity to environmental and social stimuli. The increased processing of stimuli, in turn, make such individuals more vulnerable to increased emotional reactivity, ease of overstimulation and vulnerability to stress, anxiety and

depression (Aron et al., 2012; Booth et al., 2015a; Greven et al., 2019a; Liss, et al., 2008), and to over-arousal, cognitive depletion and fatigue (Acevedo et al., 2021; Greven et al., 2019a). The construct of SPS points to a quality of sensory processing of information, and not simply sensitivity towards sensory input (Greven et al., 2019a). Aron and Aron (1997) hypothesized that children and young adults are more affected by SPS than older adults due to a lack of learning; many individuals high in SPS will learn to associate social stimuli as positive with maturity and learning. Thus, SPS may be viewed as a vulnerability factor for young adulthood, as this is a key transitioning phase with the advent of independence of living in terms of student and working life. This entails the need to form new social relations.

Individuals in this phase of life may therefore be particularly vulnerable to negative outcomes of inhibited behavior.

One of the behavioral outcomes associated with SPS is the tendency to withdraw socially. A central assumption about highly sensitive individuals is that their sensitivity to sensory stimulation places greater demands on the processing of that stimuli, and that they may need to withdraw from social stimuli more often than less sensitive individuals (Acevedo et al., 2021; Aron & Aron, 1997). Thus, social withdrawal may be seen as a useful strategy to avoid overstimulation (Aron & Aron, 1997; Lionetti et al., 2019a). To the best of my knowledge, few studies have tested this hypothesis and examined how SPS, and its different subtypes, relates to social withdrawal. Aron and Aron (1997), however, considered shyness and

unsociability, which are two different motivations of social withdrawal, to be highly relevant

to the SPS construct. They hypothesized that many shy and unsociable individuals actually have high SPS, and that SPS causes the low sociability as a strategy to avoid overstimulation (Ibid). They also suggested that shyness may originate from a sensitivity for overstimulation (Ibid).

SPS has been embraced by popular science and by some social scientists. However, there is prevailing controversy about whether SPS is a separate temperamental trait or a personality trait, or indeed may be better explained by other psychological constructs and theories.

Theory on personality and temperament, as well as Gray´s theory on the Behavioral

Inhibition System (Gray, 1981), are by many believed to adequately explain SPS equally well as the SPS construct as perceived by Aron and Aron (1997). The usefulness of the SPS construct is thus debated. Temperament and personality, as well as symptoms of social anxiety and depression are factors that may confound the hypothesized association between SPS and social withdrawal. Thus, theoretical, and empirical relations between these factors and SPS will be presented in the introduction.

Given the conceptual and behavioral relatedness and overlap between SPS, social

withdrawal, and some personality traits, the main aim of this thesis was to investigate the relationship between SPS and socially withdrawn behavior while controlling for personality traits. Furthermore, symptoms of anxiety and depression may be linked to SPS and

preference for social withdrawal and will therefore be controlled for. Investigating these relationships could help shed light on the usefulness of SPS as a separate construct, as well as contribute to the understanding of the relation to social withdrawal.

1.1 Sensory Processing Sensitivity

According to Aron and Aron (1997), Sensory Processing Sensitivity (SPS) is an innate temperamental trait that can be described as hyper-reactivity and responsivity to intense and strong sensory stimuli (Aron & Aron, 1997; Greven et al., 2019a). They hold that the human species have evolved different strategies for survival; either foraging and exploration, or a more careful and vigilant approach to the environment (Aron et al., 2012). In their view, SPS may be the underlying basis of this difference in survival strategy, more so than other

constructs utilized to describe more socially withdrawn individuals, such as shyness and introversion (Aron & Aron, 1997). Individuals with SPS are found to have greater awareness

of sensory stimulation, more behavioral inhibition (Pluess et al., 2018), deeper cognitive processing of environmental stimuli (Acevedo et al., 2014a, 2017; Jagiellowicz et al., 2011) and higher emotional and physiological reactivity (Aron et al., 2012). SPS includes sensory processing of aesthetic experiences, such as music and art, as well as other external and internal stimuli, such as loud noises; subtle changes in oneself or in others, in moods and feelings; or processing violent scenes in visual media. It is proposed that SPS is a stable personality trait that emerges in infancy, and that may be shaped further by the environmental conditions children experience while growing up (Pluess, 2015).

The Behavioral Inhibition System (BIS; Gray, 1981) is conceived as a biologically based motivational system that entails being sensitive to threatening stimuli, punishment, non- reward, and novelty, as well as the need to allow time to process stimuli further (Ibid). As such, BIS may overlap conceptually with SPS. Several studies have found a significant association between SPS and BIS (Smolewska et al., 2006; Lionetti et al., 2019a), which may indicate a similar tendency to behave in a cautious manner so as to prevent negative consequences and states which follows from becoming overwhelmed by stimuli in novel situations (Aron & Aron, 1997; Homberg et al., 2016). Aron and Aron´s theory (1997) suggests that individuals high in SPS have a more active Behavioral Inhibition System (BIS;

Gray, 1981; Aron & Aron, 1997; Smolewska, et al., 2006), and see BIS as a neurological substrate of SPS ( Aron & Aron, 1997; Smolewska et al., 2006).

Different measurement tools capture SPS, such as The Adolescent/Adult Sensory Profile (AASP; Dunn & Brown, 1997), the Sensory Processing Scale (SPS; Schoen et al., 2014), and the Highly Sensitive Person Scale (HSPS; Aron & Aron, 1997). The two former scales measure sensory modulation, which is the ability to regulate and grade responses to the sensory environment. These measurements capture different sensory styles in addition to SPS and entail either direct observations (SPS Assessment) or an extensive item pool (e.g.,

AASP=60 items). As such, they have proven useful in a clinical population to capture features of e.g., ADHD and autism spectrum disorders. The HSPS (Aron & Aron, 1997), on the other hand, was developed to capture variations of SPS in the non-clinical population, and studies utilizing the HSPS have concluded that approximately 30% of the population is higher in SPS (Pluess et al., 2018). The scale includes what Aron and Aron (1997) consider markers of increased sensitivity, such as being highly sensitive, introverted or easily

overwhelmed by stimuli, and include statements of high conscientiousness, having a rich

inner life, being startled more easily and noticing pain more strongly than others (Ibid;

Greven et al., 2019).

The HSPS was at first conceived to capture a unidimensional construct (Aron & Aron, 1997).

However, this was challenged by Smolewska, McCabe and Woody (2006), who investigated the factor structure of the HSPS, and suggested three subcategories of SPS, Aesthetic

Sensitivity (AES), referring to openness to and pleasure from aesthetic experiences; Low Sensory Threshold (LST), referring to sensitivity to subtle external stimuli; and Ease of Excitation (EOE), referring to being easily overwhelmed by internal and external stimuli or demands (Smolewska et al., 2006). This factor structure has been supported in several later studies (Booth et al., 2015; Listou Grimen & Diseth, 2016; Konrad & Herzberg, 2019;

Baryła-Matejczuk et al., 2021) and represents a development of the unidimensional model of Aron and Aron (1997). However, the proposed factor structure has not been upheld, or upheld with difficulty, in several studies across different cultures, and many items prove to have weak or no factor loadings or different factor structures (Ershova et al., 2018; Evans &

Rothbart, 2008; Greven et al., 2019a; Listou Grimen & Diseth, 2016; Rinn et al., 2018;

Şengül-İnal & Sümer, 2020) and low reliabilities for subscale scores (Smith et al., 2019).

Further, Evans and Rothbart (2007) have found that SPS, as measured by the HSPS, was better explained by temperamental constructs (negative affect and orienting sensitivity) when comparing results using the HSPS with the Adult Temperament Questionnaire (ATQ; Evans

& Rothbart, 2007). In addition, they found that measures of sensory sensitivity and sensory discomfort were uncorrelated, thus indicating that being high in SPS is unrelated to being overwhelmed by contextual stimuli and the need to withdraw from that stimuli. Aron and Aron (1997), on the other hand, have found consistently strong correlations between negative emotionality and SPS (Aron & Aron, 1997), their interpretation being that individuals high in SPS may experience both negative and positive emotions more strongly than others.

According to Aron (2010), increased negative affect for individuals high in SPS may be understood as a reaction to stress when facing overwhelming stimuli, which reinforces the need to withdraw socially (Aron, 2010). In other words, theories on personality and

temperament do not concur on the usefulness of the SPS construct. In this field there is still a knowledge gap and added empirical evidence may help shed light on whether SPS is a useful construct and help close this knowledge gap. In the following, the empirical evidence

between SPS, personality traits and preference for social withdrawal will be reviewed.

1.2 Sensitivity and preference for social withdrawal

Social preference is often treated as a unidimensional construct, but several studies have found that it may be best conceptualized as a multifaceted construct during childhood and adolescence (Asendorpf, 1990; Bowker & Raja, 2011a; Rubin & Coplan, 2004; Rubin et al., 2009). Four subtypes have been identified, namely shyness, unsociability, avoidance, and isolation (Spangler & Gazelle, 2009). These four subtypes point to different underlying motivations for social withdrawal (Asendorpf, 1990). Two distinct dimensions may be distinguished; unsociability and avoidance may be placed in a subgroup called “preference for solitude”, whereas shyness and isolation may be placed under “preference for

socialization” (Braathu, 2019).

The underlying motivations for preference for solitude seem to differ, i.e., unsociable individuals do not wish to socialize. Unsociable individuals are assumed to be less involved with peers because of a low approach motive, and not because of a high avoidance motive (Asendorpf, 1990). During childhood, these individuals seem to be more interested in playing with objects than with peers (Ibid), whereas in adolescence and young adulthood, unsociable individuals are understood as less interested in initiating interactions with peers (Bowker &

Raja, 2011a; Nelson, 2012). However, they are not seen as afraid to interact, nor as actively avoiding others (Bowker & Raja, 2011a). Avoidant individuals have a similar preference to be alone and to avoid social interaction, but this avoidance motive seems to origin in a dislike to socialize (Asendorpf, 1990; Coplan et al., 2015). Socializing may even trigger aggression due to fear of social settings or perhaps continuous negative experiences surrounding socializing and a fear of rejection (Asendorpf, 1990; Coplan et al., 2015). Individuals

classified as shy are commonly seen as wanting to socialize, but do not because of inhibition or fear, thus experiencing an internal social approach-avoidance conflict which may

contribute to social inhibition (Asendorpf, 1990; Coplan et al., 2015). Shy individuals have a heightened fear and concern of social novelty and social evaluation (Coplan et al., 2015).

Finally, in the isolated group, individuals wish to be social, but experience exclusion from groups and peer victimization (Burgess et al., 2006).

Empirical research on social withdrawal have focused mainly on shyness, and more recently on unsociability. Shyness has been related to neural activation when exposed to mild stress,

and leading to poorer social competence, lower self-esteem, anxiety, peer rejection and academic difficulties in very young children (Coplan & Armer, 2007a; Coplan et al., 2004a).

Unsociability, on the other hand, seems to be less associated with detrimental indices of socio-emotional functioning (Bowker et al., 2020; Coplan & Armer, 2007b; Nelson, 2012), as unsociable children seem to possess social skills on an equal level with sociable children when they are in social settings, and not in their preferred solitude (Rubin & Weeks, 2010).

Unsociability is seen as a more benign form of social withdrawal in westernized cultures (Bowker et al., 2017b; Coplan et al., 2015; Nelson, 2012). Some studies have found an association between unsociability and positive outcomes, such as less peer victimization (Ojanen et al., 2017) and more creativity (Bowker et al., 2017a; Long et al., 2003), based on the idea that solitude gives room for creative and new thoughts (Ibid). Other studies have found a lack of negative indices on internalizing or relationship problems in unsociable children (Coplan et al., 2010a) and youth (Nelson, 2012). The motivation for withdrawing socially, however, is less clear empirically, as unsociable individuals are hypothesized to simply prefer solitude above the company of others (Asendorpf, 1990; Rubin & Coplan, 2004). However, one study has found it difficult to separate unsociability from other forms of less benign solitude, finding that most individuals high in unsociability were also found to be high in anxiousness (Spangler & Gazelle, 2009). Coplan et al. (2007) found that social outcomes for unsociable children are less benign than are those for shy children, as unsociable children were perceived by peers as less desirable playmates than shy children (Coplan et al., 2007b). Also, unsociable youth have been found to have higher levels of depression than controls (Nelson, 2012).

Furthermore, the avoidance motive seems to origin in a dislike to socialize and is associated with aggression due to fear of social settings or perhaps continuous negative experiences surrounding socializing (Asendorpf, 1990; Coplan et al., 2015). This is supported by Bowker and Raja (2011), who found that avoidant young adolescents in the west of India are more lonely and excluded by peers than unsociable individuals (Bowker & Raja, 2011a), while Nelson (2012) found that avoidant young adults in Northern America had a higher risk of internalizing problems and lower quality in their relationships to friends and family than controls (Nelson, 2012). In another study using the same sample as the current study, Braathu (2019) found that avoidant individuals tended to report higher levels of depression and

loneliness, and lower levels of life satisfaction (Braathu, 2019).

The literature on social withdrawal has to the best of my knowledge not included SPS as a construct that may underlie or explain preference for social withdrawal or any of its subtypes.

However, Aron and Aron (1997) hypothesized that SPS may be the cause of chronic shyness (Aron & Aron, 1997). Children high in SPS would conceive of meeting strangers as

involving intense, novel and complex stimuli, leading to inhibited behavior and poor social performance, and thus representing experiences of being overwhelmed and over-aroused and eventually leading to the development of chronic shyness (Ibid). They also considered that older individuals with high SPS would in some cases be able to develop sociability through learning to see social relationships as familiar and a way to reduce arousal instead of increasing arousal (Ibid), suggesting that SPS may affect young adults´ behavior more strongly than in older individuals. Aron, Aron and Davies (2005) also provided some evidence of a link between SPS and the development of shyness, which in turn may lead to social withdrawal; they found empirical evidence that the interaction of sensitivity and adverse childhood environments may lead to negative affectivity, and hypothesized that this in turn may lead to shyness with the highly sensitive individuals being more impacted (Aron et al., 2005). However, this study did not control for other possible factors explaining social withdrawal, except for two items on social introversion. Nor did the study include other possible factors underlying, or motivating, social withdrawal other than shyness.

Another study indicated that there is a relationship between social withdrawal, cortisol stress response and HPS-axis activation, and that this activation and stress-response may partly aggravate social withdrawal, but only in depressive disorder (Bauduin et al., 2021). This study did not include any measures of sensitivity, but it does indicate a possible association between the SPS subcategories of EOE and LST with social withdrawal, as these

subcategories are associated with heightened neural activation when subjected to intense stimuli. In summary, the association between SPS and preference for social withdrawal seems to be likely. However, there are several potentially overlapping constructs that may confound this hypothesized relationship, which is further elaborated below.

1.3 Sensitivity and personality

There is no strong consensus within the research field of personality whether Sensory

Processing Sensitivity (SPS) is a valid and independent construct. Indeed, SPS has long been viewed as interchangeable with more established personality traits such as Neuroticism and

Extraversion, and not as a separate personality trait (Bolders et al., 2017; Evans & Rothbart, 2008). The following section will review the empirical evidence linking SPS with other well established Big Five personality traits. The Big Five personality traits consist of Openness to experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism, of which only Neuroticism, Extraversion and Openness have been empirically associated with SPS across studies. Therefore, only these three traits will be reviewed here.

Several studies have found that the SPS construct overlaps with personality traits with low to moderate correlations (Aron & Aron, 1997; Aron et al., 2005; Bröhl et al., 2020; Lionetti et al., 2019b; Listou Grimen & Diseth, 2016; Pluess et al., 2018; Şengül-İnal & Sümer, 2020;

Smolewska et al., 2006), thus partly supporting that SPS is not fully explained by any of the personality traits, or by a combination of traits. Of the Big Five personality traits, Aron and Aron (1997) considered the opposite pole of Extraversion to be the most relevant to the SPS construct, as the tendency to be over-aroused and socially withdrawn traditionally has been seen as part of a low Extraversion personality style (Eysenck, 1981). They found that

Extraversion correlated negatively but consistently and moderately with SPS (Aron & Aron, 1997). The correlation between Extraversion and SPS in several more recent studies is, however, found to be moderate (Listou Grimen & Diseth, 2016; Şengül-İnal et al., 2018), weak (Listou Grimen & Diseth, 2016; Smolewska et al., 2006), or lacking (Lionetti et al., 2019b).

Aron and Aron (1997) have found that SPS is related but distinct from Neuroticism, and this result has been replicated and nuanced in several later studies (Aron & Aron, 1997; Bröhl et al., 2020; Şengül-İnal et al., 2018; Smolewska et al., 2006). Neuroticism is found to correlate more consistently with all the different subcategories of the HSPS than other Big Five traits (Aron & Aron, 1997; Pluess et al., 2018; Sobocko & Zelenski, 2015), but more highly with the subcategory EOE than with LST and AES (EOE= .48; LST = .31; AES = .19; total HSP scale = .45; Smolewska et al., 2006). This has been replicated in several studies, finding weak to moderate associations between Neuroticism and the total HSPS in general, and more specifically with stronger associations between Neuroticism and EOE and, to a lesser extent, LST (Listou Grimen & Diseth, 2016; Sobocko & Zelenski, 2015). Bröhl et al. (2020) found that facets within the trait Neuroticism, when measured with the Dutch translation of the NEO-PI-3 (Hoekstra & De Fruyt, 2013), were associated with proneness to internalizing behavior, and that these facets showed the highest associations with SPS in samples of

children, older adolescents, and young adults. This may indicate that sensitive younger individuals can be more inclined to feel anxiety, depression and self-consciousness than less sensitive and older individuals (Bröhl et al., 2020).

SPS has also been reported to correlate with Openness, and the correlation is mainly

explained by the AES subcategory; individuals with a high degree of AES also have a higher degree of Openness to experience (Bröhl et al., 2020; Greven et al., 2019a; Lionetti et al., 2019a; Listou Grimen & Diseth, 2016; Smolewska et al., 2006). However, the relationship between SPS and Openness to experience has only been found among adults and not in children (Lionetti et al., 2019). This may suggest a developmental aspect of the SPS trait, as suggested by Aron and Aron (1997), which has not been properly investigated.

Based on the studies referred to in this section, it has been inferred that Openness, related to the subcategory of AES, seems to indicate the ability to enjoy pleasant stimuli to do with processing expressions of art and aesthetics, thus suggesting a positive and pleasurable dimension within the SPS construct (Greven et al., 2019). On the other hand, the trait Neuroticism has been associated mainly with EOE and, to a lesser degree, LST, and as such suggesting dimensions within the SPS construct that entail more challenging aspects and the processing of negative stimuli (Ibid). This, in turn, may lead to the experience of being overwhelmed by stimuli and the need to withdraw (Smolewska et al., 2006; Sobocko &

Zelenski, 2015). Also, the opposite pole of Extraversion seems to correlate less consistently with SPS across studies than Neuroticism and Openness. The association between SPS and these three personality traits has never, to the best of my knowledge, been explored in relation to social preference, or motivations for social withdrawal. In the following I will review preferences for social withdrawal, and how such behaviors hypothetically and empirically relate to SPS.

1.4 SPS, preference for social withdrawal and personality

Some personality traits, such as Neuroticism and the opposite pole of Extraversion, are known to contribute to socially withdrawn behavior, and popular language sometimes reflect this conceptual overlap when referring to shyness and introversion as the same construct (Balsari-Palsule & Little, 2020). The relationship between preference for social withdrawal, Neuroticism and Extraversion will be briefly outlined below.

Shyness is a subcategory of preference for social withdrawal. Shyness is also understood as a temperamental trait, and thus a potential building block for the traits Neuroticism and low Extraversion (Martin & Lease, 2020). Both Neuroticism (Soto & Tackett, 2015) and shyness (Martin & Lease, 2020) are empirically linked to anxiety and negative social outcomes for children and young adults (Nelson, 2012; Rubin & Coplan, 2010b). This relatedness of Neuroticism and shyness is also reflected in popular measures of personality, such as the Big Five Inventory. Van Zalk, Lamb and Rentfrow (2017) showed that a composite measure made from items in the subfacets Assertiveness from the Extraversion scale and Anxiety from the Neuroticism scale, were highly correlated with scores on the other scales measuring shyness, such as the Revised Cheek and Buss Shyness Scale (RCBS; Cheek, 1983; Van Zalk et al., 2017). Among social preference subcategories, high Neuroticism has been empirically associated with high shyness. Zelenski, Sobocko and Whelan (2014) found that Neuroticism predicts unhappiness much more strongly than mere unsociability or preference for solitude, and it is often found to be associated with anxiously shy individuals more so than individuals who are unsociable or prefer solitude (Zelenski et al., 2014). Furthermore, youth high in Neuroticism are more at risk for internalizing problems (Martin et al., 2015), as are shy youth (Bowker & Rubin, 2009; Karevold et al., 2012; Martin et al., 2020; Nelson, 2012). However, Neuroticism is considered an underlying personality dimension in a multitude of both

externalizing and internalizing behavioral problems that may manifest in the developmental period from childhood to young adulthood (Martin et al., 2020). Consequently, the

association between Neuroticism and preference for social withdrawal is not a simple one, as illustrated by animal research; it seems that capuchin monkeys high in Neuroticism and sociability had higher quality relationships than capuchins with differing personalities, and that monkeys high in Neuroticism and low in sociability tended to avoid social relationships in general (Balsari-Palsule & Little, 2020). Neuroticism, then, overlaps with preference for social withdrawal in general, and more specifically with shyness, and represents a potent confounding factor in the study of preference for social withdrawal, as with the study of SPS.

Introverts, with low levels of Extraversion, are found to be prone to spend more time in solitude (Zelenski et al., 2014) and are known to be more quiet, reserved, and introspective, tending to have needs to withdraw from social interaction to recharge (Balsari-Palsule &

Little, 2020). As such, there is a clear association with individuals high in SPS, who also need to withdraw from time to time in order to prevent negative effects of overstimulation

(Acevedo et al., 2021; Aron & Aron, 1997). Neuroticism and low Extraversion combined would most likely cause greater vulnerability for social withdrawal and internalizing difficulties (Soto & Tackett, 2015), and Zelensky et al. (2014) point out that the neurotic introvert tends to overlap with constructs such as sensitivity, social anxiety, shyness and loneliness (Zelenski et al., 2014).

In terms of SPS, empirical evidence seems to suggest that the SPS construct, and more

specifically the subcategory of EOE, is more strongly linked to negative social and behavioral outcomes (Greven & Homberg, 2020; Liss et al., 2008; Liss et al., 2005a) due to the tendency to be more easily overwhelmed by inner and outer stimuli, and as such may explain the need to withdraw socially. However, Neuroticism and Extraversion, are, based on empirical literature, possible confounding factors in the study of SPS and preference for social withdrawal, and are empirically linked to SPS subcategories, as well as to shyness, avoidance, and isolation. Therefore, high Neuroticism and low Extraversion, as well as depression and anxiety, may confound the relationship between SPS and preference for social withdrawal, and especially with regards to shyness, avoidance, and isolation. In terms of preference for social withdrawal, unsociability is less associated with negative social outcomes. Yet, the unsociable individual prefers to be solitary. Unsociability may therefore also be associated with EOE because of the preference to be alone. The role of Extraversion and unsociability is less clear. The avoidant individual is uncomfortable in social situations and may therefore also be expected to be the associated with EOE. SPS may help explain the discomfort of the avoidance strategy.

Finally, both SPS and preference for social withdrawal have been empirically linked to internalizing difficulties in youth, with the potential behavioral outcomes of depression and anxiety. SPS has been empirically linked to the behavioral outcomes of anxiety and

depression (Liss et al., 2008; Liss et al., 2005b; Neal et al., 2002.), and also overlaps with several facets indicative of depression and anxiety in the Big Five personality traits (Bröhl et al., 2020). Aron and Aron (1997) suggest that individuals high in SPS, and the related

tendency to be over-aroused, will naturally be more prone to anxiety and depression as adults (Aron & Aron, 1997). Anxiety and depression are similarly empirically linked to behavioral outcomes related to a preference for social withdrawal, such as shyness (Coplan & Armer, 2007b; Coplan et al., 2014), social avoidance (Coplan et al., 2018) and isolation (Rubin &

Coplan, 2004), whereas unsociability has been linked to higher levels of depression (Nelson,

2012). Empirical evidence on high SPS (Aron et al., 2005; Liss et al., 2005a), preference for social withdrawal (Bowker et al., 2017a; Coplan et al., 2015; Coplan et al., 2004a) and some personality traits (Martin et al., 2020; Soto & Tackett, 2015), suggest a greater vulnerability for negative social outcomes in adolescence and young adulthood. The complexity of social demands increasingly placed on the individual during the first two decades of life, suggests that a propensity for sensitivity of inner and outer stimuli, as well as a preference for social withdrawal, may entail greater challenges for social adjustment during all developmental phases, including early adulthood. Anxiety and depression may be the result of SPS and preference for social withdrawal, and as such may introduce bias in the models when they are controlled for (i.e., collider bias; Cole et al. 2010). Anxiety and depression may also be mediators in the association between SPS and social preferences (e.g., SPS increases anxiety and depression, which again increases preferences for being alone). Anxiety and depression will therefore only be included in the model in a last step, after analyzing the associations between SPS and social preferences without anxiety and depression. This allows comparing results from models with and without anxiety and depression and then discussing different possibilities if estimates are clearly changed after including anxiety and depression.

Gender, as well as cultural expectations, may also play a role. Aron and Aron (1997) found that women scored higher than men on the HSPS, indicating higher sensitivity in women.

However, they understood this pattern of gender-based response as perhaps reflecting a Western cultural ideal for men to be less sensitive (Aron & Aron, 1997). Nevertheless, this bias negatively influenced the statistical significance of the HSPS when gender was used as a control variable in groups of young adults and adolescents (Ibid). Of course, the higher participation of women over men in these studies may have influenced the results. Given the previous associations between SPS, age and gender, this study will control for both.

See Figure 1 for illustrated relations between SPS and social preference subscales, including the potential confounding variables Extraversion, Neuroticism, Openness, depression, and social anxiety.

Figure 1: Predictor variables to the left (EOE=Ease of Excitation, AES=Aesthetic Sensitivity, LST=Low Sensory Threshold). Outcome variables to the right (UNS=Unsociability, ISO=Isolation, SHY=Shyness, AVD=Avoidance). Confounding variables are placed below (N=Neuroticism,

E=Extraversion, O= Openness, ANX=social anxiety, DEP=depression). Age and gender may also be confounders but are not included in the figure for simplicity.

1.6 The Present study

Given the conceptual relatedness and overlap between the SPS construct and the subcategories of social preference, the aim of this thesis was to explore the relationship between SPS and preference for social withdrawal, which may contribute to understanding the different motivations for socially withdrawn behavior and its relatedness to SPS. As the HSPS has shown inconsistent factor structure and week factor loadings across several studies, the current study also investigated the factor structure of a Norwegian version of this

inventory measuring SPS in a group of young adults.

The theories presented regarding SPS (EOE, AES, and LST) and preference for social withdrawal (shyness, isolation, avoidance, and unsociability) provide valuable information concerning how individuals’ sensitivity may be associated with socially withdrawn behavior.

Considering the empirical and theoretical findings, the general hypotheses that will be tested are described below:

EOE AES

LST

UNS

AVO

SHY

ISO

N E O ANX DEP

Hypothesis regarding SPS:

Hypothesis 1. The HSPS will be accounted for by a three-factor structure comprising EOE, AES, and LST. The three-factor structure is the most recent theoretical model of SPS and is therefore seen as a useful base from which to investigate the HSPS in the current thesis.

Hypotheses regarding SPS and preference for social withdrawal:

Hypothesis 2. The HSPS is associated with social withdrawal, i.e., highly sensitive people will tend to withdraw socially more than less sensitive people. The preference for solitude dimension (unsociability and avoidance) of preference for social withdrawal is expected to be more associated with SPS than the dimension of preference for sociability (shyness and isolation).

The three factors of the HSPS are expected to differ in their relationship with different preferences for social withdrawal. The EOE sub-scale captures a person’s vulnerability for feeling stressed and overwhelmed by inner and outer stimuli. Thus, a person’s degree of EOE may contribute to his/her motivation for withdrawing socially. It is therefore expected that EOE relates to the two preferences for solitude subscale of social withdrawal (unsociability and avoidance). LST captures sensitivity to sensory stimuli such as loud noises, strong tastes, and violent visual stimuli. It is less clear how this factor is expected to be related to social preferences, and thus the relation will be explored. The third HSPS factor, AES, is expected not to be related to different preferences for social withdrawal, as this factor is associated with processing pleasant stimuli, such as art and music, and thus precluding the need to withdraw from such settings.

Hypothesis regarding SPS, preferences for social withdrawal and the Big Five personality traits:

Hypothesis 3. Different preferences for social withdrawal are associated with SPS even when personality traits are accounted for.

The personality trait of Neuroticism is considered an underlying personality dimension for externalizing and internalizing behavioral problems in young adulthood and is therefore expected to be positively associated with all social preference outcome variables. High

Neuroticism has also been associated with all HSPS factors, and especially EOE and LST.

Hence, there is a need to study the degree to which EOE and LST are associated with

preferences for social withdrawal over and above what is accounted for by high Neuroticism.

Extraversion may be expected to be associated with all types of preference for social withdrawal, but in a negative directionality. Low Extraversion is related to increased neural activation in social settings, as is EOE, and to a lesser extent, LST. Considering the expected contribution of EOE to the two preferences for solitude sub-scales of social withdrawal (unsociability and avoidance), this association is expected to uphold even when controlling for Extraversion. It is less likely that Openness to experience may contribute to social withdrawal, as this trait seems to be associated with the more pleasant aspects of SPS.

Hypothesis regarding preferences for social withdrawal, SPS, depression and social anxiety:

Hypothesis 4. Different preferences for social withdrawal are associated with SPS even when depression and social anxiety are accounted for.

Considering the added association between preferences for social withdrawal, social anxiety, and depression, then additionally controlling for depression and social anxiety may offer a more stringent test of the relationship between SPS and social withdrawal. Investigating these relationships would be an important contribution to the soundness of the SPS construct, as well as the relationship between SPS and preferences for social withdrawal. The association between EOE, AES and LST with different personality traits are expected to uphold, even when adding social anxiety and depression. If the associations are changed, anxiety and depression may have acted as confounders, mediators, or colliders.

2. Methods

The present study is part of an international study managed by Professor Robert J. Coplan at the University of Ottawa, in cooperation with different universities around the world,

including China, USA, Turkey, India, Argentina, Italy, Australia and Korea. The main study is entitled Beliefs About Social Withdrawal, which examined how young adults think about

shy behavior and investigating how this relates to adjustment outcomes. The Norwegian sub- study is managed by Associate Professor Evalill Bølstad, University of Oslo.

2.1 Participants

244 participants were recruited from the University of Oslo (UiO) and Inland Norway University of Applied Sciences (INN) through visit to lectures, e-mail, flyers, and the course credit program SONA. Inclusion criterion was enrolment in first- or second-year courses in psychology at either university. There were restrictions of age or gender. Demographics included gender (female: n=192, male: n=52), ethnicity (Caucasian: n=182, other: n= 61), year at university (median=2 years), and age group (17- 21years: n=110; 22-26 years: n=87;

and 27+ years: n=45). Data from INN was collected and added towards the end of the data collection. Hence, the demographic “which university do you belong to” was included (INN:

n=12).

2.2 Procedures

The collection of data was done from March 15th, 2018, until December 31st, 2018, through online questionnaires. The survey took approximately 30-45 minutes to complete. Before participating in the study, participants first viewed an information letter. Consent was indicated by clicking “next” on the survey. Participants were then presented with the questionnaire. A withdraw button was present on each page of the survey, providing the opportunity to discontinue participation at any time. Clicking the withdraw button led participants directly to the debriefing letter, notifying participants that there were no consequences for withdrawing, and that withdrawn data would be deleted. No participants withdrew from the present study. After completing the questionnaire, participants were directed to a debriefing letter that described the purpose of the study and contact information., Participants eligible for SONA were given a SONA-code to get course credits for their

participation in the survey. Further, participants were informed of the opportunity to sign up to win universal gift cards in the information letter, as well after the debriefing letter.

Remuneration. Students eligible for SONA received points for participation (four quarters).

All participants could win one of five universal gift cards, with a total value of NOK 1000, -.

Confidentiality. The participants who wanted to participate in the gift card contest submitted their e-mail addresses. No other sensitive information was collected, e.g., names and IP- addresses. Information linking the emails with the data were discarded. Further, e-mails were kept until the gift cards were distributed. There were no foreseen risks for identification data in the present study. Data was collected through “Nettskjema” at University of Oslo, which is a secure online survey website. Data were stored in TSD, the Service for Sensitive Data at the university, which uses updated firewall and encryption technology to protect private

information. Participants choosing to leave their e-mail addresses were directed to another URL to ensure that there would be no linkage between the participant’s answers and e-mail addresses. This information was disclosed in the informed consent and debrief letters. When a satisfactory sample size was obtained, data from the questionnaire were transferred from Nettskjema to Microsoft Excel and stored in a password- protected computer. The data in the present study is confidential and available only to students writing their thesis on this topic, as well as researchers and collaborators abroad, who are associated with this project.

Questionnaires were used to gather information for data analysis only. All personal information will be destroyed after 5 years.

2.3 Ethics

The study and thesis received ethical approval from the internal review board at the Department of Psychology, University of Oslo (IRB-number 2795534). The Norwegian Centre for Research Data (NSD) and the Regional Committees for Medical and Health Research Ethics (REK) were contacted prior to data collection, and both replied that their approval was not necessary.

2.4 Measures

The study was based on a self-report questionnaire containing ten scales. A copy of the complete survey in Norwegian is attached. Participants were asked to submit demographic information, and asked to complete scales of their sensitivity, personality traits, preference for social withdrawal, beliefs about social withdrawal, loneliness, difficult thoughts and emotions, shyness, satisfaction with life, depression, and social anxiety. The present thesis focuses on five of these ten scales (The Highly Sensitive Person Scale, The Social Preference Scale-Revised, The Big Five Inventory, The Depression, Anxiety and Stress Scale, and the Short Mood and Feeling Questionnaire).

2.4.1 Highly Sensitive Person Scale

To assess SPS, a translated version of the Highly Sensitive Person Scale (HSPS; Aron &

Aron 1997) was utilized. This scale consists of 27 items, each rated from 1 (Strongly disagree) to 5 (Strongly agree). Students were instructed to state their agreement to items describing various aspects of thoughts, feelings, and behavior that a person may have. The items in this scale reflect various aspects of sensitivity, and operationalizes three subscales of sensory processing sensitivity: 1) Ease of Excitation (EOE, e.g. «Do you find it unpleasant to have a lot going on at once?”), and 2) Aesthetic Sensitivity (AES, e.g. “Do you notice and enjoy delicate or fine scents, tastes, sounds, works of art? »), and 3) Low Sensory Threshold (LST, e.g. “Are you bothered by intense stimuli, like loud noises or chaotic scenes”). The scale was translated to Norwegian and back-translated to English by two different persons fluent in English, followed by a discussion about the differences to ensure the most accurate translation. Due to technical difficulties when constructing the online questionnaire, the study included an extra item in the HSPS. The extra item was deleted from the study before

analysis was performed. Due to this, the numbering of some items in the HSPS in the present study differs from the numbering of items in other studies. Cronbach’s alpha is a measure of internal consistency, showing how closely related a set of items are as a group. The internal consistencies for the mean SPS subscales were all acceptable: Ease of excitation (13-items; α

= 0.86), Aesthetic sensitivity (6-items; α = 0.76), and Low sensory threshold (8-items; α = 0.86).

2.4.2 The Social Preference Scale-Revised

Participants completed the 21-item Social Preference Scale-Revised (SPS-R; Bowker & Raja, 2011), which was revised from the Child Social Preference Scale (Coplan et al., 2004a). The scale has been validated in a sample of young Norwegian adults (Braathu, 2019). The scale contains four subscales of social withdrawal: 1.Shyness (e.g. “Sometimes I turn down

chances to hang out with others because I feel too shy”), 2. Unsociability (e.g. “I don’t have a strong preference for being alone or with others”, 3. Isolation (e.g. “Sometimes others don’t want me to hang out with them”), and 4. Avoidance (e.g. “I try to avoid spending time with other people”). Participants rated items according to what describes them best on a 5-point scale (1=Not at all to 5= A lot). The scale was translated to Norwegian and back-translated to English by two different individuals fluent in English, followed by a discussion of the

different translations to ensure the most accurate translation. Internal consistencies for social preferences were satisfactory, although a little low for the Unsociability subscale:

Unsociability (3-items; α = 0.63), Avoidance (4-items; α = 0.84), Isolation (4-items; α = 0.90), and Shyness (5-items; α = 0.87).

2.4.3 The Big Five Inventory

Personality traits were assessed with The Big-Five Inventory (BFI; John & Srivastava, 1999), which consists of 44 items designed to measure Openness, Neuroticism, Extraversion,

Conscientiousness, and Agreeableness. The version used in this thesis was translated to Norwegian and validated in samples of young adults and adults (Engvik & Føllesdal, 2005.).

Respondents indicated their relative agreement to items describing thoughts, feelings and behavior thought to reflect individual differences in personality on a 5-point scale (1=Not at all to 5= A lot). This thesis will focus on three personality traits in the Big Five Inventory:

Neuroticism, Extraversion and Openness. However, due to the novelty of the research question, the statistical analyses presented below will include all Big Five traits. The internal consistencies for the mean Big Five Inventory subscales were also acceptable: Extraversion (8-items; α = 0.86), Neuroticism (8-items; α = 0.86), Openness (10-items; α = 0.84),

Agreeableness (9-items; α = 0.68), and Conscientiousness (9-items; α = 0.84).

2.4.4 Short Mood and Feeling Questionnaire

To measure depression the Short Mood and Feeling Questionnaire (SMFQ; Angold et al., 1995) was used. The version used in this thesis has been translated, back-translated and validated in an independent sample of Norwegian youths (Lundervold et al., 2013; Olsen, 2015; Sund et al., 2001). The scale contains 13 items (e.g., “I didn’t enjoy anything at all”) using a 3-point scale (1=Not true to 3=True). In the present study, two items from the long version of the Mood and Feeling Questionnaire (MFQ: Angold et al., 1987; «I felt that the future had nothing positive to offer me», «I thought that life was not worth living») were added to get a broader aspect on cognitive symptoms of depression. The in total 15 items measure affective, physiological, and cognitive components of depression. In the current study, internal consistency was excellent (15-items; α=0.92).

2.4.5 Depression, Anxiety, Stress Scale (DASS)

The DASS is a 42-item self-report instrument designed to measure the three related negative emotional states of depression, anxiety and tension or stress (DASS; Lovibond & Lovibond, 1995). In this thesis, only the Anxiety scale has been utilized. The anxiety scale consists of 14 items and assesses autonomic arousal, situational anxiety, and subjective experience of

anxious affect (University of New South Wales, 2018). One item (“I found myself in

situations that made me so anxious I was most relieved when they ended”) was excluded due to a technical issue with the online questionnaire. Two items were added from Mini-SPIN, a brief scale on generalized anxiety disorder (Connor et al., 2001) in order to capture a fuller range of symptoms for social anxiety («Fear of embarrassment causes me to avoid doing things or speaking to people», « I avoid activities in which I am the center of attention »).

Subjects were asked to use a 4-point severity and frequency scale (1=Do not apply to me at all to 4=Apply to me very much, or most of the time) to rate the extent to which they have experienced each state over the past week. Internal consistency was excellent (15-items;

α=0.93).

2.5 Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics, V.27 (IBM Corp.) and

RStudio V.1.4.1717 (RStudio Team, 2020), with the packages Lavaan (Rosseel, 2012), Psych (Revelle, 2021) and EFA tools (Steiner & Grieder, 2020). To validate the Highly Sensitive Person Scale in a Norwegian population, exploratory and a confirmatory factor analysis were conducted. Then, Structural equation modeling (SEM) was used to investigate the

relationship between predictor and outcome variables, and potentially confounding variables.

2.5.1 Exploratory factor analysis

An exploratory factor analysis (EFA) was performed to explore the factor structure of the HSPS. To extract the number of factors, a Principal Components analysis (PCA) was used in this analysis. A PCA gives weighted combinations of the items and aims to account for their variance (Furr, 2011). Thus, the components are derived from the actual items (DeVellis, 2012). In a Common factor analysis, on the other hand, the composites represent hypothetical variables, the aim being to obtain an estimate of these variables (Ibid). Thus, in a common factor analysis one assumes that there are latent phenomena that affect the way people

respond to items, whereas in a PCA one is more interested in seeing what items correlate with one another (Ibid).

There are many ways to decide on how many factors to extract, such as eigenvalues, scree tests and parallel analysis (Ibid). The eigenvalue-rule is based on the absolute eigenvalues as a criterion to select factors, excluding factors with eigenvalues less than one. It is often judged as the most inaccurate method of determining which factors to retain (DeVellis, 2012;

Furr, 2011). The scree test plots the relative eigenvalues associated with the factor analysis with descending magnitudes; the recommended amount of factors are represented by magnitudes above the flattening point of the curve (DeVellis, 2012; Furr, 2011). A parallel analysis on the other hand, creates several random datasets, each with the same numbers of observations and variables as the original data, and then computes eigenvalues. If the

eigenvalues from the random data are larger than the eigenvalues from the factor analysis, the factors may be excluded from further analysis (UCLA Institute for digital research and

education, 2021). A consensus has developed in the scientific literature supporting parallel analysis as among the most accurate methods for deciding how many factors to retain, as it has been shown to give the most accurate estimate (Glorfeld, 1995; Lance at al., 2006;

DeVellis, 2012). Requirements for running a parallel analysis is that there must be correlations between independent variables. The data should also be free of outliers.

Consequently, parallel analyses were used to determine the number of factors. Parallel

analyses were developed for PCA, which do not assume underlying latent factors. When used with common factor analysis, too many factors tend to be extracted (DeVellis, 2012). Hence, parallel analysis with PCA was used in the current thesis.

An oblique rotation was then performed, allowing the factors to correlate. Parallel analyses and factor rotation were done in RStudio (RStudio Team, 2020), with the packages Psych (Revelle, 2021) and EFA tools (Steiner & Grieder, 2020).

2.5.2 Confirmatory factor analysis

Confirmatory factor analysis (CFA) was then run to test hypothesis about the HSPS. In EFA, each item is allowed to load on several factors. CFA models were constructed where each item only loaded on the factor where it had its highest loading in the EFA. Hence, the CFA provides a simpler factor model than EFA. The fit of the CFA model to the data is relevant as

each item is only included in one sub-scale when sum-scores are made. CFA evaluates the factor structure as well as facilitating model testing and revision (Furr, 2011). The fit of the CFA models was assessed by chi-square values, the Comparative Fit Index (CFI), the Root Mean Square of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR). A chi-squarestatistic is a test that measures how a model compares to actual

observed data and is often used when testing hypothesis (Hayes, 2021). CFI examines the discrepancy between the hypothesized model and the data (Costa et al., 2019). The RSMEA is an absolute measure of fit, and it is defined as the standardized difference between the observed correlation and the predicted correlation (Kenny, 2020). The SRMS is an absolute measure of fit and is the difference between the observed correlation and the predicted

correlation (Ibid). CFA models were run with the Lavaan package (Rosseel, 2012) in RStudio (RStudio Team, 2020).

2.5.3 Structural equation modeling

Next, SEM were run in Lavaan (Rosseel, 2012) to examine the associations between the HSPS sub-scales and social preference sub-scales. This was first done controlling for age and gender without controlling for the Big Five personality traits, followed by a SEM including the Big Five, and then adding DASS and SMFQ. Anxiety and depression may be results of SPS as well as social preferences, and controlling for anxiety and depression may thus result in biased estimates (i.e. collider bias). Hence, they were not included until the last model, thus providing results with and without anxiety and depression in the model.

SEM refers to a family of statistical techniques used to analyze the structural relationship between measured variables and latent variables (Kline, 2015). SEM can use all available data by using the full information maximum likelihood (FIML) estimator, which is generally advised over listwise deletion (Cham et al., 2017; Enders, 2001). In SEM, several

independent and dependent variables can be included in the same model and several

regression analysis may be run simultaneously (Tarka, 2018). SEM also allows constructing latent variables; however, this increases the number of estimated parameters in a model. SEM may be used to provide empirical support for theory, and may not be used for inferences of causality (Kline, 2015; Tarka, 2018).

Indicators of model fit used in the SEM analyses are, as in the CFA, chi-square tests, baseline fit indices (SRMR and RMSEA) and close-fit indices (CFI). The chi-square test is an

adequate measure of fit for smaller samples of 2-300 (Kenny, 2020; Klein, 2015). The

standardized root mean square residual (SRMR) is used to assess the exact fit of SEM models (Ibid), and has proven to provide accurate Type I errors in SEM models (Pavlov, 2021;

Kenny, 2020). The RMSEA is a measure of the model fit relative to the population

covariation matrix, and the complexity of the model is considered (Cangur & Ercan, 2015).

Comparative Fit Index (CFI) is a measure of the fit of a SEM model relative to the null model (Kline, 2015; Kenny, 2020). Recommended cut-offs that indicate a good fit for validation are CFI ≥.90, SRMR<.08, RMSEA<.08 (Kline, 2015; Kenny, 2020).

Multicollinearity occurs when two or more independent variables are highly correlated and may undermine the statistical significance of an independent variable. The correlation matrix was examined, and collinearity tolerance and variance inflation statistics (VIF) were tested in IBM SPSS Statistics, V.27 (IBM Corp.). Correlations should be under .80, VIF<10, and tolerance>.10 (Braunstein, 2007).

3. Results

3.1 Descriptives

Table 1 shows descriptive statistics for the mean scores of the SPS factors (EOE, AES, and LST), the social preference factors (unsociability, isolation, shyness, and avoidance), the Big Five Inventory traits (Neuroticism, Extraversion, Conscientiousness, Agreeableness, and Openness to experience), social anxiety and depression. These statistics show that data have acceptable skewness and kurtosis values.

Table 1: Descriptive Statistics for SPS factors, social preference factors, Big five inventory factors, anxiety, and depression (EOE=Ease of Excitation, AES=Aesthetic Sensitivity, LST=Low Sensory Threshold, UNS=Unsociability, ISO=Isolation, SHY=Shyness, AVD=Avoidance. N=Neuroticism, E=Extraversion, O= Openness, ANX=social anxiety, DEP=depression).

Variable Median Mean Max Min Skew Kurtosis Std. Dev.

EOE 3.42 3.33 5.00 1.00 -.29 -.37 .77

AES 3.80 3.70 5.00 1.00 -.26 -.25 .76

LST 2.57 2.63 5.00 1.00 .36 -.58 .94

UNS 3.32 3.32 5.00 1.00 -.43 -.22 .85

ISO 1.25 1.67 4.25 1.00 1.32 .79 .85

SHY 2.20 2.45 5.00 1.00 .50 -.83 1.09

AVD 1.50 1.75 5.00 1.00 1.04 1.07 .74

E .57 .60 2.29 -1.57 -.19 -.57 .86

A 1.22 1.13 2.22 -.33 -.18 -.63 .54

C .89 .90 2.22 -1.00 -.14 -.67 .68

N .88 .85 2.75 -1.25 -.054 -.57 .84

O 2.50 2.43 3.80 .60 -.24 -.50 .72

SMFQDep 1.53 1.65 3.00 1.00 .94 .18 .49

DASSAnx 1.40 1.56 3.87 1.00 1.56 2.28 .61

To investigate the bivariate relations between all scales, a correlation analysis was performed as shown in Table 2. The results show that the SPS subscales (EOE, LST, and AES) were positively correlated. Of the subscales of SPS, EOE correlates positively with all four outcome variables, with moderate correlations with shyness (r=.44, p>.01) and

avoidance (r=.38, p>.01). AES, on the other hand, has negligible correlations with all social preference variables. LST has somewhat higher correlations with social preference variables than AES and shows a moderate association with avoidance (r=.32, p>.01). EOE correlates moderately with depression (r=.37, p>.01) and social anxiety (r=.46, p>.01), while LST correlations are somewhat lower and AES correlations are negligible.

As for the association between the SPS factors and the Big Five personality factors, Neur- oticism was positively correlated with EOE (r=.63, p>.01) and LST (r=.42, p>.01). EOE (r= -.47, p>.01) and LST (r= -.27, p>.01) were negatively correlated with Extraversion. AES was strongly and positively correlated with Openness (r=.60, p>.01), as well as being

significantly and positively correlated with Conscientiousness (r=.23, p>.01). As for the correlation between Big Five personality factors, depression and social anxiety, Extraversion was negatively correlated with depression (r= -.43, p>.01) and anxiety (r= -.42, p>.01), and Neuroticism was positively correlated with both depression (r=.61, p>.01) and social anxiety (r=.54, p>.01). There was no significant correlation between Openness, depression, and social anxiety.

Table 2: Pearson Correlation Matrix among subtypes of SPS (EOE=Ease of Excitation, AES=Aesthetic Sensitivity, LST=Low Sensory Threshold), subtypes of Social Preference (UNS=Unsociability, ISO=Isolation, SHY=Shyness, AVD=Avoidance) and Big Five Inventory (N=Neuroticism, E=Extraversion, O=

Openness), DASS and SMFQ (ANX=social anxiety, DEP=depression). N=244.

Variable AES LST UNS ISO SHY AVD E A C N O SMFQDep DASSAnX

EOE .311* .637* .235* .184* .443* .377** -.471* -.199* -.062 .634* .027 .374* .457*

AES .418* .141** -.020 .016 -.009 -.034 .079 .226* .074 .603* -.007 .161**

LST .160** .115 .261* .324** -.271* -.056 .089 .417* .187* .184* .261*

UNS -.061 .066 .427** -.288* -.111 .058 .016 .133** -.042 .065

ISO .573* .293** -.387* -.286* -.118 .306* .007 .495* .295*

SHY .508** -.700* -.313* -.282* .603* -.003 .589* .550*

AVD -.529* -.440* -.161** .434* -.002 .417* .361*

E .308* .130 -.527* .044 -.430* -.418*

A .288* -.271* .042 -.318* -.249*

C -.181* .047 -.213* -.131

N -.003 .607* .543*

O .017 .112

SMFQDep .630*

DASSAnx

Note: *p < .01. **p < .05 (2-tailed).