Melissa M. Norberg, Centre for Emotional Health, School of Psychological Sciences, Macquarie University, 16 University Avenue, Room 811, Sydney, NSW, Australia.

Email: [email protected]

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Shanara Visvalingam,

Shanara Visvalingam

orcid.org/0000-0003-0426-0216

Centre for Emotional Health, School of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia

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Natasha R. Magson,

Natasha R. Magson

orcid.org/0000-0002-4470-9041

Centre for Emotional Health, School of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia

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Amie R. Newins,

Amie R. Newins

orcid.org/0000-0002-4900-2424

Department of Psychology, University of Central Florida, Orlando, Florida, USA

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Melissa M. Norberg,

Corresponding Author

Melissa M. Norberg

[email protected]

orcid.org/0000-0001-7840-9007

Centre for Emotional Health, School of Psychological Sciences, Macquarie University, Sydney, New South Wales, Australia

Correspondence

Melissa M. Norberg, Centre for Emotional Health, School of Psychological Sciences, Macquarie University, 16 University Avenue, Room 811, Sydney, NSW, Australia.

Email: [email protected]

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First published: 30 July 2023

https://doi.org/10.1111/jopy.12868

Citations: 1

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Abstract

Objective

Perfectionism is linked to a variety of mental health conditions in university students. The Perfectionism Social Disconnection Model posits that perfectionistic individuals exhibit off-putting interpersonal behaviors (i.e., rejection sensitivity and hostility), which lead to social disconnection, and in turn contribute to psychological distress. Although several longitudinal studies have found that social disconnection mediates the link between perfectionistic traits and psychological distress, less is known about how perfectionism leads to social disconnection. The present study aimed to address this gap.

Methods

A sample of 877 university students completed one survey a month for three consecutive months.

Results

Our random-intercept cross-lagged panel model results showed significant positive associations between the random intercepts of socially prescribed and self-oriented perfectionism with rejection sensitivity, hostility, and loneliness, with stronger associations for socially prescribed perfectionism. In addition, the random intercept of other-oriented perfectionism showed positive associations with hostility but not rejection sensitivity or loneliness. Moreover, almost all cross-lagged paths were nonsignificant.

Conclusion

Collectively, these findings indicate that although perfectionistic traits may be associated with rejection sensitivity and hostility to varying degrees at the between-person level, these behaviors may not cause social disconnection at the within-person level.

1 INTRODUCTION

Mental health problems appear to be highly prevalent in university student populations (Eisenberg et al., 2007; Sharp & Theiler, 2018). Levels of psychological distress among university students are often higher than those found in the general population (e.g., 83.9% vs. 29.0%; Stallman, 2010), and almost one in three first-year university students meet criteria for a psychological disorder (Auerbach et al., 2018). Increasingly, research has linked psychological distress to perfectionism (for reviews and meta-analyses, see Egan et al., 2011; Limburg et al., 2017). Given that many university students exhibit perfectionistic traits (Curran & Hill, 2019), perfectionism may be contributing to some of the psychological distress experienced by university students.

The Perfectionism Social Disconnection Model (PSDM) describes how perfectionism leads to psychological distress through social disconnection (Hewitt et al., 2006, 2017). The model describes three perfectionistic traits: self-oriented perfectionism (i.e., imposing high standards on oneself), socially prescribed perfectionism (i.e., the belief that others have high standards for oneself), and other-oriented perfectionism (i.e., imposing high standards on others; Hewitt et al., 2017). The PSDM posits that these traits lead to social disconnection through two mediating pathways: interpersonal sensitivity (i.e., rejection sensitivity) and interpersonal hostility (Hewitt et al., 2006, 2017). For example, perfectionistic individuals may be highly sensitive to cues of interpersonal rejection that increases the likeliness of perceiving rejection (Hewitt et al., 2006). In addition, perfectionistic individuals may exhibit interpersonally hostile behaviors (e.g., anger and coldness) due to their excessive focus on achievement, hyper-competitiveness, and dominance (Hewitt et al., 2006). As a result, perfectionistic individuals may isolate themselves or be rejected by others, creating an imagined or real sense of social disconnection (Hewitt et al., 2017). This sense of social disconnection from others can lead to affective reactions (e.g., negative affect, shame, and self-censure) that further perpetuate alienation, which may culminate in distress, dysfunction, and disorder over time (Hewitt et al., 2017).

Several longitudinal studies have examined the meditating role of social disconnection (e.g., social hopelessness, interpersonal discrepancies, and anti-mattering) in the link between perfectionistic traits and depressive symptoms. Social disconnection has been shown to consistently mediate the relationship between socially prescribed perfectionism and depressive symptoms; however, findings have been mixed for self-oriented and other-oriented perfectionism (for a review and meta-analysis, see Smith et al., 2020). Some studies have shown that social disconnection mediates the self-oriented perfectionism and depression link (Magson et al., 2019; Rnic et al., 2021) while others have not (Enns et al., 2005; Etherson et al., 2022; Mackinnon et al., 2014). This has led researchers to suggest that self-oriented perfectionism may only be related to certain markers of social disconnection (e.g., loneliness; Rnic et al., 2021). Furthermore, only one study has found social disconnection to explain the relationship between other-oriented perfectionism and depression (Rnic et al., 2021), with most studies neglecting to examine this trait (Enns et al., 2005; Etherson et al., 2022; Mackinnon et al., 2014; Roxborough et al., 2012).

Although several longitudinal studies have examined social disconnection as a mediator of the association between perfectionism and psychological distress, there has been no longitudinal research examining how perfectionism leads to social disconnection. Cross-sectional studies have consistently linked socially prescribed and other-oriented perfectionism with increased rejection sensitivity and interpersonal hostility, but only inconsistently linked self-oriented perfectionism with rejection sensitivity and interpersonal hostility (Abdollahi et al., 2022; Flett et al., 2014; Stoeber et al., 2017). More specifically, self-oriented perfectionism is associated with adaptive interpersonal functioning only when the shared variance between the other perfectionistic traits is controlled (Gotwals et al., 2012; Stoeber, 2011). Conversely, research that has not controlled for shared variance has found self-oriented perfectionism to be associated with adverse interpersonal functioning (Sherry et al., 2016) and poorer psychological well-being (e.g., review and meta-analysis; Smith et al., 2018). So, although it appears that at least some facets of perfectionism may lead to social disconnection through off-putting interpersonal behaviors, these pathways need to be tested within a longitudinal framework to establish the temporal sequence proposed by the PDSM.

Therefore, to extend the current literature, the present study aimed to prospectively test the mediating effect of interpersonal sensitivity and hostility on the association between the three types of perfectionism and social disconnection as specified in the first part of the PSDM using three waves of data. Based on theory and evidence, we hypothesized that both socially prescribed perfectionism and loneliness would be directly associated with rejection sensitivity and interpersonal hostility. We also predicted that rejection sensitivity and interpersonal hostility would mediate the association between socially prescribed perfectionism and loneliness. Due to previously mixed findings in the literature for self-oriented perfectionism and other-oriented perfectionism, no specific predictions were made for these traits.

2 METHODS

2.1 Participants

A total of 1040 participants were recruited for the study. However, 101 participants only partially completed the Time 1 survey and were therefore excluded as they did not provide contact details for follow-up. In addition, 62 participants failed attention checks at Time 1 resulting in the exclusion of these participants. As a result, the retained sample size at Time 1 was 877. A further 21 participants failed attention checks at subsequent waves, and these responses were also excluded from the analyses (T2 n = 14, T3 n = 7). Of the 877 participants that completed Time 1, 567 participants completed Time 2 (65%), and 441 participants completed Time 3 (50%). Most of the sample (n = 652) was recruited from an Australian university of which 632 received course credit and 20 received AUD$5. The remaining participants (n = 225) were recruited from an American university in exchange for course credit. The demographic characteristics of participants are reported in Table 1.

TABLE 1. Demographic characteristics by group with comparison analyses.

	M or n	SD or %	M or n	SD or %	M or n	SD or %	t or χ²	d or V
	Total (n = 866)		AUS (n = 644)		US (n = 222)		Comparison statistics	Effect size
Age	20.98	6.74	21.46	7.48	19.59	3.51	−3.599	0.32
Gender							6.795	0.09
Man—cisgender	272	31.4	191	29.7	81	36.5
Woman—cisgender	565	65.2	435	67.5	130	58.6
Transgender, nonbinary, or gender fluid	29	3.3	18	2.8	11	5.0
Ethnicity							195.787	0.48
Asian	165	19.1	151	23.4	14	6.3
Black or African American	25	2.9	6	0.9	19	8.6
Hispanic or Latino/a/x	64	7.4	14	2.2	50	22.5
Middle Eastern or European	136	15.7	130	20.2	6	2.7
White	365	42.1	249	38.7	116	52.3
Biracial, multiracial, or other	111	12.8	94	14.6	17	7.7
Relationship status							16.882	0.14
Single	474	54.7	367	57.0	107	48.2
Casually dating	62	7.2	44	6.8	18	8.1
In a relationship	262	30.3	174	27.0	88	39.6
Engaged, married, or divorced	68	7.9	59	9.2	9	4.1
Employment hours	14.62	13.54	16.27	13.54	9.77	12.37	−6.285	0.50

Note: Bolded test statistics indicate significance at p < 0.05. Employment hours are reported per week.

2.2 Measures

2.2.1 Perfectionistic traits

The Hewitt and Flett Multidimensional Perfectionism Scale (HF MPS-45; Hewitt & Flett, 1991) aims to assess levels of trait perfectionism in adults. The 45-item measure includes 15 items for each subscale: self-oriented perfectionism, socially prescribed perfectionism, and other-oriented perfectionism. Participants are provided with items such as “I strive to be as perfect as I can be” and asked to rate their level of agreement on a 7-point Likert-type scale from 1 (disagree) to 7 (agree). The 18 reverse-scored items were recoded, and item scores for each subscale were summed (range 15–105) with higher scores indicating greater levels of the respective trait. The HF MPS-45 has demonstrated acceptable to good internal consistency (α = 0.74–0.88; Hewitt et al., 1991) and test–retest reliability across the facets of perfectionism after a 3-month interval (r = 0.75–0.88; Hewitt & Flett, 1991).

2.2.2 Rejection sensitivity

The Rejection Sensitivity Questionnaire (RSQ; Downey & Feldman, 1996) is a 36-item questionnaire that aims to measure rejection sensitivity by determining the individual's responses to 18 theoretical situations. Each scenario (e.g., “you ask a friend to do you a big favour”) is followed by two questions, one assessing the individual's level of anxiety/concern about the outcome (e.g., How concerned or anxious would you be over his/her reaction?), and the other examining their expectation of being accepted or rejected (e.g., I would expect that he/she would want to try to help me out). Participants were asked to rate their degree of concern and expectation of rejection on a 6-point Likert-type scale from 1 (very unconcerned) to 6 (very concerned) and 1 (very unlikely) to 6 (very likely), respectively. The rejection sensitivity score was calculated by multiplying the score for the degree of concern by the reverse score for expectations of rejection and taking the mean of the resulting 18 scores. Possible scores ranged from 1 to 36 with higher scores indicating greater sensitivity to rejection. This measure has been found to demonstrate good internal consistency (α = 0.87) and acceptable test–retest reliability after a 4-month interval (r = 0.78; Ayduk et al., 2008; Downey & Feldman, 1996).

2.2.3 Interpersonal hostility

The Buss–Perry Aggression Questionnaire (BPAQ; Buss & Perry, 1992) is a 29-item measure that assesses anger, hostility, and physical and verbal aggression. Participants are provided with items such as “I tell my friends openly when I disagree with them” and asked to rate how characteristic that behavior is of them on a 5-point Likert-type scale from 1 (extremely uncharacteristic) to 5 (extremely characteristic). Negatively worded items were reverse scored. Total scores are calculated by summing individual item scores (range 29–145), with higher scores indicating greater aggression. For the current study, only the global total score was used. The BPAQ has demonstrated good internal consistency (α = 0.89) and good test–retest reliability after a 9-week interval (r = 0.80; Buss & Perry, 1992).

2.2.4 Loneliness

The UCLA Loneliness Scale (UCLA LS; Russell, 1996) is a 20-item scale designed to measure loneliness in adults. Participants are provided with items such as “I have nobody to talk to” and asked to rate how frequently they feel that way on a 4-point Likert-type scale from 0 (never) to 3 (often). The item scores were summed to obtain a total score (range 0–60), with higher scores indicating greater levels of loneliness. The UCLA Loneliness Scale has demonstrated excellent internal consistency (α = 0.94) and acceptable test–retest reliability over a 1-year period (r = 0.73; Russell, 1996).

2.2.5 Demographics

Participants were asked questions related to their age, self-identified gender, race/ethnicity, employment, and relationship status.

2.3 Procedure

This study was approved by the Human Research Ethics Committee and Institutional Review Board at the Australian and American university. Participants were recruited from two universities in different countries to increase the generalizability of study findings. The study utilized a longitudinal design, in which participants were enrolled to complete three online surveys (i.e., one survey a month for three consecutive months) during a university semester. This study was conducted as part of a larger longitudinal study. The full list of measures (along with de-identified data and code used in the current analyses) can be found in the OSF repository, and those pertaining to the current study are presented in the measures section above. Each survey took approximately 25 min to complete, and participants received a debrief form at the end of the final survey.

2.4 Statistical analyses

Data were first screened for missingness and descriptives using SPSS v.28. We first examined participant dropout using Little's (1988) Missing Completely at Random test and conducted independent samples t-tests to determine whether there were any differences between completers and noncompleters. Data were then examined for multivariate outliers and normality as per recommendations from Tabachnick and Fidell (2001). Next, independent samples t-tests and chi-squared tests were used to determine whether the Australian and United States samples differed. As a measure of effect size, Cohen's d was reported for the independent samples t-tests (0.41, 1.15, and 2.70 represent small, medium, and large sizes, respectively; Ferguson, 2009). In addition, Cramer's V (φ_c) was reported for chi-squared tests (0.2, 0.5, and 0.8 represent small, medium, and large effect sizes, respectively; Ferguson, 2009).

Random-intercept cross-lagged panel models (RI-CLPMs) were used to analyze within-person longitudinal associations over time (Muthén & Muthén, 1998–2012). Although cross-lagged panel models (CLPMs) were initially conducted, it showed that these models fit the data worse and that different conclusions would be reached using these models (see supplementary material for results of the CLPMs). There are also notable limitations of the traditional CLPM. The CLPM conflates within-person and between-person effects as it assumes there are no stable between-person differences among the scores over time (Hamaker et al., 2015; Littlefield et al., 2022; Lucas, 2023; Mulder & Hamaker, 2020). Therefore, simulation studies have shown CLPMs to produce spurious and misleading results regarding the presence, predominance, and sign of casual associations (e.g., Hamaker et al., 2015; Lucas, 2023). The RI-CLPM has been proposed as a superior method as it separates the between and within-person differences by including random-intercept factors that partial out the stable between-person differences (Hamaker et al., 2015). Using RI-CLPMs allowed us to examine whether individuals who have higher levels of perfectionism than usual (around their own trait level) might also score higher on rejection sensitivity, interpersonal hostility, and loneliness relative to their own expected scores at the following time point (i.e., within-person carry-over effects).

All cross-lagged paths were constrained to be equal across measurement occasions for all models, and the residuals of each variable were correlated within time points (Orth et al., 2021). The model fit was assessed via the comparative fit index (CFI; ≥0.90 acceptable and ≥0.95 excellent), root-mean-square-error of approximation (RMSEA; <0.08 acceptable, <0.05 excellent), and the standardized root-mean-square residual (SRMR; ≤0.08 acceptable; Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline, 2016). If a model resulted in significant cross-lagged paths across the three consecutive time periods, indirect effects were examined using the MODEL INDIRECT command. We used maximum likelihood robust estimation (MLR) to account for data non-normality and full information maximum likelihood (FIML) estimation to handle missing data as it is less biased than other methods (Muthén & Muthén, 2010). The 95% confidence intervals were estimated using percentile bootstrapping with 5000 iterations and confidence intervals that did not include zero were indicative of significant indirect effects (Biesanz et al., 2010). Benchmarks were used to interpret standardized beta sizes for cross-lagged effects (0.03, 0.07, and 0.12 for small, medium, and large effects, respectively; Orth et al., 2022).

3 RESULTS

3.1 Missing data

Missing values analyses indicated that 28.88% of values were missing across waves. However, Little's (1988) Missing Completely at Random test indicated that there were no cases that displayed systematically missing data (χ² = 1322.20, p = 0.992). Given that data appear to be not Missing at Random, unbiased results can still be obtained despite larger proportions of missing data provided that the estimated model is properly specified (Dong & Peng, 2013; Madley-Dowd et al., 2019). Moreover, a series of independent samples t-tests showed that other-oriented perfectionism at Time 1 was statistically significantly lower for those who completed Time 2 and Time 3 compared to those that did not; however, these analyses were associated with negligible effect sizes (d's = 0.19 and 0.24).

3.2 Data screening

Mahalanobis' distance analyses indicated that 11 cases exhibited multivariate outliers. As multivariate outliers can distort the results of regression analyses, these cases were excluded. This produced a final sample of 866 participants at Time 1, 562 participants at Time 2, and 436 participants at Time 3. Furthermore, univariate skewness and kurtosis values for all study variables were acceptable (less than three for skewness and <10 for kurtosis; Kline, 2016). Visual inspection of plots of standardized residuals versus predicted values indicated there were no severe violations of linearity and homoscedasticity in the data. In addition, all variance inflation factor (VIF) values were below 10 indicating no problematic multicollinearity. A bivariate correlation matrix can be found on the OSF page, and descriptive statistics can be found in Table 2. The internal consistencies for all variables at each time point were deemed acceptable to excellent (α = 0.77–0.96; see Table 2).

TABLE 2. Characteristics by group with alpha reliabilities and comparison analyses at each time point.

	M	SD	α	M	SD	M	SD	t	d
	Total			AUS		US		Comparison statistics	Effect size
Time 1	(n = 866)			(n = 644)		(n = 222)
Perfectionistic traits
Socially prescribed	56.66	13.96	0.85	56.73	14.42	56.45	12.53	−0.264	0.02
Self-oriented	72.21	15.61	0.90	71.73	15.72	73.63	15.24	1.584	0.12
Other-oriented	55.45	11.91	0.77	54.31	11.55	58.79	12.35	4.717	0.37
Rejection sensitivity	10.89	4.36	0.90	10.84	4.34	11.06	4.45	0.657	0.05
Interpersonal hostility	72.77	18.89	0.90	73.07	18.73	71.90	19.37	−0.777	0.06
Loneliness	24.05	14.96	0.95	24.12	14.78	23.84	15.53	−0.232	0.02
Time 2	(n = 562)			(n = 502)		(n = 60)
Perfectionistic traits
Socially prescribed	57.89	15.31	0.89	58.21	15.06	55.23	17.18	−1.284	0.18
Self-oriented	71.00	15.92	0.91	71.05	15.70	70.55	17.81	−0.209	0.03
Other-oriented	94.00	55.15	0.77	55.02	11.35	56.25	12.37	0.735	0.10
Rejection sensitivity	10.59	4.55	0.92	10.56	4.47	10.85	5.24	0.401	0.06
Interpersonal hostility	72.36	19.70	0.92	73.03	19.89	66.64	17.14	−2.640	0.34
Loneliness	25.28	15.24	0.95	25.26	15.23	25.45	15.41	0.084	0.01
Time 3	(n = 436)			(n = 394)		(n = 42)
Perfectionistic traits
Socially prescribed	59.36	15.93	0.91	59.17	16.05	61.17	14.88	0.819	0.13
Self-oriented	71.49	16.25	0.91	71.41	16.27	72.21	16.22	0.304	0.05
Other-oriented	55.18	12.16	0.82	54.87	12.22	58.05	11.33	1.713	0.27
Rejection sensitivity	10.01	4.85	0.93	9.89	4.70	11.14	6.10	1.273	0.23
Interpersonal hostility	71.52	20.53	0.92	71.40	20.59	72.63	20.24	0.371	0.06
Loneliness	25.17	15.63	0.96	25.03	15.54	26.51	16.56	0.548	0.09

Note: Bolded test statistics indicate significance at p < 0.05.

3.3 Participant characteristics

The demographic characteristics of the total sample stratified by country are displayed in Table 1. A series of chi-squared tests revealed statistically significant differences in gender and relationship status between the Australian and American samples, although the associated effect sizes were negligible (φ_c = 0.09–0.14). Specifically, the percentage of participants who reported identifying as a woman (cisgender), single, exclusive, or polyamorous dating relationship, or were engaged/married/divorced were significantly higher in the Australian sample than the American sample. Furthermore, a chi-squared test showed statistically significant differences in ethnicity between the Australian and American samples (φ_c = 0.48). The percentage of participants who reported being of Asian, White, Middle Eastern or European, and biracial/multiracial/other (e.g., Aboriginal or Torres Strait Islander, American Indian or Alaskan Native) were significantly higher in the Australian sample than the American sample. In addition, the percentage of participants who reported being of Black/African American, Hispanic/Latino/a/x was significantly higher in the American sample than the Australian sample. Last, independent samples t-tests showed that age and hours of employment were statistically significantly higher in the Australian sample compared with the American sample and these were associated with trivial to small effect sizes (d = 0.32 and 0.50, respectively).

The mean scores of the study variables of the total sample stratified by country are displayed in Table 2. A series of independent samples t-tests showed that other-oriented perfectionism at Time 1 was statistically significantly higher in the American sample compared with the Australian sample. In addition, interpersonal hostility at Time 2 was statistically significantly higher in the Australian sample compared with the American sample. The effect size for all comparison tests was negligible to small (d's = 0.01 to 0.36).

3.4 Random-intercept cross-lagged panel models

3.4.1 Socially prescribed perfectionism

The RI-CLPM including socially prescribed perfectionism, rejection sensitivity, hostility, and loneliness produced an acceptable fit to the data (x²(df = 29) = 59.244, CFI = 0.99, TLI = 0.99, RMSEA = 0.04, SRMR = 0.03). The parameter estimates and associations in the RI-CLPM can be found in Tables 3 and 4. We found that the random intercepts for all variables were positively and significantly correlated with each other. The autoregressive paths showed significant within-person stability in socially prescribed perfectionism (T1-T2, T2-T3), rejection sensitivity (T1-T2), interpersonal hostility (T1-T2, T2-T3), and loneliness (T1-T2). The cross-lagged paths showed that within-person increases in socially prescribed perfectionism at Time 2 led to increases in rejection sensitivity (relative to their expected scores) at Time 3 but not from Time 1 to Time 2. In addition, within-person increases in socially prescribed perfectionism did not lead to increases in interpersonal hostility or loneliness relative to their expected scores. Within-person increases in rejection sensitivity and interpersonal hostility also did not lead to increases in loneliness relative to their expected scores.

TABLE 3. Random-intercept cross-lagged panel model parameter estimates and 95% confidence intervals of the random-intercept cross-lagged path models.

Model and paths	Socially prescribed perfectionism		Self-oriented perfectionism		Other-oriented perfectionism
Random-intercept associations
Perfectionism ⇔ Rejection sensitivity	0.49 [0.40, 0.58]		0.24 [0.15, 0.34]		−0.10 [−0.20, −0.01]
Perfectionism ⇔ Interpersonal hostility	0.44 [0.36, 0.53]		0.29 [0.21, 0.39]		0.26 [0.17, 0.35]
Perfectionism ⇔ Loneliness	0.57 [0.49, 0.66]		0.32 [0.22, 0.51]		−0.01 [−0.10, 0.09]
Rejection sensitivity ⇔ Loneliness	0.60 [0.54, 0.68]		0.63 [0.55, 0.73]		0.60 [0.53, 0.68]
Interpersonal hostility ⇔ Loneliness	0.48 [0.13, 0.29]		0.48 [0.40, 0.57]		0.47 [0.39, 0.55]
Stability paths	T1-T2	T2-T3	T1-T2	T2-T3	T1-T2	T2-T3
Perfectionism	0.39 [0.28, 0.54]	0.56 [0.38, 0.68]	0.41 [−0.29, 0.59]	0.51 [−0.12, 0.70]	0.03 [−0.47, 0.40]	0.02 [−0.16, 0.46]
Rejection sensitivity	0.31 [−0.22, 0.53]	0.37 [−0.13, 0.62]	0.39 [−0.16, 0.60]	0.50 [−0.12, 0.68]	0.40 [0.09, 0.60]	0.51 [−0.07, 0.69]
Interpersonal hostility	0.35 [0.20, 0.49]	0.51 [−0.02, 0.65]	0.36 [0.24, 0.51]	0.55 [0.33, 0.68]	0.36 [0.24, 0.51]	0.55 [0.30, 0.67]
Loneliness	0.31 [−0.16, 0.52]	0.39 [−0.12, 0.65]	0.40 [0.06, 0.59]	0.52 [0.02, 0.71]	0.32 [−0.07, 0.51]	0.42 [−0.08, 0.65]
Cross-lagged paths	T1-T2	T2-T3	T1-T2	T2-T3	T1-T2	T2-T3
Perfectionism → Rejection sensitivity	0.13 [0.01, 0.37]	0.18 [0.03, 0.36]	0.02 [−0.08, 0.27]	0.02 [−0.07, 0.22]	0.02 [0.09, 0.60]	0.02 [−0.07, 0.69]
Perfectionism → Interpersonal hostility	0.00 [−0.08, 0.10]	0.00 [−0.11, 0.13]	−0.02 [−0.13, 0.08]	−0.03 [−0.12, 0.09]	−0.07 [−0.17, 0.03]	−0.06 [−0.14, 0.02]
Perfectionism → Loneliness	0.07 [−0.03, 0.24]	0.10 [−0.04, 0.30]	−0.07 [−0.19. 0.10]	−0.09 [−0.20, 0.06]	−0.02 [−0.16, 0.12]	−0.02 [−0.12, 0.12]
Rejection sensitivity → Loneliness	0.03 [−0.10, 0.18]	0.04 [−0.09, 0.22]	0.03 [−0.08, 0.17]	0.05 [−0.07, 0.22]	0.04 [−0.07, 0.19]	0.06 [−0.08, 0.25]
Interpersonal hostility → Loneliness	0.05 [−0.10, 0.27]	0.08 [−0.10, 0.34]	0.05 [−0.05, 0.24]	0.09 [−0.07, 0.35]	0.06 [−0.06, 0.25]	0.10 [−0.08, 0.35]

Note: Standardized estimates are shown above. Bolded test statistics indicate significance at p < 0.05.
Abbreviations: IH, interpersonal hostility; LON, loneliness; Perf, perfectionism; RS, rejection sensitivity; T1, Time 1; T2, Time 2; T3, Time 3.

TABLE 4. Random-intercept cross-lagged panel model associations within-time and between observed and latent variables.

Model and paths	Socially prescribed perfectionism			Self-oriented perfectionism			Other-oriented perfectionism
Associations within-time	T1	T2	T3	T1	T2	T3	T1	T2	T3
Perf ⇔ RS	−0.10 (0.19)	0.20 (0.10)	0.19 (0.06)	0.04 (0.10)	0.13 (0.06)	0.09 (0.06)	−0.06 (0.09)	0.09 (0.08)	0.07 (0.07)
Perf ⇔ IH	0.09 (0.16)	0.15 (0.07)	0.13 (0.06)	−0.08 (0.15)	0.07 (0.06)	0.06 (0.07)	0.01 (0.10)	0.06 (0.08)	−0.03 (0.06)
Perf ⇔ LON	0.06 (0.12)	0.26 (0.08)	0.24 (0.15)	−0.23 (0.11)	0.17 (0.06)	0.12 (0.06)	0.08 (0.33)	0.09 (0.10)	0.13 (0.07)
RS ⇔ IH	0.03 (0.14)	0.18 (0.12)	0.07 (0.07)	0.02 (0.14)	0.17 (0.05)	0.07 (0.06)	0.06 (0.14)	0.17 (0.05)	0.06 (0.07)
RS ⇔ LON	0.14 (0.12)	0.22 (0.10)	0.22 (0.06)	0.13 (0.12)	0.23 (0.06)	0.21 (0.06)	0.16 (0.11)	0.23 (0.07)	0.21 (0.06)
IH ⇔ LON	0.25 (0.12)	0.36 (0.09)	0.33 (0.07)	0.24 (0.12)	0.35 (0.05)	0.34 (0.06)	0.28 (0.11)	0.35 (0.06)	0.33 (0.06)
Associations b/w observed and latent variables	T1	T2	T3	T1	T2	T3	T1	T2	T3
Perfectionism	0.88 (0.03)	0.81 (0.02)	0.82 (0.02)	0.87 (0.02)	0.84 (0.04)	0.87 (0.04)	0.85 (0.02)	0.89 (0.05)	0.87 (0.02)
Rejection sensitivity	0.91 (0.02)	0.87 (0.03)	0.87 (0.03)	0.89 (0.02)	0.85 (0.02)	0.85 (0.03)	0.89 (0.02)	0.85 (0.02)	0.85 (0.03)
Interpersonal hostility	0.92 (0.02)	0.86 (0.02)	0.88 (0.02)	0.93 (0.02)	0.86 (0.02)	0.87 (0.02)	0.92 (0.02)	0.86 (0.02)	0.87 (0.02)
Loneliness	0.86 (0.02)	0.83 (0.04)	0.85 (0.03)	0.84 (0.03)	0.79 (0.04)	0.82 (0.04)	0.86 (0.02)	0.82 (0.03)	0.85 (0.03)

Note: Associations and standard errors are shown above. Bolded statistics indicate significance at p < 0.05.
Abbreviations: IH, interpersonal hostility; LON, loneliness; Perf, perfectionism; RS, rejection sensitivity; T1, Time 1; T2, Time 2; T3, Time 3.

3.4.2 Self-oriented perfectionism

The RI-CLPM including self-oriented perfectionism, rejection sensitivity, hostility, and loneliness produced an acceptable fit to the data (x²(df = 29) = 57.523, CFI = 0.99, TLI = 0.99, RMSEA = 0.03, SRMR = 0.03). We found that all random intercepts of variables were significantly positively correlated with each other (see Table 3). The autoregressive paths showed significant within-person stability over time in self-oriented perfectionism, rejection sensitivity, interpersonal hostility, and loneliness across all waves. However, the cross-lagged paths showed that within-person increases in self-oriented perfectionism did not lead to increases in rejection sensitivity, interpersonal hostility, or loneliness relative to their expected scores. In addition, within-person increases in rejection sensitivity and interpersonal hostility did not lead to increases in loneliness relative to their expected scores.

3.4.3 Other-oriented perfectionism

The RI-CLPM including other-oriented perfectionism, rejection sensitivity, hostility, and loneliness produced an acceptable fit to the data (x²(df = 29) = 43.759, CFI = 0.99, TLI = 0.99, RMSEA = 0.03, SRMR = 0.03). We found that the random-intercept estimate between other-oriented perfectionism and rejection sensitivity was significantly negatively correlated. In addition, the random-intercept estimate between other-oriented perfectionism and loneliness was not significantly related. All other random intercepts were positively and significantly correlated with each other. The autoregressive paths showed significant within-person stability in all variables except other-oriented perfectionism across waves. However, the cross-lagged paths showed that within-person increases in other-oriented perfectionism did not lead to increases in rejection sensitivity, interpersonal hostility, or loneliness relative to their expected scores. In addition, within-person increases in rejection sensitivity and interpersonal hostility did not lead to increases in loneliness relative to their expected scores. The parameter estimates and associations in the RI-CLPM can be found in Tables 3 and 4.

4 DISCUSSION

The aim of our study was to longitudinally test the pathways from perfectionistic traits to loneliness as specified in the PSDM by examining interpersonal difficulties as mediators. Our RI-CLPMs evidenced positive associations between the random intercepts of socially prescribed and self-oriented perfectionism with interpersonal difficulties (i.e., interpersonal sensitivity and hostility) and loneliness, with stronger associations for socially prescribed perfectionism. The random intercept of other-oriented perfectionism also showed a positive association with interpersonal hostility, but was negatively associated with rejection sensitivity and was not related to loneliness. Moreover, almost all cross-lagged paths were nonsignificant, suggesting that these mediational pathways do not operate at the within-person level. Overall, our RI-CLPM analyses indicate that although perfectionistic traits may be associated with off-putting interpersonal behaviors to varying degrees, these behaviors may not lead to social disconnection.

Our RI-CLPM analyses align with previous empirical research, which have shown that increased interpersonal difficulties may be most relevant to socially prescribed perfectionism compared with other forms of perfectionism (Enns et al., 2005; Etherson et al., 2022; Mackinnon et al., 2014; Magson et al., 2019). We found small-to-medium associations between socially prescribed perfectionism and interpersonal difficulties but found trivial to small associations for self-oriented and other-oriented perfectionism. This suggests that the belief that others hold unrealistic high standards may be more strongly associated with the anticipation of rejection, greater hostility, and perceived social disconnection from others over time. Given the clinical and public health significance of socially prescribed perfectionism, efforts to decrease unnecessary pressures on university students and increase their management of competing demands are crucial to improve their well-being (Flett et al., 2022).

Our RI-CLPM analyses on self-oriented perfectionism also align with previous cross-sectional research that has reported statistically significant, but trivial to small associations with rejection sensitivity (Flett et al., 2014; Magson et al., 2019) and interpersonal hostility (Abdollahi et al., 2022; Stoeber et al., 2017). Our results for other-oriented perfectionism showed its relation to certain markers of interpersonal difficulties such as interpersonal hostility consistent with previous research demonstrating its association with aggressive humor, uncaring traits, anger, and aggression (Abdollahi et al., 2022; Stoeber, 2015). These results provide further support against assertions that self-oriented and other-oriented perfectionism could reflect adaptive forms of perfectionism (e.g., Blankstein et al., 2007; Stoeber et al., 2017), and therefore, further research is needed to clarify its association with functioning.

After accounting for random intercepts (i.e., between-person trait effects) in the RI-CLPM analyses, our findings did not support the theorized mediation pathways between perfectionism, interpersonal difficulties, and loneliness at the within-person level. Although our findings showed significant autoregressive within-person stability over time across almost all variables, fluctuations in perfectionism did not predict fluctuations in interpersonal difficulties or loneliness. Post hoc power analyses indicated that our sample size may have not provided enough power to detect small-to-medium effects (~β = 0.07; Orth et al., 2022) for the RI-CLPMs (see OSF for post hoc power analysis). Although the post hoc analysis should be interpreted with caution (e.g., Madjarova et al., 2022), the lack of statistical power could explain our findings and we recommend replication of these results using larger sample sizes. Nevertheless, the RI-CLPMs did not provide support for the mediational pathways theorized by the PSDM, and therefore, further research should examine other mediators of the link between perfectionistic traits and social disconnection.

Other promising mediators based on empirical research of the association between perfectionistic traits and social disconnection include communication styles, negative expectations, and social hopelessness (Barnett & Johnson, 2016; Harper et al., 2020). For example, Barnett and Johnson (2016) found that maladaptive perfectionism had a negative indirect effect on perceived social support through communication style, more specifically preciseness and verbal aggression. Moreover, Harper et al. (2020) found that negative expectations for future social interactions and social hopelessness explained the association between socially prescribed perfectionism and loneliness using experience sampling methodology. However, both these studies employed correlational designs, and therefore, conclusions about the causality of results need to be established using longitudinal designs.

The current study has several limitations that should be considered. First, the findings may be limited to the time span investigated between waves (i.e., three waves across 3 months). Future research should investigate whether the pathways investigated may take longer to emerge (e.g., 6 months or longer as opposed to 3 months). Second, despite demographic differences between the American and Australian sample, we could not examine whether our RI-CLPM results differed dependent on country as our multigroup analyses failed to converge most likely due to a high proportion of missing data in the American sample. Third, we had a high level of participant dropout as only half of the original sample remained at Time 3. Although our missing values analysis indicated that data were not Missing at Random, we do not know whether data were Missing Completely at Random or Missing Not at Random. As data could potentially be Missing Not at Random (e.g., probability of missingness depends on the missing values), the use of maximum likelihood estimator could still lead to bias (Graham, 2009). Fourth, our study used self-report measures and future research should collect data using multisource designs such as collecting informant reports to increase the validity of the findings. Fifth, our study was restricted to three time points, which only allowed a partial test of the PSDM. Future research incorporating four or more waves of data could provide more comprehensive temporal tests of the full model inclusive of psychological distress outcomes. Sixth, the current study was limited to socially prescribed, self-oriented, and other-oriented perfectionistic traits; however, future research could explore other forms of perfectionism found in the literature (e.g., self-presentational forms and cognitions) to test the generalizability of the PSDM.

Collectively, the current findings suggest that socially prescribed, self-oriented, and other-oriented perfectionism may be related to interpersonal difficulties to varying degrees at the between-person level. Thus, intervention efforts can be directed toward helping individuals with higher levels of perfectionistic traits (relative to others) to improve their social functioning. For instance, several studies have found mindfulness to be associated with lower levels of rejection-sensitive behavior, increased perceived social connectedness, and less retribution after perceived rejection (Hafner et al., 2019; Peters et al., 2016; Sakiz & Sariçam, 2015). Furthermore, self-compassion has been found to moderate the link between rejection sensitivity and depressive symptoms such that higher self-compassion leads to lower depressive symptoms (Borawski & Nowak, 2022; Jiang et al., 2021). However, as our findings showed that off-putting interpersonal behaviors did not mediate the perfectionistic traits and social disconnection link at the within-person level, further research is needed to examine potential mediators that can be targeted to improve outcomes for university students experiencing difficulties due to perfectionism.

AUTHOR CONTRIBUTIONS

Shanara Visvalingam contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing—original draft, and writing—review and editing. Natasha R. Magson contributed to formal analysis and writing—review and editing. Amie R. Newins contributed to investigation, data curation, and writing—review and editing. Melissa M. Norberg contributed to conceptualization, methodology, writing—review and editing, and supervision.

ACKNOWLEDGMENTS

The authors would like to thank Marika Blonner for their contribution to project administration on this study. This research was not preregistered. Open access publishing facilitated by Macquarie University, as part of the Wiley - Macquarie University agreement via the Council of Australian University Librarians.

FUNDING INFORMATION

This work was supported by a Research Training Program (RTP) scholarship and a Centre for Emotional Health COVID-19 grant both awarded to the first author.

CONFLICT OF INTEREST STATEMENT

None of the authors have a conflict of interest to disclose.

ETHICS STATEMENT

This study was approved by the Human Research Ethics Committee at Macquarie University and Institutional Review Board at the University of Central Florida.

Open Research

DATA AVAILABILITY STATEMENT

The data and code generated in the current study and the full list of research measures conducted as part of the larger study are available at: https://osf.io/eub9t/?view_only=e139983ba6364b55ba7a894b88864847.

Supporting Information

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Citing Literature

Volume92, Issue4

August 2024

Pages 1024-1036

Going it alone: Examining interpersonal sensitivity and hostility as mediators of the link between perfectionism and social disconnection

Correction(s) for this article

Correction to “Going It Alone: Examining Interpersonal Sensitivity and Hostility as Mediators of the Link Between Perfectionism and Social Disconnection”

Abstract

Objective

Methods

Results

Conclusion

1 INTRODUCTION

2 METHODS

2.1 Participants

2.2 Measures

2.2.1 Perfectionistic traits

2.2.2 Rejection sensitivity

2.2.3 Interpersonal hostility

2.2.4 Loneliness

2.2.5 Demographics

2.3 Procedure

2.4 Statistical analyses

3 RESULTS

3.1 Missing data

3.2 Data screening

3.3 Participant characteristics

3.4 Random-intercept cross-lagged panel models

3.4.1 Socially prescribed perfectionism

3.4.2 Self-oriented perfectionism

3.4.3 Other-oriented perfectionism

4 DISCUSSION

AUTHOR CONTRIBUTIONS

ACKNOWLEDGMENTS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

ETHICS STATEMENT

Open Research

DATA AVAILABILITY STATEMENT

Supporting Information

REFERENCES

Citing Literature

References

Related

Information