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Relationships between Flow, Self-Concept, Psychological Skills, and Performance


The main purpose of this study was to examine psychological factors of potential predicted positive relationship between a post-event flow assessment and performance


Understanding the psychological factors that accompany successful athletic performance is a high priority for applied sport psychology, with a major area of focus being mental links to optimal performance. To advance knowledge in this area, it is important to examine specific psychological constructs with theoretical relevance to optimal performance in order to understand what psychological processes might be contributing to quality of performance. The present study examined specific links between self-concept, psychological skills and strategies, and the optimal mental state of flow, as well as relationships between flow and optimal performance.

Examination of these relationships served the purposes of furthering the study of antecedents of flow state in sport, as well as investigating the relationship between flow and quality of athletic performance.

The first and primary construct examined was flow. Flow is an optimal psychological state that occurs when there is a balance between perceived challenges and skills in an activity (Csikszentmihalyi, 1990). It is a state of concentration so focused that it amounts to absolute absorption in an activity. Research on flow in sport and exercise has increased in recent years (e.g., Jackson, 1992; 1995; 1996; Jackson, Kimiecik, Ford, & Marsh, 1998;
Jackson & Marsh, 1996; Kimiecik & Stein, 1992), and Csikszentmihalyi (1992) has encouraged application of flow theory to physical activity settings, which is where some of his initial research into flow began (Csikszentmihalyi, 1975). Based on their respective research findings, Jackson and Csikszentmihalyi (1999) have recently written a book describing flow in sport and how to attain this optimal mental state. Knowledge of factors associated with the attainment of flow is an important goal for those interested in the quality of athletes’ experience and performance in competition.

Theoretically, flow, as an optimal mental state, would be expected to be associated with optimal athletic performance as well as providing an optimal experience. Flow is generally viewed as a peak performance state, and there is some support for this assumption (e.g., Jackson & Roberts, 1992; McInman & Grove, 1991). Nonetheless, more research is needed to empirically examine the relationship between flow and performance in sport.


Correlational support for a positive relationship between ratings of flow and perceptions of peak performance was obtained by Jackson and Roberts (1992) who asked athletes to reflect on their best performances, and found flow characteristics to be endorsed. Other writers (e.g., McInman & Grove, 1991; Privette & Bundrick, 1991) have concluded that flow and peak performance share many similar characteristics, but should still be viewed as conceptually distinct. Privette and Bundrick (1991) distinguished the two concepts by defining flow as an intrinsically rewarding experience and peak performance as optimal functioning. Jackson (1996) distinguished between the two concepts by describing peak performance as a standard of accomplishment, while flow describes a psychological state.
Assessments of flow tied to specific performances are important to obtain, in order to further knowledge of how these two factors relate to each other. In a recent study by Jackson and colleagues (Jackson et al., 1998), correlational support was obtained for a relationship between self-reported flow state and ratings of perceived success with both measures taken after a competitive event. Beyond identifying any associations between flow and peak performance, it is important to ascertain whether it is possible to promote the state of flow. That is, is there a set of conditions or factors that are positively associated with athletes being able to attain flow? A preliminary study that examined this question (Stein, Kimiecik, Daniels, & Jackson, 1995) failed to identify any substantive relationship between the psychological constructs: goals, competence, and confidence, and attainment
of flow in three different sport settings: a weekend tennis tournament, college basketball activity classes, and amateur senior golf. More recently, Jackson et al. (1998) did find associations between flow and three psychological variables: intrinsic motivation (positive), perceived ability (positive), and cognitive anxiety (negative). Further research is needed to understand possible psychological antecedents of flow, at both the immediate level of the flow experience (state) as well as propensity to experience flow in general in physical activity (dispositional flow). The present study examined relationships between two sets of psychological factors predicted to be positively related to flow: athletic self-concept and psychological skills. Both dispositional and state flow experiences were included in order to examine relationships with self-reported flow experiences in general as well as with self-reported flow within particular competitive events. 


We chose to examine associations between athletic self-concept and flow because there is evidence that self-concept facilitates other favorable outcomes in sport and exercise, as well as being a desired outcome of participation itself (Marsh, Hey, Johnson, & Perry, 1997). For example, components of physical self-concept have been associated with athletic participation (e.g., Jackson & Marsh, 1986), fitness indicators (e.g., Marsh &
Redmayne, 1994), and self-esteem (e.g., Sonstroem, 1997). The significance of the physical self is evidenced by the wealth of research that is accumulating in this area, illustrated in a recent volume dedicated to the topic (Fox, 1997). Fox writes that physical self-concept has come to be viewed as an important determinant of behavior and a contributor to mental health and well-being. However, little is known about the relationship
of athlete self-concept to flow experiences. A related construct, perceived ability, has been shown to be positively related to flow (Jackson et al., 1998; Jackson & Roberts, 1992). Perceived ability can be thought of as a general assessment of self-concept related to ability in particular situations. 


Self-concept researchers (e.g., Marsh, 1997; Marsh et al., 1997) favor a multidimensional approach to self-concept assessment, where several relevant dimensions of self-concept are used, rather than a unitary construct. Recently, Marsh et al. (1997) have developed a self-report instrument that assesses dimensions of athletic self-concept. Based on multidimensional self-concept theory, a sport-specific assessment of self-concept would be predicted to relate more closely to optimal sport experiences than a generalized self-concept assessment, and was thus selected as the instrument of choice in the present study. Athletic self-concept was expected to be positively related to flow based on the positive associations that have been found between perceived ability and flow (Jackson et al., 1998; Jackson & Roberts, 1992). Positive perceptions of one’s athletic prowess were expected to positively influence the challenge-skill balance equation critical to flow as well as to enable the performer to focus on the task and not have the concerns of the out-of-flow performer. Of the multiple dimensions of athletic self-concept (Marsh et al., 1997) those assessing mental skills, sport skills, and overall performance were expected to relate most closely to sport flow experiences.

The second area examined for potential relevance to understanding athletes’ flow experiences was their strategic use of psychological skills. The skills involved in regulating arousal, processing information, and managing emotions are particularly important for competitive athletes (Thomas, Murphy, & Hardy, 1999). These skills are commonly targeted in training programs and have been found to differentiate successful and unsuccessful athletes (Mahoney, Gabriel, & Perkins, 1987; Thomas & Over, 1994). Flow is not an easy state to attain, and getting in to flow involves a certain level of psychological skills, such as ability to control attention (Csikszentmihalyi, 1990). The importance of psychological skills to athletic performance is well documented in the sport psychology literature (e.g., Hardy, Jones, & Gould, 1996; Williams & Krane, 1998). Recently, Thomas et al. (1999) have developed a self-report instrument to measure athletes’ psychological skills and performance strategies. Eight areas are assessed including self-talk, emotional control, automaticity, goal-setting, imagery, activation, negative thinking, and relaxation. In general, it was expected that the more proficient athletes are at using psychological skills in their sport, the more likely they will experience flow due to developing greater control over their thoughts and emotions during performance. More specifically, the psychological factors predicted to be most related to flow experience were automaticity, the absence of negative thinking, goal-setting, emotional control, and relaxation. These skills were considered to be most conceptually relevant to the dimensions of flow from the eight psychological skill areas assessed by the Thomas et al. (1999) instrument. 


In summary, the relationships between flow and two sets of psychological constructs, athletic self-concept and psychological skills, were examined in order to increase understanding of how these constructs may be associated with flow experiences. In addition, flow was expected to demonstrate positive associations with performance assessments and it was considered important to empirically examine this relationship due to a lack of research directly examining the flow-performance link.


A total of 236 athletes, representing three sports, were involved in this two-part study, which included dispositional (Part 1) and state assessments (Part 2). The three sports represented were orienteering in this study, although quite different from each other, did share some similar qualities: all involved a structured race format, were subject to prevailing
environmental conditions, and were of a continuous nature. In addition to the continuous structured race format, the majority of performances involved endurance events. These similarities meant that the findings would have potential transferability to sports sharing similar characteristics. Other considerations in participant selection were to obtain a large number of athletes competing at different levels, to include both males and females, and to include diversity in age. The sample included athletes competing at n = 112), surf lifen = 92), and road cycling (n = 32). The sports selected for inclusion a broad range of performance levels. Of the 231 athletes for whom relevant data were available, 10.4% were competing at an international level, 41.6% national, 6.1% junior national, 35.9% state, and 6.1% at club level. 


The number of years the participants had been taking part in their sport ranged from 0.25 to 45 years (male and female competitors (34% female, 66% male). The age range of
the participants was 16 to 73 years.

Participants were asked to report the time spent per week on various aspects of their training. Because the data were not normally distributed, the median and inter-quartile (IQR) ranges were calculated. The sample median for total training time was 10 hours per week, with the IQR also being 10 hours. This total time was comprised of the physical, technical,
and psychological aspects of their training. For physical aspects of training, a median of 7 hours was spent per week, with an IQR of 6 hours. The median time spent on technical aspects was 1.5 hours, with an IQR of 2 hours. Mental training had a median of 0 hours per week, with an IQR of 1 hour.

M = 9.7, SD = 7.4). All three sports involvedM = 29.8, SD = 13.9).


The four psychological inventories and the performance assessment used in this study are described below. 
subscales obtained in this study are shown in Table 1. All alphas were
above acceptable levels (.72 to .96).
1 Coefficient alphas for the inventory
Flow State Scale (FSS).
of flow, as discussed by Csikszentmihalyi (e.g., 1990) and supported
in qualitative research with athletes (e.g., Jackson, 1996). The instrument
is designed to assess flow experience in a particular situation,
with respondents instructed to answer the questions in relation to a specified
event. The dimensions assessed via the nine factor scale are challengeskill
balance, action-awareness merging, clear goals, unambiguous feedback,
concentration on the task at hand, sense of control, loss of self-consciousness,
time transformation, and autotelic experience. Each dimension
comprises a subscale of the total inventory, and is assessed by four items
on a five-point Likert-type scale, ranging from 1
This instrument assesses the nine theorized dimensions(Strongly disagree) to 5
(Strongly agree)
of the subscales to be acceptable, ranging from .81 to .86, with a
. Research by Jackson and Marsh (1996) showed the reliability
upon request.


Further information about the psychological inventories can be obtained from the authors mean alpha of .83. Further, confirmatory factor analyses supported the nine factor model, as well as a hierarchical model with one higher order global flow factor. As discussed by Marsh (Marsh, 1998; Marsh & Jackson, 1999) establishing the validity of a psychological construct is an ongoing process which should involve both within and between-network studies. 


While the confirmatory factor analysis approach represents the within network approach, the between-network approach involves attempting to establish a logical, theoretically consistent pattern of relations between

Table 1
Means, Standard Deviations, and Coefficient Alphas
for the Psychological Scales
Questionnaire Subscale
M) (SD) a (M) (SD) a
Challenge-Skill Balance 3.86 0.58 0.80 3.76 0.68 0.76
Action-Awareness Merging 3.38 0.70 0.85 3.11 0.85 0.88
Clear Goals 4.13 0.63 0.80 4.14 0.59 0.82
Unambiguous Feedback 4.03 0.66 0.86 3.62 0.75 0.81
Concentration on the Task 3.67 0.74 0.89 3.66 0.90 0.91
Sense of Control 3.73 0.65 0.84 3.50 0.84 0.89
Loss of Self-Consciousness 3.33 0.83 0.72 3.48 0.88 0.79
Transformation Time 2.87 0.88 0.80 2.82 0.86 0.87
Autotelic Experience 4.11 0.58 0.77 3.48 1.09 0.92
M) (SD) a
Skills 4.28 1.10 0.93
Body 4.06 1.27 0.96
Aerobic 4.17 1.15 0.92
Anaerobic 3.45 1.37 0.96
Mental 4.31 1.01 0.90
Performance 4.16 1.03 0.94
M) (SD) a
Activation 3.47 0.78 0.81
Relaxation 3.42 0.81 0.88
Imagery 3.26 0.99 0.88
Goal-Setting 3.78 0.86 0.85
Self-Talk 3.23 0.95 0.85
Automaticity 2.86 0.79 0.77
Emotional Control 3.48 0.74 0.82
Negative Thinking 2.29 0.76 0.82
Note. DFS
Description Questionnaire;
= Dispositional Flow Scale; FSS = Flow State Scale; EASDQ = Elite Athlete Self-TOPS = Test of Performance Strategies.
measures of flow and other constructs. As part of this process, studies by
Jackson et al. (1998) and Marsh and Jackson (1999) have demonstrated
empirical support for psychological constructs hypothesized to be substantially
related to flow.
Dispositional Flow Scale (DFS)
scale was developed to assess propensity to experience flow in physical
activity (Jackson et al., 1998). This scale is essentially a parallel version of
the FSS, with items re-worded to assess frequency of flow experience while
participating in physical activity. A 5-point Likert-type scale, ranging from
1 (
of the nine subscales has been found to be at acceptable levels, ranging
from .70 to .88 (Jackson et al., 1998). Confirmatory factor analyses have
shown that the dispositional version has a similar structure to the FSS,
with both a nine factor first order model and a higher order model receiving
support (Marsh & Jackson, 1999). However, in confirmatory factor
analyses conducted to date, there is stronger support for the first order than
the higher order global flow model for both the DFS and the FSS (Jackson
& Marsh, 1996; Marsh & Jackson, 1999). Similar to the FSS, betweennetwork
approaches described in Jackson et al. (1998) and Marsh and Jackson
(1999) have demonstrated expected relationships between the DFS
and other psychological constructs.
. 2 A dispositional version of the flowNever) to 5 (Always) is used to assess the dispositional items. Reliability
Elite Athlete Self-Description Questionnaire (EASDQ)
of self-concept developed by Marsh et al. (1997) covers six areas: skills,
body, aerobic fitness, anaerobic fitness, mental competence, and overall
performance, with four to five items per subscale (Marsh et al., 1997). A 6-
point Likert-type scale is used to assess the items, ranging from 1 (
to 6 (
alphas ranging from .83 to .89, with a mean of .85. The hypothesized six
factor and higher order structure were supported by confirmatory factor
analyses with two different elite athlete groups (Marsh et al., 1997).
. This measureFalse)True). Reliability estimates were found by Marsh et al. to be good,
Test of Performance Strategies (TOPS)
by Thomas et al. (1999) measures athletes’ use of psychological skills
and strategies in competition on eight subscales: activation, relaxation, imagery,
goal setting, self-talk, emotional control, negative thinking and automaticity.
The same skills and strategies are also measured at practice
except for negative thinking which is replaced by a measure of attentional
. This instrument recently developed
Scale (Jackson et al., 1998), to more accurately reflect what it purports to measure.
The Dispositional Flow Scale has been renamed from its original name, Trait Flow
control. Each subscale, identified by exploratory factor analysis, consists
of four items and responses are made on a 5-point Likert-type scale, ranging
from 1 (
ranges from .74 to .80 with a mean of .78 (Thomas et al., 1999). Because of
the large number of measures used in the present study, and the substantial
overlap between the constructs measured in competition and practice, the
data from the TOPS practice subscales were not included in the present
Never) to 5 (Always). Reliability of the competition subscales
Performance data.
finishing position data in a specified event. Athletes were asked to rate
their performance in the event being assessed, in comparison to all other
similar competitions in which they had participated. Ratings were on an
11-point scale, ranging from 0 (
The sub-sample of orienteers were also asked to report the number of minutes
of errors they made on the course, which is a common performance
assessment made in this sport. In addition to these self-report measures,
finishing position was recorded and used as a gross assessment of performance.
This objective measure of performance was used as each of the
sports involved a race format.
Performance was assessed by both self-report andExtremely low) to 10 (Extremely high).
Contact was initially made with each sport’s organizing body, and subsequently
with coaches and event administrators. Informed consent procedures
were followed, and athletes interested in taking part in the study
were asked to complete the questionnaires, which were organized into packets,
and distributed prior to competition by the sport’s administrators and/
or coaches. Completed questionnaires were either collected at the conclusion
of competition or mailed back to the investigators in pre-paid envelopes.
Participants were asked to complete two questionnaires: the first in
their own time but not directly before or after competing; and a second,
shorter questionnaire to be completed after a specified competitive event.
The first questionnaire included all the dispositional measures (DFS,
EASDQ, TOPS) plus demographic information. The post-competition questionnaire
was completed by a subsample of the respondents (
included the FSS and performance-related questions. Respondents were
asked to indicate the length of time between when they finished their event
and when they completed the post-competition questionnaire. As the data
were not normally distributed, median and IQR were calculated. The median
time to completion of the post-competition questionnaire was 6.25
n = 208), and
hours, with the IQR being 21 hours. Thus, there was considerable variability
in when participants completed this questionnaire.
Questionnaire return rates across the three sports were as follows:
orienteering 55%, surf life saving 48% (Part 1) and 32% (Part 2), and cycling
19%. Less structured support was obtained from the cycling association
contacted for this study, resulting in a low return rate from this group.
Extreme weather conditions during the collection of the life saving competition
data may have affected the Part 2 return rates for this sport.
Predicted and Analyzed Relationships
Predictions were made regarding expected relationships between the
psychological factors assessed by the flow scales (FSS/DFS), the self-concept
scale (EASDQ), and the test of psychological skills and strategies
(TOPS). Because the EASDQ and TOPS assess factors at a dispositional
level, it was expected that relationships between these scales and flow would
be greater for dispositional than for state flow. Self-concept factors expected
to be most highly related to flow were: overall performance, mental
competence, and sport skills. The remaining three scales from the EASDQ,
assessing perceptions of body, aerobic, and anaerobic fitness, were not
expected to be strongly related to flow.
Of the psychological skills assessed by the TOPS, all except negative
thinking were expected to be positively associated with flow, with the following
factors predicted to be most strongly related: automaticity, negative
thinking (inverse relationship), goal setting, emotional control, and
relaxation. Finally, flow state was predicted to be positively associated
with self-ratings of performance, and negatively associated with errors in
orienteering and overall finishing position across the three sports (i.e., the
more errors and the higher the finishing position, the less self-reported
To assess the predicted relationships between flow, self-concept, and
psychological skills, hierarchical regression and canonical correlation analyses
were conducted. To examine the relationship of flow state to performance,
standard multiple regressions were conducted on the performance
criteria, using the FSS responses as the predictor variables.
Descriptive Results
The means and standard deviations for the psychological variables are
shown in Table 1. The scores on the flow scales indicated moderate endorsement
of the items for the group as a whole, with the average across
the subscales for the dispositional items being 3.68, and 3.51 for the state
items (on a 5-point scale). Similar moderate endorsement of items for the
group as a whole was found for the self-concept measure (4.07 on a 6-
point scale) and the psychological skills use scale (3.22 on a 5-point scale).
Bivariate correlations between the dispositional flow subscales and the
self-concept and psychological skills subscales are shown in Table 2. Significant
correlations (
p < .01) ranged between .17 and .70, with a median
correlations for the FSS (not shown), which ranged between .18 and
.48, with a median
and psychological skills subscales ranged between -.01 and .66, with a
was no evidence of a problem with bivariate collinearity among the psychological
variables. The issue of multicollinearity was addressed more
directly in the regression analyses, described below.
= .38. This set of correlations was higher than the parallel set of significantr = .25. Bivariate correlations between the self-conceptr = .37. Thus, although correlations were moderate to high, there
Relationships Among the Psychological Factors
Hierarchical regressions with global flow factors.
global flow scores were associated with differences in self-concept and
psychological skills, hierarchical multiple regressions were conducted, using
pairwise deletion for missing values, with dispositional and state global
flow as the criterion variables respectively. The eight TOPS and six EASDQ
factors were included as independent variables for the dispositional and
state flow regressions, with the addition of the nine dispositional flow factors
in the prediction of state flow. Due to the large number of independent
variables and the fact that some shared substantial variance, as indicated
by the bivariate correlations, a tolerance analysis was conducted for
multicollinearity, as recommended by Tabachnick and Fiddell (1996). The
tolerance test showed all variables to be within acceptable limits and
multicollinearity was not evident.
The hierarchical regression procedure allowed for examination of
the level of prediction of each set of psychological variables on the criterion,
dispositional or state flow. Change in
To assess whetherR2 was calculated in two ways:
Table 2
Correlations Between Dispositional Flow (DFS), TOPS, and EASDQ Factors
FLOW Chal-Skill Action- Clear Goals Unambig. Concentrat. Sense of Loss of Self- Time Autotelic Flow
(DFS) Balance Awareness Feedback on Task Control Conscious. Transform. Experience Total
Activation .47 .34 .46 .38 .48 .47 .14 .01 .33 .55
Relaxation .47 .32 .42 .31 .43 .46 .28 –.08 .31 .52
Imagery .40 .33 .53 .31 .42 .37 .02 .04 .31 .48
Goal Setting .39 .21 .61 .38 .41 .42 .01 –.09 .33 .46
Self-Talk .36 .14 .44 .18 .37 .36 .07 .08 .31 .41
Automaticity .09 .49 –.04 .13 .06 .11 .14 .09 –.03 .20
Emot. Control .41 .28 .44 .32 .58 .54 .27 –.03 .36 .57
Neg. Think. –.42 –.30 –.47 –.31 –.52 –.53 –.33 .07 –.29 –.56
Skill .67 .50 .45 .47 .38 .49 .16 –.13 .15 .55
Body .32 .38 .25 .26 .23 .26 .01 –.01 .17 .33
Aerobic .40 .39 .24 .30 .25 .24 .06 –.07 .06 .33
Anaerobic .28 .33 .31 .35 .24 .27 –.03 .01 .22 .35
Mental .59 .40 .65 .45 .68 .62 .15 –.02 .44 .70
Performance .66 .47 .59 .39 .57 .61 .17 –.06 .40 .67
Note. Correlations greater than .16 are statistically significant at

p < .01 (two-tailed).
regression equation first, providing information on how much variance
each predictor variable set could explain of the criterion, without regard to
any of the other constructs; and (b)
variable was entered into the regression equation last, providing information
on how much unique variance could be explained by each set of
predictors after controlling for all other constructs. The test procedure in
SPSS was used to calculate the second set of values (SPSS, 1994, 6.1 Syntax
Reference Guide, p. 627). This procedure was adopted because there
was not a strong a priori reason for entering the variables in a particular
order, but by following the two-step approach, it was possible to examine
each variable singly and in combination with other variables. Furthermore,
because the variables did share some common variance, it was considered
a better approach to running independent standard multiple regressions.
Both self-concept (
accounted for substantial amounts of variance in dispositional flow when
entered into the regression equation first. There was considerable overlap
between these two predictors however, as the amount of unique variance
the prediction of state flow from these same two predictors plus the
addition of the dispositional flow factors. Considerably less variance in
state flow was accounted for by the dispositional predictors, total
R2 ch1, the value of R2 when each predictor variable was entered into theR2ch2, the value of R2 when each predictorR2 ch1 = .53) and psychological skills (R2 ch1 = .58)R2ch2) each accounted for was low, 6 and 10% respectively. The total R2 =F(14, 220) = 27.47, p = .0000. The second regression equation examinedR2 = .44,
initial variance (
explained more variance in state flow than self-concept (
but there was substantial overlap in their contributions (psychological skills
(23, 183) = 6.33, p = .0000. Dispositional flow accounted for the mostR2 ch1 = .36) as well as the greatest amount of unique varianceR2ch2 = .13) in state flow. Psychological skills use (R2ch1 = .30) initiallyR2ch1 = .18),R2 ch2 = .06; self-concept: R2 ch2 ns).
Canonical correlation analyses.
flow, self-concept, and athletes’ psychological skill use, canonical
correlation analysis was employed. This analysis allowed for examination
of which factors comprising the respective constructs were contributing
most to the global relationships. Two canonical correlations were run to
examine the relationship between the subscales of dispositional and state
flow respectively, with the subscales of the self-concept (EASDQ) and
psychological skills (TOPS) variables.
The first canonical correlation analysis involved the DFS subscales as
criterion variables and the EASDQ and TOPS subscales as the predictor
To further examine the relationship between
set. The omnibus multivariate test of significance indicated that a significant
relationship was present between these sets of variables, Wilks’s
lambda = .05,
indicated that four canonical functions were statistically significant,
however, only the first predicted more than 10% of the variance in the
criterion set and consequently only the first function will be interpreted.
The canonical correlation between the two sets of variables was
with the redundancy index for the predictor set indicating that this covariate
could explain 30% of the variance in the criterion set.
The canonical loadings for the first function are shown in Table 3. Canonical
loadings greater than .30 are considered meaningful (Pedhazur,
1982). The highest loadings for the flow set were challenge-skill balance
(.83), sense of control (.79), clear goals (.76), concentration (.75), and action-
awareness merging (.67). Amongst the predictor variables, the selfconcept
performance (.88), mental competence (.87), and sk

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