Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field of musical performers.

Flow is a positive and optimal state of mind, during which people are highly motivated and absorbed in the activity they are doing. It is an experience that can occur in any area of life. One of the measurement instruments which is most commonly used to evaluate this construct is the Flow State Scale-2 (FSS-2). This instrument has been used in different languages, mainly in the field of sport. In this research work, the FSS-2 has been translated into Spanish and administered to 486 musicians from different regions of Spain in order to examine the psychometric properties. A version which uses six dimensions from the original questionnaire has been used-those that constitute the experience of flow-and four alternative models have been analysed (Six related factors model, two second order factor models and a bifactor model).The results revealed that the dimension time could be controversial and more research could be needed. In general terms, the six-factor model (RMSEA = .04; GFI = .99; AGFI = .99) and a second-factor one (RMSEA = .033; GFI = .99; AGFI = .99) are solutions consistent with previous studies and show that the questionnaire can be considered a reliable (Alphas of the dimensions range from .81 to .94; Omegas from .86 to .97; and mean discrimination of the dimensions from .64 to .88) and useful tool, both in clinical and educational contexts, as well as an instrument for the evaluation of this construct in future research. However, the results of this study also suggest that flow can be explored in greater depth in musicians, taking into account that the writing of the original items was based on the experience of athletes and that the role of time in flow needs to be investigated.

Introduction

The concept of flow has aroused much interest in recent years, leading to a great deal of research related to this topic. According to Csikszentmihalyi [1], flow is a positive and optimal state of mind during which people are highly motivated and absorbed in the activity they are doing, and it is an experience that can occur in any area of life. For the conceptualisation of flow, Csikszentmihalyi [1] used a phenomenological model of consciousness based on information theory. Based on this approach, it was proposed that a mental event could be better understood when the way it was being experienced was directly observed. The optimal experience, or flow, would occur when the information that reaches consciousness is consistent with the goals set. In this way, the activity flows effortlessly and there is no reason to question one's own ability, while there is at the same time a positive feedback and a strengthening of self-confidence. The experience of flow is significant because it is a determining factor in order to make the present moment more enjoyable, but also because it strengthens self-confidence and advances the development of personal skills.

Csikszentmihalyi proposed a multidimensional model which, based on what he calls "the phenomenology of enjoyment", defines the state of flow by means of the following eight components [1]:

Challenge-skill balance (1). This dimension aims to apprehend the balance between one's own abilities and the objective to be achieved.

Concentration on the task (2). This refers to the way the capacity for attentional self-control is manifested while the activity being evaluated is in process.

Clear goals and clear feedback (3 and 4). The goals which one is looking to achieve are unequivocally established and, during the completion of the task, their gradual achievement can be evaluated in a reliable and precise manner.

Action-awareness fusion (5). This indicates the synchrony between awareness and action. From a phenomenological perspective, it implies the perception that one is acting without effort, with a deep involvement that moves the concerns and pressures of daily life away from consciousness.

Sense of control (6). This refers to the presence of a feeling of control over one's actions, or more accurately, it implies a lack of concern regarding losing control.

Loss of self-consciousness (7). This indicates the degree to which the task is absorbing and central at that existential moment. In relation to this experience there is a lack of concern for any other interest or concern which, at other times, could be central to the person's life.

Transformation of time (8). This dimension refers to the altered perception of the passing of time while the activity is taking place.

The combination of all these elements leads to a deep feeling of enjoyment that rewards the person, what he calls: autotelic experience [1]. This is a distinctive and fundamental factor of the flow experience and describes the highly positive emotional value of this experience.

Based on this theoretical model, research into the flow experience has been conducted following different procedures and using different measurement techniques. One of the lines that has generated a large amount of research is that which has been carried out in the field of sport. In this context, a scale has been proposed in order to measure the optimal experience, the Flow State Scale (FSS) [2, 3], which has been used in numerous research projects. The FSS is an instrument that enables one to evaluate the eight theoretical dimensions outlined above, but which adds a scale in order to measure the autotelic experience. It therefore presents a questionnaire which proposes nine factors. In addition, the scale, taken as a whole, can be considered as a tool for measuring “global flow”.

Subsequently, in order to improve the psychometric properties of the scale, the FSS-2 questionnaire was developed [4, 5]. The FSS-2 questionnaire was designed in order to evaluate the flow experience in the context of physical activity, but it has also been used for the evaluation of flow in other activities such as musical performance [5]. The scales that compose it provide values for internal consistency of between .83 and .92. The goodness-of-fit indices are considered good for two proposed models: a model of nine first-order factors (Comparative Fit Index, CFI = .939; Chi-Square Non-Normed Fit Index, NNFI = .931; Root Mean Square Error of Approximation, RMSEA = .051) and a hierarchical model with nine first-order factors and one second-order factor (CFI = .93; NNFI = .920; RMSEA = .054). Only the "transformation of time" dimension presents a factor loading which is relatively low (.23) to one second-order factor ("global flow"). The authors [5] thus suggest that the “transformation of time” factor not be taken into account when calculating global flow.

Subsequently, a reduced version of FSS-2 [6] was developed: the Short State Flow Scale, consisting of nine elements. This version of the FSS-2 has been validated using a varied sample of activities, including musical performance.

Currently, research can be found that addresses adaptations and validations, in different languages and countries, both, of the original scale, the FSS, and the improved one, the FSS-2 [7, 8, 9, 10, 11, 12]. These studies regarding the adaptation of the FSS have also been carried out in Spanish [13]. However, these adaptations and validations have been carried out mainly in the field of sport, ignoring other situations in which the experience of flow could also be considered of interest.

Given the potential utility of the flow construct, in order to understand the state of optimal experience, the need to research it in musical activity, particularly in musical performers, has also been considered. Thus, Wrigley and Emmerson examined the properties of FSS-2 in a sample of Australian musicians [14]. Their results showed internal consistency values of between .81 and .92 for the scales of the instrument. The goodness-of-fit indices showed good results for the model of nine first-order factors (CFI = .96; Tucker-Lewis Index, TLI = .96; RMSEA = .04). In the case of the hierarchical model with nine first-order factors and one second-order factor, all the scales predicted the flow state, with all the regression weights within a significant critical proportion. In almost all cases the beta values exceeded .30, varying between .46 and .85. Only one exception was found to these results, and that is in relation to the “transformation of time” factor, a dimension which, as has been mentioned previously when presenting research in the field of sport, repeatedly shows low values.

Flow has been linked to increases in motivation, improved competition, and growth of individual abilities [15]. It has been pointed out that it contributes to improvement in terms of the technical and expressive training of musicians [16], while at the same time determining an increase in the time devoted to musical practice [17]. Therefore, although the FSS-2 is not a specific measuring instrument used to evaluate the experience of flow in musicians, numerous studies have used it in the context of musical performance. Thus, the flow experience has been studied as a desirable state of musicians during their performances, because it could enhance the quality and positive experience of the performance [14]. Other research has used the FSS-2 to assess the experience of the flow state in musicians and their conclusions contain certain implications for the development of musical learning [18]. Flow state has also been related to performance anxiety [18, 19], emotional intelligence [20], to the style of the music being performed [21], and the situation in which the musical performance takes place [21]. Certain aspects of the environment which may be facilitators or inhibitors of the flow experience have also been described [22]. Finally, some researchers have focused on flow from a social perspective, which considers not only optimal performance, but also the optimal interaction between two or more people [23, 24].

Recently, flow theory has begun to distinguish between the conditions that are necessary to give rise to flow and the psychological components that constitute the experience of flow [25, 26]. Therefore, in order to enter flow, an appropriate balance between the skills and the challenges that a person faces is deemed necessary, as well as having clear objectives which are proximate to the action and, also, that there is clear and immediate feedback. The other dimensions would be the subjective experience of the flow state: concentration on the task, action-awareness, sense of control, loss of self-consciousness, transformation of time and autotelic experience [25, 26]. This research is conducted within this theoretical framework, in line with other authors who have measured flow state in musicians taking this reformulation into account [18].

In view of all of the above, the need to adapt the FSS-2 to Spanish and to look at the psychometric properties of the instrument in a population composed specifically of musicians was considered. The aim was to have a tool available which could be used to assess flow state in musicians. On the one hand, it was hypothesised that the results of the psychometric analysis of the FSS-2 scale adapted and translated into Spanish in a population of performing musicians would be consistent with the results obtained in other studies that have examined the psychometric properties of the FSS-2. On the other hand, a second hypothesis was presented according to which, if the factors of skill-challenge balance, clear objectives and clear feedback are conditions in order to enter the flow state, they will positively correlate with the other factors that represent the flow experience or state. This correlation can therefore be deemed as supporting the criterion validity.

The results of this study share many similarities with those of other research projects [8, 4, 9, 10, 14]. The six scales considered as well as the global flow scale present values that indicate good internal consistency and discrimination. Moreover, the structural models analysed present good goodness-of-fit indices for a model of six first-order related factors and a hierarchical model of six first-order factors and one second-order. Following the theoretical model indicated, we have used the three FSS-2 scales that measure the preconditions to enter flow as criteria measurements and to see how they relate to the six scales that measure flow state. The results show how the scale that measures the goals for the action, once partial correlations are made and the effect of the other two conditions for flow is controlled, goes from being significantly correlated with all the dimensions of the questionnaire (except the dimension that measures transformation of time) to not be significant and practically zero.

Materials and methods

The research has been carried out following the standards recommended for research on human participants from the code of ethics of the European Community and the American Psychological Association´s Ethical Standards for Research and Publication. The research was approved by the Bioethics Committee of the UNED. We have guaranteed privacy in the processing of data. Participation in the study was voluntary and anonymous.

Participants

A sample of 558 participants was obtained, including music students as well as amateur and professional musicians. They were all Spanish speakers and came from different regions of Spain. As a criterion for inclusion, it was established that participants would have a well-established relationship with musical performance (students, professionals, amateurs), specifically at least two years of study; as a criterion for exclusion a minimum age of 18 was established. As a consequence of these criteria, 72 persons were excluded, so in the end the total number of participants was 486, with an age range between 18 and 83 years old (mean 38.17 and SD = 12.91). Men accounted for 38.90% of the sample (mean age 38.91; SD = 12.97), while 60.50% were women (mean age 37.77; SD = 12.90). Three participants preferred not to answer this questionnaire (0.6%; mean age 31.33; SD = 9.07). Participation in the study was voluntary, with no financial or academic reward.

Procedure

The sample was obtained by means of snowball sampling. Through the UNED’s social networks and communications tools, all interested persons were offered the possibility of participating in this research by completing a survey that was submitted online. The form was published via the Google Forms tool, in which the EFIM questionnaire was included. The questions were organised in such a way that it was “mandatory” to answer all of them (Google signals this requirement with a red asterisk at the end of each question). The participants thus answered all the questions in the survey and there were no cases where the answers to any of the questions set by the tool were missing. The time required to complete the survey was approximately 15 minutes. Addressees were informed that participation, which was anonymous and voluntary, consisted of filling out a Google form, in which the Spanish adaptation of the FSS-2, the scale “Estado de Fluidez para Intérpretes Musicales” (EFIM) was included.

The scale “Estado de Fluidez para Intérpretes Musicales”, EFIM (https://www.mindgarden.com/100-flow-scales#horizontalTab2). This is a 24-item questionnaire that measures the Flow State. It consists of six scales, each with four items and conceptually different: Action-awareness merging (it will be merging); Total concentration on the task at hand (concentration); Sense of control (control); Loss of self-consciousness (consciousness); Transformation of time (time); and Autotelic experience (autotelic). To assess the degree of agreement with the formulation of each element, on the original scale, the FSS-2, a Likert scale with five anchor points (1 to 5) [5] was used. However, reports have been submitted indicating that larger amplitudes of the scale appear to improve the sensitivity and accuracy of the measurements [27, 28, 29, 30, 31], while other reports challenge the use of the central categories in these types of scales, such as 3 in the case of using a scale of 1 to 5, suggesting that it may affect both the accuracy of the measurements and the validity of the inferences made [32, 33, 34, 35, 36]. In addition, in our cultural field it is usual and widespread to use scales from 0 to 10 when almost any object or event has to be evaluated or assessed [37]. Due to these considerations, the questionnaire that was presented to the participants was answered on a Likert scale of 0 to 10 points. On the other hand, when using these types of scales, it is suggested that verbal labels associated with extreme scores be included, so that they guide the trend of the values: zero is labelled with a “totally disagree” and ten with “totally agree” [37], so that, the higher the score, the greater the flow state. The Spanish version of this questionnaire was developed in accordance with the guidelines of the International Test Commission [38], and using the back-translation method based on the original English version: 1) the original version was translated into Spanish by a bilingual group expert in psychology; 2) the new version was translated back into English by a different translator, also bilingual and a psychologist; and 3) the discrepancies arising were discussed and the appropriate corrections made to the new version of the EFIM. With the form, which included socio-demographic questions in addition to the EFIM, a pilot test was carried out with 20 musicians. The results of this test were satisfactory since the respondents did not report any difficulties understanding the questionnaire.

The remaining 3 scales of the FSS-2 that were not included in the EFIM were used as criteria, as they correspond to the dimensions that are necessary conditions in order to generate the flow state. These are: Challenge-skill balance (balance) (in this sample: Alpha = .75; Omega = .83; mean discrimination = .59); Clear goals (goals) (Alpha = .90; Omega = .90; mean discrimination = .78); Unambiguous feedback (feedback) (Alpha = .86; Omega = .87; mean discrimination = .56).

Statistical analyses

In order to obtain evidence of construct validity of the instrument in a Spanish sample, we tested the original model proposed by Jackson and Eklund [4] using the Confirmatory Factor Analysis (CFA) procedure [39]. After analysing the goodness-of-fit indices and the patterns of correlations between the latent variables, we also tested alternative models.

Although the items are ordinal in nature, given that there are 11 response options, the univariate normality test for asymmetry and kurtosis was analysed in order to guide the election of the estimation method which was most suitable [40, 41, 42]. We used polychoric correlations (see S1 File), and as an estimation method that of Robust Unweighted Least Squares (RULS), given the large number of variables and the distribution of the items [40, 43, 44].

The statistical analyses were carried out using the following applications: PRELIS 2.30, LISREL 8.8 [45, 46] and SPSS 24.0.0.0 [47].

Results

Descriptive statistics

Table 1 shows the basic descriptive statistics of the items. We would like to highlight that all the items have a negative skewness and, although there are 11 points in the response scale, neither of them has a normal distribution.

Table 1

Basic description of the items.

Items	Mean	SD	Skewness	Kurtosis	Normality test (Skewness and Kurtosis) Chi-Square	P-value
1	7.00	2.49	-.80	.01	42.62	< .01
2	7.94	1.90	-.95	.86	64.29	< .01
3	7.27	2.05	-.84	.46	49.51	< .01
4	6.49	2.93	-.59	-.63	43.49	< .01
5	6.77	3.10	-.89	-.27	52.37	< .01
6	8.25	1.94	-1.66	3.23	162.01	< .01
7	6.61	2.34	-.59	-.08	25.14	< .01
8	7.27	2.45	-.89	.13	50.89	< .01
9	7.37	2.09	-.84	.46	48.94	< .01
10	6.20	2.94	-.48	-.84	65.48	< .01
11	6.83	3.02	-.93	-.09	53.92	< .01
12	7.87	2.34	-1.34	1.40	108.01	< .01
13	6.20	2.56	-.50	-.39	23.49	< .01
14	7.79	1.94	-.94	.92	64.61	< .01
15	6.73	2.39	-.78	.29	42.41	< .01
16	6.12	2.94	-.45	-.84	62.47	< .01
17	6.93	2.89	-.93	.04	54.08	< .01
18	8.16	2.01	-1.54	2.85	146.42	< .01
19	6.52	2.40	-.67	.06	31.56	< .01
20	7.72	1.99	-1.03	1.10	74.96	< .01
21	6.85	2.31	-.72	.08	35.85	< .01
22	6.10	2.96	-.39	-.92	78.47	< .01
23	6.78	2.89	-.86	-.09	47.71	< .01
24	8.19	2.04	-1.45	2.32	131.41	< .01

Confirmatory factor analysis

According to the original structure proposed by Jackson and Eklund [4], the dimensions of the instrument are grouped into: merging (1, 7, 13, and 19); concentration (2, 8, 14 and 20); control (3, 9, 15, and 21); consciousness (4, 10, 16 and 22); time (5, 11, 17 and 23); and autotelic (6, 12, 18 and 24). It is thus a related six-factor model (Model 1).

As a result of carrying out a CFA on the model 1 (six related factors), the following global goodness-of-fit indices were obtained (see Table 2): χ2 (d.f. = 237; p < .001) = 341.46; RMSEA = .04 with an interval at 90% (.03 to .05) (values < 0.08 are adequate); GFI = .99; AGFI = .99; CFI = 1.00; TLI = 1.00; (values > 0.90 are adequate) SRMR = .04 (values < 0.10 are adequate); and BIC = -295.27 (the lower the better) [48].

Table 2

Global goodness-of-fit indices of the four models.

	χ2	d.f.	P	RMSEA	GFI	AGFI	CFI	TLI	SRMR	BIC
Model 1	341.46	237	< .01	.04	.99	.99	1.00	1.00	.04	-295.27
Model 2	1292.39	238	< .01	.10	.92	.90	.96	.95	.14	656.97
Model 3	340.54	231	< .01	.03	.99	.99	1.00	1.00	.04	-280.07
Model 4	304.19	228	< .01	.03	.99	.99	1.00	1.00	.05	-308.36

Model 1 = Six related factors model; Model 2 = five first-order factors explained by one second-order factor except for time; Model 3 = six first-order factors explained by one second-order factor; Model 4 = bifactor model.

The standardised solution for model 1 is shown in Table 3.

Table 3

Standardized solution for Model 1 (M1) and Bifactor Model (BM). The last column shows the loading in the general factor of the bifactor model (B).

Item	Merging		Concentra.		Control		Conscious.		Time		Autotelic		B
	M1	BM	M1	BM	M1	BM	M1	BM	M1	BM	M1	BM
1	.70*	.26*											.53*
7	.74*	.52*											.65*
13	.74*	.75*											.81*
19	.86*	.66*											.55*
2			.82*	.62*									.03
8			.82*	.23*									.80*
14			.93*	.57*									.51*
20			.92*	.58*									.72*
3					.84*	.27							.88*
9					.90*	.10							.59*
15					.91*	.47							.14*
21					.91*	.10							.79*
4							.84*	.67*					.49*
10							.91*	.72*					.76*
16							.88*	.67*					.88*
22							.96*	.67*					.58*
5									.74*	.84*			.21*
11									.88*	.89*			.81*
17									.98*	.93*			.59*
23									.97*	.87*			.75*
6											.92*	.40*	.89*
12											.91*	.43*	.64*
18											.94*	.45*	.23*
24											.92*	.59*	.78*

Concentra. = Concentration; Conscious. = Consciousness.

* p < .05

These results could be considered as a good fit. However, after inspecting the factor correlations (see Table 4), we found that time presents low relation with the other five factors of the models.

Table 4

Correlations between factors.

	merging	concentration	control	consciousness	time	autotelic
merging	1.00
concentration	.50*	1.00
control	.67*	.83*	1.00
consciousness	.55*	.49*	.67*	1.00
time	.27*	.15*	.06	.06	1.00
autotelic	.59*	.76*	.82*	.54*	.23*	1.00

* p < .05

This result is consistent with previous studies that found that the relation between time and the rest of the dimensions is weak [8, 4, 9, 10, 14]. Supported by this result, it could be of interest to test a model with a second-order factor which explained these five factors, but don’t explain time (model 2). The goodness-of-fit indices of this new model (Model 2) were: χ2 (d.f. = 238; p < .001) = 1292.39; RMSEA = .10 with an interval at 90% (.09 to .11); GFI = .92; AGFI = .90; CFI = .96; TLI = .95; SRMR = .14; BIC = 656.97. The significant increase of χ2 of 950.93 for 1 degree of freedom indicates that Model 2 is a significant deterioration on Model 1 (six related factors) (see Table 2). That is, Model 1 fits better than Model 2 (five first-order factors explained by one second-order factor except for time). Otherwise, the modification index suggests including the parameter that related time with the general second-order factor (flow). This modification can be considered reasonable because, in accordance with what has been described in previous studies into flow [8, 4, 9, 10, 14], it was decided to propose a second-order factor model made up of one higher-order factor (Model 3). The goodness-of-fit indices of Model 3 were: χ2 (d.f. = 231; p < .001) = 340.54; RMSEA = .03 with an interval at 90% (.02 to .04); GFI = .99; and AGFI = .99; CFI = 1.00; TLI = .99; SRMR = .04; BIC = -280.07. As in Model 1 (six related factors), these results provide empirical support for the structure proposed (six factors explained by one second order factor) (see Table 2). Given these results, it could be considered that Models 1 (six related factors) and 3 (second order factor) present the same goodness of fit. Therefore, both of them could be used. The decision regarding which one should be used just depends on theoretical background.

The completely standardised solution of the structural model is shown in Fig 1.

Fig 1

Completely standardised solution of the structural model for Model 3.

Nevertheless, given the apparent contradictory results regarding the fit and definition of time in the flow state dimension, a bifactor model was tested in order to obtain evidence as to whether the items of time could be considered in the same way as the items of the other dimensions, or if conversely, these items have complementary hues related to flow. The goodness-of-fit indices of Model 4 were: χ2 (d.f. = 228; p < .001) = 304.19; RMSEA = .03 with an interval at 90% (.02 to .04); GFI = .99; and AGFI = .99; CFI = 1.00; TLI = 1.00; SRMR = .05; BIC = -308.36 The standardised solution is shown in the last column of Table 3. As we can see, for consciousness, and especially for time, the items tend to show higher loadings in its original dimensions. This result is consistent with Model 3 (second order factor) where both dimensions are the most poorly predicted by flow state (see Fig 1). On the other hand, the loadings show that practically all the items reflect both a general factor and a specific factor, with the exception of Items 2, 9 and 21 (see Table 3).

Reliability

The basic psychometric characteristics of the dimensions obtained in the CFA of Model 1 show that both the reliability of the scales and their average discrimination are adequate (Table 5). All six scales have Cronbach's alpha values above .80, the weighted construct omega presents values above .89 [49, 50] and their average discrimination is greater than .30 [51].

Table 5

Cronbach’s α, omega, and mean discrimination.

	Reliability (Cronbach’s alpha)	Weighted Construct Ωω	Mean discrimination
Merging	.81	.86	.64
concentration	.89	.94	.79
Control	.94	.94	.86
consciousness	.93	.97	.84
Time	.92	.97	.82
autotelic	.95	.95	.88
FLOW	.92	.99	.58

Criterion validity

In order to analyse the criterion validity, we calculated the Pearson bivariate correlation index between each of the six subscales, the global flow, and the scores on: a) balance, b) goals, c) feedback. Given the inter-relation between these scales, we also obtained the partial correlation between each subscale, global flow included, and each one of the three measures referred to above, controlling in each case the effect of the two remaining scales that are conditions for entering flow (Table 6).

Table 6

Pearson correlations (r) and partial correlations (pr) between the dimensions of the EFIM and the criteria measurements (scales: balance, goals and feedback).

	balance		goals		feedback
	r	pr	r	pr	r	pr
Merging	.53**	.35**	.39**	-.02	.43**	.10*
Concentration	.58**	.21**	.58**	.04	.67**	.36**
Control	.69**	.35**	.65**	-.04	.79**	.52**
consciousness	.39**	.14**	.36**	-.03	.44**	.22**
Time	.16**	.19**	.05	-.01	.02	-.09*
autotelic	.61**	.39**	.47**	-.08	.56**	.24**
Global flow	.69**	.42**	.58**	.04	.66**	.32**

* p < .05

** p < .01

In the measurements of balance, goals and feedback, the correlations obtained with the 6 dimensions and global flow obtained a significance level of p < .01. This is except for the time scale with goals and feedback which are not significant. However, once the influence of the remaining subscales on each one of the dimensions had been controlled by means of partial correlations, the relationship between goals and all dimensions disappeared, global flow included, so that correlations ceased to be significant and close to 0 (Table 6). In feedback, the relationship is maintained with all dimensions and global flow, except with time, but with a slight decrease as well as a significance level of p < .05 on merging (pr = .10). Instead, the relationship with time (pr = -.09) is reversed and is significant to p < .05. Finally, the Pearson correlations obtained between balance and all dimensions, global flow included, in the partial correlations the relationship was maintained but there was a slight decrease in the direct relationship, except with time (pr = .19), which was slightly greater (Table 6).

Discussion

This study has examined the factorial structure of the EFIM with a sample of Spanish musicians. As Calvo et al. [13] discussed in their work, the translation of this tool into Spanish and its adaptation can provide a very fruitful line of work and research. Moreover, according to Csikszentmihalyi [52], flow appears in all areas of life and is closely related to the satisfaction of the activities we do. Therefore, this instrument can be modified for use in various areas of psychology, such as the psychology of music. In addition, we consider that the flow variable may be highly significant in psychophysiological processes that affect musicians in general and professional musicians in particular. It is not always easy for professional musicians to achieve a good level of performance, at the same time as personal satisfaction and well-being. Therefore, we believe that taking this variable into account in musicians would allow us to provide useful information for its application and for future research and interventions in musicians.

In this research, a structure of six factors that correspond to the scales of merging, concentration, control, consciousness, time and autotelic was analysed. This is in line with work by other authors [18] who have used these scales to measure the flow variable, specifically, an alignment of the item with the highest factor loading in the original studies [4]. These scales are defined as the six core components of the flow experience. The balance, goals and feedback dimensions are considered preconditions for flow and inherent to the task [25, 26] and in this research we have taken them into account as measurements for criteria validity.

The results of this study are like those of other research projects [8, 4, 9, 10, 14] in many ways. The six scales and global flow present good internal consistency and discrimination, and the structural Models 1 and 3 show good goodness-of-fit indices: Model 1 for a model of six related first-order factors and Model 3 for a hierarchical model of six first-order and one second-order factor. Therefore, either of the two models can be used to measure flow state. However, it should be considered that Model 3 is more in line with the theoretical framework, since, theoretically, the factors that make up the instrument are the elements that constitute the flow state [2]. In this way, the importance of each of the factors in the global flow state can be quantified, which can provide valuable information about their influence in achieving an optimal psychophysiological state [13].

The results show how time, despite its weak relationship with the rest of the dimensions (results which are in keeping with previous research [8, 4, 9, 10, 14]), is explained by a general flow factor (Model 3). Despite this weak relationship, we can consider that time is related to merging and autotelic, which would suggest it is part of the flow experience, but more related to the gratification of the experience and the fusion of action and thought, than with the other three dimensions, with which it may even maintain an inverse relationship that is hidden in bivariate correlations and apparently diminishes its relationship with both merging and autotelic and with global flow. According to Stavrou and Zervas [11], it is possible that global flow is actually multidimensional. The results suggest that merging, time and autotelic could fall within the same category, in which one perceives the most sensory and emotional part of the experience. An experience which, in addition, would be less controllable by the musician since it is a consequence of the state in which they find themselves. Meanwhile, concentration, control and consciousness could belong to another category which involves an assessment of cognitive aspects. It is possible that people are more familiar with thinking about them and, as the questionnaire states, in the statements regarding these three dimensions, respondents can respond not only as to whether they felt they were concentrating, in control of what they were doing and not worrying about the comments of others; they can also respond as to the attitude they adopted before playing, either because of their volition to do so or because they already rehearse putting into operation those cognitive mechanisms of concentration, thought control or attention to movement. These aspects are thus more controllable by the person. This could also help explain that time is a central component of the experience of flow as a sensation in different activities, although it is possible, as previous studies in sports psychology discuss [8, 4, 5, 9, 10], that is not thus either in all activities, or in all cases equally, due to factors such as the demands of the activity or personal characteristics. As Jackson and Eklund [5] comment, future research may determine whether, or not, time depends on certain situations, types of activities or types of individuals.

In this research, a bifactor model was also analysed of which the goodness-of-fit indices are similar to those of Models 1 and 3. In addition, it allows us to observe how time reflects the general flow factor, while the high factor loading in its dimension reflect a specific part of that factor itself. This result is interesting given that, on the one hand, it supports the theoretical framework that considers time as an essential component of the experience of flow, despite obtaining, in general, low correlations with the other dimensions. On the other hand, it also gives rise to the need, as we mentioned above, to clarify what that specific contribution would consist of. The analysis of the bifactor model also shows that three items obtain low factor loadings. On the one hand, Items 9 and 21 obtain a loading of .10 in their control factor, which suggests that they are better explained by the general flow factor and not so much by its specific dimension. In fact, control presents only Item 15 with a relatively high loading. If we analyse how the control items are worded (https://www.mindgarden.com/100-flow-scales#horizontalTab2), we observe that only Item 15: “I had a feeling of total control”, differs from 3, 9 and 21, in that feeling of control is not focused on a specific matter, while the other items detail that the person felt or had a sensation of control over what he or she was doing or, specifically, over his or her own body. This difference as regards specifying over what they have control may explain why Item 15 has a factor loading that reflects that it is also explained by a specific factor and, meanwhile, the other three items seem to be better explained by the general flow factor, which would suggest the possibility of modifying and analysing them in order to explain the significance of these items in the control factor. On the other hand, Item 2, concentration, obtains a low load in the general flow factor, of .03 to be precise. One possible explanation is that the wording of Item 2: “My attention was completely focused on what I was doing”, unlike the other items on the concentration scale, speaks of focused attention and not specifically of concentration or of keeping one’s mind on the specific task. Although the difference is very subtle, it may suggest that, at the specific moment of performance, people can sense the level of concentration for that task and, at the same time, that there are other moments, before, during or just after the performance, where the focus is on different stimuli and that this is not related to the state of flow during the performance.

It is also interesting to note how goals lose their relationship with the dimensions of the questionnaire once feedback and balance are controlled in the partial correlations. This result suggests that the clear goals in order to enter flow, which are important in the field of sport, where athletes set objectives and goals such as revalidating markers or improving them, are not the same type of objectives musicians set themselves. Taking into account that the items of the questionnaire were drawn up in relation to the field of sport, the sample of musicians may not identify with the statements as they are expressed in the questionnaire. This does not mean that musicians do not have clear goals because they do, both as regards the activity itself, and during a performance or during rehearsals and the preparation of a work, but it is probably not reflected in the scale of goals in such a way that musicians view it in that sense, as it could be with an affirmation of the type: I have a clear idea of how I am going to interpret the work, or, I have prepared the work with a clear technical-interpretative idea, for example. Beyond this possibility, there may also be differences as regards the contribution of goals to flow due to the focus of musical activity in our culture. It may be reflective of how in our country musical activity is not planned and musicians do not set goals in the same way that an Anglo-Saxon musician would do.

Among the limitations of the research it is worth mentioning that the snowball sampling technique, since it is not probabilistic, cannot guarantee that it is a representative sample of each one of the regions of Spain. However, it was possible to ensure that the selection of the initial persons to whom the form was provided were from different regions of Spain and from different fields of music. Moreover, because it was launched via the social networks of the UNED, including those related to the field of music, the potential number of participants that could be reached increased. Another more specific limitation of this study is that, although it is considered more appropriate to complete the flow scale right at the end of the activity or task [2], the musicians were asked to answer the questions using the last musical activity in which they had participated as the "key situation", given the fact that musicians may not be available right after a performance or other type of musical event. However, this could enable us to generalise the factorial structure, in our case, of the EFIM in environments where respondents are not available after a performance [8], in this sense in order to obtain more validity evidences and be able to generalised the model tested into different groups (gender, instruments used, years of experience,…) an invariance analysis should be explore in forthcoming researches.

Finally, this study provides a validated tool to assess the state of flow in musicians. The validation of this instrument may have clinical and educational implications, since the use of the questionnaire allows one to identify significant aspects of what facilitates or inhibits a musical performance or of the learning itself. It can also be used for future research where researchers wish to measure the flow variable. However, the results of this study also suggest that flow can be explored in greater depth in musicians. Taking into account that the writing of the original items was based on the experience of athletes, for future research it would be more appropriate to initiate a line with a quantitative as well as qualitative approach to capture the flow experience of musicians in all its breadth: how they live their experiences and how they describe them. This could lead to a rewording of some items of the scales and another study of both the relationship between the criteria measurements and the dimensions of the questionnaire as possible alternative models.

Polychoric correlations matrix.

(PDF)

Click here for additional data file.

27 Dec 2019

PONE-D-19-31155

Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field of musical performers

PLOS ONE

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Additional Editor Comments:

The work entitled “Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field ofm usical performers” is of great interest in the assessment field. The research is very stimulating. It contains new scientific knowledge and provides comprehensive information for further development in this productive line of research. This research is well-argued and clearly worthy of publication. As minor comments I would like to say:

0.- Add empirical results in the abstract.

1.- Add relevant references to understand psychometric procedures used in this research: Muñiz J, Fonseca-Pedrero E. Ten steps for test development. Psicothema. 2019 Feb;31(1):7-16.

2.- Add the main goals and hypotheses at the end of the intro.

3.- Add more information about sampling procedure and sample characteristics. In particular, add more information about the all sociodemographic characteristics of the sample, e.g., ethnicity, socio-economic status, etc. Here we have convenience sample, and we know the limitations related to this kind of samples.

4.-Do you have any information about non-response? Describe inclusion/exclusion criteria if part of the data was excluded from the analysis. Were outliers removed from the data? Please, Which method did you use to deal with missing data in the analyses? What variables are related to missing data?

5.- Please, add subsections in Results (descriptive, CFA, reliability).

6.- Please, test other CFA models, e.g., bifactor model, unidimensional model, etc.

7.-Please, add other CFA goodness of fit indices (e.g., BIC, TLI).

8.-Please explain the constrains of the present study (e.g., not use infrequency or social desirability scale, sample and sampling, self-reports, etc.).

9.-Please, delete from table 1, N=486.

10.- Add as supplementary material, the polychoric correlations matrix.

11.- Please add information from the ethical committee.

12.- Please, following the PLOS ONES standards.

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2. We noticed you have some minor occurrence of overlapping text with the following previous publication, which needs to be addressed:

Ashinoff, Brandon K., and Ahmad Abu-Akel. "Hyperfocus: the forgotten frontier of attention." Psychological research (2019): 1-19.

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

3. Please include a copy in the original language of the adapted questionnaire you developed, as Supporting Information.

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field of musical performers aims to provide validity evidence for the Spanish adaptation of the Flow State Scale-2 (FSS-2) in a sample of musicians. The adapted scale is called Estado de Flujo para Intérpretes Musicales (EFIM). The original FSS-2 scale has 9 factors whereas the Spanish version (EFIM) is shorter, containing 6 of the 9 original factors.

I think the manuscript is good, it is well structured and well written. I enjoyed reading it, and for that, I would like to congratulate the authors. The study meets the expected conditions for a piece of research and I recommend its publication. I do, however, have a few minor questions that I believe could help to improve the paper. I would like to hear the authors’ responses.

- The authors state that the EFIM scale contains the six factors that “provide the highest factorial loads in the original studies” (referring to FSS-2). However, this is not entirely accurate: in the study by Jackson & Eklund (2002) neither Time nor Consciousness were among the six factors with the highest factorial loads. Instead, Goals and Balance were among those six factors, but were not included in the EFIM. Therefore believe the authors should redefine the reasons for choosing the six factors for the Spanish adaptation.

- In terms of how the 9 original factors of the FSS-2 were managed, I have another question. In another part of the paper, the authors indicate that the three factors from the FFS-2 not included in the EFIM (Feedback, Goals and Balance) “are considered preconditions for Flow” (although I’m not sure about this statement). They use this to justify the use of these three factors in the predictive validity section. However, isn’t it a little opportune to take a part (in this case the three segregated factors of the FSS-2) to justify the whole (in this case the EFIM)? Imagine if we validated a short version of the verbal factor of the WISC-R (for example, with the subscales Information, Similarities and Comprehension) and to demonstrate the predictive validity of these three scales we correlate them with the Vocabulary scale of the original test? Aren’t the authors doing the same here? Given that it seems as though the authors have information for the nine original scales of the FSS-2, have they tried to fit a 9-factor model? What were the results? Even if they do not give this information in the published manuscript, I would very much like to read their response, and if possible, see the fit data for the nine-factor model.

- One question about the wording. In lines 307-311, the authors state that “In order to analyse the criterion validity we calculated the Pearson bivariate correlation index between each of the six subscales, the global flow, and the scores on: a) balance, b) goals, c) feedback. Given the inter-relation between these scales, we also obtained the partial correlation between each subscale, global flow included, and each one of the three measures referred to above, controlling in each case the effect of the remaining scales” which seems to indicate that they analysed the effect between each pair of variables, discounting the effect of the other subscales. However, later (lines 395-396) they state “It is also interesting to note how goals lose their relationship with the dimensions of the questionnaire once feedback and balance are controlled in the partial correlations”. It is not clear which variables they used as controls to estimate the partial correlations.

- I am Spanish, and this comment comes from me thinking if I had to find the EFIM in a Spanish Psychology Test library. My history of classical philosophy professor said that we should require a name (whether a journal or a test etc.) to “determine and explain the content” [González Escudero, S. (1989). A propósito del nombre. Psicothema, 1(1), 5-6]. I’m afraid that the translation “Estado de Flujo para Intérpretes Musicales” does not do this. I think that in the context of the test the term flow could be translated into Spanish as : fluidez, plenitud, entusiasmo, compleción, enardecimiento or maybe even effluxion (which is at least the most similar to the original English, although there is the risk of a second meaning in Spanish: “un mal parto” a bad birth). Would it be better to make a “less literal” translation of the term Flow for potential Spanish users?

- In the references, there are some errors: the separation of words is incorrect in some cases (for example TheSpanishJournal of Psychology), there are inconsistencies when using journal names (for example: ,: Journal of Sport andExercise Psychology (sic) and Journal of Sport & Exercise Psychology), journals that are not capitalized (Frontiers in psychology, Experimental aging research), poorly referenced authors (for example, ¿Lopez SJ or Lopez JS?), variations in citing books or manuals in the order “City: publisher”. There are various other errors. Similarly, there are various studies that do not have a DOI where one is available. Please review all of the references: a poorly cited reference will not appear in search engines or impact indexers later.

- I note that there is a lot of information about the construction of Likert scales. Here are three (APA style) references that I am sure will interest the authors and may be included in their paper:

* Calderón, C., Navarro, D., Lorenzo-Seva, U., & Ferrando, P. J. (2019). Multidimensional or essentially unidimensional? A multi-faceted factor analytic approach for assessing the dimensionality of tests and items. Psicothema, 31(3), 450-457. https://doi.org/10.7334/psicothema2019.153

* Suárez-Alvarez, J., Pedrosa, I., Lozano, L. M., García-Cueto, E., Cuesta. M., & Muñiz, J. (2018) Using reversed items in Likert scales: A questionable practice. Psicothema, 30(2), 149-158. doi: 10.7334/psicothema2018.33

* Lozano, L. M., García-Cueto, E., & Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology, 4(2), 73-79. doi:10.1027/1614-2241.4.2.73

In summary, it is very good work methodologically. The small, formal defects and issues about evidence of validity can, I believe, be corrected without too much trouble by the authors, and the manuscript subsequently published in PLOS-One.

Reviewer #2: I would like to thank the authors for their submission to PLOS ONE. I enjoyed reading this excellent article and I only have a few minor suggestions to make. My comments are below:

- In the Results section (Confirmatory Factor Analysis) I would recommend including a summary table that gives the goodness of fit indices X2, RMSEA, GFI, AGFI, NFI and SRMR regarding the models compared in the text: model 1 (266); model 2 (280) and model 3 (288). This table would help the reader to compare the models and reinforce the arguments in favor of selecting the model with the best fit.

- In the light of those goodness of fit indicators, it is interesting because the removal of time as a constituent factor of the six-factor model 1 results in a new, five-factor model 2 with indicators suggesting a worse fit (in GFI, AGFI and in NFI). Although the authors do comment on this in the discussion section, from a psychometric point of view it is counterintuitive that on removing the factor that clearly provides less to the solution, the fit of the model is substantially worse. In the end, what role, in psychometric terms, does the factor Time play? Might it not be understood as an inconsistency or a certain amount of instability in the construct the authors want to validate?

- In the discussion (349-355) the authors explain their reasons for using a six-factor scale that excludes the factors balance, goals and feedback rather than the nine-factor scale produced for the original study, according to Jackson and Eklund, 2002. They state that the six remaining factors are those with the highest factorial loadings, although the cited study indicates that the three excludable factors due to the lowest loadings were in fact time, consciousness and feedback. The authors should consider reframing their argument for choosing to exclude balance and goals.

-Please consider including the DOI in the references wherever it is available, for example, in the following:

14. Wrigley WJ, Emmerson SB. The experience of the flow state in live music performance. Psychology of Music. 2013;41: 292-305. DOI: 10.1177/0305735611425903

16. Custodero LA. Seeking challenge, finding skill: Flow experience and music education. ArtsEducationPolicy Review. 2002;103: 3–9. https://doi.org/10.1080/10632910209600288

18. Fullagar CJ, Knight PA, Sovern HS. Challenge/skill balance, flow, and

performance anxiety. Applied Psychology. 2013;62: 236-259. DOI:10.1111/j.1464-0597.2012.00494.x

20. Srinivasan N, Gingras B. Emotional intelligence predicts individual differences in proneness for flow among musicians: the role of control and distributed attention. Frontiers in psychology. 2014;5: 608 https://doi.org/10.3389/fpsyg.2014.00608

37. Hambleton RK, Merenda P, Spielberger C. Adapting educational and psychological tests for cross-cultural assessment. Hillsdale: Lawrence Erlbaum Publishers; 2005. DOI: 10.1007/S11336-007-9014-3

42. Holgado FP, Chacón S, Barbero I, Vila, E. Polychoric 553 versus Pearson correlations 554 in exploratory and confirmatory factor analysis of ordinal variables. Quality & 555 Quantity. 2010;44: 153-166. https://doi.org/10.1007/s11135-008-9190-y

46. Yanuar F, Devianto D, Marisa S, Zetra, A. Consistency test of reliability index in SEM model. Applied Mathematical Sciences. 2015;9: 5283-5292 http://dx.doi.org/10.12988/ams.2015.56446.

In general, this article is in an excellent state and, with a few additions and clarifications, it will be a significant contribution to the literature on flow. The authors should be thanked for their excellent work in this valuable project.

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Reviewer #1: No

Reviewer #2: No

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30 Jan 2020

Dear Dr. Eduardo Fonseca-Pedrero,

First, we would like to thank you all, the comments that you have done so kindly about our work. Sincerely, we believe that with your comments, this work has improved in its results and, even, they open more questions which we believe can be further investigated.

Next, you can read our responses about all the comments that you have asked us. We hope the responses are to your liking.

Thank you very much.

We look forward to hearing your feedback.

Editor Comments:

0.- Add empirical results in the abstract.

Finally, we have rewritten the summary in this way:

Flow is a positive and optimal state of mind, during which people are highly motivated and absorbed in the activity they are doing. It is an experience that can occur in any area of life. One of the measurement instruments which is most commonly used to evaluate this construct is the Flow State Scale-2 (FSS-2). This instrument has been used in different languages, mainly in the field of sport. In this research work, the FSS-2 has been translated into Spanish and administered to 486 musicians from different regions of Spain in order to examine the psychometric properties. A version which uses six dimensions from the original questionnaire has been used - those that constitute the experience of flow - and four alternative models have been analysed (Six factor model, two second order factor models and a bifactorial model). The results revealed that the dimension time could be controversial and more research could be needed. In general terms, the six-factor model and a second-factor one are solutions consistent with previous studies and show that the questionnaire can be considered a reliable and useful tool, both in clinical and educational contexts, as well as an instrument for the evaluation of this construct in future research. However, the results of this study also suggest that flow can be explored in greater depth in musicians, taking into account that the writing of the original items was based on the experience of athletes and that the role of time in flow needs to be investigated.

1.- Add relevant references to understand psychometric procedures used in this research: Muñiz J, Fonseca-Pedrero E. Ten steps for test development. Psicothema. 2019 Feb;31(1):7-16.

The new reference has been included in the following paragraph:

“In order to obtain evidence of construct validity of the instrument in a Spanish sample, we tested the original model proposed by Jackson and Eklund [4] using the Confirmatory Factor Analysis (CFA) procedure [39].”

2.- Add the main goals and hypotheses at the end of the intro.

The following paragraph has been added in the paper:

On the one hand, it was hypothesised that the results of the psychometric analysis of the FSS-2 scale adapted and translated into Spanish in a population of performing musicians would be consistent with the results obtained in other studies that have examined the psychometric properties of the FSS-2. On the other hand, a second hypothesis was presented according to which, if the factors of skill-challenge balance, clear objectives and clear feedback are conditions in order to enter the flow state, they will positively correlate with the other factors that represent the flow experience or state. This correlation can therefore be considered as supporting the criterion validity.

3.- Add more information about sampling procedure and sample characteristics. In particular, add more information about the all sociodemographic characteristics of the sample, e.g., ethnicity, socio-economic status, etc. Here we have convenience sample, and we know the limitations related to this kind of samples. 4.- Do you have any information about non-response? Describe inclusion/exclusion criteria if part of the data was excluded from the analysis. Were outliers removed from the data? Please, Which method did you use to deal with missing data in the analyses? What variables are related to missing data?

The following information relating to the issues you raised has been added in the paper:

A sample of 558 participants was obtained, including music students as well as amateurs and professionals. They were all Spanish speakers and came from different regions of Spain. As a criterion for inclusion, it was established that participants would have a well-established relationship with musical performance (students, professionals, amateurs), specifically at least two years of study; as a criterion for exclusion a minimum age of 18 was established. As a consequence of these criteria, 72 subjects were excluded.

The form was published via the Google Forms tool, in which the EFIM questionnaire was included. The questions were organised in such a way that it was “mandatory” to answer all of them (Google signals this requirement with a red asterisk at the end of each question). The participants thus answered all the questions in the survey and there were no cases where the answers to any of the questions set by the tool were missing.

5.- Please, add subsections in Results (descriptive, CFA, reliability).

Two new subsections have been included:

- Descriptive Statistics

- Reliability

6.- Please, test other CFA models, e.g., bifactor model, unidimensional model, etc.

We really appreciated this suggestion. We have conducted a bifactorial model, and in a way it helped to understand the previous results regarding the time dimension. We have included the following paragraph:

Nevertheless, given the apparent contradictory results regarding the fit and definition of time in the flow state dimension, a bifactorial model was tested in order to obtain evidence as to whether the items of time could be considered in the same way as the other items of the other dimensions, or if conversely, these items have complementary hues related to flow. The goodness-of-fit indices of Model 3 were: �2 (d.f. = 231; p < .001) = 304.19; RMSEA = .028 with an interval at 90% (.019 to .036); GFI = .99; and AGFI = .99; CFI = 1.00; NFI = .99; SRMR = .05. The standardised solution is shown in the last column of Table 2. As we can see, for consciousness, and especially for time, the items tend to show higher loadings in its original dimensions. This result is consistent with Model 3 where both dimensions are the most poorly predicted by flow state.

The results are discussed in the Discussion section.

7.-Please, add other CFA goodness of fit indices (e.g., BIC, TLI).

TLI index also is named NNFI, that were reported. However, according to the reviewer

suggestion the name has been modified to TLI, that may be is more known.

8.-Please explain the constrains of the present study (e.g., not use infrequency or social desirability scale, sample and sampling, self-reports, etc.).

Regarding the constraints of the study, the following comments have been added:

Among the limitations of the research it is worth mentioning that the snowball sampling technique, since it is not probabilistic, cannot guarantee that it is a representative sample of each and every one of the regions of Spain. However, it was possible to ensure that the selection of the initial subjects to whom the form was provided from different regions of Spain and from different fields of music. Moreover, because it was launched via the social networks of the UNED, including those related to the field of music, the potential number of subjects that could be reached increased. *was

9.-Please, delete from table 1, N=486.

Okay, the column has been removed.

10.- Add as supplementary material, the polychoric correlations matrix.

We will submit the polychoric correlations matrix as Supporting Information.

11.- Please add information from the ethical committee.

In the materials and methods section, we have added the following paragraph:

“The research has been carried out following the standards recommended for research on human subjects from the code of ethics of the European Community and the American Psychological Association´s Ethical Standards for Research and Publication. The research was approved by the Bioethics Committee of the UNED. We have guaranteed privacy in the processing of data. Participation in the study was voluntary and anonymous.”

Journal Requirements:

2. We noticed you have some minor occurrence of overlapping text with the following previous publication, which needs to be addressed:

Ashinoff, Brandon K., and Ahmad Abu-Akel. "Hyperfocus: the forgotten frontier of attention." Psychological research (2019): 1-19.

Okay, we have taken note of the overlapping in that publication. We have worded it as follows:

Recently, flow theory has begun to distinguish between the conditions that are necessary to give rise to flow and the psychological components that constitute the experience of flow [25, 26]. Therefore, in order to enter flow, an appropriate balance between the skills and challenges that a person faces is deemed necessary, as well as having clear objectives which are proximate to the action and, also, that there is clear and immediate feedback. The other dimensions would be the subjective experience of the flow state: concentration on the task, action-awareness, sense of control, loss of self-consciousness, transformation of time and autotelic experience [25, 26].

3. Please include a copy in the original language of the adapted questionnaire you developed, as Supporting Information.

Okay, we will submit it as Supporting Information

Reviewer #1:

1.- The authors state that the EFIM scale contains the six factors that “provide the highest factorial loads in the original studies” (referring to FSS-2). However, this is not entirely accurate: in the study by Jackson & Eklund (2002) neither Time nor Consciousness were among the six factors with the highest factorial loads. Instead, Goals and Balance were among those six factors, but were not included in the EFIM. Therefore, believe the authors should redefine the reasons for choosing the six factors for the Spanish adaptation.

Thanks for the comment. It is clearly a mistake due to confusion during the writing and translation. The correct paragraph reads as follows:

In this research, a structure of six factors that correspond to the scales of merging, concentration, control, consciousness, time and autotelic was analysed. This is in line with work by other authors [18] who have used these scales to measure the flow variable, specifically, an alignment of the item with the highest factorial load in the original studies [4].

2.- In terms of how the 9 original factors of the FSS-2 were managed, I have another question. In another part of the paper, the authors indicate that the three factors from the FFS-2 not included in the EFIM (Feedback, Goals and Balance) “are considered preconditions for Flow” (although I’m not sure about this statement). They use this to justify the use of these three factors in the predictive validity section. However, isn’t it a little opportune to take a part (in this case the three segregated factors of the FSS-2) to justify the whole (in this case the EFIM)? Imagine if we validated a short version of the verbal factor of the WISC-R (for example, with the subscales Information, Similarities and Comprehension) and to demonstrate the predictive validity of these three scales we correlate them with the Vocabulary scale of the original test? Aren’t the authors doing the same here? Given that it seems as though the authors have information for the nine original scales of the FSS-2, have they tried to fit a 9-factor model? What were the results? Even if they do not give this information in the published manuscript, I would very much like to read their response, and if possible, see the fit data for the nine-factor model.

The reviewer raises a significant theoretical question. In our research, we have chosen a model which is consistent with our theoretical hypothesis concerning the structure of flow.

We believe that this structure of six factors that correspond to the experience or state of flow and the three factors that are the conditions in order for that state to be achieved is well argued in the theoretical framework of the theory of flow by Nakamura and Csíkszentmihályi. The experience of flow, as a state a person finds themselves in and about which this person can speak, refers to the assessments about the internal state itself regarding the activity or performance in a task. These assessments are the result of the observation of one's own experience, which can be in the form of sensations, emotions or cognitive functioning. However, the skill-challenge balance, having objectives which are proximate to the action and immediate clear feedback, do not reflect the experience of flow itself as an internal state. Although the subjective belief of the balance between skills/challenges in order to enter a state of flow is significant, it is also important that this balance be objectively real, even if the person is not aware of this. This means that, for example, in learning processes, students can reach a state of flow, rather than a state of anxiety, if that balance is ensured. Furthermore, although the objectives which are proximate to the action entail that the person is clear about what he or she has to do, and, therefore, they are able to assess it, it is the result of that clarity that probably translates into a state of control and concentration, for example. With immediate feedback, something similar would happen. The sources of feedback can be diverse and also external to the person, and the FSS-2 includes the assessment made by the person as to how their performance is proceeding. It is therefore a reflection as to whether or not he or she is aware of what they are doing and how they are doing it, but the internal state would relate to the ability to keep their focus on those feedback elements.

Furthermore, research has been conducted where the dimensions that are deemed conditions to enter flow have been controlled, especially the dimension of skill-challenge balance in activities such as online games or learning situations.

Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and flow of online learning. Computers in human behavior, 21(5), 745-771.

Rheinberg, F., & Vollmeyer, R. (2003). Flow-Erleben in einem Computerspiel unter experimentell variierten Bedingungen.

Keller, J., & Bless, H. (2008). Flow and regulatory compatibility: An experimental approach to the flow model of intrinsic motivation. Personality and social psychology bulletin, 34(2), 196-209.

Moller, A. C., Csikszentmihalyi, M., Nakamura, J., & Deci, E. L. (2007). February. Developing an experimental induction of flow. In Poster presented at the Society for Personality and Social Psychology Conference, Memphis, TN.

Moller, A. C., Meier, B. P., & Wall, R. D. (2010). Developing an experimental induction of flow: Effortless action in the lab. In B. Bruya (Ed.), Effortless attention: A new perspective in the cognitive science of attention and action (p. 191–204). MIT Press. https://doi.org/10.7551/mitpress/9780262013840.003.0010

Therefore, this theoretical definition of flow is what we have tried to investigate empirically by means of the CFA. Given the results obtained, we believe that our hypothesis was able to be empirically sustained.

However, below we provide the results for the model with nine dimensions:

Degrees of Freedom = 558

Normal Theory Weighted Least Squares Chi-Square = 2298.33 (P = 0.0)

Satorra-Bentler Scaled Chi-Square = 879.57 (P = 0.00)

Estimated Non-Centrality Parameter (NCP) = 321.57

90 Percent Confidence Interval for NCP = (244.88; 406.18)

Minimum Fit Function Value = 1.42

Population Discrepancy Function Value (F0) = 0.74

90 Percent Confidence Interval for F0 = (0.56; 0.93)

Root Mean Square Error of Approximation (RMSEA) = 0.036

90 Percent Confidence Interval for RMSEA = (0.032; 0.041)

P-Value for Test of Close Fit (RMSEA < 0.05) = 1.00

Expected Cross-Validation Index (ECVI) = 2.52

90 Percent Confidence Interval for ECVI = (2.34; 2.71)

ECVI for Saturated Model = 3.06

ECVI for Independence Model = 148.44

Chi-Square for Independence Model with 630 Degrees of Freedom = 64498.87

Independence AIC = 64570.87

Model AIC = 1095.57

Saturated AIC = 1332.00

Independence CAIC = 64753.66

Model CAIC = 1643.95

Saturated CAIC = 4713.71

Normed Fit Index (NFI) = 0.99

Non-Normed Fit Index (NNFI) = 0.99

Parsimony Normed Fit Index (PNFI) = 0.87

Comparative Fit Index (CFI) = 0.99

Incremental Fit Index (IFI) = 0.99

Relative Fit Index (RFI) = 0.98

Critical N (CN) = 316.85

Root Mean Square Residual (RMR) = 0.046

Standardised RMR = 0.046

Goodness-of-Fit Index (GFI) = 0.99

Adjusted Goodness-of-Fit Index (AGFI) = 0.99

Parsimony Goodness-of-Fit Index (PGFI) = 0.83

While this model also presents appropriate fit indices, there are some issues to be considered:

1. We have tried to follow a theory driven perspective, and our interest is thus focused on the model presented in the paper.

2. If we compare this model with the one presented in the paper, the relative fit index based on Chi-squared test, is worse in the model with nine dimensions. On the other hand, it is a logical result because its complexity is higher. This model is also tenable but it is not consistent with our theoretical position presented in the paper.

Nonetheless, if the reviewer considers that it could be of interest to present this result in the paper, please let us know. We felt that the paper was sufficient in length, and the presence of more results could hinder the understanding of the paper by potential readers with several results which do not relate to the main objective.

3.- One question about the wording. In lines 307-311, the authors state that “In order to analyse the criterion validity we calculated the Pearson bivariate correlation index between each of the six subscales, the global flow, and the scores on: a) balance, b) goals, c) feedback. Given the inter-relation between these scales, we also obtained the partial correlation between each subscale, global flow included, and each one of the three measures referred to above, controlling in each case the effect of the remaining scales” which seems to indicate that they analysed the effect between each pair of variables, discounting the effect of the other subscales. However, later (lines 395-396) they state “It is also interesting to note how goals lose their relationship with the dimensions of the questionnaire once feedback and balance are controlled in the partial correlations”. It is not clear which variables they used as controls to estimate the partial correlations.

Thanks for the comment. It is indeed confusing. The paragraph could be clarified as follows:

Given the inter-relation between these scales we also obtained the partial correlation between each subscale, global flow included, and each one of the three measures referred to above, controlling in each case the effect of the two remaining scales that are conditions for entering flow (Table 6).

4.- I am Spanish, and this comment comes from me thinking if I had to find the EFIM in a Spanish Psychology Test library. My history of classical philosophy professor said that we should require a name (whether a journal or a test etc.) to “determine and explain the content” [González Escudero, S. (1989). A propósito del nombre. Psicothema, 1(1), 5-6]. I’m afraid that the translation “Estado de Flujo para Intérpretes Musicales” does not do this. I think that in the context of the test the term flow could be translated into Spanish as : fluidez, plenitud, entusiasmo, compleción, enardecimiento or maybe even effluxion (which is at least the most similar to the original English, although there is the risk of a second meaning in Spanish: “un mal parto” a bad birth). Would it be better to make a “less literal” translation of the term Flow for potential Spanish users?

We really like your assessment of the term, and we find that, although there is a lot of literature - both academic and informative - concerning flow where it is translated as “estado de flujo”, the term "fluidez" or even "fluencia" is also used. We believe that with regard to musicians, translating it as "fluidez" may be more accurate because it conveys the quality of expressing oneself with a certain degree of ease and spontaneity.

5.- In the references, there are some errors: the separation of words is incorrect in some cases (for example TheSpanishJournal of Psychology), there are inconsistencies when using journal names (for example: ,: Journal of Sport andExercise Psychology (sic) and Journal of Sport & Exercise Psychology), journals that are not capitalized (Frontiers in psychology, Experimental aging research), poorly referenced authors (for example, ¿Lopez SJ or Lopez JS?), variations in citing books or manuals in the order “City: publisher”. There are various other errors. Similarly, there are various studies that do not have a DOI where one is available. Please review all of the references: a poorly cited reference will not appear in search engines or impact indexers later.

We have reviewed the references.

6.- I note that there is a lot of information about the construction of Likert scales. Here are three (APA style) references that I am sure will interest the authors and may be included in their paper:

Thank you very much. We will add the following reference:

* Lozano, L. M., García-Cueto, E., & Muñiz, J. (2008). Effect of the number of response categories on the reliability and validity of rating scales. Methodology, 4(2), 73-79. doi:10.1027/1614-2241.4.2.73

Reviewer #2:

1.- In the Results section (Confirmatory Factor Analysis) I would recommend including a summary table that gives the goodness of fit indices X2, RMSEA, GFI, AGFI, NFI and SRMR regarding the models compared in the text: model 1 (266); model 2 (280) and model 3 (288). This table would help the reader to compare the models and reinforce the arguments in favor of selecting the model with the best fit.

Thanks for the suggestion. This summary table with the goodness-of-fit indices has been added to the paper:

Table 2. Global goodness-of-fit indices of the four models.

�2 d.f. p RMSEA GFI AGFI CFI NFI SRMR

Model 1 341.46 237 <.001 .036 .99 .99 1.00 .99 .04

Model 2 1292.39 238 <.001 .10 .92 .90 .96 .95 .14

Model 3 340.54 231 <.001 .033 .99 .99 1.00 .99 .04

Model 4 304.19 228 <.001 .028 .99 .99 1.00 .99 .05

Model 1= Related six-factor model; Model 2= five first-order factors and one second-order factor (not time); Model 3= six first-order factors and one second-order factor; Model 4= bifactorial model.

2.- In the light of those goodness of fit indicators, it is interesting because the removal of time as a constituent factor of the six-factor model 1 results in a new, five-factor model 2 with indicators suggesting a worse fit (in GFI, AGFI and in NFI). Although the authors do comment on this in the discussion section, from a psychometric point of view it is counterintuitive that on removing the factor that clearly provides less to the solution, the fit of the model is substantially worse. In the end, what role, in psychometric terms, does the factor Time play? Might it not be understood as an inconsistency or a certain amount of instability in the construct the authors want to validate?

We have analysed a bifactorial model that we have included in the article and which we have commented on in the discussion section. The goodness-of-fit indices are similar to those of Models 1 and 3. However, it allows us to observe how time reflects the general flow factor, while the high factorial loads in its dimension reflect a specific part of that factor itself. This result, on the one hand, supports the theoretical framework that considers time as an essential component of the experience of flow, despite obtaining, in general, low correlations with the other dimensions. On the other hand, it also gives rise to the need, as we state in the article, to clarify what that specific contribution would consist of.

3.- In the discussion (349-355) the authors explain their reasons for using a six-factor scale that excludes the factors balance, goals and feedback rather than the nine-factor scale produced for the original study, according to Jackson and Eklund, 2002. They state that the six remaining factors are those with the highest factorial loadings, although the cited study indicates that the three excludable factors due to the lowest loadings were in fact time, consciousness and feedback. The authors should consider reframing their argument for choosing to exclude balance and goals.

Thank you for the comment. It is clearly a mistake due to confusion during the writing and translation. The corrected paragraph reads as follows:

In this research, a structure of six factors that correspond to the scales of merging, concentration, control, consciousness, time and autotelic has been analysed. This is in line with work by other authors [18] who have used these scales to measure the flow variable, specifically, an alignment of the item with the highest factorial load in the original studies [4].

4.- Please consider including the DOI in the references wherever it is available.

Thank you very much. We have included all the references that we have found.

Thank you very much.

Best regards,

Laura Moral-Bofill

Pablo Holgado Tello

Submitted filename: EN_Response to Reviewers_para_traduccion.docx

Click here for additional data file.

26 Feb 2020

PONE-D-19-31155R1

Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field of musical performers

PLOS ONE

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Additional Editor Comments (if provided):

1.- Add empirical information in the abstract (eg. reliability).

2.- APA style: better talk about persons, not subjects (see participants section).

3.- Used only two decimals (see method).

4.- Add SPSS reference.

5.- Please, explain all CFA models tested.

6.- Please test one factor model.

7.- Please check typo: bifactorial model (bifactor model).

8.- Table 3. Add p values.

9.- CFA: add BIC values and IC 90% RMSEA.

10.- Table 4, add p values.

11.- Please, add CFA measurement invariance analyses.

12.- Please, add information about the psychometric properties of: Challenge-skill balance (balance); Clear goals (goals); Unambiguous feedback (feedback).

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10 Mar 2020

Additional Editor Comments (if provided):

1.- Add empirical information in the abstract (eg. reliability).

We have included some global fit indexes for the model of 6 related factor and the second order factor model. Also, the alphas range of the dimensions have been included. The new abstract is:

“Flow is a positive and optimal state of mind, during which people are highly motivated and absorbed in the activity they are doing. It is an experience that can occur in any area of life. One of the measurement instruments which is most commonly used to evaluate this construct is the Flow State Scale-2 (FSS-2). This instrument has been used in different languages, mainly in the field of sport. In this research work, the FSS-2 has been translated into Spanish and administered to 486 musicians from different regions of Spain in order to examine the psychometric properties. A version which uses six dimensions from the original questionnaire has been used - those that constitute the experience of flow - and four alternative models have been analysed (Six related factors model, two second order factor models and a bifactor model).The results revealed that the dimension time could be controversial and more research could be needed. In general terms, the six-factor model (RMSEA = .04; GFI = .99; AGFI = .99) and a second-factor one (RMSEA = .033; GFI = .99; AGFI = .99) are solutions consistent with previous studies and show that the questionnaire can be considered a reliable (Alphas of the dimensions range from .81 to .94; Omegas from .86 to .97; and mean discrimination of the dimensions from .64 to 88) and useful tool, both in clinical and educational contexts, as well as an instrument for the evaluation of this construct in future research. However, the results of this study also suggest that flow can be explored in greater depth in musicians, taking into account that the writing of the original items was based on the experience of athletes and that the role of time in flow needs to be investigated”.

2.- APA style: better talk about persons, not subjects (see participants section).

Thank you very much for your advice. The use of persons has been corrected along the text.

3.- Used only two decimals (see method).

Done

4.- Add SPSS reference.

Done

5.- Please, explain all CFA models tested.

In order to facilitate the reading, we have included a brief description after each model label. For example, if we refer model 1, in brackets we have include “six related factors”. The same for the rest of model labels. The theoretical sense of each model and the references that support them are described in the text but is true that the reading was difficult only with the model labels. Also, we have tried to explain better the models.

6.- Please test one factor model.

According to the reviewer suggestion we have examined the one factor model. The fit indexes are the following:

Degrees of Freedom = 254

Normal Theory Weighted Least Squares Chi-Square = 6700.39 (P = 0.0)

Satorra-Bentler Scaled Chi-Square = 3722.06 (P = 0.0)

Chi-Square Corrected for Non-Normality = 26692.64 (P = 0.0)

Estimated Non-centrality Parameter (NCP) = 3468.06

90 Percent Confidence Interval for NCP = (3274.39 ; 3669.03)

Minimum Fit Function Value = 10.31

Population Discrepancy Function Value (F0) = 7.97

90 Percent Confidence Interval for F0 = (7.53 ; 8.43)

Root Mean Square Error of Approximation (RMSEA) = 0.18

90 Percent Confidence Interval for RMSEA = (0.17 ; 0.18)

P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00

Expected Cross-Validation Index (ECVI) = 8.77

90 Percent Confidence Interval for ECVI = (8.32 ; 9.23)

ECVI for Saturated Model = 1.38

ECVI for Independence Model = 63.42

Chi-Square for Independence Model with 276 Degrees of Freedom = 27540.03

Independence AIC = 27588.03

Model AIC = 3814.06

Saturated AIC = 600.00

Independence CAIC = 27709.90

Model CAIC = 4047.63

Saturated CAIC = 2123.29

Normed Fit Index (NFI) = 0.86

Non-Normed Fit Index (NNFI) = 0.86

Parsimony Normed Fit Index (PNFI) = 0.80

Comparative Fit Index (CFI) = 0.87

Incremental Fit Index (IFI) = 0.87

Relative Fit Index (RFI) = 0.85

Critical N (CN) = 37.15

Root Mean Square Residual (RMR) = 0.19

Standardized RMR = 0.19

Goodness of Fit Index (GFI) = 0.86

Adjusted Goodness of Fit Index (AGFI) = 0.84

Parsimony Goodness of Fit Index (PGFI) = 0.73

The fit indices are not adequate, may be, because the construct respond to the structure of the theoretical position presented in the paper. Given these results we understand that present this model has not interest and, probably, will make more difficult the reading to potential readers. Nonetheless if the reviewer consider that could be of interest present this model in the paper, please, let us know it.

7.- Please check typo: bifactorial model (bifactor model).

Done

8.- Table 3. Add p values.

According to reviewer suggestion we have included an asterisk in each significant parameter, as is usual reporting results of SEM (a note in each table has been included to explain this issue). We have not included the exact p value because most of them are under .01, then we will need more than two decimals and the tables will contain a lot of information. But the relevant information about the significance of the parameters has been added.

9.- CFA: add BIC values and IC 90% RMSEA.

According to reviewer suggestion, we have included BIC in the table 2 and the IC for RMSEA along the text where the fit indices of each model are commented.

10.- Table 4, add p values.

we have included an asterisk in each significant parameter

11.- Please, add CFA measurement invariance analyses.

In all probability the reviewer is considering one of the key elements of an adequate study of validity. In this sense, invariance analysis is necessary to obtain empirical evidences about the construct validity of the scale. If we are considering different populations or groups, and if we suspect that could be found differences in how these groups understand the measured concept, an invariance analysis should be done in order to guarantee that we are measuring with the same conditions (concepts, reliability) between samples. In this research we have not considered conjectures about differences among defined groups as gender, age, instruments, or years of practice, for example. As the reviewer indicate, this could be an interesting issue to be examine, however in this paper, as a first step, our objective was to adapt the FSS-2 to Spanish and to look at the psychometric properties of the instrument in a population composed specifically of musicians. An adequate analysis of the invariance exceed the objective and extension of this paper (another different paper could be done), because an adequate invariance analysis could be extensive in results and discussion if variance is found and firstly we must review the literature to investigate in which variables the multigroup perspective should be considered (gender, instrument, years of practice,…).

In order to recognize the importance of this issue in the limitations we have added the following text: “…in this sense in order to obtain more validity evidences and be able to generalised the model tested into different groups (gender, instruments used, years of experience,…) an invariance analysis should be explore in forthcoming researches”

Finally, a technical limitation to carry out this kind of analysis is that given the complexity of the tested models the sample size do not is big enough to split it by any moderator variable as gender for example.

12.- Please, add information about the psychometric properties of: Challenge-skill balance (balance); Clear goals (goals); Unambiguous feedback (feedback).

According with this suggestion the alphas, omegas and mean discrimination of these dimensions has been added in the instrument section. The new paragraph is:

“…The remaining 3 scales of the FSS-2 that were not included in the EFIM were used as criteria, as they correspond to the dimensions that are necessary conditions in order to generate the flow state. These are: Challenge-skill balance (balance) (in this sample: Alpha = .75; Omega = .83; mean discrimination = .59); Clear goals (goals) (Alpha = .90; Omega = .90; mean discrimination = .78); Unambiguous feedback (feedback) (Alpha = .86; Omega = .87; mean discrimination = .56).”

Submitted filename: respond to reviewers_2_2.docx

Click here for additional data file.

16 Mar 2020

Adaptation to Spanish and psychometric study of the Flow State Scale-2 in the field of musical performers

PONE-D-19-31155R2

Dear Dr. Pablo Holgado-Tello,

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PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: