Perfectionism, worry, rumination, and distress: A meta-analysis of the evidence for the perfectionism cognition theory

Abstract

The perfectionism cognition theory (PCT) provides a theoretical account of the cognitive mechanisms of perfectionism, with an emphasis on the function of cognitive perseveration. The framework of PCT provides three central themes: (1) that both self-oriented perfectionism (SOP) and socially prescribed perfectionism (SPP) are correlated with rumination and worry; (2) that perfectionists are more likely to suffer from interrelated forms of cognitive perseveration; (3) that rumination and worry are important variables that contribute to vulnerability to emotional distress and physical illness. A meta-analysis was performed to examine the three themes of PCT. The results suggested that both SOP and SPP were positively correlated with worry and rumination. Moderator analyses suggested that both SOP and SPP are correlated with various forms of cognitive perseveration and meta-analytic mediation models suggested worry and rumination contribute to the relationship between perfectionism and distress. The findings of this meta-analysis provide evidence to support the three themes of PCT and shed light on the mechanisms and processes of perfectionism cognition.

Full text

PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
Perfectionism, Worry, Rumination, and Distress: A Meta-Analysis of the Evidence for
the Perfectionism Cognition Theory
Abstract
The perfectionism cognition theory (PCT) provides a theoretical account of the
cognitive mechanisms of perfectionism, with an emphasis on the function of cognitive
perseveration. The framework of PCT provides three central themes: (1) that both
self-oriented perfectionism (SOP) and socially prescribed perfectionism (SPP) are
correlated with rumination and worry; (2) that perfectionists are more likely to suffer
from interrelated forms of cognitive perseveration; (3) that rumination and worry are
important variables that contribute to vulnerability to emotional distress and physical
illness. A meta-analysis was performed to examine the three themes of PCT. The
results suggested that both SOP and SPP were positively correlated with worry and
rumination. Moderator analyses suggested that both SOP and SPP are correlated with
various forms of cognitive perseveration and meta-analytic mediation models
suggested worry and rumination contribute to the relationship between perfectionism
and distress. The findings of this meta-analysis provide evidence to support the three
themes of PCT and shed light on the mechanisms and processes of perfectionism
cognition.
Keywords: rumination, worry, cognitive perseveration, perfectionism cognition
theory
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
1. Introduction
During the past two decades, important studies have provided empirical evidence
for and theoretical models of perfectionism (e.g. Frost, Marten, Lahart, & Rosenblate,
1990; Hewitt & Flett, 1991; Stoeber & Otto, 2006). Researchers have agreed that
perfectionism is a vulnerability factor for psychological issues like depression and
anxiety (e.g. Flett, Madorsky, Hewitt, & Heisel, 2002; Frost & DiBartolo, 2002).
Recently, Flett, Nepon and Hewitt (2016) proposed the perfectionism cognition theory
(PCT) to provide a theoretical account of the cognitive mechanisms of perfectionism,
with an emphasis on the function of cognitive perseveration. As two comparable
forms of cognitive perseveration, rumination and worry play an important role in the
perfectionism-distress link because perfectionists who are at risk of mental health
issues tend to ruminate and worry. Therefore, the correlation between cognitive
perseveration and perfectionism could shed light on the cognitive processes and
mechanisms of perfectionism.
1.1. Perfectionism
Perfectionism is defined as the setting of and striving for excessively high
standards of personal performance and overly critical evaluation of oneself (Frost et
al., 1990). Early researchers treated perfectionism as a unitary construct, describing
perfectionism as a fear of failure due to poor self-esteem (Beck, 1976; Ellis, 1962;
Missildine, 1963), and early unidimensional perfectionism measurements, such as the
perfectionism subscales of the Eating Disorder Inventory (Garner, Olmstead, &
Polivy, 1983) and the Burns Perfectionism Scale (Burns, 1980), were adopted.
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Hamachek (1978) was the first to propose a multifarious conceptualisation of
perfectionism and to suggest that the different dimensions of perfectionism have
significant functional influence on positive and negative outcomes. Consequently,
researchers developed multidimensional perfectionism measurements that provided
theoretical and empirical advances in terms of the multidimensional construct of
perfectionism (Stoeber & Otto, 2006).
The Multidimensional Perfectionism Scale (MPS-F) was the first
multidimensional perfectionism measurement to assess six dimensions of
perfectionism, including personal standards, organisation, concern over mistakes,
doubts about actions, parental expectations, and parental criticism (Frost et al., 1990).
The MPS-F has been widely used due to its good psychometric properties (Frost &
DiBartolo, 2002). Hewitt and Flett (1991) subsequently looked at the clinical features
of perfectionism and reviewed the relevant literature and developed the
Multidimensional Perfectionism Scale (MPS-HF; Hewitt & Flett, 1991). The MPS-
HF emphasises the importance of both intrapersonal and interpersonal factors and has
three dimensions: self-oriented perfectionism (SOP), socially prescribed
perfectionism (SPP), and other-oriented perfectionism (OOP). Other
multidimensional perfectionism measurements that assess traits of perfectionism have
also emerged, such as the Almost Perfect Scale-Revised (APS-R; Slaney, Rice,
Mobley, Trippi, & Ashby, 2001), the Perfectionism Inventory (PI; Hill et al., 2004),
and the Perfectionism Questionnaire (PQ; Rhéaume, Ladouceur, & Freesto, 2000).
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This meta-analysis adopted MPS-HF as the measurement for perfectionism, because
the PCT focuses on SOP and SPP.
In the multidimensional model of perfectionism proposed by Hewitt and Flett
(1991), the three forms of perfectionism relate to beliefs, motivations, and behaviours.
Self-oriented perfectionists believe that becoming perfect is important to themselves
and set very high personal standards. If a self-oriented perfectionist fails to meet these
standards, they will criticise themselves. In comparison, socially prescribed
perfectionists believe that becoming perfect is important to others and that they need
to be perfect for others. If socially prescribed perfectionists fail to meet their high
expectations, they believe that they will be criticised by others.
In both SOP and SPP, there is an element of self-criticism, whereas other-
oriented perfectionists set high standards for others and expect others to become
perfect. If others fail to meet these standards, other-oriented perfectionists will
criticise them. Therefore, SOP and SPP focus on criticism of the self, whereas OOP
focuses on criticism of others (Hewitt & Flett, 2004). It is clear that concerns about
criticism of the self are correlated with various forms of perseverative cognition (Flett
et al., 2016), therefore, the framework of PCT focuses on SOP and SPP, but not OOP.
SOP has both adaptive and maladaptive features. On the one hand, self-oriented
perfectionists strive for high standards by using adaptive strategies, such as
achievement motivation (Klibert, Langhinrichsen-Rohling, & Saito, 2005), self-
efficacy (Mills & Blankstein, 2000), and positive affect (Frost, Heimberg, Holt,
Mattia, & Neubauer, 1993). On the other hand, SOP is also found to correlate with
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depressive symptoms (e.g. Hewitt & Flett, 1991; Olson & Kwon, 2008) as unrealistic
self-standards and harsh evaluations of the self may generate stress associated with
failure and negative emotions. In contrast, SPP is only associated with negative
psychological outcomes, such as depressive symptoms (Klibert, Langhinrichsen-
Rohling, & Saito, 2005), trait anger (Dunkley & Blankstein, 2000), suicide proneness
(Blankstein, Lumley, & Crawford, 2007), and procrastination (Xie, Yang, & Chen,
2018).
1.2. Worry and rumination
Worry and rumination are similar, but distinguishable forms of perseverative
cognition. Worry is defined as thoughts and images about affectively negative and
relatively uncontrollable future events (Borkovec, Ray, & Stober, 1998; Borkovec,
Robinson, Pruzinsky, & DePree, 1983). Rumination is characterised by repetitive,
intrusive, and past-oriented cognitions about negative experiences (Papageorgiou &
Siegle, 2003). Worry and rumination have similar thought processes (i.e. repetitive
and negative) and are both chronic (Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008),
but the type of goal orientation associated with them differs. Worry is more future-
oriented, while rumination is considered to be much more past-oriented (Watkins,
Moulds, & Mackintosh, 2005). It should also be noted that worry is focused on
events, while rumination is focused on feelings (Nolen-Hoeksema, Wisco, &
Lyubomirsky, 2008).
Self-report questionnaires are typically adopted to assess worry and rumination.
The most common measurement of worry is the Penn State Worry Questionnaire
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(PSWQ; Meyer, Miller, Metzger, & Borkovec, 1990), which is widely used to assess
trait worry. Similarly, the Intolerance of Uncertainty Scale (IUS; Buhr & Dugas,
2002) assesses worry-related cognitive factors. The most common measurements of
rumination are the Response Styles Questionnaire (RSQ; Nolen-Hoeksema &
Morrow, 1991) and the Ruminative Response Scale (RRS; Treynor, Gonzalez, &
Nolen-Hoeksema, 2003), which require participants to recall depressive experiences
and to assess the degree of their ruminative symptoms. Although these two
questionnaires are the most widely used, researchers also have developed other self-
report questionnaires of rumination (Whitmer & Gotlib, 2013), such as the
Rumination-Reflection Questionnaire (RRQ; Trapnell & Campbell, 1999) and
Rumination About an Interpersonal Offense (RIO; Wade, Vogel, Liao, & Goldman,
2008).
Individual differences and situational characteristics have been found to be the
determinants of perseverative cognition. Low self-confidence in problem-solving
abilities causes worrisome thinking (Davey, 1994) and a low internal locus of control
causes rumination (Watson , Clark, & Tellegen, 1984). Gender is a common factor
that impacts on the ruminative response style according to the response styles theory
(RST; Nolen-Hoeksema, 1987) and Nolen-Hoeksema, Girgus, and Seligman (1991)
found that women are more likely to be ruminative. Personality traits like neuroticism
and perfectionism are found to be important predictors of perseverative cognition
(Blankstein & Lumley, 2008; Wänke & Schmid, 1996). Several models of
perseverative cognition also highlight the importance of goals as the failure of goal
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attainment leads to repetitive thought, which is likely to continue until the goal is
achieved (Higgins & Carver, 1990; Martin & Tesser, 1996). These models suggest that
the disruption of goals that are important to an individual’s life is more likely to cause
perseverative cognition.
Several findings have provided evidence that perseverative cognition is
correlated with distress and that it can maintain and exacerbate negative affect in
several ways. Repetitive thoughts make individuals dwell on their problems rather
than solve them or reduce the difficulties they face, which causes a high level of
perceived stress (Just & Alloy, 1997; Nolen-Hoeksema, 2000). Perseverative
cognition can prolong negative affect as individuals tend to interpret their life events
in an irrational and negative way (Nolen-Hoeksema, Morrow, & Fredrickson, 1993).
Distress can also trigger worry and rumination, which then makes individuals feel
more distressed, leading to an emotional and cognitive vicious cycle (Spasojević &
Alloy, 2001). Lyubomirsky and Tkach (2004) suggested that the correlation between
perseverative cognition and distress is due to impaired motivation, biased memory,
and poor health behaviours.
1.3. Perfectionism Cognition Theory
PCT is a theoretical model that provides a framework for perfectionism to
explicate its cognitive processes and mechanisms (Flett et al., 2016). This model
highlights the importance of two forms of perseverative cognition: worry and
rumination. The framework of PCT provides three central themes: (1) that both SOP
and SPP are correlated with rumination and worry; (2) that perfectionists are more
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likely to suffer from interrelated forms of cognitive perseveration; (3) that rumination
and worry are important variables that contribute to vulnerability to emotional distress
and physical illness.
The first theme of PCT focuses on the relationship between perfectionism and
perseverative cognition and suggests that perfectionists are more likely to be chronic
overthinkers. Perfectionists fear negative evaluations by themselves and others, which
can be magnified by worry and rumination. It is clear that evaluation fears, which
activate various forms of cognitive perseveration, can contribute to the relationship
between perfectionism and cognitive perseveration. Self-evaluative and socially
evaluative concerns are important to the nature of perfectionism and provide a
theoretical account of perseverative cognition among perfectionists. The findings of
empirical studies have provided evidence that both SOP and SPP are correlated with
worry and rumination in college and community samples (Olson & Kwon, 2008;
Randles, Flett, Nash, McGregor, & Hewitt, 2010; Senra, Merino, & Ferreiro, 2017;
Wilson, Hunter, Rasmussen, & McGowan, 2015).
Although there are evidences supported the links between perfectionism and
perseverative cognition, the magnitude of the correlation is still unclear. Meta-analysis
is a statistical method to aggregate and synthesize the existing findings from those
studies which focus on a particular topic (Glass, 1976). From this perspective, meta-
analysis is an appropriate method to evaluate the magnitude of correlations between
perfectionism and perseverative cognition. However, to the best of our knowledge,
there is no meta-analysis in terms of perfectionism and perseverative cognition.
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Therefore, the first aim of this study is to meta-analysis method to evaluate the
magnitude of the four comparable correlations between perfectionism and
perseverative cognition (SOP and worry, SPP and worry, SOP, and rumination, and
SPP and rumination).
The second theme of PCT assumes that both SOP and SPP are correlated with
various forms of cognitive perseveration. Flett et al. (2016) used case studies to
survey several types of cognitive perseveration, including mistake rumination, post-
event overthinking, and social comparison rumination, which can exacerbate and
prolong the stress and distress of perfectionists. A range of stressors can elicit various
types of cognitive perseveration that make perfectionists more likely to be concerned
about possible or actual failures. The constant pressure from the physical and social
environment can impair cognitive capacity in chronically stressed perfectionists and
empirical studies indicate that perfectionists are prone to many types of cognitive
perseveration (see Cox & Chen, 2014; Hewitt & Flett, 2007; Treynor, Gonzalez, &
Nolen-Hoeksema, 2003).
Clinical cases of perfectionists can provide evidence regarding the link between
perfectionism and various forms of cognitive perseveration, but research comparing
these links is minimal. There are various types of cognitive perseveration, therefore, it
is necessary to compare the correlations between perfectionism and the multiple types
of overthinking. To assess different forms of cognitive perseveration, researchers
adopt different measurements of worry and rumination, as mentioned above.
Moderator analysis can examine every group of correlation that depends on the
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measurements of the included studies. Therefore, the second aim of this study is to
evaluate the correlations between perfectionism and the various forms of cognitive
perseveration.
The third theme of PCT states that worry and rumination can contribute to both
mental and physical health outcomes in perfectionists. Perseverative cognition causes
perfectionists to have more intense negative emotions and stress reactions, which can
lead to perseverating perfectionists having reduced attention and cognitive capacity
and suffering from emotional and physical exhaustion. The cognitive perseveration
hypothesis proposed by Brosschot, Gerin and Thayer (2006) highlights the role of
perseverative cognition in response to a variety of stressors and suggests that worry
and rumination prolong stress-related responses and lead to poor health outcomes.
Many existing studies show that worry and rumination are correlated to psychological
distress among perfectionists (Blankstein & Lumley, 2008; Cox & Chen, 2014;
Nepon, Flett, Hewitt, & Molnar, 2011), but the correlation between cognitive
perseveration and physical illness among perfectionists has not been explored
empirically. Therefore, the present meta-analysis only focuses on the mental health of
perseverating perfectionists.
PCT highlights two contributors (rumination and worry) to the perfectionism
cognitive process and suggests that they mediate the link between perfectionism and
distress. O’Connor, O’Connor and Marshall (2007) found that rumination mediates
the correlation between SOP/SPP and depression and hopelessness, while Short and
Mazmanian (2013) found that both rumination and worry mediate the correlation
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between SPP and negative affect. To the best of our knowledge, however, there is a
lack of studies that explore the mediating role of worry in the link between SOP and
mental health. To illuminate the mechanisms of perfectionism cognition, it is therefore
necessary to explore whether worry and rumination mediate the link between
SOP/SPP and psychological distress.
PCT is a theoretical model of cognitive perfectionism’s processes and
mechanisms that provides a better understanding of the role of cognitive perseveration
in perfectionists’ psychological and physical health. The three aims of the present
meta-analysis reflect the three themes of the PCT. The first aim is to evaluate the
magnitude of the correlation between perfectionism and perseverative cognition. The
second aim is to examine the correlations between two dimensions of perfectionism
and the various forms of cognitive perseveration. The third aim is to examine the
mediating roles of cognitive perseveration between multidimensional perfectionism
and distress.
2. Methods
2.1. Literature search
The literature search was conducted using seven databases (ERIC, MEDLINE,
ProQuest Dissertations and Thesis, PsycARTICLES, PsycINFO, PubMed, and Web of
Science) and by using the following search terms: (perfectionism or perfection or
perfectionist or perfectionistic) and (rumination or ruminate or ruminative or worry
or overthinking or repetitive thought or repetitive think or cognitive perseveration).
The publication date was limited to between 1991 and 2018 because the MPS-HF was
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first developed in 1991 (Hewitt, et al., 1991). In addition, one chapter of the book by
Flett et al. (2016) that first proposed PCT was reviewed and the reference list was
examined for potential studies. Finally, we contacted several authors via email to
request potential studies and unpublished data sets. The literature search was finalised
in March 2018.
2.2. Inclusion criteria
Each study included in this meta-analysis had to meet four inclusion criteria: the
MPS-HF had to have been used to measure perfectionism; at least one form of
cognitive perseveration, including rumination and worry, had to have been measured;
the correlation coefficient r had to have been reported; and the study had to be written
in English. If a published study and an unpublished study (dissertation) used the same
data set, the published study was included. After screening the full text of the articles,
15 studies met the inclusion criteria. The study selection procedure (see Fig.1)
followed the PRISMA (Preferred Reporting Items for Systematic Review and Meta-
Analyses) guidelines (Moher, Liberati, Tetzlaff, & Altman, 2009).
Insert Fig. 1 about here
Fig.1. The PRISMA diagram (Moher et al., 2009).
2.3. Coding
Two authors coded the effect sizes and the characteristics of studies
independently. Any disagreement in the coding was resolved before conducting any
analyses. The correlation coefficient r was coded as the measure of effect size and
every study included in this meta-analysis had to have reported at least one effect size.
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We extracted four comparisons of the correlation between perfectionism and cognitive
perseveration: (i) SOP and worry (k = 7, n = 1278); (ii) SPP and worry (k = 7, n =
1278); (iii) SOP and rumination (k = 30, n = 5339); (iv) SPP and rumination (k = 31, n
= 5387). Mean age, percentage of females, country of origin, publication type,
rumination measures, worry measures, depression measures, and anxiety measures
were coded for moderators. Table 1 shows the studies included in this meta-analysis
and their corresponding study characteristics. To test the mediation model, we coded
four correlation matrices: (i) SOP, worry, depression, and anxiety (k = 7, n = 1278);
(ii) SPP, worry, depression, and anxiety (k = 7, n = 1278); (iii) SOP, rumination,
depression, and anxiety (k = 36, n = 6285); (iv) SPP, rumination, depression, and
anxiety (k = 37, n = 6333).
Insert Table 1 about here
2.4. Data analysis
2.4.1. Overall estimated effect sizes. A meta-analysis is used to estimate the
effect sizes from a series of empirical studies addressing the same question (Field &
Gillett, 2010). We conducted the current meta-analysis by using the correlation
coefficient r as the measure of effect size. Although Hedges and Olkin (1985)
suggested that Fisher’s r-to-z transformation can be performed for combining
correlation coefficients, researchers do not completely agree with this procedure (see,
Field, 2001; Hafdahl, 2009; Hunter & Schmidt, 1990). As a meta-analytic mediation
model requires raw correlation matrices, the correlations in this meta-analysis were
not transformed to Fisher’s z for the sake of consistency. In addition, the 95%
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confidence intervals of the overall mean effect sizes were reported. Fixed-effects
models are adopted for studies with homogeneity, whereas random-effects models are
adopted for studies with heterogeneity (Cooper, Hedges, & Valentine, 2009). A
significant Q value represents the statistical heterogeneity between studies and
indicates the variation among the effect sizes. I2 reflects the percentage of total
variability due to heterogeneity (Higgins, Thompson, Deeks, & Altman, 2003).
As studies with significant findings are more likely to be published than those
with findings supporting the null hypothesis (Field & Gillett, 2010), publication bias
should be estimated in a meta-analysis. Three strategies were adopted to assess
publication bias in the current meta-analysis. Firstly, Duval and Tweedie’s (2000) trim
and fill method was used to assess the symmetry of the effect size distributions in the
funnel plot. This method estimates the number of missing studies according to the
most extreme results in the meta-analysis and recalculates the estimated effect size to
make the funnel plot more symmetrical. Publication bias exists when the estimated
effect size changes significantly after using the trim and fill method.
Secondly, the fail-safe N method was used to assess publication bias by
calculating the number of studies that would be needed to change the conclusion from
significant to non-significant (Vevea &Woods, 2005). If the fail-safe N is greater than
5k + 10 (k is the number of effect sizes), the result can be considered robust. Thirdly,
Begg’s adjusted rank test (Begg & Mazumdar, 1994) and Egger’s linear regression
test (Egger, Smith, Schneider, & Minder, 1997) were examined for each meta-
analysis. Begg’s adjusted rank test computes Kendall’s tau for the correlation between
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the effect size and the sample size and Egger’s linear regression test assesses the
asymmetry of the funnel plots for every effect size. When the statistical p value is less
than .05, it indicates that there is publication bias in the meta-analysis. The metafor
package (Viechtbauer, 2010) was performed for overall effect sizes analyses in the R
environment.
2.4.2. Moderator analyses. Moderators were tested for in the correlations
between perfectionism and cognitive perseveration. As two types of moderators
(categorical and continuous) exist, we conducted corresponding analyses to examine
the moderators that accounted for the heterogeneity in the true effects. Subgroup
analyses were conducted for categorical moderators and meta-regressions were
conducted for continuous moderators (Borenstein, Hedges, Higgins, & Rothstein,
2009). Moderator analyses were also carried out in the metafor package.
2.4.3. Meta-analytic mediation model. To examine the mediating role of
cognitive perseveration between multidimensional perfectionism and distress, we
proposed four mediation models. In these models, two dimensions of perfectionism
(SOP and SPP) were the independent variables, rumination and worry were the
mediating variables, and distress was the dependent variable. Distress was a latent
variable loading from depression and anxiety as these are the most common distress
symptoms. Four correlation matrices were extracted to test the mediation models.
Two-stage structural equation modelling (TSSEM) was performed to test these
four mediation models (Cheung, 2015; Cheung & Chan, 2005; Cheung & Chan,
2009). TSSEM combines meta-analysis procedure and structural equation modelling.
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In the first stage, the pooled correlation matrix is estimated and the homogeneity of
the correlation matrices is tested. In the second stage, the proposed model is tested by
using the fixed-effects model and the random-effects model according to the results of
the homogeneity test from the first stage. The path coefficients and the standard errors
of those path coefficients were calculated to conduct the Sobel test, the Aroian test,
and the Goodman test for the mediation tests. The metaSEM package (Cheung, 2015)
and the lavaan package (Rosseel, 2012) were used to test the mediation effect in the R
environment.
3. Results
3.1. Study characteristics
Fifteen articles were included in the current meta-analysis (see Table 1) and four
published articles reported seven samples for the effect sizes of the SOP-worry link
and the SPP-worry link. A total of 1,278 participants with a mean age of 22.18 (78%
female) were from one country (Canada). The 12 articles (two unpublished
dissertations and 10 published journals) reported 30 samples for the effect size of the
SOP-rumination link. A total of 5,339 participants with a mean age of 25.62 (70%
female) were from four countries (Canada, Spain, UK, and USA). Thirteen articles
(two unpublished dissertations and 11 published journals) reported 31 samples for the
effect size of the SPP-rumination link. A total of 5,387 participants with a mean age of
25.61 (70% female) were from five countries (Australia, Canada, Spain, the UK, and
the USA). The number of effect sizes for the SPP-rumination link was larger than that
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for the SOP-rumination link because one article reported only one dimension of
perfectionism (SPP).
3.2. Overall estimated effect sizes
Each comparison of the two dimensions of perfectionism and the two forms of
cognitive perseveration were examined. Table 2 shows the overall mean effect sizes
(with corresponding confidence interval), the estimates of heterogeneity (Q value and
I2), and the results of the publication bias tests, i.e. results of Duval and Tweedie’s
(2000) trim and fill method, fail-safe N, and estimates of Begg’s adjusted rank test and
Egger’s linear regression test.
Insert Table 2 about here
3.2.1. SOP and worry. The overall mean correlation between SOP and worry
was significant: r = 0.33, with a 95% CI from 0.28 to 0.38, p < 0.001 under the fixed-
effects model. The results of the homogeneity tests showed existing homogeneity
across studies (Q = 5.00, p = 0.54, I2 = 0). There were no missing studies according to
Duval and Tweedie’s (2000) trim and fill method (ka = 0) and the overall mean
correlation remained the same (rb = 0.33, p < 0.001). The result of fail-safe N was 416,
which suggested that the result was robust. The results of Begg’s adjusted rank test
(Kendall's tau = -0.24, p = 0.56) and Egger’s linear regression test (z = -0.32, p =
0.75) were not significant, suggesting that there was no potential publication bias.
3.2.2. SPP and worry. The overall mean correlation between SPP and worry
was significant: r = 0.43, with a 95% CI from 0.38 to 0.47, p < 0.001 under the fixed-
effects model. The results of the homogeneity tests showed existing homogeneity
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across studies (Q = 6.46, p = 0.54, I2 = 18%). There were no missing studies
according to Duval and Tweedie’s (2000) trim and fill method (ka = 0) and the overall
mean correlation remained the same (rb = 0.43, p < 0.001). The result of fail-safe N
was 878, which suggested that the result was robust. The results of Begg’s adjusted
rank test (Kendall's tau = -0.14, p = 0.77) and Egger’s linear regression test (z = -0.03,
p = 0.98) were not significant, suggesting that there was no potential publication bias.
3.2.3. SOP and rumination. The overall mean correlation between SOP and
rumination was significant: r = 0.20, with a 95% CI from 0.17 to 0.22, p < 0.001
under the fixed-effects model. The results of the homogeneity tests showed existing
homogeneity across studies (Q = 40.97, p = 0.07, I2 = 34%). Three studies were
imputed by Duval and Tweedie’s (2000) trim and fill method, but the interpretation of
the overall mean correlation did not change (rb = 0.19, p < 0.001). The result of fail-
safe N was 2,379, which suggested that the result was robust. The results of Begg’s
adjusted rank test (Kendall's tau = -0.01, p = 0.91) and Egger’s linear regression test
(z = 1.36, p = 0.17) were not significant, suggesting that there was no potential
publication bias.
3.2.4. SPP and rumination. The overall mean correlation between SPP and
rumination was significant: r = 0.32, with a 95% CI from 0.27 to 0.36, p < 0.001
under the random-effects model. The results of the homogeneity tests showed existing
heterogeneity across studies (Q = 97.56, p < 0.001, I2 = 69%). Nine studies were
imputed by Duval and Tweedie’s (2000) trim and fill method, but the interpretation of
the overall mean correlation did not change (rb = 0.27, p < 0.001). The result of fail-
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safe N was 6,941, which suggested that the result was robust. The results of Begg’s
adjusted rank test (Kendall's tau = -0.02, p = 0.92) and Egger’s linear regression test
(z = 0.83, p = 0.40) were not significant, suggesting that there was no potential
publication bias.
3.3. Moderator analyses
Several moderator analyses were conducted to examine the moderation effects of
mean age, percentage of females, country of origin, publication type, and measures
using the mixed-effects model. A summary of coefficients (k, N, r, 95% CI, df, and
Qwithin) among categorical moderators is shown in Table 3. A summary of coefficients
(intercept, ß, SE, 95% CI for ß, and Z) among continuous moderators is shown in
Table 4. Publication type significantly moderated the correlation between SOP and
rumination (Qwithin = 7.36, p < 0.01) and country significantly moderated the
correlation between SPP and rumination (Qwithin = 21.11, p < 0.001).
Insert Table 3 about here
Insert Table 4 about here
3.4. Mediation analysis
To explore the mediating role of cognitive perseveration between perfectionism
and distress, corresponding correlation matrices were extracted (as mentioned in the
coding procedure). Four mediation models were proposed using TSSEM. In the first
stage, the correlations among variables were calculated using the same method as the
estimation of effect sizes. In addition, the homogeneity of the correlation matrices was
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
estimated by the value of fit indices, including the chi-square test (

2), degree of
freedom (df), the root-mean-square error of approximation (RMSEA), the
standardised root mean square residual (SRMR), and the comparative fit index (CFI).
In the second stage, the fit indices of the mediation models were estimated to test
these four proposed models. To examine the significance of the indirect effects, raw
regression coefficients for the associations and their standard errors were calculated to
conduct the Sobel test, the Aroian test, and the Goodman test.
3.4.1. Mediating role of worry between SOP and distress. In the first stage, all
the correlations among variables were significant, indicating that it was appropriate to
conduct TSSEM. The model-fitting statistics (

2 = 7.26, df = 10, RMSEA = 0.00,
SRMR = 0.03, CFI = 1.00) indicated homogeneity of the correlation matrices,
therefore, the TSSEM analysis used a fixed-effects model in both stages. In the
second stage, the fit indices of the mediation model were

2 = 0.62, df = 1, RMSEA =
0.00, SRMR = 0.02, and CFI = 1.00, indicating that the proposed model fitted the data
very well. Fig. 2 shows the proposed mediation model and the corresponding
parameters. The results show that the indirect effect of SOP on distress via worry
(Sobel test = 7.77, p < 0.001; Aroian test = 7.76, p < 0.001; Goodman test = 7.79, p <
0.001) was significant, indicating that worry plays a mediating role in the relationship
between SOP and distress.
Insert Fig. 2 about here
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
Fig.2. Path model of the mediating role of worry in the relationship between SOP and
distress. N (sample size) = 1278.
3.4.2. Mediating role of worry between SPP and distress. In the first stage, all
the correlations among variables were significant, indicating that it was appropriate to
conduct TSSEM. The model-fitting statistics (

2 = 8.35, df = 10, RMSEA = 0.00,
SRMR = 0.04, CFI = 1.00) indicated homogeneity of the correlation matrices,
therefore, the TSSEM analysis used a fixed-effects model in both stages. In the
second stage, the fit indices of the mediation model were

2 = 1.35, df = 1, RMSEA =
0.02, SRMR = 0.02, and CFI = 1.00, indicating that the proposed model fitted the data
very well. Fig. 3 shows the proposed mediation model and the corresponding
parameters. The results show that the indirect effect of SPP on distress via worry
(Sobel test = 6.80, p < 0.001; Aroian test = 6.79, p < 0.001; Goodman test = 6.81, p <
0.001) was significant, indicating that worry plays a mediating role in the relationship
between SPP and distress.
Insert Fig. 3 about here
Fig.3. Path model of the mediating role of worry in the relationship between SPP and
distress. N (sample size) = 1278.
3.4.3. Mediating role of rumination between SOP and distress. In the first
stage, all the correlations among variables were significant, indicating that it was
21

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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
appropriate to conduct TSSEM. The model-fitting statistics (

2 = 590.60, df = 125,
RMSEA = 0.15, SRMR = 0.12, CFI = 0.84) indicated heterogeneity of the correlation
matrices, therefore, the TSSEM analysis used a random-effects model in both stages.
In the second stage, the fit indices of the mediation model were

2 = 4.17, df = 1,
RMSEA = 0.02, SRMR = 0.04, and CFI = 1.00, indicating that the proposed model
fitted the data very well. Fig. 4 shows the proposed mediation model and the
corresponding parameters. The results show that the indirect effect of SOP on distress
via worry (Sobel test = 6.84, p < 0.001; Aroian test = 6.82, p < 0.001; Goodman test =
6.85, p < 0.001) was significant, indicating that rumination plays a mediating role in
the relationship between SOP and distress.
Insert Fig. 4 about here
Fig.4. Path model of the mediating role of rumination in the relationship between SOP
and distress. N (sample size) = 6285.
3.4.4. Mediating role of rumination between SPP and distress. In the first
stage, all the correlations among variables were significant, indicating that it was
appropriate to conduct TSSEM. The model-fitting statistics (

2 = 609.13, df = 128,
RMSEA = 0.15, SRMR = 0.12, CFI = 0.88) indicated heterogeneity of the correlation
matrices, therefore, the TSSEM analysis used a random-effects model in both stages.
In the second stage, the fit indices of the mediation model were

2 = 1.13, df = 1,
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
RMSEA = 0.00, SRMR = 0.02, and CFI = 1.00, indicating that the proposed model
fitted the data very well. Fig. 5 shows the proposed mediation model and the
corresponding parameters. The results show that the indirect effect of SPP on distress
via worry (Sobel test = 6.48, p < 0.001; Aroian test = 6.47, p < 0.001; Goodman test =
6.50, p < 0.001) was significant, indicating that rumination plays a mediating role in
the relationship between SPP and distress.
Insert Fig. 5 about here
Fig.5. Path model of the mediating role of rumination in the relationship between SOP
and distress. N (sample size) = 6333.
4. Discussion
The overall purpose of the present study was to examine the three themes of PCT
by conducting a meta-analysis. The specific aims were to evaluate the magnitude of
the correlations between perfectionism and perseverative cognition, to examine the
correlations between two dimensions of perfectionism and various forms of cognitive
perseveration, and to examine the mediating roles of cognitive perseveration between
multidimensional perfectionism and distress. Correspondingly, we first calculated the
overall estimated effect sizes of four comparable correlations between perfectionism
and cognitive perseveration. We then analysed two moderators, especially the
measurements of worry and rumination, and finally examined four mediation models
to test the roles of worry and rumination in the relationships between SOP and SPP
and psychological distress.
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
This study is the first meta-analytic review of the relationship between
perfectionism and cognitive perseveration and finds that both SOP and SPP are
positively correlated with worry and rumination. These results are consistent with the
previous qualitative summary (Flett et al., 2016) and provide further evidence to
support the first theme of PCT. The findings also suggest that according to Cohen’s
(1992) guidelines, SOP has a medium positive relationship with worry and a small-to-
medium positive relationship with rumination, while SPP has a medium-to-large
positive relationship with worry and a medium positive relationship with rumination.
As described above, both SOP and SPP capture the evaluation fears that activate
worry and rumination. Perfectionists tend to perceive constant pressure in their life,
focus on negative events, fear being criticised, and subsequently worry about the
future and ruminate about the past (Flett & Hewitt, 2002; Hewitt & Flett, 1996).
The present understanding of perfectionism dimensions indicates that
perfectionism involves two higher order dimensions: perfectionistic strivings (PS) and
perfectionistic concerns (PC) (see Stoeber & Otto, 2006). SOP as an indicator of PS is
regarded as an adaptive form of perfectionism correlated with positive characteristics,
whereas SPP as an indicator of PC is regarded as a maladaptive form of perfectionism
correlated with negative characteristics. The results of this study indicate that SOP
and SPP have a positive relationship with worry and rumination, however, which is
not consistent with this model of perfectionism. Some other models of perfectionism
also regard SOP as adaptive dimension of perfectionism (see Bieling, Israeli, &
Antony, 2004; Dunkley & Blankstein, 2000). Stoeber and Otto (2006) outlined that
24

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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
PS and PC are typically correlated and they show considerable overlap. Consequently,
PS (an equivocal dimension) links to both adaptive and maladaptive outcomes.
To investigate this problem, the relationships between the dimensions of
perfectionism and the outcomes were examined in three reviews of perfectionism by
conducting both bivariate and partial correlations (i.e. controlling for correlations in
different dimensions of perfectionism) (Gotwals, Stoeber, Dunn, & Stoll, 2012; Hill &
Curran, 2015; Stoeber & Otto, 2006). To compare the partial correlation coefficients
with the bivariate correlation coefficients, researchers could understand correlations
between PS and PC. In the future, researchers may take the results of partial
correlations among dimensions of perfectionism into consideration to explore the
effects of the pure dimensions of perfectionism. Moreover, PCT states that other
elements of evaluative concerns perfectionism (concerns over mistakes and doubts)
are correlated with worry and rumination, therefore future studies can adopt various
measurements of perfectionism to examine the relationship between two higher order
dimensions (PS and PC) and cognitive perseveration. The Big Three Perfectionism
Scale (BTPS) developed by Smith and colleagues (2016) could provide an
understanding of the correlations between SOP and rumination and worry.
Specifically, the BTPS disentangles SOP from self-worth contingencies and this might
make sense of the link between the rigid insistence of perfectionists and rumination
and worry.
In terms of moderation, the current study examined two moderators: measures of
worry and rumination. The results of the subgroup analyses suggest that most
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
correlations between perfectionism and various forms of cognitive perseveration are
positively significant, although two correlations were not significant when studies
adopted CERQ as the rumination measure. The findings of this meta-analysis provide
evidence that partly supports the second theme of PCT. Some forms of cognitive
perseveration mentioned in PCT have not been examined in the current study,
however, because there is a lack of specific measurements for these types of cognitive
perseveration.
In the four comparisons of the correlation between perfectionism and cognitive
perseveration, only the correlation between SPP and rumination showed existing
heterogeneity across studies. Country may alter this relationship, indicating that the
link between SPP and rumination in English-speaking countries is stronger than in
Spain. This could be because Senra, Merino and Ferreiro (2017) adopted the Spanish
version of the MPS and the RRS in which some items of measurement might be
adjusted for Spanish, or because cultural differences impact this relationship. It is
evident that perfectionism and its correlates show cultural variations (e.g. Stoeber,
Kobori, & Tanno, 2013). Considering the cultural differences in the link between
perfectionism and cognitive perseveration, future studies could compare the influence
of Western and Eastern culture on this relationship.
In terms of mediation, the current study examined four mediation models and
found that worry and rumination have mediating roles in the relationship between
perfectionism and distress. The findings of this meta-analysis provide evidence to
support the third theme of PCT, which states that worry and rumination contribute to
26

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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
the link between perfectionism and distress. The findings are partly inconsistent with
the previous research, however. For example, Short and Mazmanian (2013) found that
worry and rumination mediate the relationship between SPP and negative affect, but
that SOP was not significantly correlated with distress. In contrast to the findings of
Short and Mazmanian, some studies provide evidence that the link between SOP and
distress is significant (e.g. Hewitt & Flett, 1991; Olson & Kwon, 2008). This meta-
analysis synthesises several studies and finds a significantly positive relationship
between SOP and distress and a mediating role of rumination and worry in this
relationship. The results of the meta-analytic mediation model illuminate the
mechanisms of perfectionism cognition and provide a better understanding of the link
between perfectionism and distress.
To examine the mediating roles of worry and rumination in the link between
perfectionism and distress, longitudinal studies would be appropriate to test mediation
models and help understand causality. Olson and Kwon (2008) designed a four-week
longitudinal study to examine perfection, rumination, and depression, and found that
perfectionism interacted with rumination and stress and could predict depression.
More longitudinal studies like this are needed as they could help identify the
dynamics of the perfectionism-distress relationship and might clarify the cognitive
process over time.
In fact, the third theme of PCT proposes that cognitive perseveration not only
contributes to psychological distress, but also contributes to physical illness among
perfectionists. The cognitive perseveration hypothesis mentioned in the introduction
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
suggests that cognitive perseveration may negatively impact physical health by
prolonging the stress response (Brosschot, Gerin, & Thayer, 2006). Several studies
provide evidence that cognitive perseveration is correlated with poor physical well-
being, e.g. cardiovascular disease (Brosschot, Van, & Thayer, 2007; Chalmers,
Quintana, Abbott, & Kemp, 2014), an impoverished immune system (Thomsen et al.,
2004), and poor sleep quality (Zoccola, Dickerson, & Lam, 2009). To the best of our
knowledge, however, only one study has explored the link between perfectionism and
health symptoms from a cognitive perspective that highlights the role of
perfectionistic automatic thoughts (Flett, Molnar, Nepon, & Hewitt, 2012). Future
studies could explore the contributory roles of worry and rumination in the
relationship between perfectionism and physical illness.
It should be noted that this meta-analysis has some limitations. Firstly, all the
studies included examined perfectionism using the MPS-HF (Hewitt & Flett, 1991),
because PCT focuses on SOP and SPP. PCT also states that other elements of
evaluative concerns perfectionism, such as concern over mistakes (COM) and doubts
about actions (DAA), are correlated with worry and rumination, but the current study
did not examine other dimensions of perfectionism. Future studies could examine
other dimensions of perfectionism by using several different measurements. Secondly,
all the studies included in this meta-analysis were done with non-clinical sample.
When researchers try to study perfectionism in clinical samples, they prefer to use the
MPS-F because of it’s good psychometric properties (Frost & DiBartolo, 2002).
Theoretically, the PCT could provide an explanation for both clinical and non-clinical
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PERFECTIONISM, WORRY, RUMINATION, AND DISTRESS
samples, so future studies could examine the PCT in clinical samples. Thirdly,
although the number of studies in this area is growing, the literature is relatively small
and only 15 articles met the inclusion criteria for this meta-analysis. Lastly, the
mediation analyses required correlation matrices for perfectionism, worry, rumination,
depression, and anxiety, but some of the included research used more than one
measurement to evaluate a variable. This would lead to a large inflation of effect
sizes, which should be taken into consideration when interpreting the results.
To conclude, the current study provides evidence for the three themes of PCT.
Firstly, it was found that both SOP and SPP were positively correlated with worry and
rumination. Specifically, SOP had medium and small-to-medium positive
relationships with worry and rumination, respectively, while SPP had medium-to-
large and medium positive relationships with worry and rumination, respectively.
Secondly, there was evidence that both SOP and SPP were correlated with various
forms of cognitive perseveration. Thirdly, the mediation results suggested that worry
and rumination have mediating roles in the relationship between perfectionism and
distress. In other words, cognitive perseveration contributes to mental health
outcomes among perfectionists. Overall, the findings of this meta-analysis support the
main arguments of PCT and shed light on the mechanisms and processes of
perfectionism cognition.
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