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Journal of Personality Assessment Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hjpa20
The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of CriterionRelated Validity a
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Jarrod A. Johnson , Heidi N. Keiser , Evan M. Skarin & Scott R. Ross a
Department of Psychological Sciences, Purdue University
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Department of Psychology, University of Minnesota–Twin Cities
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The Guildhall, Southern Methodist University
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Department of Psychology, DePauw University Published online: 13 Mar 2014.
To cite this article: Jarrod A. Johnson, Heidi N. Keiser, Evan M. Skarin & Scott R. Ross (2014) The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of Criterion-Related Validity, Journal of Personality Assessment, 96:4, 465-470, DOI: 10.1080/00223891.2014.891524 To link to this article: http://dx.doi.org/10.1080/00223891.2014.891524
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Journal of Personality Assessment, 96(4), 465–470, 2014 C Taylor & Francis Group, LLC Copyright ISSN: 0022-3891 print / 1532-7752 online DOI: 10.1080/00223891.2014.891524
The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of Criterion-Related Validity JARROD A. JOHNSON,1 HEIDI N. KEISER,2 EVAN M. SKARIN,3 AND SCOTT R. ROSS2,4 1
Department of Psychological Sciences, Purdue University Department of Psychology, University of Minnesota–Twin Cities 3 The Guildhall, Southern Methodist University 4 Department of Psychology, DePauw University
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The Dispositional Flow Scale–2 (DFS–2; Jackson & Eklund, 2002) may be one of the most promising measures for assessing Csikszentmihalyi’s (1990) construct of “autotelic personality.” Despite strong internal validity, external validity of the DFS–2 remains open. We used 2 methods to provide evidence for external validity: (1) multiple-time assessments of experience sampling (1,856 entries generated over 7 days) to derive aggregate indices of criterion validity; and (2) single-time assessments of flow and personality for additional criterion-related validity. For single-time assessments of flow, we used a modified version of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984). To assess personality, we included a measure of the Five-factor traits using the Revised NEO Personality Inventory (Costa & McCrae, 1992). A path model of NEO domains, DFS–2 global scores, and experience sampling aggregates fit the data well.
Flow is an experiential state that is intrinsically rewarding and defined by total immersion and enjoyment (Csikszentmihalyi, 1990). Csikszentmihalyi (1975) coined the term flow to describe observations of optimal human experience across multiple tasks and activities. Studies at least partially support a key assumption that a balance of challenge and skill level gives rise to flow at the state level (Engeser & Rheinberg, 2008; Keller, Ringelham, & Blomann, 2011). The flow experience has been variously described, but common descriptions include a merging of action and awareness, lack of self-consciousness, intense concentration, clear goals, unambiguous feedback, a sense of control, a distorted sense of time, and a desire to repeat the experience (Csikszentmihalyi, 1975, 1990). The relative balance of challenge and skill level is often used to index flow using an experience sampling method (ESM) wherein individuals report their experience in real time. Due in part to the limitations of retrospective self-report, complications of having participants self-assess for abstruse experiences (Fiske, 1971), and the ability of the ESM to identify the contingencies of behavior (e.g., Scollon, Kim-Prieto, & Diener, 2003), the ESM has become the standard in flow assessment. However, the difficulty of obtaining ESM data has prompted the use of single-time measurements of flow propensity with varying levels of content validity. A recently derived measure of flow propensity, the Dispositional Flow Scale–2 (DFS–2; Jackson & Eklund, 2002) possesses strong content validity by including nine subscales, each of which assesses a major component of flow. Previous studies examining the psychometric properties of the DFS–2 have focused mostly on the internal validity. In this study, we take a closer look at the external validity (criterion-related and Received October 1, 2012; Revised January 22, 2014. Editor’s Note: This manuscript was accepted under the Editorship of Gregory Meyer. Address correspondence to Scott R. Ross, Department of Psychology, DePauw University, 7 Larabee Street, Greencastle, IN 46135; Email: srross@ depauw.edu
convergent) and examine the relationship of the DFS–2 to the Five-factor model (FFM) of personality traits (Costa & McCrae, 1992).
AUTOTELIC PERSONALITY Investigations of flow have led to the concept of an autotelic personality, roughly defined as the tendency to engage in an activity “for its own sake” (i.e., intrinsic motivation; Csikszentmihalyi, 1975, 1990). Autotelic personality can be operationalized in the context of the ESM in two ways: frequency of experiencing flow (e.g., Asakawa, 2004) and quality of the flow experience (e.g., Carli, Fave, & Massimini, 1988). In this investigation, we view autotelic personality as a constellation of traits that represent an increased propensity to experience flow across different situations. The investigation of autotelic personality provides a fresh take on flow research and a fruitful avenue for future research endeavors, as most studies to date have focused on flow propensity within the context of a chosen “high-flow” activity (e.g., music, sports, arts, etc.). Consistent with trait theory, we conceptualize autotelic personality as the tip of a spectrum propensity for experiencing flow across a wide range of activities and not simply specific situational contexts—in short, cross-situational consistency. An established measure of flow propensity would aid researchers in exploring the correlates of autotelic personality and establishing the construct within a larger nomological network (Cronbach & Meehl, 1955) while allowing them to avoid the tangled logistics of ESM research.
DISPOSITIONAL MEASURES OF FLOW To date, few dispositional measures of flow propensity have been put forth in the literature. Csikszentmihalyi and Larson (1984) were the first in developing the Flow Questionnaire (FQ), a measure used for assessing individual differences in flow propensity via semistructured interviews. The FQ assesses several major components of flow, but it lacks content validity in capturing the entire phenomological flow experience. The Flow State Scale (FSS; Jackson & Marsh, 1996) is a state
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466 measure of flow with highly similar item content to the DFS–2 in which individuals respond to their experience in the activity in which they have just participated. The DFS–2 (Jackson & Eklund, 2002) is a dispositional (or “trait”) measure of flow that assesses how respondents generally feel when taking part in a given activity. Both measures possess much stronger content and construct validity than the FQ as they assess all nine of Csikszentmihalyi’s (1975, 1990) major flow components. Despite the comprehensiveness of both the FSS and the DFS–2, few studies have examined the ability of these instruments to function as indicators of autotelic personality. The DFS–2’s strong psychometric properties of internal consistency, content validity (i.e., construct representativeness), and factorial validity, in addition to its dispositional focus, make it a prime candidate for a single-time measure of flow propensity. Although the DFS–2 instructions refer to dispositional tendencies within the context of a particular domain (e.g., music, sports, arts, etc.), they could easily be tailored so that the items assess general dispositional tendencies (i.e., life). Previous studies have shown that the “flow condition” can cut across any particular activity (Massimini & Carli, 1988). By targeting responses to general life activities, the DFS–2 can assess cross-situational consistency and provide a putative measure of flow propensity.
CRITERION-RELATED VALIDITY OF THE DFS–2 Despite valuable studies supporting the internal validity of the DFS–2 (Jackson & Eklund, 2002; Jackson, Martin, & Eklund, 2008), the criterion-related validity of the DFS–2 remains open, with convergent validity restricted to examinations of interrelationships among DFS–2 subscales (see Wang, Liu, & Khoo, 2009). Both behavioral criteria (e.g., ability to predict frequency of flow experiences) and characterological criteria (e.g., relationships with known personality constructs) are needed for establishing the DFS–2 as a measure of autotelic personality. The ESM provides the best avenue for behavioral criterion validation of the DFS–2. If the DFS–2 reflects greater flow propensity, then higher scores should be associated with more time spent in flow. The well-established domains of the FFM of personality provide appropriate characterological criteria and an established nomological network in which to identify the trait constituents of autotelic personality. An autotelic personality theoretically represents a specific constellation of personality traits; if the DFS–2 assesses autotelic personality in terms of flow propensity, then DFS–2 scores should bear specific, predictable relationships with FFM domains. Of these, Neuroticism, Conscientiousness, Extraversion, and Openness should be particularly relevant to autotelic personality. Individuals low in Neuroticism should be free of distraction and have reduced self-consciousness. Those high in Conscientiousness should be more cognizant of their goals and more capable of directing their activities toward achieving those goals in an efficient manner, with increased concentration, control, and motivation. Aspects of Extraversion, including energy and activity, might propel individuals into tasks that they find enjoyable, increasing task engagement and desire to repeat the experience. Openness to Experience could underlie greater absorption or immersion in activities. Recently, Ull´en et al. (2012) found partial support for these predictions, where Neuroticism and Conscientiousness accounted for 22% of the variance in a newly developed Swedish measure of flow in everyday life activities. Additionally, Ross and Keiser (2014) found that Revised NEO Personality Inventory (NEO PI–R) domains of Neuroticism and Conscientious-
ness, as well as Extraversion and Agreeableness accounted for at least 38% of the variance in DFS–2 global scores and over 50% in a multivariate factor reflecting the DFS–2 components aimed at general life activities.
THIS STUDY This study advances the literature by examining the DFS–2’s potential as a measure of autotelic personality. Convergent validity was assessed via the FQ in dichotomous and Likertscaled formats. Criterion-related validity was assessed using the ESM as an aggregate behavioral criterion of flow propensity. In addition, we examined all flow indexes in relation to the FFM domains to provide further criterion-related validity. To our knowledge, no previous study has attempted to predict flow experiences (e.g., ESM sampling) using a dispositional flow measure. Until recently, previous studies had not examined flow within the context of the FFM. Employing the FFM in flow research should help to elucidate the construct of flow in an established nomological network. We hypothesize that the DFS–2 will be related to the FQ and predict frequency of ESM flow experiences. The DFS–2 should also be strongly associated with lower Neuroticism and higher Conscientiousness and exhibit at least modest relationships with other FFM domains (Extraversion +, Openness +). Finally, it is hypothesized that the DFS–2 will demonstrate stronger criterion-related validity than the FQ, because of the DFS–2’s higher content validity and construct representativeness. METHOD Participants Fifty-two undergraduate students (M = 19.2 years, SD = 1.12) at a selective Midwestern liberal arts college in the United States were recruited for participation. Participants were randomly selected from courses for participation and were contacted via e-mail. One participant failed to complete all measures and was excluded from analyses. Participants received $30 or extra credit in psychology courses as payment for completion of the study. Procedure Participants attended a mandatory training session explaining the process of the ESM, the methods of the study, and the procedure for filling out the Experience Sampling Forms (ESFs). Students were randomly assigned to fill out the singleadministration self-report questionnaires before or after completing the ESM portion of the study. Participants received six signals per day for 7 days, yielding 42 possible responses per participant, for a total of 1,856 samplings. Signals were sent via text message to cell phones between the hours of 10 a.m. and 10 p.m. using Windows Live Messenger. This 12-hr time period was divided into six 2-hr intervals; each signal was sent randomly within one of these intervals, with no signal arriving within 1 hr of the previous signal. Participants were instructed to fill out an ESF as soon as possible after receiving the signal, with their entries based on the activity they were engaged in at the moment they were signaled. Instructions were given that an ESF should not be filled out more than 30 min after receiving a signal. Two different operationalizations of flow were utilized, the first being derived from Asakawa’s (2004) method. For the first
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DISPOSITIONAL FLOW SCALE–2 operationalization (FlowPercent), experiences of challenge and skill had to exceed the group average and be within one raw score point of each other; individuals’ total such experiences were divided by their total number of responses and multiplied by 100 to determine the percentage of time they were in the flow experience, or FlowPercent. A secondary ESM flow indicator called InTheZone was also included. The operationalization of InTheZone was the mean score for the ESF item measuring the extent to which participants felt they were “in the zone” when they were signaled. In the zone is an American colloquialism used by athletes to denote the flow phenomenon (Weinberg & Gould, 2006). An InTheZone experience is commonly understood to include feelings of intense concentration, effortless action, and absorption in the moment. This second definition allows a within-method measure of flow and validity check to which we can compare our challenge-skill derived flow index.
Measures Experience Sampling Form. The ESF was completed by participants each time they received a signal. The measure is composed of items designed to assess affect (e.g., “happy,” “friendly”), activation or potency (e.g., “strong,” “active,” “focused”), and motivation (e.g., “wish to be somewhere else,” “motivation”), as well as items concerning the participant’s current environs and activities (see Asakawa, 2004; Massimini & Carli, 1988). Additionally, the ESF contains two items assessing perceived challenge and skill for the current activity in which the participant is engaged; these items were used to assess the presence of flow. All nonsetting items are Likert scaled, ranging from 1 (very little) to 9 (very much). Investigators have used the ESM to assess the subjective experiences of flow across various samples (e.g., Japanese college students, Asakawa, 2004; Italian high school students, Carli et al., 1988; working adults, Csikszentmihalyi & LeFevre, 1989; and U.S. high school students, Shernoff, Csikszentmihalyi, Shneider, & Shernoff, 2003). Dispositional Flow Scale–2. The DFS–2 (Jackson & Eklund, 2002, 2004) is a dispositional measure of flow that assesses how respondents generally feel when taking part in a given activity. It was constructed based on Csikszentmihalyi’s (1975, 1990) nine proposed components of flow. Each component is assessed with four items on a Likert scale, ranging from 1 (never) to 5 (always). Coefficient alpha values for the nine subscales ranged from .80 for Autotelic Experience to .91 for Clear Goals. DFS–2 total scores had an internal consistency of .91. Instructions for the DFS–2 were modified to target “any activity in life,” rather than participants’ experiences in a specific activity. This modification allowed for the opportunity to test the DFS–2 as a measure of autotelic personality. Flow Questionnaire. The FQ (Csikszentmihalyi & Larson, 1984) consists of two separate questionnaires that were combined to create a four-item response questionnaire used to assess previous flow experiences. Consistent with Asakawa (2010), four quotations describing firsthand accounts of the flow experience are given, and the participant indicates whether or not he or she has had such an experience (e.g., “My mind isn’t wandering. I am not thinking of something else. I am totally involved in what I am doing. My body feels good. I don’t seem to hear anything. The world seems to be cut off from me. I am less aware of myself and my problems.”). Responses were calculated in
467 TABLE 1.—Correlations among autotelic personality measures and ESM flow indexes. Variable FlowPercent InTheZone FQDRtot FQStot
FlowPercent
InTheZone
FQDRtot
FQStot
DFS–2
—
.33∗ — — —
.12 .22 — —
.24 .01 .74∗∗
.34∗ .00 .31∗ .60∗∗
Note. ESM = experience sampling method; InTheZone = average of responses to the ESM item “In the zone”; FQDRtot = total score from dichotomous response items of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984); FQStot = summed scores from scales added to Flow Questionnaire items; DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002). ∗ p < .05 level (2-tailed). ∗∗ p < .01 level (2-tailed).
two ways. First, dichotomous responses were summed to form the FQ Dichotomous Response Total (FQDRtot). Second, we extended the range of responses to improve the psychometric properties by adding a 7-point Likert scale with anchors of very rarely and extremely often. Responses were summed to create the FQ Scale Total (FQStot). These variables were used to assess reciprocal convergent validity for the FQ and DFS–2.
NEO Personality Inventory–Revised. The NEO PI–R (Costa & McCrae, 1992) is a 240-item questionnaire used to assess the five FFM personality domains (i.e., Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). Items are scored on a 5-point Likert scale ranging from strongly agree to strongly disagree. Sample items include “I am not a worrier” (Neuroticism) and “I have a very active imagination” (Openness). Domain-level alpha coefficients ranged from .86 for Conscientiousness to .93 for Neuroticism. RESULTS Autotelic Personality Measures and Flow Indexes Zero-order correlations were calculated to determine relationships among all flow indexes. The DFS–2 was significantly related to the primary ESM flow index, FlowPercent, although it was unrelated to the secondary flow index, InTheZone (see Table 1). We also examined the relationships between specific DFS–2 subscales and other flow indexes. FQStot was strongly related to DFS–2 total scores and most DFS–2 subscales. The DFS–2 subscales Clear Goals and Autotelic Experience trended toward significance in this analysis (p < .06), with only DFS–2 Unambiguous Feedback being clearly unrelated (p > .50). The DFS–2 subscales Transformation of Time, Autotelic Experience, and Loss of Self Consciousness all correlated significantly with FlowPercent (rs ranging from .33–.41, all p < .05). In The Zone did not correlate significantly with any DFS–2 subscale. Autotelic Personality and FFM Personality Zero-order correlations and multiple regression analyses were used to examine ESM flow indexes vis-`a-vis FFM domains. With the exception of InTheZone scores with Neuroticism (see Table 2), no relationships between FFM domains and ESM flow indexes were detected (p > .05). DFS–2 scores exhibited strong relationships with Neuroticism and Conscientiousness. FQStot scores correlated significantly with Neuroticism and approached significance with Conscientiousness (p = .068). A multiple regression analysis using NEO PI–R domains to
JOHNSON, KEISER, SKARIN, ROSS
468 TABLE 2.—Correlations of autotelic personality measures and ESM flow indexes with Five-factor model domains. Variable Neuroticism Extraversion Openness Agreeableness Conscientiousness
FlowPercent
InTheZone FQDRtot FQStot −.34∗ .09 .05 .18 −.18
−.12 .20 .06 −.10 .26
−.11 .19 .19 .22 .19
−.40∗∗ .09 .09 .26 .29
DFS–2 −.64∗∗ .32∗ .15 −.14 .64∗∗
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Note. ESM = Experience sampling method; InTheZone = average of responses to the ESM item “in the zone”; FQDRtot = total score from dichotomous response items of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984); FQStot = summed scores from scales added to Flow Questionnaire items; DFS–2 = modified Dispositional Flow Scale–2 (Jackson & Eklund, 2002). ∗ p < .05 level (2-tailed). ∗∗ p < .01 level (2-tailed).
predict DFS–2 total scores predicted 52% of the variance in DFS–2 scores (p < .001) with Neuroticism (β = −.47, p < .005) emerging as a significant predictor and Conscientiousness approaching significance (β = .30, p < .10). Overall, the DFS–2 was strongly related to FFM personality.
Path Model To examine the relationships among basic personality (FFM), DFS–2 global flow, and ESM flow indexes, a path model was developed. Means, standard deviations, and Cronbach’s alphas for path model variables are listed in Table 3. Zero-order correlations in this sample suggested including the following covariances: Neuroticism with Conscientiousness (r = −.66, p < .001), Extraversion with Openness (r = .36, p < .05), and Extraversion with Conscientiousness (r = .44, p < .01). These were added to the model. The a priori path model fit well, χ 2(3, N = 51) = 3.342, ns, comparative fit index (CFI) = .990, root mean squared error of approximation (RMSEA) = .047. None of the effects of the FFM domains on FlowPercent were significant, nor were the effects of Extraversion and Openness on DFS–2 total scores; consequently, these parameters were successively fixed to zero. Model fit did not degrade significantly, χ 2 diff(6, N = 51) = 2.912, ns, CFI > .99, RMSEA < .001. Because they were unrelated to both FlowPercent and DFS–2 total scores, Extraversion and Openness were dropped from the model. The simpler model fit well, χ 2(2, N = 51) = 2.087, ns, CFI = .998, RMSEA = .029. The model is depicted in Figure 1.
TABLE 3.—Means, standard deviations, and Cronbach’s alpha values for path model variables. Variable Neuroticism Extraversion Openness Conscientiousness DFS–2 FlowPercent
M
SD
Cronbach’s alpha
92.0 117.6 115.2 117.7 125.9 8.9%
19.7 20.2 20.7 17.5 13.7 7.4%
.90 .91 .92 .84 .91
Note. DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002). The original items were modified so that they referred to general experience in any activity in life rather than within the context of a specific activity.
FIGURE 1.—Path model of Five-factor model personality, autotelic personality, and flow experience. DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002); FlowPercent = percentage of time spent in flow, as defined by the percent of experience sampling method responses involving challenge and skill endorsed above the sample mean for those items and within one point of each other. Listed parameter estimates are unstandardized/completely standardized. ∗∗∗ p < .005.
DISCUSSION The findings reported here provide strong, initial support for the use of the DFS–2 as a measure of autotelic personality. The DFS–2 predicted a moderate amount of the variation in time spent in flow (FlowPercent), as defined by challenge-skill balance in the ESM. In addition, the strong relationship between the Likert-scaled version of the FQ and DFS–2 total scores provides reciprocal convergent validity for the modified FQ and the DFS–2. However, the original dichotomous response version of the FQ generally failed to correlate significantly with time spent in flow and the FFM. Researchers should take caution when using the FQ and only use a Likert-scaled format in assessing flow when using this measure. Many component subscales of the DFS–2 also predicted ESM indexes and were related to the Likert-scaled FQ index. Additionally, our results seem to indicate little to no direct relationship between flow propensity as measured by behavioral ESM indexes and FFM personality. The simplest explanation is a lack of statistical power to detect small effects between the ESM and FFM. Additionally, differences in method likely contributed to small effects for aggregate ESM indexes with a single-administration trait measure of the FFM. Conversely, we found moderate to strong relationships between the DFS–2 and FFM personality, with overlapping variance exceeding 50%. Although this estimate is likely inflated by model overfitting, it does suggest considerable overlap between a putative measure of general flow propensity (e.g., the DFS–2, targeting general life activities) and FFM traits, a finding that is consistent with recent investigations of flow and the FFM (see Ross & Keiser, 2014; Ull´en et al., 2012). Further, it is generally consistent with recently published findings that highlight overlap between personality traits and the global DFS–2 index of flow at 38% to over 50%, depending on the multivariate model employed (see Ross & Keiser, 2014). NEO Neuroticism and Conscientiousness were especially potent predictors of global flow, consistent with recent findings by Ross and Keiser (2014) and Ull´en et al. (2012). Individuals who score highly on the DFS–2 should be less anxious and selfconscious, with clearer goal focus and persistence than lower scoring individuals. In addition, a modest to moderate effect was found for Extraversion and the DFS–2, which might reflect the increased reward sensitivity of extraverts (Keiser & Ross, 2011)
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DISPOSITIONAL FLOW SCALE–2 and those in flow (de Manzano, Theorell, Harmat, & Ull´en, 2010). Although the 95% confidence interval for this correlation is 0.05 to 0.55 in this sample, we have reason to believe that this finding is not sample-specific. Although not found by Ull´en et al. (2012) using a different global index of flow in life activities, the effect of extraversion (r = .35) was found by Ross and Keiser (2014) in a much larger sample using the NEO PI–R and the DFS–2 adapted in the same way as for this study. Our findings lend themselves to a path model of flow constructs (see Figure 1). At one end lies personality constructs, and at the other end lies flow experiences as measured by the ESM. Our index of basic personality, the NEO PI–R reflecting the FFM, is the most proximal measure to basic personality dimensions. On the other end, the ESM flow index is the most proximal measure to flow experiences as states. In between, we find the DFS–2 as a stable measure of flow propensity. If this model were accurate, we would expect to find stronger relations to flow indexes as we move from the trait-personality end toward the state-flow end. Such a model fits our data quite well. The DFS–2 appears to have entirely accounted for the relationships that Neuroticism and Conscientiousness have with ESM flow propensity, although we might have lacked the power to detect any covariance that the DFS–2 did not cover. Despite these findings, the sample size in this study does not allow for sufficient power to clearly answer this question. A major limitation of the path modeling is that the likelihood of rejecting our proposed model was low. In setting alpha at either the .05 or .10 levels for chi-square test of model fit, it gives us a 6% or 11% power, respectively, to reject the model. Using an RMSEA index of .10 gives us a 22% power to reject the model. Consequently, because of sample size, this model might provide more heuristic or exploratory value for future researchers rather than act as a test of the direct effects of Neuroticism, Conscientiousness, or both in terms of DFS–2 scores. Similarly, sample-specific inflations of certain correlations (e.g., between NEO Neuroticism and Conscientiousness at .66; most studies find correlations between these NEO domains in the .20–.30 range, as in Ross, Canada, & Rausch, 2002) suggest the need for replication using larger samples. For example, although only Neuroticism and Conscientiousness accounted for significant variance in DFS–2 global scores, Ross and Keiser (2014) found that Extraversion and Agreeableness were also important. Null findings for Openness suggest that it might have less to do with characterizing autotelic personality as measured by flow propensity than originally hypothesized. The correlation between ESM index of FlowPercent might indicate a theoretical cap on the strength of relationship that cross-method flow propensity measures could exhibit with FlowPercent. The trait flow measures, however, use a one-time self-report method. This cross-method, within-method distinction is important, as the within-method comparison would possess the greatest potential for demonstrating a high correlation because of common methods variance (Campbell & Fiske, 1959). The effect size in predicting ESM flow states from the DFS–2 is consistent with previous ESM studies predicting specific behaviors from traits (Scollon et al., 2003) and might be as high as can be expected when only a limited slice of a particular trait’s manifestations is analyzed (see Fleeson & Gallagher, 2009). Although challenge-skill balance might be important to flow, it is only one part of the experience (Engeser & Rheinberg, 2008). Autotelic personality should have other manifestations
469 beyond this balance (i.e., propensity to experience the other major components of flow). Despite these limitations, the DFS–2 predicted the ESM flow index as well as the “in the zone” mean from the ESM itself. These issues are particularly pervasive in ESM studies that typically, although not always, include small sample sizes in the range of 40 to 60 participants (see Verduyn, Delvaux, Van Coillie, Tuerlinckx, & Van Mechelen, 2009). Our findings advance the literature by providing convergent and criterion-related validity for the DFS–2 as a measure of autotelic personality. We showed that a potential measure of autotelic personality succeeds in predicting the classic indicator of flow states in situ. Future investigations should include larger sample sizes that would possess the sensitivity and power necessary to discern the subtle relationships among basic personality, autotelic personality, and the flow experience.
REFERENCES Asakawa, K. (2004). Flow experience and autotelic personality in Japanese college students: How do they experience challenges in daily life? Journal of Happiness Studies, 5, 123–154. doi:10.1023/B:JOHS.0000035915.97836.89 Asakawa, K. (2010). Flow experience, culture, and well-being: How do autotelic Japanese college students feel, behave, and think in their daily lives? Journal of Happiness Studies, 11, 205–223. doi:10.1007/s10902-008-9132-3 Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56, 81–105. doi:10.1037/h0046016 Carli, M., Fave, A., & Massimini, F. (1988). The quality of experience in the flow channels: Comparison of Italian and U.S. students. In I. Csikszentmihalyi & M. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 364–383). New York, NY: Cambridge University Press. Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI–R) and NEO Five-Factor Inventory (NEO–FFI) professional manual. Odessa, FL: Psychological Assessment Resources. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281–302. doi:10.1037/h0040957 Csikszentmihalyi, M. (1975). Play and intrinsic rewards. Journal of Humanistic Psychology, 15, 41–63. doi:10.1177/002216787501500306 Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper & Row. Csikszentmihalyi, M., & Larson, R. (1984). Being adolescent: Conflict and growth in the teenage years. New York, NY: Basic Books. Csikszentmihalyi, M., & LeFevre, J. (1989). Optimal experience in work and leisure. Journal of Personality and Social Psychology, 56, 815–822. doi:10.1037/0022-3514.56.5.815 de Manzano, O., Theorell, T., Harmat, L., & Ull´en, F. (2010). The psychophysiology of flow during piano playing. Emotion, 10, 301–311. Engeser, S., & Rheinberg, F. (2008). Flow, performance and moderators of challenge-skill balance. Motivation and Emotion, 32, 158–172. Fiske, D. (1971). Measuring the concepts of personality. Oxford, UK: Aldine. Fleeson, W., & Gallagher, P. (2009). The implications of Big Five standing for the distribution of trait manifestation in behavior: Fifteen experiencesampling studies and a meta-analysis. Journal of Personality and Social Psychology, 97, 1097–1114. doi:10.1037/a0016786 Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The Flow State Scale–2 and Dispositional Flow Scale–2. Journal of Sport and Exercise Psychology, 24, 33–150. Retrieved from EBSCOhost. Jackson, S. A., & Eklund, R. C. (2004). The Flow Scales manual. Morgantown, WV: Fitness Information Technology. Jackson, S., & Marsh, H. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport and Exercise Psychology, 18, 17–35. Retrieved from EBSCOhost. Jackson, S. A., Martin, A. J., & Eklund, R. C. (2008). Long and short measures of flow: The construct validity of the FSS–2, DFS–2, and new brief counterparts. Journal of Sport and Exercise Psychology, 30, 561–587.
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470 Keiser, H. N., & Ross, S. R. (2011). Carver and White’s BIS/FFS/BAS scales and domains and facets of the Five factor model of personality. Personality and Individual Differences, 51, 39–44. doi:10.1016/j.paid.2011.03. 007 Keller, J., Ringelham, S., & Blomann, F. (2011). Does skills-demands compatibility result in intrinsic motivation? Experimental test of a basic notion proposed in the theory of flow experiences. Journal of Positive Psychology, 6, 408–417. Massimini, F., & Carli, M. (1988). The systematic assessment of flow in daily experience. In Optimal experience: Psychological studies of flow in consciousness (pp. 266–287). New York, NY: Cambridge University Press. Ross, S. R., Canada, K. E., & Rausch, M. K. (2002). Self-handicapping and the Five factor model of personality: Mediation between neuroticism and conscientiousness. Personality and Individual Differences, 32, 1173–1184. Ross, S. R., & Keiser, H. N. (2014). Autotelic personality through a Five-factor lens: Individual differences in flow propensity. Personality and Individual Differences, 59, 3–8.
JOHNSON, KEISER, SKARIN, ROSS Scollon, C. N., Kim-Prieto, C., & Diener, E. (2003). Experience sampling: Promises and pitfalls, strengths and weaknesses. Journal of Happiness Studies, 4, 5–34. doi:10.1023/A:1023605205115 Shernoff, D., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18, 158–176. doi:10.1521/scpq.18.2.158.21860 ¨ Almeida, R., Magnusson, P. E., Pedersen, N. L., Ull´en, F., de Manzano, O., Nakamura, J., . . . Madison, G. (2012). Proneness for psychological flow in everyday life: Associations with personality and intelligence. Personality and Individual Differences, 52, 167–172. doi:10.1016/j.paid.2011.10.003 Verduyn, P., Delvaux, E., Van Coillie, H., Tuerlinckx, F., & Van Mechelen, I. (2009). Predicting the duration of emotional experience: Two experience sampling studies. Emotion, 9, 83–91. Wang, C., Liu, W., & Khoo, A. (2009). The psychometric properties of Dispositional Flow Scale–2 in Internet gaming. Current Psychology: Research and Reviews, 28, 194–201. doi:10.1007/s12144-009-9058-x Weinberg, R., & Gould, D. (2006). Foundations of sport and exercise psychology (4th ed.). Champaign, IL: Human Kinetics.
Journal of Personality Assessment Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hjpa20
The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of CriterionRelated Validity a
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Jarrod A. Johnson , Heidi N. Keiser , Evan M. Skarin & Scott R. Ross a
Department of Psychological Sciences, Purdue University
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Department of Psychology, University of Minnesota–Twin Cities
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The Guildhall, Southern Methodist University
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Department of Psychology, DePauw University Published online: 13 Mar 2014.
To cite this article: Jarrod A. Johnson, Heidi N. Keiser, Evan M. Skarin & Scott R. Ross (2014) The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of Criterion-Related Validity, Journal of Personality Assessment, 96:4, 465-470, DOI: 10.1080/00223891.2014.891524 To link to this article: http://dx.doi.org/10.1080/00223891.2014.891524
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Journal of Personality Assessment, 96(4), 465–470, 2014 C Taylor & Francis Group, LLC Copyright ISSN: 0022-3891 print / 1532-7752 online DOI: 10.1080/00223891.2014.891524
The Dispositional Flow Scale–2 as a Measure of Autotelic Personality: An Examination of Criterion-Related Validity JARROD A. JOHNSON,1 HEIDI N. KEISER,2 EVAN M. SKARIN,3 AND SCOTT R. ROSS2,4 1
Department of Psychological Sciences, Purdue University Department of Psychology, University of Minnesota–Twin Cities 3 The Guildhall, Southern Methodist University 4 Department of Psychology, DePauw University
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The Dispositional Flow Scale–2 (DFS–2; Jackson & Eklund, 2002) may be one of the most promising measures for assessing Csikszentmihalyi’s (1990) construct of “autotelic personality.” Despite strong internal validity, external validity of the DFS–2 remains open. We used 2 methods to provide evidence for external validity: (1) multiple-time assessments of experience sampling (1,856 entries generated over 7 days) to derive aggregate indices of criterion validity; and (2) single-time assessments of flow and personality for additional criterion-related validity. For single-time assessments of flow, we used a modified version of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984). To assess personality, we included a measure of the Five-factor traits using the Revised NEO Personality Inventory (Costa & McCrae, 1992). A path model of NEO domains, DFS–2 global scores, and experience sampling aggregates fit the data well.
Flow is an experiential state that is intrinsically rewarding and defined by total immersion and enjoyment (Csikszentmihalyi, 1990). Csikszentmihalyi (1975) coined the term flow to describe observations of optimal human experience across multiple tasks and activities. Studies at least partially support a key assumption that a balance of challenge and skill level gives rise to flow at the state level (Engeser & Rheinberg, 2008; Keller, Ringelham, & Blomann, 2011). The flow experience has been variously described, but common descriptions include a merging of action and awareness, lack of self-consciousness, intense concentration, clear goals, unambiguous feedback, a sense of control, a distorted sense of time, and a desire to repeat the experience (Csikszentmihalyi, 1975, 1990). The relative balance of challenge and skill level is often used to index flow using an experience sampling method (ESM) wherein individuals report their experience in real time. Due in part to the limitations of retrospective self-report, complications of having participants self-assess for abstruse experiences (Fiske, 1971), and the ability of the ESM to identify the contingencies of behavior (e.g., Scollon, Kim-Prieto, & Diener, 2003), the ESM has become the standard in flow assessment. However, the difficulty of obtaining ESM data has prompted the use of single-time measurements of flow propensity with varying levels of content validity. A recently derived measure of flow propensity, the Dispositional Flow Scale–2 (DFS–2; Jackson & Eklund, 2002) possesses strong content validity by including nine subscales, each of which assesses a major component of flow. Previous studies examining the psychometric properties of the DFS–2 have focused mostly on the internal validity. In this study, we take a closer look at the external validity (criterion-related and Received October 1, 2012; Revised January 22, 2014. Editor’s Note: This manuscript was accepted under the Editorship of Gregory Meyer. Address correspondence to Scott R. Ross, Department of Psychology, DePauw University, 7 Larabee Street, Greencastle, IN 46135; Email: srross@ depauw.edu
convergent) and examine the relationship of the DFS–2 to the Five-factor model (FFM) of personality traits (Costa & McCrae, 1992).
AUTOTELIC PERSONALITY Investigations of flow have led to the concept of an autotelic personality, roughly defined as the tendency to engage in an activity “for its own sake” (i.e., intrinsic motivation; Csikszentmihalyi, 1975, 1990). Autotelic personality can be operationalized in the context of the ESM in two ways: frequency of experiencing flow (e.g., Asakawa, 2004) and quality of the flow experience (e.g., Carli, Fave, & Massimini, 1988). In this investigation, we view autotelic personality as a constellation of traits that represent an increased propensity to experience flow across different situations. The investigation of autotelic personality provides a fresh take on flow research and a fruitful avenue for future research endeavors, as most studies to date have focused on flow propensity within the context of a chosen “high-flow” activity (e.g., music, sports, arts, etc.). Consistent with trait theory, we conceptualize autotelic personality as the tip of a spectrum propensity for experiencing flow across a wide range of activities and not simply specific situational contexts—in short, cross-situational consistency. An established measure of flow propensity would aid researchers in exploring the correlates of autotelic personality and establishing the construct within a larger nomological network (Cronbach & Meehl, 1955) while allowing them to avoid the tangled logistics of ESM research.
DISPOSITIONAL MEASURES OF FLOW To date, few dispositional measures of flow propensity have been put forth in the literature. Csikszentmihalyi and Larson (1984) were the first in developing the Flow Questionnaire (FQ), a measure used for assessing individual differences in flow propensity via semistructured interviews. The FQ assesses several major components of flow, but it lacks content validity in capturing the entire phenomological flow experience. The Flow State Scale (FSS; Jackson & Marsh, 1996) is a state
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466 measure of flow with highly similar item content to the DFS–2 in which individuals respond to their experience in the activity in which they have just participated. The DFS–2 (Jackson & Eklund, 2002) is a dispositional (or “trait”) measure of flow that assesses how respondents generally feel when taking part in a given activity. Both measures possess much stronger content and construct validity than the FQ as they assess all nine of Csikszentmihalyi’s (1975, 1990) major flow components. Despite the comprehensiveness of both the FSS and the DFS–2, few studies have examined the ability of these instruments to function as indicators of autotelic personality. The DFS–2’s strong psychometric properties of internal consistency, content validity (i.e., construct representativeness), and factorial validity, in addition to its dispositional focus, make it a prime candidate for a single-time measure of flow propensity. Although the DFS–2 instructions refer to dispositional tendencies within the context of a particular domain (e.g., music, sports, arts, etc.), they could easily be tailored so that the items assess general dispositional tendencies (i.e., life). Previous studies have shown that the “flow condition” can cut across any particular activity (Massimini & Carli, 1988). By targeting responses to general life activities, the DFS–2 can assess cross-situational consistency and provide a putative measure of flow propensity.
CRITERION-RELATED VALIDITY OF THE DFS–2 Despite valuable studies supporting the internal validity of the DFS–2 (Jackson & Eklund, 2002; Jackson, Martin, & Eklund, 2008), the criterion-related validity of the DFS–2 remains open, with convergent validity restricted to examinations of interrelationships among DFS–2 subscales (see Wang, Liu, & Khoo, 2009). Both behavioral criteria (e.g., ability to predict frequency of flow experiences) and characterological criteria (e.g., relationships with known personality constructs) are needed for establishing the DFS–2 as a measure of autotelic personality. The ESM provides the best avenue for behavioral criterion validation of the DFS–2. If the DFS–2 reflects greater flow propensity, then higher scores should be associated with more time spent in flow. The well-established domains of the FFM of personality provide appropriate characterological criteria and an established nomological network in which to identify the trait constituents of autotelic personality. An autotelic personality theoretically represents a specific constellation of personality traits; if the DFS–2 assesses autotelic personality in terms of flow propensity, then DFS–2 scores should bear specific, predictable relationships with FFM domains. Of these, Neuroticism, Conscientiousness, Extraversion, and Openness should be particularly relevant to autotelic personality. Individuals low in Neuroticism should be free of distraction and have reduced self-consciousness. Those high in Conscientiousness should be more cognizant of their goals and more capable of directing their activities toward achieving those goals in an efficient manner, with increased concentration, control, and motivation. Aspects of Extraversion, including energy and activity, might propel individuals into tasks that they find enjoyable, increasing task engagement and desire to repeat the experience. Openness to Experience could underlie greater absorption or immersion in activities. Recently, Ull´en et al. (2012) found partial support for these predictions, where Neuroticism and Conscientiousness accounted for 22% of the variance in a newly developed Swedish measure of flow in everyday life activities. Additionally, Ross and Keiser (2014) found that Revised NEO Personality Inventory (NEO PI–R) domains of Neuroticism and Conscientious-
ness, as well as Extraversion and Agreeableness accounted for at least 38% of the variance in DFS–2 global scores and over 50% in a multivariate factor reflecting the DFS–2 components aimed at general life activities.
THIS STUDY This study advances the literature by examining the DFS–2’s potential as a measure of autotelic personality. Convergent validity was assessed via the FQ in dichotomous and Likertscaled formats. Criterion-related validity was assessed using the ESM as an aggregate behavioral criterion of flow propensity. In addition, we examined all flow indexes in relation to the FFM domains to provide further criterion-related validity. To our knowledge, no previous study has attempted to predict flow experiences (e.g., ESM sampling) using a dispositional flow measure. Until recently, previous studies had not examined flow within the context of the FFM. Employing the FFM in flow research should help to elucidate the construct of flow in an established nomological network. We hypothesize that the DFS–2 will be related to the FQ and predict frequency of ESM flow experiences. The DFS–2 should also be strongly associated with lower Neuroticism and higher Conscientiousness and exhibit at least modest relationships with other FFM domains (Extraversion +, Openness +). Finally, it is hypothesized that the DFS–2 will demonstrate stronger criterion-related validity than the FQ, because of the DFS–2’s higher content validity and construct representativeness. METHOD Participants Fifty-two undergraduate students (M = 19.2 years, SD = 1.12) at a selective Midwestern liberal arts college in the United States were recruited for participation. Participants were randomly selected from courses for participation and were contacted via e-mail. One participant failed to complete all measures and was excluded from analyses. Participants received $30 or extra credit in psychology courses as payment for completion of the study. Procedure Participants attended a mandatory training session explaining the process of the ESM, the methods of the study, and the procedure for filling out the Experience Sampling Forms (ESFs). Students were randomly assigned to fill out the singleadministration self-report questionnaires before or after completing the ESM portion of the study. Participants received six signals per day for 7 days, yielding 42 possible responses per participant, for a total of 1,856 samplings. Signals were sent via text message to cell phones between the hours of 10 a.m. and 10 p.m. using Windows Live Messenger. This 12-hr time period was divided into six 2-hr intervals; each signal was sent randomly within one of these intervals, with no signal arriving within 1 hr of the previous signal. Participants were instructed to fill out an ESF as soon as possible after receiving the signal, with their entries based on the activity they were engaged in at the moment they were signaled. Instructions were given that an ESF should not be filled out more than 30 min after receiving a signal. Two different operationalizations of flow were utilized, the first being derived from Asakawa’s (2004) method. For the first
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DISPOSITIONAL FLOW SCALE–2 operationalization (FlowPercent), experiences of challenge and skill had to exceed the group average and be within one raw score point of each other; individuals’ total such experiences were divided by their total number of responses and multiplied by 100 to determine the percentage of time they were in the flow experience, or FlowPercent. A secondary ESM flow indicator called InTheZone was also included. The operationalization of InTheZone was the mean score for the ESF item measuring the extent to which participants felt they were “in the zone” when they were signaled. In the zone is an American colloquialism used by athletes to denote the flow phenomenon (Weinberg & Gould, 2006). An InTheZone experience is commonly understood to include feelings of intense concentration, effortless action, and absorption in the moment. This second definition allows a within-method measure of flow and validity check to which we can compare our challenge-skill derived flow index.
Measures Experience Sampling Form. The ESF was completed by participants each time they received a signal. The measure is composed of items designed to assess affect (e.g., “happy,” “friendly”), activation or potency (e.g., “strong,” “active,” “focused”), and motivation (e.g., “wish to be somewhere else,” “motivation”), as well as items concerning the participant’s current environs and activities (see Asakawa, 2004; Massimini & Carli, 1988). Additionally, the ESF contains two items assessing perceived challenge and skill for the current activity in which the participant is engaged; these items were used to assess the presence of flow. All nonsetting items are Likert scaled, ranging from 1 (very little) to 9 (very much). Investigators have used the ESM to assess the subjective experiences of flow across various samples (e.g., Japanese college students, Asakawa, 2004; Italian high school students, Carli et al., 1988; working adults, Csikszentmihalyi & LeFevre, 1989; and U.S. high school students, Shernoff, Csikszentmihalyi, Shneider, & Shernoff, 2003). Dispositional Flow Scale–2. The DFS–2 (Jackson & Eklund, 2002, 2004) is a dispositional measure of flow that assesses how respondents generally feel when taking part in a given activity. It was constructed based on Csikszentmihalyi’s (1975, 1990) nine proposed components of flow. Each component is assessed with four items on a Likert scale, ranging from 1 (never) to 5 (always). Coefficient alpha values for the nine subscales ranged from .80 for Autotelic Experience to .91 for Clear Goals. DFS–2 total scores had an internal consistency of .91. Instructions for the DFS–2 were modified to target “any activity in life,” rather than participants’ experiences in a specific activity. This modification allowed for the opportunity to test the DFS–2 as a measure of autotelic personality. Flow Questionnaire. The FQ (Csikszentmihalyi & Larson, 1984) consists of two separate questionnaires that were combined to create a four-item response questionnaire used to assess previous flow experiences. Consistent with Asakawa (2010), four quotations describing firsthand accounts of the flow experience are given, and the participant indicates whether or not he or she has had such an experience (e.g., “My mind isn’t wandering. I am not thinking of something else. I am totally involved in what I am doing. My body feels good. I don’t seem to hear anything. The world seems to be cut off from me. I am less aware of myself and my problems.”). Responses were calculated in
467 TABLE 1.—Correlations among autotelic personality measures and ESM flow indexes. Variable FlowPercent InTheZone FQDRtot FQStot
FlowPercent
InTheZone
FQDRtot
FQStot
DFS–2
—
.33∗ — — —
.12 .22 — —
.24 .01 .74∗∗
.34∗ .00 .31∗ .60∗∗
Note. ESM = experience sampling method; InTheZone = average of responses to the ESM item “In the zone”; FQDRtot = total score from dichotomous response items of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984); FQStot = summed scores from scales added to Flow Questionnaire items; DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002). ∗ p < .05 level (2-tailed). ∗∗ p < .01 level (2-tailed).
two ways. First, dichotomous responses were summed to form the FQ Dichotomous Response Total (FQDRtot). Second, we extended the range of responses to improve the psychometric properties by adding a 7-point Likert scale with anchors of very rarely and extremely often. Responses were summed to create the FQ Scale Total (FQStot). These variables were used to assess reciprocal convergent validity for the FQ and DFS–2.
NEO Personality Inventory–Revised. The NEO PI–R (Costa & McCrae, 1992) is a 240-item questionnaire used to assess the five FFM personality domains (i.e., Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). Items are scored on a 5-point Likert scale ranging from strongly agree to strongly disagree. Sample items include “I am not a worrier” (Neuroticism) and “I have a very active imagination” (Openness). Domain-level alpha coefficients ranged from .86 for Conscientiousness to .93 for Neuroticism. RESULTS Autotelic Personality Measures and Flow Indexes Zero-order correlations were calculated to determine relationships among all flow indexes. The DFS–2 was significantly related to the primary ESM flow index, FlowPercent, although it was unrelated to the secondary flow index, InTheZone (see Table 1). We also examined the relationships between specific DFS–2 subscales and other flow indexes. FQStot was strongly related to DFS–2 total scores and most DFS–2 subscales. The DFS–2 subscales Clear Goals and Autotelic Experience trended toward significance in this analysis (p < .06), with only DFS–2 Unambiguous Feedback being clearly unrelated (p > .50). The DFS–2 subscales Transformation of Time, Autotelic Experience, and Loss of Self Consciousness all correlated significantly with FlowPercent (rs ranging from .33–.41, all p < .05). In The Zone did not correlate significantly with any DFS–2 subscale. Autotelic Personality and FFM Personality Zero-order correlations and multiple regression analyses were used to examine ESM flow indexes vis-`a-vis FFM domains. With the exception of InTheZone scores with Neuroticism (see Table 2), no relationships between FFM domains and ESM flow indexes were detected (p > .05). DFS–2 scores exhibited strong relationships with Neuroticism and Conscientiousness. FQStot scores correlated significantly with Neuroticism and approached significance with Conscientiousness (p = .068). A multiple regression analysis using NEO PI–R domains to
JOHNSON, KEISER, SKARIN, ROSS
468 TABLE 2.—Correlations of autotelic personality measures and ESM flow indexes with Five-factor model domains. Variable Neuroticism Extraversion Openness Agreeableness Conscientiousness
FlowPercent
InTheZone FQDRtot FQStot −.34∗ .09 .05 .18 −.18
−.12 .20 .06 −.10 .26
−.11 .19 .19 .22 .19
−.40∗∗ .09 .09 .26 .29
DFS–2 −.64∗∗ .32∗ .15 −.14 .64∗∗
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Note. ESM = Experience sampling method; InTheZone = average of responses to the ESM item “in the zone”; FQDRtot = total score from dichotomous response items of the Flow Questionnaire (Csikszentmihalyi & Larson, 1984); FQStot = summed scores from scales added to Flow Questionnaire items; DFS–2 = modified Dispositional Flow Scale–2 (Jackson & Eklund, 2002). ∗ p < .05 level (2-tailed). ∗∗ p < .01 level (2-tailed).
predict DFS–2 total scores predicted 52% of the variance in DFS–2 scores (p < .001) with Neuroticism (β = −.47, p < .005) emerging as a significant predictor and Conscientiousness approaching significance (β = .30, p < .10). Overall, the DFS–2 was strongly related to FFM personality.
Path Model To examine the relationships among basic personality (FFM), DFS–2 global flow, and ESM flow indexes, a path model was developed. Means, standard deviations, and Cronbach’s alphas for path model variables are listed in Table 3. Zero-order correlations in this sample suggested including the following covariances: Neuroticism with Conscientiousness (r = −.66, p < .001), Extraversion with Openness (r = .36, p < .05), and Extraversion with Conscientiousness (r = .44, p < .01). These were added to the model. The a priori path model fit well, χ 2(3, N = 51) = 3.342, ns, comparative fit index (CFI) = .990, root mean squared error of approximation (RMSEA) = .047. None of the effects of the FFM domains on FlowPercent were significant, nor were the effects of Extraversion and Openness on DFS–2 total scores; consequently, these parameters were successively fixed to zero. Model fit did not degrade significantly, χ 2 diff(6, N = 51) = 2.912, ns, CFI > .99, RMSEA < .001. Because they were unrelated to both FlowPercent and DFS–2 total scores, Extraversion and Openness were dropped from the model. The simpler model fit well, χ 2(2, N = 51) = 2.087, ns, CFI = .998, RMSEA = .029. The model is depicted in Figure 1.
TABLE 3.—Means, standard deviations, and Cronbach’s alpha values for path model variables. Variable Neuroticism Extraversion Openness Conscientiousness DFS–2 FlowPercent
M
SD
Cronbach’s alpha
92.0 117.6 115.2 117.7 125.9 8.9%
19.7 20.2 20.7 17.5 13.7 7.4%
.90 .91 .92 .84 .91
Note. DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002). The original items were modified so that they referred to general experience in any activity in life rather than within the context of a specific activity.
FIGURE 1.—Path model of Five-factor model personality, autotelic personality, and flow experience. DFS–2 = Dispositional Flow Scale–2 (Jackson & Eklund, 2002); FlowPercent = percentage of time spent in flow, as defined by the percent of experience sampling method responses involving challenge and skill endorsed above the sample mean for those items and within one point of each other. Listed parameter estimates are unstandardized/completely standardized. ∗∗∗ p < .005.
DISCUSSION The findings reported here provide strong, initial support for the use of the DFS–2 as a measure of autotelic personality. The DFS–2 predicted a moderate amount of the variation in time spent in flow (FlowPercent), as defined by challenge-skill balance in the ESM. In addition, the strong relationship between the Likert-scaled version of the FQ and DFS–2 total scores provides reciprocal convergent validity for the modified FQ and the DFS–2. However, the original dichotomous response version of the FQ generally failed to correlate significantly with time spent in flow and the FFM. Researchers should take caution when using the FQ and only use a Likert-scaled format in assessing flow when using this measure. Many component subscales of the DFS–2 also predicted ESM indexes and were related to the Likert-scaled FQ index. Additionally, our results seem to indicate little to no direct relationship between flow propensity as measured by behavioral ESM indexes and FFM personality. The simplest explanation is a lack of statistical power to detect small effects between the ESM and FFM. Additionally, differences in method likely contributed to small effects for aggregate ESM indexes with a single-administration trait measure of the FFM. Conversely, we found moderate to strong relationships between the DFS–2 and FFM personality, with overlapping variance exceeding 50%. Although this estimate is likely inflated by model overfitting, it does suggest considerable overlap between a putative measure of general flow propensity (e.g., the DFS–2, targeting general life activities) and FFM traits, a finding that is consistent with recent investigations of flow and the FFM (see Ross & Keiser, 2014; Ull´en et al., 2012). Further, it is generally consistent with recently published findings that highlight overlap between personality traits and the global DFS–2 index of flow at 38% to over 50%, depending on the multivariate model employed (see Ross & Keiser, 2014). NEO Neuroticism and Conscientiousness were especially potent predictors of global flow, consistent with recent findings by Ross and Keiser (2014) and Ull´en et al. (2012). Individuals who score highly on the DFS–2 should be less anxious and selfconscious, with clearer goal focus and persistence than lower scoring individuals. In addition, a modest to moderate effect was found for Extraversion and the DFS–2, which might reflect the increased reward sensitivity of extraverts (Keiser & Ross, 2011)
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DISPOSITIONAL FLOW SCALE–2 and those in flow (de Manzano, Theorell, Harmat, & Ull´en, 2010). Although the 95% confidence interval for this correlation is 0.05 to 0.55 in this sample, we have reason to believe that this finding is not sample-specific. Although not found by Ull´en et al. (2012) using a different global index of flow in life activities, the effect of extraversion (r = .35) was found by Ross and Keiser (2014) in a much larger sample using the NEO PI–R and the DFS–2 adapted in the same way as for this study. Our findings lend themselves to a path model of flow constructs (see Figure 1). At one end lies personality constructs, and at the other end lies flow experiences as measured by the ESM. Our index of basic personality, the NEO PI–R reflecting the FFM, is the most proximal measure to basic personality dimensions. On the other end, the ESM flow index is the most proximal measure to flow experiences as states. In between, we find the DFS–2 as a stable measure of flow propensity. If this model were accurate, we would expect to find stronger relations to flow indexes as we move from the trait-personality end toward the state-flow end. Such a model fits our data quite well. The DFS–2 appears to have entirely accounted for the relationships that Neuroticism and Conscientiousness have with ESM flow propensity, although we might have lacked the power to detect any covariance that the DFS–2 did not cover. Despite these findings, the sample size in this study does not allow for sufficient power to clearly answer this question. A major limitation of the path modeling is that the likelihood of rejecting our proposed model was low. In setting alpha at either the .05 or .10 levels for chi-square test of model fit, it gives us a 6% or 11% power, respectively, to reject the model. Using an RMSEA index of .10 gives us a 22% power to reject the model. Consequently, because of sample size, this model might provide more heuristic or exploratory value for future researchers rather than act as a test of the direct effects of Neuroticism, Conscientiousness, or both in terms of DFS–2 scores. Similarly, sample-specific inflations of certain correlations (e.g., between NEO Neuroticism and Conscientiousness at .66; most studies find correlations between these NEO domains in the .20–.30 range, as in Ross, Canada, & Rausch, 2002) suggest the need for replication using larger samples. For example, although only Neuroticism and Conscientiousness accounted for significant variance in DFS–2 global scores, Ross and Keiser (2014) found that Extraversion and Agreeableness were also important. Null findings for Openness suggest that it might have less to do with characterizing autotelic personality as measured by flow propensity than originally hypothesized. The correlation between ESM index of FlowPercent might indicate a theoretical cap on the strength of relationship that cross-method flow propensity measures could exhibit with FlowPercent. The trait flow measures, however, use a one-time self-report method. This cross-method, within-method distinction is important, as the within-method comparison would possess the greatest potential for demonstrating a high correlation because of common methods variance (Campbell & Fiske, 1959). The effect size in predicting ESM flow states from the DFS–2 is consistent with previous ESM studies predicting specific behaviors from traits (Scollon et al., 2003) and might be as high as can be expected when only a limited slice of a particular trait’s manifestations is analyzed (see Fleeson & Gallagher, 2009). Although challenge-skill balance might be important to flow, it is only one part of the experience (Engeser & Rheinberg, 2008). Autotelic personality should have other manifestations
469 beyond this balance (i.e., propensity to experience the other major components of flow). Despite these limitations, the DFS–2 predicted the ESM flow index as well as the “in the zone” mean from the ESM itself. These issues are particularly pervasive in ESM studies that typically, although not always, include small sample sizes in the range of 40 to 60 participants (see Verduyn, Delvaux, Van Coillie, Tuerlinckx, & Van Mechelen, 2009). Our findings advance the literature by providing convergent and criterion-related validity for the DFS–2 as a measure of autotelic personality. We showed that a potential measure of autotelic personality succeeds in predicting the classic indicator of flow states in situ. Future investigations should include larger sample sizes that would possess the sensitivity and power necessary to discern the subtle relationships among basic personality, autotelic personality, and the flow experience.
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