Person‐specific priorities in solitude

Abstract

Objective

People value solitude in varying degrees. Theories and studies suggest that people's appreciation of solitude varies considerably across persons (e.g., an introverted person may value solitude more than an extraverted person), and solitude experiences (i.e., on average, people may value some functions of solitude, e.g., privacy, more than other functions, e.g., self‐discovery). What are the unique contributions of these two sources?

Method

We surveyed a quota‐based sample of 501 US residents about their perceived importance of a diverse set of 22 solitude functions.

Results

Variance component analysis reveals that both sources contributed to the variability of perceived importance of solitude (person: 22%; solitude function: 15%). Crucially, individual idiosyncratic preferences (person–by‐solitude function interaction) had a substantial impact (46%). Further analyses explored the role of personality traits, showing that different functions of solitude hold varying importance for different people. For example, neurotic individuals prioritize emotion regulation, introverted individuals value relaxation, and conscientious individuals find solitude important for productivity.

Conclusions

People value solitude for idiosyncratic reasons. Scientific inquiries on solitude must consider the fit between a person's characteristics and the specific functions a solitary experience affords. This research suggests that crafting or enhancing positive solitude experiences requires a personalized approach.

1. INTRODUCTION

Solitude, the state of being alone without interacting with others, is a common experience (Larson, 1990). Although being alone has been often assumed to be a negative experience (e.g., loneliness; Hawkley & Cacioppo, 2010), emerging research suggests solitude can be rewarding, enjoyable, and functional (Coplan, Hipson, et al., 2019). In fact, people voluntarily seek solitude, considering it an important part of everyday life (Chua & Koestner, 2008; Lay et al., 2020; Long et al., 2003).

Not everyone sees solitude as equally important. Perceived importance of solitude (i.e., the extent to which people value being alone without interacting with others) may determine people's priorities, shape their solitary experiences, and guide their solitude‐seeking behaviors. For example, a person who does not recognize the importance of solitude may not carve out enough time alone for their own well‐being (Coplan, Hipson, et al., 2019). Prior work suggests that the perceived importance of a solitude experience varies considerably due to two sources. Some perspectives (Burger, 1995; Coplan, Ooi, et al., 2019) suggest that it varies by the person, such that the key differences in perceived importance of solitude are due to differences between people. Other perspectives (Long et al., 2003; Ost Mor et al., 2021; Wesselmann et al., 2021) highlight the varieties of functions solitude provides (e.g., relaxation, emotion regulation), such that the key differences in perceived importance of solitude are due to differences between the specific solitude functions. An open question is what are the relative contributions of these two sources of variance? Gaining a better understanding of this question is important because its answer will help guide researchers to focus on individual differences or the function of solitude experiences in their theorizing. This question has not been answered, in part, because researchers focus on the sources of variation (individual differences or the different functions of solitude experiences) separately. In this research, we assess sources of variation of perceived solitude importance. This approach has been applied in several research areas including person perception (Hehman et al., 2017), situation perception (Rauthmann & Sherman, 2019), and morally relevant attitudes (Kodapanakkal et al., 2021). Our central aim is to quantify the contributions of individual differences and solitude functions in variance of solitude perception, specifically, perceived importance of solitude.

1.1. The role of individual differences in perceived importance of solitude

Past work suggests that perceived importance of solitude is guided by the characteristics of a person. Some studies have focused on dispositional factors. For example, being relatively low on the Big Five personality dimension of Extraversion is often considered a highly relevant disposition in both the scientific literature (Burger, 1995; Leary et al., 2003; Tse et al., 2021) and the media (e.g., Sabatin, 2020). Another relevant disposition is the general preference for solitude (Burger, 1995). Preference for solitude is defined as the individual differences in the motivation for solitude. Similar constructs include unsociability (Coplan & Weeks, 2010) or the affinity for aloneness (Goossens, 2014). Additionally, one recent study showed that dispositional autonomy, defined as “the tendency to regulate from a place of self‐congruence, interest, and self‐volition” was linked with the extent to which people value time alone (Nguyen et al., 2018).

Other studies have focused on demographic variables. For example, researchers have examined age as a relevant factor (Coplan, Ooi, et al., 2019; Lay et al., 2020; Ost Mor et al., 2021). Older adults may consider solitude to be more important and functional than younger age groups, such as children and college students (Coplan, Ooi, et al., 2019). Consistent with this speculation, the link between desire for solitude and decreased positive affect was found among middle‐aged adults, but not older adults (Lay et al., 2020). Together, these studies support the perspective that individual differences are an important source of variation in perceived importance of solitude.

1.2. The role of solitude functions in perceived importance of solitude

Another source of variation in perceived importance of solitude is the function of solitude experiences. Solitude represents a general, broad experience. It is perhaps not surprising that solitude has a wide range of functions. Some studies focused on a particular function of solitude, such as coping with social exclusion (Ren et al., 2016, 2021) or emotion regulation (Nguyen et al., 2018). Other studies have attempted to develop a taxonomy for solitude experiences (Long et al., 2003; Ost Mor et al., 2021; Wesselmann et al., 2021) or simply document different types of solitary activities (Hipson et al., 2021). These studies revealed a variety of solitude functions. For example, emotion regulation, despite being referred to using different terms (e.g., a way to control stress, Ost Mor et al., 2021; meditation, Hipson et al., 2021) appeared to be an important function of being alone (Nguyen et al., 2018; Wesselmann et al., 2021). Concentration on cognitively demanding tasks (e.g., problem‐solving) is another commonly documented function of solitude experience (Hipson et al., 2021; Long et al., 2003; Ost Mor et al., 2021; Wesselmann et al., 2021). In addition, a number of other solitude functions were reported by participants such as relaxation, self‐reflection and discovery, and autonomy. Importantly, some solitude functions may be more valued than others. For example, in one study, college students rated problem‐solving as the most important function of solitude (M = 5.44, SD = 1.36; on a 1–7 scale) and spirituality (e.g., being close to one's God or faith) as the least important (M = 3.55, SD = 1.52; Long et al., 2003), showing that specific solitude functions drive variation in perceived importance of solitude.

1.3. Current research

As we have described above, past work suggests two theoretical perspectives on sources of variation in perceived importance of solitude. One perspective suggests that perceived importance of solitude is driven by individual differences. For example, some people value solitude more than others, and this individual difference is stable and generalizes across various functions of solitude. In contrast, the other perspective suggests that perceived importance of solitude is driven by the function of solitude experiences. For example, some functions of solitude experiences are more valuable than others, and there is some consensus regarding which functions are more important. Figure 1 provides a visual illustration of these two perspectives using hypothetical data (this figure is inspired by figures 1–3 in Kodapanakkal et al., 2021).

FIGURE 1.

Illustration of the two existing theoretical perspectives on variation in perceived importance of solitude (Kodapanakkal et al., 2021): Variation due to person (top panel) and solitude function (bottom panel). The x‐axis represents different solitude functions, the y‐axis represents importance ratings, and the colored lines represent different individuals. All data are hypothetical.

Which theoretical perspective is more useful for better understanding perceived importance of solitude? Put differently, how much does perceived importance of solitude arise from the characteristics of a person vs. the function of solitude? To understand this question (Question 1), we quantified the unique contributions of the two sources of variance—individual differences and solitude functions. We applied variance component analysis using cross‐classified models (Martinez et al., 2020) to address this fundamental question in solitude research. Note that as this was the first attempt to quantify the different sources of variance in perceived importance of solitude, we focused on data from one country (i.e., the US) in this research as a starting point. We acknowledge that cultural background is an interesting and important factor, and future research should consider collecting globally diverse samples.

In addition to the main research question of this project (as described above), we also sought to gain a richer understanding of the specific personality predictors of solitude importance ratings (Question 2). Past research has studied specific predictors in isolation. Here, we explored which personality variables, specifically among the Big Five personality dimensions, explain variation in the importance ratings of each solitude function. We chose to focus on the personality variables within the Big Five framework (instead of narrow traits outside of the Big Five framework) for two reasons. First, research suggests that the Big Five is an effective organizing framework for a range of psychological traits (Bainbridge et al., 2022; Goldberg, 1993; John et al., 2008). Second, solitude research is currently in its early stages. Using the Big Five framework allowed us to get a broad initial picture of how perceived importance of solitude is associated with individual difference variables. We encourage researchers to go beyond the Big Five framework and explore the relevance of narrow traits such as preference for solitude (Burger, 1995) and dispositional autonomy (Nguyen et al., 2018) in future research.

Below we first report analyses of two existing data sets as a pilot study. We then present a preregistered study which was designed to replicate the preliminary findings while addressing the limitations of the pilot study. All analyses were conducted using R (R Core Team, 2022). All research materials, data, and analysis scripts are publicly available at the Open Science Framework (https://osf.io/qn5zf/).

2. PILOT STUDY

We conducted secondary data analysis of two existing data sets. Both data sets were collected as part of the first author's PhD dissertation to answer a different research question (Ren, 2016).

2.1. Method

2.1.1. Participants

Both samples were drawn from mass prescreening of the undergraduate psychology participant pool at Purdue University. Sample 1 (2014 Spring) has a total number of 741 participants and Sample 2 (2014 Fall) has a sample of 801 participants. Sample characteristics are summarized in Table 1.

TABLE 1.

Sample characteristics (pilot study).

		Sample 1	Sample 2
Age	Years: M (SD)	19.53 (1.53)	19.06 (1.51)
	<missing>	0	1
Gender	Male	353	301
	Female	388	500
	<missing>	0	0
Ethnicity	Native Hawaiian/Pacific Islander	1	3
	Hispanic or Latino	29	19
	Native American/American Indian a	[not listed]	1
	Asian	129	117
	Asian Indian a	[not listed]	24
	Black or African American	23	22
	White, Caucasian, or European	546	595
	Other	8	10
	<missing>	5	10
International	No	631	680
	Yes	106	119
	<missing>	4	2
English as a native language	No	124	142
	Yes	617	659
	<missing>	0	0
Year in college	Freshman	353	448
	Sophomore	220	109
	Junior	99	178
	Senior	69	66
	<missing>	0	0
Relationship status	Not involved	408	476
	Involved	321	310
	<missing>	12	15

^a Two response options (native American/American Indian, Asian Indian) were not listed in the Ethnicity question in Sample 1.

2.1.2. Procedure and materials

Participants were brought into a laboratory and directed to individual cubicles to complete a prescreening survey consisting of several unrelated questionnaires on a computer.

Stimuli: Solitude functions

Participants were told that they would be presented with several descriptions about spending time alone. We explicitly defined “spending time alone” as “no social contact with anyone either in person or over electronic media, such as cell phones or the Internet.” The solitude descriptions included: (1) solitude as emotion regulation (“To spend time alone to deal with my negative emotions such as depression, anxiety, frustration etc.”), (2) solitude as avoiding unpleasant interaction (“To be alone so that I can avoid unpleasant social interactions.”), (3) solitude as concentration (“To be apart from other people to avoid distractions such as when I want to concentrate on schoolwork”), (4) solitude as relaxation (“To be by myself relaxing and recovering from exhaustion”), (5) solitude as self‐reflection (“To spend time alone to reflect on my own thoughts and feelings.”), and (6) solitude as independence (“To be away from people to seek independence and autonomy”). These six solitude functions were selected based on the common functions of solitude identified in previous research using college student samples (Long et al., 2003; Wesselmann et al., 2014).

Outcome measure: Importance ratings

After reading each solitude description, participants reported to what extent they value this function of solitude (e.g., “how important is it for me to spend time alone to deal with my negative emotions?”) on a 7‐point scale (1 = not at all, 7 = extremely). Each participant rated all six functions of solitude experiences.

Personality predictors

Personality predictors were measured using the Five‐Factor Model Rating Form (Samuel et al., 2013). This is a brief Big Five personality measure that assesses both the five broad dimensions and the lower‐order personality facets proposed by Costa and McCrae (1995). Each of the five dimensions consisted of six facets (30 facets in total; for example, gregariousness, trust, self‐discipline) with each facet measured using one item. Each item was anchored at both the low and high ends by a set of 2–3 adjectives (e.g., gregariousness: withdrawn, isolated vs. sociable, outgoing; 1 = extremely low, 2 = low, 3 = neither high nor low, 4 = high, 5 = extremely high).

Demographics/background predictors

Participants reported their age, gender (male, female ¹ ), ethnicity (Native Hawaiian/Pacific Islander, Hispanic or Latino, Native American/American Indian, Asian, Asian Indian, Black or African American, White, Caucasian, or European, Other; recoded as White, Caucasian, or European = 0, all other options = 1), ² International student (no, yes), English language as the native language (no, yes), year in school (Freshman, Junior, Senior, Sophomore), and current relationship status (romantically involved: yes, no).

2.2. Results

Figure 2 provides a visual presentation of variation in perceived importance of solitude for each of the six solitude functions. Following past work (Martinez et al., 2020), we examined the correlational patterns of the ratings by calculating interrater correlations. We computed pairwise Pearson correlations between raters (i.e., participants). ³ On average, the interrater agreement was low in both samples (Sample 1 average r = 0.129, SD = 0.453; Sample 2 average r = 0.140, SD = 0.450), showing that there was little consensus in which solitude functions were perceived to be the most important.

FIGURE 2.

Variation in perceived importance of solitude across solitude functions (pilot study). The x‐axis represents the six solitude functions and the y‐axis represents participants’ importance ratings. The colored lines represent different individuals randomly selected from each sample. The solitude functions are arranged from left to right in descending order of mean importance ratings in a merged dataset consisting of both samples. Overall, we observed that (a) there was variation across the solitude functions (i.e., some functions were more valued than others), and (b) there was variation across individuals (e.g., participant 268 valued solitude more than participant 142). We also see that people value different solitude functions differently. This source of variation is examined in the preregistered study.

2.2.1. Question 1

To what extent do individual differences and differences between solitude functions explain variation in perceived importance of solitude?

Analytical approach

We conducted variance component analysis using intercept‐only cross‐classified multilevel models (importance ratings were cross‐classified by individuals and solitude functions). This approach allowed us to quantify the contribution of each of the two sources of variation (individuals and solitude functions) to the variability of the outcome (perceived importance of solitude). We carried out the following steps:

1. We fit a cross‐classified multilevel model (see, e.g., chapter 7 of Hox et al., 2017), with the individual and the solitude function as the two random effects in the model. For example, such a model can be fitted using the lme4 package (version 1.1.23) in R (Bates et al., 2015) as follows:

   $$\mathrm{lme}4::\text{lmer}\left(\text{rating}~1+\left(1|\text{individual}\right)+\left(1|\text{function}\right),\text{data}\right)$$

2. We extracted the estimated variances of each random effect from the fitted model: individual ($s_{\text{individual}}^2$), solitude function ($s_{\text{function}}^2$), and the residual variance ($s_{\text{error}}^2$).

3. We calculated the intraclass correlation coefficient (ICC) for the individual random effect as:

   $$\frac{s_{\mathit{\text{individual}}}^2}{s_{\mathit{\text{individual}}}^2+s_{\mathit{\text{function}}}^2+s_{\mathit{\text{error}}}^2}$$
   Similarly, we calculated the ICC for the solitude function random effect as

   $$\frac{s_{\mathit{\text{function}}}^2}{s_{\mathit{\text{individual}}}^2+s_{\mathit{\text{function}}}^2+s_{\mathit{\text{error}}}^2}$$

The ICC for a particular component thus represents the proportion (or percentage after multiplying by 100%) contributed by that component to the total variance. This information allowed us to determine how much variance is attributable to individuals versus solitude functions. We further constructed 95% confidence intervals using model‐based parametric bootstraps for mixed models (Davison & Hinkley, 1997). For each bootstrap sample, steps 1–3, as described above, were carried out to calculate the ICCs for both components. We generated 10,000 bootstrap samples using the “bootMer” function in the “lme4” package (Bates et al., 2015), with all other options set at their default levels.

Findings

In Sample 1, individual differences accounted for 30.2% (95% CI = [25.8, 34.3]) of the total variance of importance ratings. Solitude functions accounted for 8.0% (95% CI = [1.4, 18.4]), which was much less than the variation due to individual variation (difference between these two components: 22.2%, 95% CI = [8.1, 31.9]). Variation due to the residual component was 61.7% (95% CI = [54.5, 67.5]), indicating that most of the variance was unexplained. This pattern of results was consistent in Sample 2. Individual differences accounted for 35.2% (95% CI = [30.4, 39.5]). Functions of solitude experiences accounted for 8.0% (95% CI = [1.4, 18.5]; difference between the two components: 27.2%, 95% CI = [12.4, 37.1]). The residual component accounted for 56.8% (95% CI = [50.1, 62.3]), indicating again that most of the variance was unexplained.

These results revealed three things. First, overall, across the solitude functions, individuals differed in how much they valued time alone. This is consistent with the theoretical perspectives that focus on the role of individual differences in perceived importance of solitude (e.g., Burger, 1995; Coplan & Weeks, 2010; see Figure 1: Top panel). Second, there was little consensus on which functions of solitude experience were perceived to be the most important. This finding is inconsistent with the theoretical perspectives that focus on the role of solitude functions in perceived importance of solitude (e.g., Long et al., 2003; see Figure 1: Bottom Panel). Finally, the substantial contribution of the residual component suggested that it would be worthwhile to consider estimating the interaction between individual and solitude function. More details regarding this interaction component are presented in the discussion section of this study. ⁴

2.2.2. Question 2

What personality predictors explain the variation in perceived importance of solitude?

Analytical approach

To gain a richer understanding of the specific personality predictors of solitude importance ratings, we explored whether or not personality variables explain variance in each of the six importance ratings (outcome variables) above and beyond demographic variables. We compared a model including a personality variable with a model without the given variable to evaluate the relevance of each Big Five dimension in turn (VanderWeele, 2017). ⁵ In our analysis, we merged the two samples and conducted our analyses using the merged dataset with a dummy coded variable indicating the sample. For each given personality dimension and each given outcome, we carried out the following steps:

1. We fit a linear regression model of the outcome. Predictors were the demographic/background variables and the sample variable (in total eight predictors).

2. We added a personality variable to the linear regression model.

3. We carried out an ANOVA comparing the two models above. Finally, we calculated the p‐value for the F‐test.

We repeated the steps above for all possible combinations of personality variables and outcomes (5 × 6 = 30 model comparisons). We used the false discovery rate approach (a more powerful approach compared with the Bonferroni adjustment; Benjamini & Hochberg, 1995) to adjust the p‐values for multiple comparisons. A significant adjusted p‐value (<0.05) indicates that a given personality variable explains variation in a given outcome. To inspect the direction of the effects (i.e., a positive effect or a negative effect; which cannot be inferred from p‐values), we will also summarize the unstandardized regression coefficients of the personality variables below. ⁶

Findings

All significant model comparison p‐values (after adjustment) are visualized in Figure 3, with darker colors corresponding to smaller p‐values. p‐values that are larger than the alpha level (0.05) are not visible (i.e., in color white). All unstandardized regression coefficients of the personality variables are visualized in Figure 4, with darker colors corresponding to larger estimates. Positive estimates are in the color red, and negative estimates are in the color blue. We observed three findings. First, all the personality dimensions emerged as relevant predictors of the outcome variables, with some dimensions (Extraversion, Neuroticism, and Conscientiousness) more relevant than other dimensions (Agreeableness, Openness). Second, dimensions that were relevant for one solitude function were not necessarily relevant to other solitude functions. For example, Conscientiousness was a relevant predictor of participants' ratings of solitude as concentration, but not solitude as emotion regulation. Finally, the same personality variable may have effects of opposite signs (Figure 4). For example, Agreeableness was positively related to importance ratings of solitude as self‐reflection, but negatively related to ratings of solitude as independence. This suggests that it is useful to quantify the interaction between individual and solitude function in variance component analysis.

FIGURE 3.

Model comparison p‐values (pilot study). We compared whether or not a model including a Big Five personality dimension explained more variation in each of the six importance ratings compared to a model without the given dimension. Model comparison p‐values are adjusted for multiple testing. Adjusted p‐values are visualized, with darker colors corresponding to smaller p‐values. p‐values that are larger than the alpha level (.05) are not visible (i.e., in color white). The x‐axis represents the personality dimensions and the y‐axis represents the solitude functions. The solitude functions are arranged from top to bottom in descending order of mean importance ratings.

FIGURE 4.

Unstandardized estimates of the Big Five dimensions (pilot study). For each of the six ratings and each personality dimension, we fitted models with predictors including the demographic/background variables, one variable indicating the sample, and one personality dimension. The unstandardized estimates of the personality dimensions are visualized, with darker colors corresponding to larger estimates. Positive estimates are in the color red, and negative estimates are in the color blue. Black boarders indicate significant model‐comparison p‐values (see Figure 3 for p‐values). The x‐axis represents the personality dimensions and the y‐axis represents the solitude functions. The solitude functions are arranged from top to bottom in descending order of mean importance ratings.

2.3. Discussion

We sought to quantify the contributions of individual differences and solitude functions in variance of people's perceived importance of solitude. Analyses of two datasets involving 1542 college students suggest that a solitude importance rating is guided more by the characteristics of a person, and less by the function of solitude experiences. We also explored which of the Big Five personality dimensions were relevant predictors of people's perceived importance of solitude. We found that in addition to Extraversion, which has often been considered a key predictor of perceived importance of solitude (e.g., Tse et al., 2021), Neuroticism and Conscientiousness are also important personality predictors. Building on these preliminary findings, we conducted a study to further explore people's perceptions of solitude. The new study was designed to address three limitations of the pilot study, as we describe below.

First, there was a substantial amount of unexplained variance. This might be because of measurement error, but it could also be due to missing variance components. One way to address both of these issues is to include repeated measures of each solitude experience. The repeated measures help reduce measurement error and give us the additional degrees of freedom necessary to estimate the variance due to a person × solitude function interaction component (Martinez et al., 2020). Without repeated ratings, the variance of the interaction component cannot be disentangled from the residual variance (Hehman et al., 2017; Martinez et al., 2020). This variance component represents more person‐specific idiosyncratic priorities. For example, if some people value having time alone to concentrate on their work, but not to cope with negative emotions, and other people value time alone to cope with their negative emotions, but not to seek autonomy, this variation would not be fully captured by either the person or the function variance component. Instead, this person‐specific and more idiosyncratic solitude priority are captured by the person × solitude function interaction component. See Figure 5 for a visual illustration (Kodapanakkal et al., 2021). Therefore, to reduce measurement error and estimate an additional variance component, participants in the new study rated each solitude function twice.

FIGURE 5.

Illustration of a missing variance component due to idiosyncratic solitude priorities (Kodapanakkal et al., 2021). The x‐axis represents different solitude functions, the y‐axis represents perceived importance ratings, and the colored lines represent different individuals. All data are hypothetical.

Second, one reason that we observed a relatively small contribution of solitude functions could be due to the limited stimulus set in the pilot data (n = 6 solitude functions). These solitude functions may not have adequately captured the range of solitude functions and only represented the functions where there was some degree of consensus among participants. Simulation studies have shown that the number of the stimulus set is essential for improving the precision of the estimates (Martinez et al., 2020). Thus, in our preregistered study, we expanded the functions of solitude experiences to cover a wider range of functions.

Finally, the pilot study used data from college students in the US. Given that age is a relevant factor in people's attitude toward solitude (e.g., Coplan, Ooi, et al., 2019), using young adult samples limits our confidence in the generalizability of our findings. Therefore, our preregistered study recruited a quota‐based representative sample of the US population.

3. PREREGISTERED STUDY

The study was preregistered at https://osf.io/eavq5/.

3.1. Methods

3.1.1. Sampling plan

We requested a representative sample of US residents on Prolific. Prolific use benchmarks from the US Census Bureau to stratify the requested sample size across three demographics: age (18 years and older ⁷ ), gender, and ethnicity (https://researcher‐help.prolific.co/hc/en‐gb/articles/360019238413). To determine sample size, we used past work on variance component analysis as guidelines. Simulation studies show that a minimum of 60 participants provide reasonable estimates, and a larger sample size improves the precision of the estimates (Martinez et al., 2020). For budget considerations (~2000 euros), we requested 500 participants on Prolific. Participants were paid at an hourly rate of 8.98 pounds. The survey lasted about 10 min. A total of 501 participants completed the survey. Based on the preregistered data exclusion criteria (i.e., participants who fail both attention checks, ⁸ or skip all items in blocks 1 and 2), no participants were removed from the analysis. The final sample size is 501 participants. ⁹ See Table 2 for sample characteristics.

TABLE 2.

Sample characteristics (preregistered study).

Age (years)	M = 45.79, SD = 16.36
	18–27	91
	28–37	87
	38–47	84
	48–57	83
	58+	156
	<missing>	0
Gender	Woman	253
	Man	240
	Gender non‐conforming	3
	Transgender	1
	Two‐spirit	0
	Another (please specify)	2
	I prefer not to answer	2
	<missing>	0
Ethnicity	Native Hawaiian/Pacific Islander	0
	Hispanic or Latinx	27
	Asian, Black or African American	95
	White, Caucasian, or European	370
	Other	9
	<missing>	0
Education	Less than a high school degree	7
	High school graduate	52
	Some college but no degree	118
	Associate degree in college	68
	Bachelor's degree in college	177
	Master's degree	65
	Doctoral degree/Professional degree (JD, MD)	13
	<missing>	1
Political orientation	M = 5.02, SD = 3.36 (1 = extremely liberal, 10 = extremely conservative)
Religion	Agnostic	82
	Atheist	73
	Hindu	3
	Buddhist	9
	Muslim	7
	Jewish	11
	Orthodox such as Greek or Russian Orthodox	5
	Mormon	1
	Roman Catholic	57
	Protestant	125
	Not listed	46
	Nothing in particular	81
	<missing>	1
Employment	Employed	297
	Unemployed	70
	Student	28
	Retired	77
	Other	29
	<missing>	0
Income (12‐month household income)	Less than $10,000	32
	$10,000 – $19,999	60
	$20,000 – $29,999	51
	$30,000 – $39,999	42
	$40,000 – $49,999	46
	$50,000 – $59,999	54
	$60,000 – $69,999	28
	$70,000 – $79,999	28
	$80,000 – $89,999	26
	$90,000 – $99,999	70
	$100,000 – $149,999	32
	More than $150,000	1
	<missing>	1
Relationship status	Married/living together with a partner	256
	In a steady relationship but not living together	36
	Dating someone	16
	Single	192
	<missing>	1
Living situation	Living alone	114
	Living with others	387
Perceived covid threat	Threat to the self: M = 2.76, SD = 1.98
	Threat to others: M = 3.62, SD = 1.88
	1 = not at all concerned, 7 = very concerned

3.1.2. Procedure and materials

Consistent with the pilot study, at the beginning of the survey, participants read that they would be presented with several descriptions about spending time alone, which was defined as no social contact either in person or virtually. The survey consisted of four blocks. In the first two blocks, participants rated the importance of a variety of solitude functions. Following recent work as a guideline (Martinez et al., 2020), we expanded the stimulus set beyond the six functions of solitude experiences in the pilot study. Building on the solitude functions that were documented in past studies (Long et al., 2003; Nguyen et al., 2018; Ost Mor et al., 2021; Ren et al., 2021; Wesselmann et al., 2021), we compiled an initial list of solitude functions including the functions we used in the pilot data (a total of 23 functions were identified. See here for the initial list: https://osf.io/qn5zf/). We further edited this list in four ways: identical categories were merged (e.g., “concentration on schoolwork” and “facilitating achievement” were merged), broad categories were split into separate categories (e.g., “experience in nature or abroad” were split into “nature” and “traveling”), one category that represents a negative and undesirable solitude experience was removed (“loneliness: You feel self‐conscious, anxious, or depressed; you long for interpersonal contact.” Long et al., 2003), and one category that is suggested by research on the behavioral immune system was added (reducing risks of infection; Murray & Schaller, 2016). For each category, the descriptions in the original studies were used as much as possible, but light edits were applied in order to improve clarity. The final list of solitude functions (n = 22) and the descriptions for participants are listed in Table 3. The solitude functions are arranged in descending order of mean importance ratings (means and standard deviations are included in the table). Participants rated the importance of these solitude functions in two blocks (blocks 1 and 2). Solitude functions were randomized in each block.

TABLE 3.

Solitude functions and the descriptions for participants.

Functions	Description for participants	Source	M	SD
Privacy	You are alone to have privacy	Wesselmann et al. (2021)	5.96	1.18
Quietness	You are alone to enjoy a quiet space	Ost Mor et al. (2021)	5.91	1.16
Inner peace	You are alone to feel calm and relaxed, free from the pressures of everyday life	Long et al. (2003)	5.86	1.13
Relaxation	You are alone to relax and recover from exhaustion	Pilot	5.76	1.28
Achievement	You are alone to avoid distractions so that you can focus, work, learn, manage your tasks, and gain achievements	Pilot; Ost Mor et al. (2021)	5.53	1.26
Self‐reflection	You are alone to reflect on your own thoughts and feelings	Pilot	5.53	1.34
Leisure	You are alone for leisure activities and hobbies (e.g., baking, listening to music, reading a book, gardening, etc.)	Long et al. (2003)	5.42	1.36
Escapism	You are alone to escape daily tasks, social engagements, duties, and “background noises” from time to time	Ost Mor et al. (2021)	5.34	1.35
Problem‐solving	You are alone to think about specific problems or decisions you are facing, and you attempt to come to some resolution	Long et al. (2003)	5.10	1.31
Emotion regulation	You are alone so that you can cope with your negative emotions such as grief, depression, anxiety, frustration, stress, etc.	Pilot; Nguyen et al. (2018); Ost Mor et al. (2021)	5.00	1.64
Anonymity	You are alone to act in whatever ways you feel like at the moment, without concern for social niceties or what others might think	Long et al. (2003)	4.99	1.66
Independence	You are alone in seeking independence and autonomy	Pilot	4.82	1.57
Avoiding unpleasant interaction	You are alone so that you can avoid certain social contacts you expect to be unpleasant (e.g., conflicts with others, being ignored and excluded, receiving prejudicial treatment)	Pilot; Ren et al. (2016); Ren et al. (2021)	4.70	1.70
Creativity	You are alone to create. Being alone may stimulate novel ideas or innovative ways of expressing yourself, whether actually in art, poetry, or intellectual pursuits, or whimsically in daydreaming with a purpose	Long et al. (2003)	4.69	1.75
Health	You are alone in recovering from an illness or physical discomfort	Wesselmann et al. (2021)	4.64	1.68
Nature	You are alone to explore and connect with nature	Long et al. (2003); Wesselmann et al. (2021)	4.58	1.77
Routines	You are alone because of routines or habits (e.g., shopping, driving, physical exercises)	Ost Mor et al. (2021); Wesselmann et al. (2021)	4.57	1.50
Self‐discovery	You are alone to gain insight into your fundamental values and goals and you come to realize your unique strengths and weaknesses	Long et al. (2003)	4.52	1.54
Traveling	You are alone and being away from a familiar environment (e.g., traveling abroad alone). This experience enables freedom from restraints, language, customs, and habits. It also enables resetting one's mind and regaining energy	Ost Mor et al. (2021)	3.96	1.77
Reducing infection risks	You are alone to reduce in‐person contact. This way you can avoid risks of infection	Murray and Schaller (2016)	3.82	2.01
Intimacy	You are alone to feel close to someone you care about, for example, an absent friend or lover, or perhaps a deceased relative (such as a beloved grandparent); the absence of the person only strengthens your feeling of closeness	Long et al. (2003); Wesselmann et al. (2021)	3.70	1.80
Religious experience	You are alone to experience a spiritual connectedness. Being closer to one's God or faith is a way to feel serene, guided, or open to other dimensions	Ost Mor et al. (2021)	3.69	2.28

In block 3, participants completed the Five‐Factor Model Rating Form (Samuel et al., 2013), the same measure we used in the pilot study. The items were randomized in this block.

In block 4, similar to the pilot study, participants completed several demographic/background questions. Because data collection took place during the ongoing COVID‐19 pandemic, participants' attitudes toward solitude may be influenced by their perceived risks of infection. Therefore, two questions were included to assess participants' concerns about COVID‐19. All variables collected in block 4 are summarized in Table 2. The questions were presented in the order they are listed in Table 2.

3.2. Results and discussion

Figure 6 provides a visual presentation of variation in perceived importance of solitude for each of the 22 solitude functions. Following the preregistered data analysis plan, we first examined the correlational patterns of the ratings. We calculated intrarater reliability (i.e., test–retest reliability) by computing Pearson correlations between the two time points for the same participant. On average, intrarater reliability was high (average r = 0.757, SD = 0.229). We also computed pairwise Pearson correlations between raters (i.e., participants). Consistent with the pilot study, there was low consensus in which solitude functions were perceived to be the most important (average r = 0.216, SD = 0.258). ¹⁰ In what follows, we report the main analyses we conducted to answer the two research questions. ¹¹

FIGURE 6.

Variation in perceived importance of solitude across solitude functions (preregistered study). The x‐axis represents the 22 solitude functions and the y‐axis represents participants’ importance ratings. The colored lines represent different individuals randomly selected from the sample. The solitude functions are arranged from left to right in descending order of mean importance ratings. Overall, we observed that (a) there was variation across the solitude functions (i.e., some functions were more valued than others); (b) there was variation across individuals (e.g., participant 286 valued solitude more than participant 86); and (c) people have idiosyncratic solitude priorities (i.e., people value different solitude functions differently).

3.2.1. Question 1

To what extent do individual differences, differences between solitude functions, and idiosyncratic solitude priorities explain variation in perceived importance of solitude?

Analytical approach

We conducted variance component analysis using a cross‐classified multilevel model. The importance ratings were the outcome variable, and the random intercepts were individuals, solitude functions, individuals × solitude functions, block, block × individuals block × solitude functions. We fitted this model using the lme4 package (version 1.1.23) in R (Bates et al., 2015) as follows:

$$\begin{array}{c}\mathrm{lme}4::\text{lmer}(\text{rating}~1+\left(1|\text{individual}\right)+\left(1|\text{function}\right)\hfill \\ +\left(1|\text{individual}:\text{function}\right)+\left(1|\text{block}\right)\hfill \\ +\left(1|\text{block}:\text{individual}\right)+\left(1|\text{block}:\text{function}\right),\text{data})\hfill \end{array}$$

ICCs for each component (including the residual component) were calculated. Based on the findings of the pilot study, we expected that there would be little variance due to solitude functions. We also expected that individuals (the individual component, and the individual × function component) would have a substantial contribution to the total variance.

Findings

ICCs for each component are visualized in Figure 7. Individual differences accounted for 22% of the total variance, and solitude functions accounted for a similar proportion of the total variance (15%; the difference between these two components: 6.2%, 95% CI [−4.5, 15.9]). Importantly, idiosyncratic solitude priorities (i.e., individual by function) had a substantial contribution to the total variance (46%), making it the component with the highest ICC. Finally, there was little variance due to block (block, block × individual, block × function), suggesting that people's ratings across the two blocks were consistent and reliable.

FIGURE 7.

ICC by component (preregistered study).

3.2.2. Question 2

What personality predictors explain the variation in perceived importance of solitude?

Analytical approach

We explored which personality variables explain variance in the importance ratings of each function above and beyond demographic/background variables, using the same procedure we described in the pilot study. We did not have specific hypotheses, but we expected that the relevance of a particular personality variable would depend on specific solitude functions. A correlation matrix of the Big Five dimensions and all 22 solitude functions is presented in Supplementary Materials.

Findings

Consistent with the pilot study, we plotted all significant model comparison‐adjusted p‐values (Figure 8) and all unstandardized regression coefficients of the personality variables (Figure 9). The pattern of the results resembles that of the pilot study: (1) all five personality dimensions emerged as relevant predictors of solitude ratings, with some dimensions more relevant than others; (2) the relevance of a particular dimension depended on the specific solitude functions; and (3) a particular dimension may have effects of opposite signs. In addition, we observed similar results regarding the functions that were examined in the pilot study. For example, emotion regulation was negatively predicted by Extraversion and positively predicted by Neuroticism; achievement was positively predicted by Conscientiousness.

FIGURE 8.

Model comparison p‐values (preregistered study). We compared whether or not a model including a Big Five personality dimension explained more variation in each of the 22 importance ratings compared to a model without the given dimension. Model comparison p‐values are adjusted for multiple testing. Adjusted p‐values are visualized, with darker colors corresponding to smaller p‐values. p‐values that are larger than the alpha level (.05) are not visible (i.e., in color white). The x‐axis represents the personality dimensions and the y‐axis represents the solitude functions. The solitude functions are arranged from top to bottom in descending order of mean importance ratings.

FIGURE 9.

Unstandardized estimates of the Big Five dimensions (preregistered study). For each of the 22 solitude ratings and each personality dimension, we fitted models with predictors including the demographic/background variables and one personality dimension. The unstandardized estimates of the personality dimensions are visualized, with darker colors corresponding to larger estimates. Positive estimates are in the color red, and negative estimates are in the color blue. Black boarders indicate significant model‐comparison p‐values (see Figure 8 for p‐values). The x‐axis represents the personality dimensions and the y‐axis represents the solitude functions. The solitude functions are arranged from top to bottom in descending order of mean importance ratings.

Including a wide range of solitude functions in the preregistered study also allowed us to uncover new findings. One notable observation concerns Openness. While Openness did not stand out as a particularly relevant dimension based on the pilot data, it emerged as a significant predictor of several functions that are newly included in the preregistered study: creativity, nature, religious experience, and self‐discovery. This finding reaffirms our approach of considering solitude and solitude‐seeking behavior as a complex and broad experience with a variety of distinct functions.

Another new observation is that several solitude functions (10 out of 22 functions we examined) were not predicted by any of the Big Five personality dimensions. For example, privacy was ranked as the most important solitude function, but none of the personality dimensions emerged as relevant predictors. Similarly, none of the personality dimensions were relevant predictors of the function of reducing infection risks. These results suggest that personality is not always linked to perceived importance of solitude. We will return to this finding in the General Discussion.

4. GENERAL DISCUSSION

People value solitude in varying degrees. What are the sources of variation in people's perceived importance of solitude? Previous research suggests that the perceived importance of solitude varies considerably due to two sources: individual differences (e.g., Burger, 1995; Coplan, Ooi, et al., 2019), and differences among various functions of being alone (e.g., Long et al., 2003; Wesselmann et al., 2021). In this research, we quantified the relative contributions of these two sources of variance (and their interaction) using variance component analysis. We first analyzed a pilot study involving two US college student samples (N = 1542, n = 6 functions of solitude). Building on the pilot study, we preregistered and conducted a study with a quota‐based US representative sample (N = 501, n = 22 functions of solitude). Results reveal that perceived importance of solitude is guided by both a person's characteristics and a solitary experience's function. Crucially, a substantial amount of variance was explained by the individual by function interaction component, suggesting that person‐specific idiosyncratic solitude priorities are key to understanding people's perceived value of solitude.

We also asked a second question in the current research: Who values solitude? The answer to this question depends on the specific solitude functions. Across both studies, all five dimensions explained variance in solitude importance ratings above and beyond the (measured) demographic and background variables. For example, participants who scored higher (vs. lower) in Neuroticism perceived the emotion regulation function of solitude to be more important. Participants who scored higher (vs. lower) in Extraversion considered it to be less important to seek solitude in order to feel calm and relaxed. Participants who scored higher (vs. lower) in Contentiousness particularly cared about the chance to get things done when being alone. Participants who scored higher (vs. lower) in Openness placed more value on creativity and self‐discovery in solitude.

4.1. Implications

These findings highlight that it is important to consider people's unique priorities for solitude. The existing research has either focused on a general preference for solitude (e.g., dispositional preference for solitude, Burger, 1995; dispositional autonomy, Nguyen et al., 2018), or examined the different types of solitude experiences (Ost Mor et al., 2021; Wesselmann et al., 2021). While these perspectives are useful, the present results are mostly in line with the perspective that people are drawn to solitude for different reasons (Figure 5). As illustrated in Figure 5, each person has their own unique priorities. Person A might value solitude for privacy reasons, person B might seek solitude to be productive, and person C enjoys quiet and relaxing time in solitude. Theories that integrate these unique preferences have the potential to provide a more complete account of people's perceptions of solitude, the consequences of spending time alone, and solitude‐seeking behaviors.

The findings on idiosyncratic solitude priorities have implications for developing interventions on cultivating “positive solitude” (Ost Mor et al., 2021) or “solitude skills” (Thomas, 2021). It is possible that there is not a “cookie‐cutter” intervention to increase people's satisfaction with alone time. Rather, a personalized approach that improves the fit between a person's characteristics and the functions of a solitary experience is required. People might reap the most benefits from solitary experiences that match their priorities. For example, a 15‐min session of alone time in a quiet room might be what one person needs to feel recharged and rested, but for another, it might be exactly the inspiration they need to harm themselves and others (Pfattheicher et al., 2021; Wilson et al., 2014). Similarly, just as a solo experience exploring and connecting with nature might increase well‐being for one person, it might not be the right solitude experience for another person. Therefore, to build a positive solitude experience, the first step is to understand a person's personality and priorities. This information would allow the possibility to cultivate a personalized solitude space where individuals could create and transform their alone time into a tailored experience that is fulfilling and enjoyable.

The current research shows that the link between personality and solitude can be counterintuitive and complex. Extraversion has been considered a key, if not the sole, Big Five personality dimension that is relevant to people's attitudes toward solitude (Leary et al., 2003; Sabatin, 2020; Tse et al., 2021). The current research shows that all Big Five personality dimensions are relevant personality variables and that they depend on which solitude functions are of interest. In addition, the prevalent assumption that extraverted people do not value solitude as much as their introverted peers (e.g., Sabatin, 2020) is invalid. As Figure 9 shows, although extraversion was negatively associated with the ratings of some solitude functions (e.g., quietness, inner peace), it was positively associated with the ratings of others (e.g., nature, intimacy). These nuances must be carefully considered in order to establish a more accurate understanding of the link between personality and people's solitude attitudes or behaviors.

Notably, the link between personality and solitude can be weak or nonexistent. As Figure 8 shows, for nearly half of the solitude functions we examined (10 out of 22), the importance ratings were not predicted by any of the Big Five personality dimensions. These findings suggest that having an appreciation for solitude does not necessarily reflect an individual's personality. This is important, considering that laypeople readily infer personality or individual characteristics from a target's solitude preferences. For example, past work has shown that a solitude‐seeking target is judged to be cold, introverted, and unlikeable (Ren & Evans, 2021; Ren & Stavrova, 2022). Deviating from these lay beliefs, the current research shows that valuing time alone is not necessarily linked to, and may even have nothing to do with an individual's personality. This discrepancy adds to our understanding of common misconceptions of solitude seeking, and the potential consequences of expressing an appreciation of time alone (Ren et al., 2023; Ren & Evans, 2021).

Finally, the current research highlights the importance of clearly defining solitude for participants. Without a clear definition, participants are likely to interpret “solitude” freely. For example, one participant might interpret “solitude” as an experience that is characterized by quietness and relaxation, whereas another participant might interpret “solitude” as an experience that allows for self‐discovery and creativity. Given the wide range of solitude functions, simply asking participants about solitude can easily lead to uninterpretable results. We recommend researchers to explicitly define solitude in their research and to participants. Suppose researchers are interested in studying participants' experience with solitude as emotion regulation, researchers may consider providing a specific definition of solitude in their instruction (e.g., “We would like to ask you a few questions about solitude. We are particularly interested in your experience with solitude when you choose to spend time alone in order to regulate your emotions.”). Offering such a definition to participants helps to clarify what solitude means to avoid ambiguity.

4.2. Limitations and future directions

The current research has limitations. In this section, we focus on two limitations and suggest future extensions. The first limitation concerns the samples we used. We collected data from college students and residents in one nation (the US), which limited our ability to assess cross‐cultural differences in broader cultural contexts. Past research suggests that people from different cultural backgrounds may view solitude differently, suggesting that culture may account for variations in perceived importance of solitude (Ding et al., 2015; Jiang et al., 2019; Oyserman et al., 2002). For example, it might be possible that people from individualistic cultures value solitude more than people from collectivist cultures. It is also possible that people from different cultural groups value different solitude functions. We encourage researchers to make use of variance component analysis to quantify the contribution of the culture component. The results of such an analysis can be used to develop specific hypotheses regarding the role of culture. To achieve this, future research should consider collecting globally diverse samples.

We also collected data at one time point. Future work may consider collecting people's ratings of solitude functions across multiple occasions using daily diary methods or experience sampling designs. This type of data would provide an opportunity to evaluate the variability of people's judgments across occasions, offering insights into the extent to which solitude perceptions fluctuate within a person in daily life. This type of data would also allow researchers to examine whether people's perceived value of solitude corresponds to how they actually feel in solitude situations (e.g., a person values traveling alone, but does the person really enjoy it when they are traveling alone?).

Finally, people's solitude values may change over developmental stages (Coplan, Ooi, et al., 2019). To what extent do people's solitude priorities change across the life span? To answer this question, longitudinal studies that track individuals over an extended period of time and through different developmental stages (e.g., from adolescence to late adulthood) would be incredibly valuable. At the request of a reviewer, we have calculated the correlations between participants' age and the solitude importance ratings (see Supplementary Materials). The associations ranged from −0.13 to 0.16. The strongest associations were observed between age and solitude as religious experience (0.16), emotion regulation (−0.13), self‐discovery (−0.13), and avoiding unpleasant social interactions (−0.13). These preliminary findings may serve as a starting point for future investigations of people's values across the life span using longitudinal data.

5. CONCLUSION

Previous research suggests that some people value solitude more than others; some solitude functions are perceived to be more important than others. While we observed evidence for both of these theoretical perspectives, the present research reveals the key to understanding people's perceived value of solitude is person‐specific idiosyncratic priorities. Instead of asking who values solitude or what kind of solitude is the most enjoyable, a more fruitful scientific inquiry must focus on the fit between a person's characteristics and the specific functions a solitary experience affords.

AUTHOR CONTRIBUTIONS

Conceptualization, methodology, data curation, formal analysis, writing—original draft, writing—review & editing, visualization: Dongning Ren: Conceptualization, methodology, formal analysis, writing—original draft, writing—review & editing: Wen Wei Loh: Conceptualization, methodology, writing—review & editing: Joanne M. Chung: Conceptualization, methodology, writing—review & editing: Mark J. Brandt.

FUNDING INFORMATION

No external funding source.

CONFLICT OF INTEREST STATEMENT

No conflict of interest to disclose.

ETHICS STATEMENT

The study has been approved by the Ethics Review Board of the School of Social and Behavioral Sciences of Tilburg University (protocol number RP621).

Supporting information

Data S1: Supporting information

Ren, D. , Loh, W. W. , Chung, J. M. , & Brandt, M. J. (2025). Person‐specific priorities in solitude. Journal of Personality, 93, 12–30. 10.1111/jopy.12916

DATA AVAILABILITY STATEMENT

All data are made available on the Special Issue's OSF page, so that they can be readily accessed by readers of the issue.