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1. Psychology Learning and Teaching
Volume 12 Number 1 2013
www.wwwords.co.uk/PLAT
4 http://dx.doi.org/10.2304/plat.2013.12.1.4
Adaptive Perfectionism, Maladaptive Perfectionism
and Statistics Anxiety in Graduate Psychology Students
VICTORIA COMERCHERO & DOMINICK FORTUGNO
Touro College, New York, USA
The current study examined if correlations between statistics anxiety and dimensions of perfectionism
(adaptive and maladaptive) were present amongst a sample of psychology graduate students (N = 96).
Results demonstrated that scores on the APS-R Discrepancy scale, corresponding to maladaptive
perfectionism, correlated with higher levels of statistics anxiety with several subscales of the Statistics
Anxiety Rating Scale (STARS). Results also demonstrated negative correlations between bilingualism
and dimensions of statistics anxiety. Implications for future research and practical implications are
presented.
Introduction
Much to the chagrin of many social science majors, statistics is generally a required course for both
graduation as well as advancement in master’s and doctoral level programs. Many students regard
statistics as the most difficult and least pleasant course (Berk & Nanda, 1998). Additionally, research
has indicated that courses in statistics produce perhaps the highest rates of anxiety especially within
non-mathematics majors (Zeidner, 1991). Statistics anxiety has been defined as anxiety that occurs
as a result of encountering statistics in any form and at any level (Onwuegbuzie, DaRos, & Ryan,
1997). According to research, prevalence rates of statistics anxiety within graduate social science
students have ranged from 75% to 80% (Onwuegbuzie, Slate, Patterson, Watson, & Schwartz,
2000). Related to this, statistics examinations have been demonstrated to be more anxiety inducing
than other types of tests (Baloglu, 2003; Onwuegbuzie & Seaman, 1995). Statistics anxiety is
considered to be situation specific because it tends to emerge when students are learning statistical
applications, vocabulary, formulae or applying statistics in a particular context (Benson & Bandalos,
1989, cited in Onwuegbuzie, 2004).
More specifically, Cruise, Cash, and Bolton (1985) identified the following six components of
statistics anxiety upon which the STARS (Statistics Anxiety Rating Scale) was based: (1): worth of
statistics; (2) interpretation anxiety; (3) test and class anxiety; (4) computational self-concept; (5) fear
of asking for help; and (6) fear of statistics teachers.
Statistics anxiety has been linked to serious negative psychological outcomes including
depression, anger, negative emotionality as well as physiological problems and procrastination in
the enrolment of required statistics courses (Onwuegbuzie et al., 1997). From an educational
perspective research has consistently demonstrated negative associations between statistics anxiety
and course performance (Lalonde & Gardner, 1993; Onwuegbuzie, 1995; Onwuegbuzie & Seaman,
1995).
In a comprehensive review of the literature on statistics anxiety, three categories of
antecedent variables were identified as being linked to the causes of the phenomenon. Specifically,
Onwuegbuzie and Wilson (2003) described these as situational, dispositional and environmental
antecedents. Situational antecedents were described as factors which surround the student, such as
previous exposure to statistics courses and number of maths courses completed. Environmental

2. Adaptive Perfectionism, Maladaptive Perfectionism and Statistics Anxiety
5
antecedents were described as interpersonal factors related to classroom experiences, such as
students’ experiences with their statistics professor. Finally, dispositional antecedents were
identified as interpersonal factors that are endemic to each individual student’s personality or
makeup. Amongst the dispositional attributes identified was perfectionism (Onwuegbuzie & Daley,
1999; Walsh & Ugumba-Agwunobi, 2002).
Over the past several decades, perfectionism has been defined as a multidimensional
personality construct (Frost, Heimberg, Holt, Mattia, & Neubauer, 1993; Frost, Marten, Lahart, &
Rosenblate, 1990; Hewitt & Flett, 1990; Slaney, Rice, Mobley, Trippi & Ashby, 2001). Most
recently, Slaney et al., (2001) identified two major perfectionism dimensions --- ‘extreme or
excessive striving for perfection’ and ‘a disposition to regard anything short of perfection as
unacceptable’ (p. 131). Based on this premise, the authors developed and refined the Almost Perfect
Scale Revised (Slaney et el., 2001). The APS-R consists of two subscales, measuring two distinct
aspects of perfectionism. The adaptive perfectionism subscale is measured by the High Standards
component. This assesses the standards one sets for performance and the second, maladaptive
perfectionism, measures the perceived difference between the standards one sets for his or her
behavior and actual performance. In sum, high standards are considered to be related to the
adaptive perfectionism component, and the discrepancy corresponds to the maladaptive or
negative component.
Research applying the adaptive/maladaptive classification has linked the maladaptive
component to higher levels of problematic characteristics, such as high concerns about making
mistakes, intense and persistent self-criticism, a chronic perceived sense of inadequacy and inability
to reach goals and desired standards, as well as greater emotional distress and lower interpersonal
adjustment and poor adjustment in college students (Gilman & Ashby, 2003; Rice & Dellwo, 2002).
Furthermore, maladaptive perfectionists perceive higher rates of failure, experience mistakes as
catastrophic, and tend to undermine their successes with lower levels of self-esteem also
documented (Ashby, LoCicero, & Kenny, 2003; Rice & Slaney, 2002). Elevated levels of depression
and anxiety amongst graduate students prone to maladaptive perfectionism have been found
(Chang & Rand, 2000; Grzegorek, Slaney, Franze, & Rice, 2004; Martin, 2005; Wang, Yuen, &
Slaney, 2009; Yoon & Lau, 2008). In contrast, adaptive perfectionism has been linked to students
having high levels of striving without being excessively punitive or dissatisfied with their
performance or themselves. Adaptive perfectionism has also been associated with positive affect
and healthy psychological adjustment (Rice & Mirzadeh, 2002; Saboonchi & Lundh, 2003). Similar
findings have been replicated with college student populations, diverse cultural groups, couples and
children (Mobley, Slaney & Rice, 2005; Rice & Preusser, 2002). While a large body of research has
correlated adaptive perfectionism with positive psychological outcomes and maladaptive
perfectionism with negative correlates, no study to date has explored these with respect to statistics
anxiety. Only two studies have even examined perfectionism as a variable with respect to statistics
anxiety.
In a seminal study, Onwuegbuzie and Daley (1999) explored the relationship between
perfectionism and statistics anxiety. The authors employed Hewitt and Flett’s (1990)
multidimensional perfectionism scales. Results demonstrated that graduate students who held
unrealistic standards for others (other-oriented perfectionists) and those who maintained a
perceived need to attain standards and expectations prescribed by significant others (e.g., socially
prescribed perfectionists) generally had higher levels of statistics anxiety associated with
interpretation anxiety, test and class anxiety, computational self-concept and fear of asking for help.
One limitation of this study suggested by the authors was that no attempt was made to
differentiate normal perfectionists from neurotic perfectionists as described by Hamachek (1978).
Results of a subsequent study (Walsh & Ugumba-Agwunobi, 2002) examined the
relationships between statistics anxiety perfectionism while controlling for trait anxiety and
procrastination. Results indicated modest relationships between interpersonal perfectionism and
components of statistics anxiety. Intrapersonal perfectionism, trait anxiety and procrastination were
variables contributing to statistics anxiety. The authors concluded that fear of failure and
evaluation concern may be most responsible for statistics anxiety.
Based on the results of the two aforementioned studies, research applying the
multidimensional construct of perfectionism was warranted. No study to date has employed the
use of the adaptive/maladaptive perfectionism classification system developed by Slaney et al.

3. Victoria Comerchero & Dominick Fortugno
6
(2001) as it correlates to statistics anxiety. It follows that in examining relationships between
adaptive perfectionism, maladaptive perfectionism and statistics anxiety amongst psychology
graduate students would be a natural next step in the small body of research exploring relationships
between perfectionism variables and statistics anxiety.
Therefore the purpose of this study was to examine how adaptive and maladaptive
perfectionism dimensions as defined and measured by Slaney et al. (2001) in the APS-R correlated
to the six dimensions of statistics anxiety as assessed through the STARS (Cruise & Wilkins, 1980).
Method
Participants
Participants were drawn from the graduate school of psychology in a small northeastern college in
the USA. They were all enrolled in graduate-level statistics or applied research methods courses.
Participants were told that the purpose of the research was to examine attitudes towards
themselves and specific graduate school courses. Informed consent was obtained from all students
who participated in the study. The final sample included 75 females (78%) and 21 males (22%). The
mean age of the students was 27.5, with a range of 19-54, indicating a varied sample size with
respect to that variable. Approximately 87% of the sampled students had taken statistics as an
undergraduate; however, there was variance in terms of how much time had elapsed since the
course was completed. Ethnic identification was predominantly White/Caucasian (66%) with
African American (11%), with Hispanic/Latino (9%) comprising the majority of the remainder of
the sample. Approximately half of the students identified themselves as bilingual (43.4%). For their
completion of the survey, students were awarded several points on midterm examinations as an
incentive for participation.
Measures
In addition to a demographic questionnaire, two scales were administered to assess the main
constructs of interest. The Almost Perfect Scale Revised (APS-R) authored by Slaney et al. (2001) is
a 23-item self-report measure designed to assess levels of perfectionism. Respondents use a 7-point
Likert type scale (1 = strongly disagree, 7 = strongly agree) in responding to the items. The APS-R
is made up of three subscales: High Standards (7 items), Order (4 items), and Discrepancy (12
items). In this study only the Discrepancy scales and High Standards scales were used. The
Discrepancy scale measures to what degree respondents perceive themselves as failing to meet
personal standards for performance. Psychometric support for the subscales and three APS-R
dimensions has been extensively documented. Numerous exploratory and confirmatory factor
analyses have supported the factor structure of the measure (Slaney et al., 2001; Suddarth & Slaney,
2001). Convergent validity of APS-R scores has been demonstrated through significant positive
correlations with other perfectionism measures (Ashby & Rice, 2002). Concurrent validity has also
been supported through correlations with scores derived from other measures expected to be
associated with perfectionism such as self-esteem, depression, anxiety and shame (Ashby, Rice, &
Martin, 2006). Test-retest correlations suggest stability for APS-R scores over a three-week interval,
ranging from .72 to .83 (Grzegorek et al., 2004) and over 8-10-week intervals ranging from .76 to .87
(Rice & Aldea, 2006) Reliability has been strong as well, with Slaney et al. reporting a coefficient
alpha of .92 for the Discrepancy subscale.
To measure statistics anxiety the Statistics Anxiety Rating Scale (STARS) was used. The
STARS, developed by Cruise and Wilkins (1980), is a 51-item, 5-point Likert format instrument
which assesses anxiety in a wide range of academic situations. It consists of two parts. The first part
presents 23 situations often associated with statistics anxiety. The second part consists of 28
statements dealing with or related to statistics.
Six subscales assessing different dimensions of statistics anxiety are included. Factor one
(‘Worth of Statistics’) corresponds to a student’s perception of how valuable they see statistics
courses. Hence a person scoring high on this dimension would see little or no value in taking a
statistics courses. A student who shows elevated scores on this factor also believes that statistics
does not fit well with his or her personality, indicating a negative attitude toward statistics (Cruise

4. Adaptive Perfectionism, Maladaptive Perfectionism and Statistics Anxiety
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et al., 1985). Factor two, entitled ‘Interpretation Anxiety’ corresponds to one’s anxiety arising from
interpreting statistical data. For example, this type of anxiety may emerge when a student is asked
to decide which type of statistical test to use or how to interpret a null hypothesis (Cruise et al.,
1985). The third factor, ‘Test and Class Anxiety’, deals with anxiety related to taking either a
statistics course or examination. Students who score high on this factor may experience
significantly elevated anxiety when enrolling in or taking a statistics course, solving statistical
problems, or taking an actual statistics test (Cruise et al., 1985). Factor four, ‘Computation Self-
Concept’, entails anxiety associated with computing mathematical problems. Students scoring high
on this component experience anxiety because it involves mathematical calculations and
contributes to a student feeling uncertain or inadequate when comprehending statistics (Cruise et
al., 1985). A high score on the fifth factor, ‘Fear of Asking for Help’, reveals an apprehension of
asking a fellow student or professor for assistance with statistics problems (Cruise et al., 1985).
Finally, the sixth factor, ‘Fear of Statistics Teachers’, relates to the student’s perceptions of the
statistics teacher. The student scoring high on this factor questions the ‘humanness’ of the teacher.
This person views the statistics teacher as being less personal and human (Cruise et al., 1985).
Procedure
The questionnaires were administered to small groups of 10-20 students at the beginning or end of
class instruction. Participants were guaranteed anonymity of their responses and confidentiality of
the data since no personal identifying information was asked for on the questionnaires. Completion
of the entire survey packet typically required 20-30 minutes. Surveys were counterbalanced to
control for order effects. In some cases the APS-R was administered first and in the other half of the
cases the STARS was administered at the beginning. At the completion of participation, all subjects
were given a written debriefing statement explaining the purpose of the study.
Results
The purpose of the present study was to explore the correlations between perfectionism and
statistics anxiety by examining differences between adaptive and maladaptive perfectionism, as well
as relationships between perfectionism (adaptive and maladaptive) and other demographic
variables.
Several demographic variables were collected as part of this study, including participants’
gender, age, and number of hours worked. Participants were also asked whether they were
bilingual and whether they had completed an undergraduate statistics course. It should be noted
that participant age demonstrated a strong negative correlation with completion of an
undergraduate statistics course (rpbis = -.36, p < .01), suggesting younger students were more likely
to have encountered statistics during undergraduate study. No significant correlation was observed
between number of hours worked and scales for statistics value, anxiety, or fears. Surprisingly,
work hours were positively correlated with the APS-R Standards scale (r = .24, p < .05), suggesting
students working a larger number of hours actually demonstrated lower expectations and tendency
toward perfectionism than their peers.
Demographic variables were compared to results of both the STARS statistics anxiety survey
and APS-R perfectionism survey. Age demonstrated a significant positive correlation with
participants’ value of statistics worth (r = .52, p < .01), indicating that younger students tended to
place a greater sense of value in statistics. Women reported significantly greater values than men
for structure and organization t(93) = 2.20, p = .03, d = .55, 95% CI [.05, 1.05], but also reported
higher levels of total statistics anxiety than men t(93) = 2.42, p = .02, d = .61, 95% CI .[.11, 1.11]. In
terms of language acquisition, bilingual students reported significantly less anxiety interpreting
statistical results than their monolingual peers t(93) = 2.45, p = .02, d = .50, 95% CI [.09, .90] and
less fear requesting assistance with statistical concepts t(93) = 2.06, p = .04, d = .42, 95% CI [.02,
.83]. There were no differences between bilingual and monolingual students in terms of
perfectionism according to the APS-R.
Students who had completed an undergraduate statistics course reported significantly more
positive perceptions of the value of statistics t(93) = 2.59, p = .01, d = .77, 95% CI [.18, 1.37] and

5. Victoria Comerchero & Dominick Fortugno
8
significantly less fear of asking for assistance with statistical concepts t(93) = 2.15, p = .03, d = .64,
95% CI [.05, 1.23]. Students with undergraduate statistics experience did not report differences in
perfectionism.
Table 1. Correlations between the STARS and APS-R.
Standards Order Discrepancy
Worth .05 .17 .03
Anx-Int .09 .06 .23*
Anx-Class .21* .13 .27**
Anx-Total .14 .13 .18
Computation .08 .10 .10
Fear-Help .04 -.16 .22*
Fear-Teacher .14 .11 .23*
Notes. Worth = worth or value of statistics; Anx-Int = anxiety related to interpreting statistics results; Anx-Class = anxiety
related to statistics classes or coursework; Anx-Total = total score for statistics anxiety; Computation = self-concept for
computation; Fear-help = fear of asking for assistance with statistics concepts; Fear-teacher = fear of or dehumanization
of statistics instructors; Standards = standards of perfectionism or expectations; Order = level of structure, organization;
Discrepancy = maladaptive scale indicating low self-esteem or perception of efficacy, statistics performance. *p < .05. **p
< .01.
Table 1 depicts correlations between the STARS and APS-R. A clear relationship emerged between
higher ratings on the APS-R Discrepancy scale, which suggest low self-esteem or self-efficacy, and
several STARS scales for statistics anxiety and fear. Specifically, the Discrepancy scale correlated
significantly with anxiety about interpreting statistics results, performance at statistics coursework,
fear of asking for assistance with statistics concepts, and fear of statistics teachers. The APS-R
Standards scale correlated positively with anxiety related to statistics classes and coursework,
indicating higher expectations and tendency towards perfectionism correlated with anxiety
regarding statistics performance in an academic setting. The APS-R Order scale did not correlate
significantly with STARS scales for statistics value, anxiety, or fears.
Discussion
The purpose of the present study was to explore the correlations between perfectionism and
statistics anxiety. Although researchers (Onwuegbuzie & Daley, 1999; Walsh & Ugumba-
Agwunobi, 2002) have found positive correlations between perfectionism and statistics anxiety
amongst graduate students, the current study adds to this emerging body of research by examining
differences between adaptive and maladaptive perfectionism, as well as relationships between
perfectionism (adaptive and maladaptive) and other demographic variables. The current study was
the first to examine the adaptive and maladaptive dimensions according to the APS-R (Slaney et al.,
2001).
The most prominent results of the current study involve the links between maladaptive
perfectionism and statistics anxiety. Graduate students who displayed higher scores on the APS-R
Discrepancy scale also demonstrated greater levels of statistics anxiety on several STARS scales.
The current results indicate a clear relationship between discrepant (maladaptive) perfectionism
and statistics anxiety, and suggest that students with low self-efficacy and poor levels of satisfaction
with their own academic performance are at risk for increased anxiety interpreting statistics results,
lower performance at statistics coursework, greater fear of asking for assistance with statistics
concepts, and increased fear of statistics teachers. Taken together, these factors may serve to
severely inhibit performance in statistics courses and exacerbate the student’s already damaged
sense of performance in this area. In addition to the APS-R Discrepancy scale, higher expectations
and a tendency towards perfectionism, as measured by the Standards scale, also occurred with
greater levels of statistics anxiety regarding academic performance. No relationship could be found
between the APS-R Order scale and statistics anxiety.
Results also suggest demographic and experience factors may help mediate statistics anxiety.
Younger students tended to place a greater sense of value in statistics, which was most likely

6. Adaptive Perfectionism, Maladaptive Perfectionism and Statistics Anxiety
9
related to the fact that these students were more likely to have completed an undergraduate
statistics course. Undergraduate statistics experience was related to substantially more positive
perceptions of the value of statistics, as well as significantly less fear asking for assistance with
statistical concepts. These results offer some evidence that requiring statistics in undergraduate
programs may help facilitate a reduction in statistics anxiety during graduate study. Additionally,
the tendency for younger students to have lower levels of statistics anxiety coincides with some
previous research suggesting that nontraditional students have higher levels of statistics anxiety
(Bell, 2003). Given that the mean age of the current sample was 27.5 and ages ranged from 19-54,
older students comprising the study were more likely to be non-traditional, in that they were
working adults, many of whom were returning to school to study after some time away from the
classroom.
One factor that may mediate statistics anxiety is bilingualism. Bilingual students reported
significantly less anxiety interpreting statistical results compared to their monolingual peers and less
fear requesting assistance with statistical concepts. It is worth noting that this difference was not
mediated by increased levels of perfectionism. The current findings suggest a link between foreign-
language acquisition and learning statistics, and support previous research by Lalonde and Gardner
(1993), who demonstrated a successful model for predicting statistics performance for psychology
students using a learning model developed within the context of second-language learners.
Finally, women reported significantly higher levels of overall statistics anxiety than men,
despite demonstrating higher levels of structure and organization. Although some evidence has
been found for gender differences in maths anxiety, a number of studies offer conflicting results
(Hsiao & Chiang, 2011). Moreover, these gender variations may be moderated by differences
between men and women in respect to self-concept, perceived self-efficacy, and internal versus
external attribution of failure (Bandalos, Yates, & Thorndike-Christ, 1995).
The results of this study provide a starting point to examine the role of maladaptive and
adaptive perfectionism as they relate to statistics anxiety. Perfectionism, and specifically
maladaptive perfectionism, as a possible dispositional antecedent as described by Onwuegbuzie and
Wilson (2003) emerged from the findings. The current investigation was limited by a relatively
small sample size and thus replications with larger sample sizes that also control for other
dispositional variables such as procrastination are warranted. Additionally, future studies may wish
to examine the role of demographic variables such as gender and bilingualism as they pertain to
relationships between perfectionism and statistics anxiety in more depth.
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VICTORIA COMERCHERO* PhD, is an assistant professor of school psychology at Touro
college, New York, USA, and a licensed psychologist. Victoria currently teaches courses in statistics,
research design, psychopathology and school consultation. Her research includes studies pertaining
to various areas including: perfectionism, training of school psychologists, and grief and
bereavement. Correspondence: victoria.comerchero@touro.edu
DOMINICK A. FORTUGNO PhD, is an assistant professor and program director of the school
psychology program at Touro College, New York, USA. Dominick currently teaches courses in
statistics, psychometrics and assessment. His research includes studies pertaining to autism,
program evaluation and scale development. Correspondence: dominick.fortugno@touro.edu
*Contact author
Manuscript received 31 January 2012
Revision accepted for publication 21 May 2012