Reliability and validity of the Arabic version of coach-athlete relationship questionnaire: ACART-Q

Abstract

This study had a twofold purpose: (a) to create an Arabic version of the Coaches-Athletes Relationship Questionnaire (CART-Q) and (b) to assess its psychometric qualities in the context of Tunisia, as well as its sensitivity in other sports populations. The researchers conducted an online survey of 594 amateur athletes from different sports clubs in Tunisia. For the exploratory factor analysis, 157 athletes aged 22.72 ± 3.78 years were randomly selected, while confirmatory data were collected from 482 athletes aged 22.74 ± 7.63. The study found a three-factor structure through principal component and confirmatory factor analyses. CFA fit indices showed acceptable results for both second-order and first-order. The scale had excellent internal consistency for all three factors and total score, but only partially established sensitivity for the complementarity dimension. Gender and type of sport showed significant differences for the complementarity dimension. Cronbach’s Alpha, McDonald’s ω, and Guttmann’s λ6 coefficients were all high at 0.88, 0.89, and 0.94, respectively. Overall, the results suggest ACART-Q is a valid instrument for assessing the coach/athlete relationship in the Tunisian context. The study provided valuable insights into its psychometric qualities and the questionnaire could be used by coaches, researchers, and athletes to improve their relationships and performance.

Keywords:

• Player
• trainer
• questionnaire
• closeness
• commitment
• complementarity

1. Introduction

Sports performance is influenced by both athlete’s intrapersonal and interpersonal aspects (Cece et al., 2023; Chu et al., 2023). Intrapersonal aspects may include mental toughness, self-confidence, motivation, and emotional regulation of the athlete. On the other hand, interpersonal aspects in sports may include communication, cooperation, and collaboration with coaches, teammates, opponents, and spectators. Thus, to increase performance, the development of interpersonal variables such us coach-athlete relationship would be essential (Davis et al., 2023; Fonteyn et al., 2022; Jowett, 2017; Jowett & Chaundy, 2004; Jowett & Ntoumanis, 2004; Vella et al., 2013). The social environment that exists in the coach-athlete relationship is what is used to identify, describe and operationalize the quality of the interaction between them (Davis et al., 2023; Jowett, 2007). Understanding interpersonal relationships requires taking into account their tripartite (e.g., affective, cognitive and behavioral components) (Shi & Burapajana, 2023). From the viewpoint of the athletes, positive and solid relationships between coaches and athletes are crucial because they offer comfort during times of stress in sports or in everyday life (Davis et al., 2023; Li et al., 2020; Wachsmuth et al., 2018). Accordingly, to this specific literature, the need for further study of coach-athlete relationships is eminent. Several previous studies have focused on the content of the relationship that coaches and athletes develop (Freire et al., 2022; Jowett et al., 2023; Simons & Bird, 2022; Stephen et al., 2022; Zhao & Jowett, 2022).

Many frameworks have been developed to explain the relationship between athletes and their coaches (Jowett & Ntoumanis, 2004), however a large body of studies in this field adopted a three-factor model which called the 3C (Jowett & Chaundy, 2004; Jowett & Ntoumanis, 2004). This model has been sparked by a conceptual paradigm known as the closeness, commitment, and complementarity model of the coach-athlete interaction (Ahmad et al., 2021; Balduck & Jowett, 2010). The coach-athlete relationship is viewed by the 3C as a situational occurrence in which emotional closeness, commitment-related thoughts, and complementary behaviors are intertwined (Gustafsson et al., 2023; Jowett & Timson-Katchis, 2005; Vigário et al., 2020). The term closeness (affective component) describes how emotionally connected with another in the coach—athlete relationship, including through affection, respect, trust, and appreciation (Jowett & Ntoumanis, 2004). The term commitment (cognitive component) refers to the connection between coach’s and athlete’s goals and ideas regarding the formation of a strong and enduring alliance between them (Balduck & Jowett, 2010). The concept of complementarity (behavioral component) refers to voluntary cooperative interactions in the coach-athlete relationship (Balduck & Jowett, 2010; Jowett & Ntoumanis, 2004).

The Coach-Athlete Relationship Questionnaire (CART-Q) was the psychometric scale based on the conceptualization of the 3C model which has developed and validated to assess closeness, commitment and complementarity (Ahmad et al., 2021; Jowett & Chaundy, 2004; Jowett & Ntoumanis, 2004). The CART-Q is an instrument that evaluates the closeness, commitment, and complementarity (Balduck & Jowett, 2010; Jowett & Ntoumanis, 2004; Woolliams et al., 2021; Yang & Jowett, 2012). It can be used to assess coaches’ and athletes’ perspectives or meta-perspectives (Davis et al., 2023; Freire et al., 2022). The findings of the CART-Q can help coaches create situations that foster a good connection between coaches and athletes as well as useful information for experts in the field of sport psychology (Ahmad et al., 2021).

Studies in this field aim to create a universal theory that leads to more generalizable study findings across cultures and nations (Yang & Jowett, 2012). Therefore, it is necessary to assess how well the theory and operationalization of the CART-Q (Balzadeh et al., 2019; Jowett & Ntoumanis, 2004; Kovács et al., 2021; Şahin et al., 2020) have been adjusted to the specifics of cultural variety. Several adequate transcultural validation of the CART-Q were conducted (See additional Table A1), for example: in Belgium (Balduck & Jowett, 2010), Greece (Jowett & Ntoumanis, 2003), Brazil (Vieira et al., 2015), China (Yang & Jowett, 2010), Turkey (Altintaş et al., 2012), and Kuwait (Ahmad et al., 2021).

Understandably, many Arab athletes aspire to participate in competitive sports and extend their athletic careers, making the coach-athlete relationship an essential part of sports psychology studies. By developing a coach-athlete relationship questionnaire specific to the Western Arab region, sport psychology professionals can gain a deeper understanding of the cultural dynamics that influence this relationship. This understanding can inform culturally sensitive interventions, coach education programs, and athlete support services, ultimately promoting positive coaching practices and enhancing athletes’ well-being and performance in the region. This serves to emphasize once again the importance of employing and validating the Arabic versions of the CART-Q (Ahmad et al., 2021).

A recent study was conducted on the validation of the CART-Q in Arabic language in Kuwait (Ahmad et al., 2021). However, the results of this study should be interpreted with caution because the sample sizes were significantly inadequate and the male athletes were dominant. This significant gender difference comes down to the cultural differences that discourage the existence of women in sports in Kuwait (Ahmad et al., 2021). In addition, it is important to emphasize that Kuwait’s particular circumstances (Kuwait’s temporary absence from international sport) may have potential implications for the generalizability of the study’s findings. The authors stated that during the period when this study took place some athletes displayed comparatively lower levels of ambition, engaged in fewer hours of training and demonstrated reduced motivation to improve their skills or pursue a sporting career (Ahmad et al., 2021). Even if this instrument was intended for an Arabic-speaking population, it turns out to be unsuitable for a Tunisian population given the difference in dialect. Indeed, it is important to note that Arabic dialects can be divided into two major categories based on their geographic distribution: eastern dialects (Levantine Arabic, Gulf Arabic, and Egyptian Arabic) and western dialects (Maghreb Arabic) (Alorifi, 2008). In particular, Tunisian Arabic, which has been strongly affected by a number of languages, including Berber, Turkish, Italian, Spanish, and French. It is distinguished by having different morphology, syntax, pronunciation, and vocabulary from the, modern standard Arabic and even from other Arabic dialects (Zribi et al., 2013, 2017).

Sport occupies an essential place in Tunisian society. Like all other athletes, Tunisians are dependent on their coaches on a technical and tactical level, but also on a relational level. Therefore, understanding the nature of the coach-athlete relationship in the Tunisian sports population is essential. As such, to understand the nature of this relationship it would be essential to use robust and valid measuring instruments for the Tunisian culture and sporting environment.

Despite the multitude of cross-cultural validation studies of the CART-Q, and to the knowledge of the authors, there is no tool for measuring the coach-athlete relationship adapted to the Tunisian population. The objective of this study was to adapt and validate an Arabic version of the direct perspective version of the questionnaire CART-Q, to test its psychometric properties in a Tunisian context and verify its sensitivity for different athletic populations.

2. Methods

2.1. Participants

The latest statistics published in 2018 by the National Institute of Statistics (available on the site: http://www.ins.tn/statistiques/80) indicated that the number of licensed Tunisian athletes is 145,684 with a margin of progression that does not exceed 3.5% over 10 years. Therefore, the minimum sample size will be calculated from a population estimated at around 167,268.

The minimum sample is estimated at 384 (5 % Margin of Error 95 %, confidence interval and 50 % of fraction of interesting responses) athletes according (see Formula, Additional file 1).

From December 2022 until February 2023, we collected cross-sectional data from a Google Forms based survey, which includes athletes’ socio-demographic data (sex and age), number of years of experience and types of sport (team and individual).

The exclusion criteria involve: having a coach-athlete relationship of less than 6 months, being a non-Arabic-speaking Tunisian athlete and being under 18 years old.

The athletes practiced individual sports such as athletics (n = 52), swimming (n = 22), fitness (n = 54), aerobics (44), tennis (n = 24), judo (n = 30), taekwondo (n = 42) and cross-fit (n = 44). While team sports involve football (n = 84), handball (n = 71), basketball (n = 81) and volleyball (n = 46).

After collecting 678 specimens from various clubs and sports halls in several regions in Tunisia, a number of specimens with incomplete and missing data (n = 84) were discarded.

A total of 594 amateur athletes (46.80% female) were retained from various sports clubs in Tunisia. The level of experience varies between 3 years and 17 years. All athletes are competitive in different levels of competition in the Tunisian championships and participate in regular training (See Table 1).

Table 1. Summary of the sample characteristics

	Exploratory sample	Confirmatory sample	Total
n (%)	157 (26,43%)	437 (73,57%)	594 (100%)
Gender
Female (%)	n = 68 (43,31%)	n = 212 (48,5%)	n = 280 (47,1%)
Male (%)	n = 89 (56,69%)	n=(225;51,5%)	n = 314 (52,9%)
Age	22,72 ± 3,78	22,74 ± 4,38	22,73 ± 4,23
Experience level	10,63 ± 2,52	7,63 ± 2,54	8,42 ± 2,86
Type of sport
Individual (%)	n = 82 (52,23%)	n = 230 (52,63%)	n = 312 (52,53%)
Team (%)	n = 75 (47,77%)	n = 207 (47,37%)	n = 282 (47,47%)

Two distinct populations were recruited to conduct the exploratory and confirmatory analyses. Exploratory data were collected from 157 athletes aged 22.72 ± 3.78 years randomly selected from the data. The subjects were recruited from both sexes, female (n = 68; 43.31%) and male (n = 89; 56.69%), belonging to the students at the Higher Institute of Physical Education and Sport of Tunisia. The subjects has 10.63 ± 2.52 years expertise in individual sports (n = 82, 52.23 %) and team sports (n = 75, 47.77 %).

Confirmatory data were collected from 482 students who were aged 18–34 years (M = 22.74, SD = 7.63) and has a 7.63 ± 2.54 years of expertise. Athletes of both genders (48.5 % female) are divided into individual sports athletes (n = 230, 52.6 %) and team sports athletes (207, 47.36 %).

3. Instrument

3.1. The coach-athlete relationship questionnaire (CART-Q)

An Arabic-language version of the CART-Q (Additional Table 2) was used to assess athletes’ perception of the coach. The questionnaire consists of 11 items, which are conceptualized in three sub-scales: direct perspective (3 items; example: “I like my coach”); direct commitment (3 items; example: “I am committed to my coach”; 3 items for direct complementarity, for example, “When I am coached by my coach, I am sensitive to his efforts”) For this study, the factorial structure of the CART-Q version was used in Tunisia and was prepared with direct perspectives Items that were scored from 1 (strongly disagree) to 7 (strongly agree).

Table 2. Descriptive statistics of the ACART-Q items (n = 157)

Items	Exploratory sample				Confirmatory sample
	Mean	Std. Deviation	Skewness	Kurtosis	Mean	Std. Deviation	Skewness	Kurtosis
I1	4,40	1,39	−0,03	−0,86	4,07	1,30	0,26	−0,44
I2	4,36	1,32	−0,24	−0,16	4,12	1,27	0,10	−0,60
I3	4,46	1,37	−0,11	−0,43	4,14	1,36	0,09	−0,68
I4	4,46	1,50	−0,47	−0,55	4,10	1,43	0,23	−0,67
I5	4,41	1,45	−0,32	−0,60	4,27	1,48	0,21	−0,72
I6	4,53	1,58	−0,41	−0,59	4,05	1,37	0,26	−0,38
I7	4,39	1,44	−0,30	−0,32	4,08	1,33	0,11	−0,55
I8	4,28	1,36	0,02	−0,46	4,06	1,38	0,29	−0,58
I9	4,35	1,48	−0,09	−0,84	4,04	1,38	0,34	−0,50
I10	4,36	1,40	−0,19	−0,69	4,04	1,37	0,33	−0,49
I11	4,39	1,43	−0,12	−0,52	3,97	1,35	0,26	−0,58

Cronbach’s internal consistency coefficients for the three subscales for the direct and meta versions of the initial version were respectively 0.82 and 0.85 for closeness, 0.70 and 0.82 for commitment, 0.65 and 0.82 for complementarity.

4. Procedures

In the first stage, a committee made up of a professional translator, professor of linguistics, and three professors of social sciences translated the coach-athlete relationship questionnaire (CART-Q). A reverse translation procedure was conducted to see the robustness of the adapted version. This procedure was described by Hambleton (Hambleton, 1993).

The participants in this study were invited thanks to a distribution via the social network Facebook or by e-mail. They were informed that there was no obligation to take part in the study, and that a possible refusal did not have to be justified. The study was described as focusing on the quality of the coach-athlete relationship, without any clarification on the notions of commitment in order to limit response bias.

5. Data analyses

Statistical analyses were performed using IBM SPSS version 26.0 for Windows. The reliability of the instrument was tested by the open-source software JASP. Whereas Lavaan’s R package (R Studio) was adopted for confirmatory factor analysis; preliminary data analysis was performed by Skewness and Kurtosis normality tests.

A preliminary data analysis was performed to review the quality of the data collected and to inspect if there are any anomalies or outliers. Missing data were excluded from the analysis for all items. Subsequently, univariate normality tests (Skewness and Kurtosis) performed. Kurtosis values larger than 3 and asymmetry values greater than 7 were deemed to be non-Gaussian.

The Pearson correlation matrix was examined to exam correlation between items. We used low (<0.35), moderate (between 0.36 and 0.67), and strong (>0.67) thresholds for Pearson’s correlation coefficients Since significance levels are not very useful for examining relationships in large samples (De Winter et al., 2016).

Principal component analysis was performed using the unweighted least squares method with Promax rotation and Kaiser-Mayer-Oklins (KMO) normalization. A KMO value greater than 0.60, a significant χ2 value and an eigenvalue greater than 1 are indicators of an adequate factorial solution.

The reliability of the instrument was examined simultaneously by Cronbach’s, McDonald’s ω coefficient, and Guttmann’s λ6. The recommended threshold for these indexes is 0.70 for acceptability and 0.80 for good reliability.

The structure of the questionnaire for the entire population was carried out by confirmatory factor analysis (CFA) based on the robust Diagonally Weighted Least Squares (DWLS) procedure as estimator.

Several CFA indices were a priori defined and used to examine the model: (1) the χ2; (2) χ2/DF, (3) the comparative adjustment index (CFI); (4) Tucker-Lewis Index (TLI); (5) Adjusted Good of Fit Index (AGFI), (6) Good of Fit Index (GFI), (7) Standardized Root Mean Square Error (SRMR), and (8) Root Mean Square Error of Approximation (RMSEA).

Hu and Bentler recommendation suggest that to adopt the model, GFI and AGFI must have values higher than 0.90. Values of TLI and CFI higher than 0.95 indicate an excellent model fit. For a decent model fit and an adequate model fit, the RMSEA value should be less than 0.06.

The comparison in each sub-scale and total score was carried out by the Multivariate analysis of covariance (MANCOVA) and Univariate analysis of covariance (ANCOVA) with age and experience number as covariates. In addition, Effect size (Eta Squared) was examined for each comparison.

6. Results

Table 2 shows the descriptive statistics (means and standard deviations) and the Skewness and Kurtosis coefficients. The normality coefficients prove the normality of the distributions. Skewness values greater than 2 and Kurtosis values greater than 3 indicate that the item is not normally distributed (Westfall & Henning, 2013). No anomaly for the data collected was highlighted.

7. Principal component analysis

Before carrying out the principal component analysis, we performed the Pearson correlation matrix (Table 3). Correlation coefficients implied a high correlation between items of each sub-scales and moderate correlation between the items of each dimension with the items of the other dimensions.

Table 3. Pearson correlation matrix of the ACART-Q items

	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10
I2	,729**	-
I3	,795**	,781**	-
I4	,298**	,315**	0,138	-
I5	,255**	,265**	0,117	,747**	-
I6	,233**	,250**	0,135	,781**	,791**	-
I7	,287**	,339**	,164*	,739**	,770**	,787**	-
I8	,378**	,405**	,363**	,340**	,363**	,332**	,389**	-
I9	,364**	,379**	,378**	,232**	,290**	,188*	,238**	,712**	-
I10	,395**	,456**	,425**	,274**	,274**	,276**	,293**	,703**	,723**	-
I11	,417**	,431**	,433**	,370**	,387**	,363**	,404**	,778**	,738**	,732**

Footnote: * : p < 0.05; **:p < 0.01.

The scree plot’s aim, as illustrated in Figure 1, is to choose variables with Eigenvalues greater than 1. The gathered data and the simulated data, revealed a three-factor solution: the factors retained had to be above the cut-off line parallel to the axis of the Eigenvalues (intersection for Eigenvalue = 1).

Figure 1. Scree plot of the ACART-Q.

The results of the principal component factor analysis with Kaiser Normalization and Promax rotation suggested the extraction of three factors that explain 76.3% of the total variance. The first, the second and the third factors explained 46.7 % (Eigenvalue = 5.14), 18.80% (Eigenvalue = 2.07) and 10.80 % (Eigenvalue = 1.19), respectively (See Table 4).

Table 4. Factor loadings, Eigenvalues and variance explained (%) of the ACART-Q score’s

	Factor 1	Factor 2	Factor 3	Eigenvalues	Variance explained (%)
I6	0.927			5,14	46,70%
I5	0.870
I7	0.860
I4	0.855
I9		0.897		2,07	18,80%
I11		0.846
I8		0.842
I10		0.816
I3			0.967	1,19	10,80%
I1			0.848
I2			0.813

8. Reliability

The reliability of the scale was determined by calculating Cronbach’s, McDonald’s ω coefficient, and Guttmann’s λ6 coefficients of the three dimensions and the total scale.

As shown in Table 5, the values of the internal consistency coefficients for committing were all satisfactory (ω = 0.91, α = 0.91, λ6 = 0.87). For closeness, the coefficients were ω = 0.93, α = 0.93, λ6 = 0.91. While for the complementarity dimension, the values of the internal consistency indices were 0.92 for ω and α and the value of λ6 state was 0.89. For all the items, the value of Cronbach’s Alpha, McDonald’s ω, and Guttmann’s λ6 coefficients were 0.88, 0.89 and 0.94 respectively. These values are greater than 0.80 and show good reliability of the scale.

Table 5. Consistency coefficients of the ACART-Q

	Estimâtes	McDonald’s ω	Cronbach’s α	Guttman’s λ6
Commitment	Value	0.91	0.91	0.87
	95% CI Low	0.89	0.88	0.84
	95% CI High	0.93	0.93	0.90
Closeness	Value	0.93	0.93	0.91
	95% CI Low	0.91	0.91	0.88
	95% CI High	0.95	0.95	0.93
Complementarity	Value	0.92	0.92	0.89
	95% CI Low	0.89	0.89	0.86
	95% CI High	0.94	0.94	0.92
Total score	Value	0.88	0.89	0.94
	95% CI Low	0.857	0.87	0.94
	95% CI High	0.91	0.92	0.96

9. Confirmatory factor analysis

The value of chi2/ddl is 1.69, the GFI index is 0.97, and the AGFI index is 0.95. Moreover, CFI is 0.98 and TLI is 0.98, likewise the two indices RMR and RMSEA were respectively 0.068 and 0.039. All the fit indices are adequate and comply with the values recommended in the literature (Mueller & Hancock, 2008; Schreiber et al., 2006). As a result, the indices from the CFA show a consistent factor structure (Figure 2).

Figure 2. First-order factor model for the ACART-Q.

For the second-order model, the value of Khi2 is chi2/ddl is 1.66, the GFI index is 0.99, the AGFI index is 0.98. Moreover, CFI is 0.98 and TLI is 0.98, likewise the two indices RMR and RMSEA were respectively 0.058 and 0.051. All the fit indices are adequate and comply with the values recommended in the literature (Mueller & Hancock, 2008; Schreiber et al., 2006). The results of the indices from the CFA show a factor structure that is similar to the first-order model (see Figure 3).

Figure 3. Second-order factor model for the ACART-Q.

10. Sensitivity

Overall MANCOVA results revealed no difference on interaction, gender and sport type: Wilks’ Lambda = 0.99; F (_{3, 429}) = 0.12 (Eta = 0.01) and Wilks’ Lambda = 0, 99; F (_{3, 429}) = 1.20, Eta = 0.008) for gender and sport type respectively.

For age, results demonstrate a Wilks’ Lambda = 0.99 with F (_{3, 429}) = 0.12 (Eta = 0.001). In contrast a significant difference was found for expertise: Wilks’ Lambda = 0.77, F (_{3, 429}) = 43.79. (Eta = 0.23). Finally, no differences were found for interaction, Wilks’ Lambda was 0.99, F (_{3, 429}) = 0.1 (Eta = 0.001). In addition, univariate statistics showed no difference for the interaction. While, for females a higher significant score was showed in Complementarity 5 for gender F (1, 431) = 7.76). The test revealed (Eta = 0.018) and type of sport F (1, 431) = 10.5 (Eta = 0.024) (See Table 6).

Table 6. Comparison of ACART-Q scores

		Females				Males				Gender	Sport	Gender*Sport
		M	SD	S	K	M	SD	S	K
Commitment	Co	4.03	1.17	0.2	-0.5	3.97	1.09	0.1	-0.4	0.21 (0)	1.19(0.03)	0.14(0)
	Ind	4.18	1.13	0.3	-0.3	4.24	1.15	0.2	-0.2
Closeness	Co	4.05	1.14	0.1	-0.1	3.95	1.08	0.2	-0.2	0.76(00.2)	1.16(0.03)	0.29(0.001)
	Ind	4.21	1.14	0.3	0.1	4.26	1.12	0.1	0.2
Complementarity	Co	3.91	1.16	0.2	0.2	3.87	1.03	0.1	0.1	7.76 (0.018)**	10.5 (0.024)**	0.46(0.001)
	Ind	4.05	1.25	0.1	-0.1	4.23	1.27	0.3	0.3
Total score	Co	3.99	0.87	0.2	0.2	3.93	0.77	0.1	0.1	0.044(0.000)	3.41(0.008)	0.28(0.001)
	Ind	4.15	0.92	0.2	0.2	4.24	0.83	0.2	0.3

Footnote: **: p < 0.01.S=Skewness, K=Kurtosis.

11. Discussion

The objectives of the present study were: (a) to adopt a translated version in Arabic language of the direct version of the questionnaire on the coach-athlete relationship (CART-Q), (b) to test its psychometric properties in a Tunisian context and (c) verify its sensitivity for different sports populations.

The results of the principal component analysis and confirmatory factor analysis suggest a three-factor structure. In addition, the CFA fit indices reveal that both structures are acceptable. In addition, the internal consistency of the scale was excellent for the three factors and the total score. However, the sensitivity of the scale was only partially established for the complementarity dimension. The results highlighted the three-factor structure in accordance with the pre-established theoretical conceptualization and initial validation (Jowett & Ntoumanis, 2004). Similar results in factorial structure were reported by the first psychometric work conducted on the CART-Q (Jowett & Ntoumanis, 2004) among 214 British coaches and athletes. The results of factor confirmatory analysis suggested a three-factor model. However, the fit indices of the two models (first order and high order) suggested acceptable factorial structure (Jowett & Ntoumanis, 2004). The confirmatory factor analysis consolidated the cross-cultural validations by a solution with three similar first-order and second-order factors. Our results are in congruence with other previous adaptation studies (Ahmad et al., 2021, Balduck & Jowett, 2010, Jowett & Ntoumanis, 2003, Vieira et al., 2015, Yang & Jowett, 2010, (Altintaş et al., 2012)– (Tojari et al., 2013)). For example, the CFA results of the validation of the Belgian CART-Q revealed two acceptable models (the first-order three-factor model and a hierarchical factor model) (Balduck & Jowett, 2010). Despite the acceptability of these two models, the addition of an error covariance was necessary to improve their fit. The added error parameter was due to the similarity between two proximity items: “I respect my athlete” and “I appreciate the sacrifices my athlete has made in order to improve his performance”. This similarity was referred by the authors to cultural nuances. Similarly, the results of the validation of the Chinese Coach-Athlete Relationship Questionnaire revealed that the higher-order model and the first-order three-factor model were the best-fit models to understand the nature and quality of the relationship Chinese coach-athlete (Yang & Jowett, 2010). On the other hand, it is important to note that the items of the complementarity subscale were more salient in Tunisian athletes than in Chinese athletes. Complementarity aims to capture the cooperative aspects in the coach-athlete relationship. However, complementarity involves two main sets of interactions. One set of interactions involving cooperative behaviors between coach and athlete and another set of interactions involving coach dominance (Jowett, 2007). Therefore, we can refer this difference in salience of the items of complementarity between the Tunisian sports population and their Chinese counterparts to the difference of the training systems. The athlete training system in Eastern countries was still considered harsh and coach-led (Yang & Jowett, 2010). Consequently, interactions involving coach dominance would be more predominant than interactions involving cooperation between coach and athletes. On the other hand, the athlete training system in Tunisia would be based on complementarity in these two sets of interactions.

Moreover, the classic internal consistency indices confirmed that the measurement scale is reliable for the three components. Several previous validation studies in different country are in line with the reliability results (Balduck et al., 2011; Jowett, 2009; Jowett & Ntoumanis, 2003, 2004; Yang & Jowett, 2010). However, their study was consolidated by a criterion validity that we could not demonstrate (Jowett & Ntoumanis, 2004). Indeed, interpersonal satisfaction was used as a criterion variable for the quality of coach-athlete relationships, and the results supported the predictive validity of the CART-Q.

A previous study in Arabic context conducted on 259 young Kuwaiti athletes aimed on the validation of direct and meta-perspective versions of the Arabic language coach-athlete relationship questionnaire. The results of this research determined the quality of the coach-athlete relationship to the athlete’s satisfaction with individual performance according to type of sport, duration of sport and sport performance. The results of this research also showed robustness fit indices for the direct version.

Our findings revealed a significant difference in complementarity between female/male and individual/team sports athletes. Concerning gender, our results are in line with Mohd Kassim et al (Mohd Kassim et al., 2020). who investigated the athlete’s perception of the coach-athlete relationship in team sports. The results of this study revealed that there were no differences for the three dimensions of the coach-athlete relationship between genders.

In line with our findings, Ahmad et al (Ahmad et al., 2021) showed a significant differences in the levels of coach-athlete relationship between individual and team athletes (Ahmad et al., 2021) In contrast, Woolliams et al (Woolliams et al., 2021) suggested no significant differences between individual team sports athletes concerning complementarity. However, athletes in team sports need to be personally and instrumentally interdependent. In team sports, athletes have less opportunity to interact with their coach having to compete with other athletes for face-to-face interpersonal transactions (Woolliams, 2015). According to Ahmad et al (Ahmad et al., 2021), the coach-athlete relationship is more important in individual sports, because the leadership and guidance of the coach is very important for the athletes who are in direct contact with them. Our results can be also, explained by cultural differences.

In another study, Rhind et al (Rhind et al., 2012), suggested that individual athletes felt both closer and more committed to their coach. Furthermore, athletes who performed in individual sports also perceived that their coach felt closer, more committed, and complementary than athletes who performed in team sports.

The findings from the adapted questionnaire can guide coaches in tailoring their coaching approaches to individual athletes. By understanding the unique dynamics of each coach-athlete relationship, coaches can adapt their communication styles, motivational strategies, and support mechanisms to better meet the needs of each athlete. This personalized approach can foster stronger relationships and enhance athlete development.

Coaches can explore the relationship between the adapted questionnaire scores and various athlete performance outcomes. By examining correlations between the questionnaire scores and criterion variables such as athlete satisfaction, motivation, self-efficacy, or performance indicators, coaches can gain insights into the influence of the coach-athlete relationship on these outcomes. This understanding can inform coaching strategies aimed at optimizing athlete performance and well-being. The adapted questionnaire can help establish a benchmark for evaluating coach-athlete relationships within a specific coaching context. Coaches can administer the questionnaire periodically to monitor changes and progress in their relationships over time. This ongoing assessment allows coaches to track the effectiveness of their relationship-building efforts and make adjustments as needed.

12. Study limitations

This study included some limitations that need to be explained. The sample was taken for this study only on athletes and therefore, it did not include a complete dyad (meta-perspective version).

In addition, the lack of concurrent validity testing of the instrument with other instruments of a comparable nature is one of the main limitations of the study. The instrument has also only been tried on a single population that resides in a single nation.

Continued study may also clarify the features of the questionnaire and offer more proof of convergent and discriminant validity. Although this research represents the first and only attempt to validate the culturally appropriate ACART-Q in a Tunisian population, future research should endeavour to examine whether the validity of the ACART-Q factor structure using larger samples as well as in other Arab cultures.

Also, there are additional limitations to consider. Firstly, the study’s cross-sectional design restricts the ability to determine causality or track changes in the coach-athlete relationship over time. Secondly, relying on self-report measures introduces potential biases or inaccuracies in the data. Lastly, the exclusion of professional athletes from the study limits the applicability of the findings to this specific population. The meta-perspective refers to the way one thinks that the other feels, thinks and behaves towards him (Jowett, 2006, 2009; Rhind et al., 2012). Cross-assessing coaches/athletes’ direct and meta-perspectives can allow researchers to assess the level of assumed similarity, actual similarity, and empathy. Therefore, research aimed at validating the meta-perspective version of the ACART-Q would be necessary among the two actors.

13. Conclusion

The results of the exploratory factor analysis, confirmatory factor analysis, reliability and sensitivity suggest that the ACART-Q is a valid instrument to assess the coach/athlete relationship in the Tunisian context.

Results from the Arabic version of the CART-Q could help researchers and practitioners investigate the nature of the coach-athlete relationship among Arabic-speaking nations. Additionally, the CART-Q could help coaches and athletes understand the steps that lead to a quality coach-athlete relationship while studying the obstacles that stand in the way of a harmonious relationship. Once coaches and athletes understand what builds their relationship, they can achieve their goals together and achieve an overall state of well-being. The CART-Q constructs have shown that high satisfaction is strongly associated with a high quality, successful, harmonious and supportive coach-athlete relationship. CART-Q is very beneficial for Arabic countries in the MENA region, as the sports field in this region is growing and developing rapidly.

14. Declarations

14.1. Ethics approval and consent to participate

The research was given the go-ahead by the Institute of Sport and Physical Education of Kef, Jendouba University, Tunisia, which has its own local ethics committee (code 13–2023). The survey was designed and carried out in accordance with Checklist for Reporting Results of Internet E-Surveys (CHERRIES) (Eysenbach, 2004, 2012). While, the policy and terms of Google Forms as our survey platform ensure the respect of confidentiality, privacy and security of users. In the response form, no personal information was obtained (e.g., names, addresses, and phone numbers).

Moreover, this study was undertaken following the legal standards of the Helsinki declaration in (1964 and 2013) and its corresponding amendments. In addition, the participants voluntarily joined the study and were asked to submit an informed consent through Google forms, without financial incentives.

Author contributions

H.J, O.H, N.K, I.D, N.G and M.R: conception and design. H.J, N.K, N.G, M.B.A, M.R, O.H and M.B.A: analysis and interpretation of the data. I.D, H.J, N.G, M.S, N.K, O.H and M.B.A: drafting of the paper. I.D, H.J, N.K, N.G, A.N, M.R, M.S and M.B.A: revising it critically for intellectual content. All authors gave their final approval to the version that will be published.

Consent for publication

All authors approved of the final version to be published and agree to be accountable for any part of the work.

Availability of data and materials

The data that support the findings of this study are openly available upon request from the corresponding author.

Acknowledgments

Open access provided by Qatar National Library.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

APPENDIX

Additional Files

Additional File 1

Table

n = N x/((N-1) ME^2 + x)Where: x = Z(^c/100)^2r(100-r)Margin of error   ME = Sqrt [^(N ^− n)x/n(N-1)]N is the population size, r is the fraction of interesting responses, and Z(c/100)is the critical value for the confidence level.

Table A1. Transcultural studies of the CART-Q

Authors	n	Country	Participants Demographics	Psychometric proprieties	Key Findings
Yang & Jowett (2010)	246	China	115 males and 131 females Mage* = 22.11±3.33	All α> 0.70 CFI=0.96; TLI=0.95. RMSEA = 0.06	Three higher-order generic instrument
Vieira et al. (2015)	364	Brazil	151 females and 213 males Mage =22.11± 4.86	All α> 0.70 CFI = 0.96; TLI=0.94 and RMSEA =0.08	Three second-order model
Balduck et al. (2011)	401	Belgian, Dutch speaking	316 males and 85 females Mage=23 ± 5.52	All α> 0.70 CFI=0.95; TLI=0.93 and RMSEA=0.10	Three higher-order model
Ahmad et al. (2021)	259	Kuwait	187 males and 72 females Mage =22 ±4.61	α =n/a CFI=0.98; TLI=n/a and RMSEA= 0.06	Three first-order model
Sanz-Fernández & Alonso-Arbiol (2023)	162	Spain	92 males and 70 females Mage =26.41 ±8.98	All α> 0.70 CFI=0.97; TLI=0.95 and RMSEA=0.078	Trifactorial second-order model
Altintas et al. (2012)	151	Turkey	62 Females and 89 Males Mage = 15.66 ±1.87	All α> 0.70 TLI=0.98; CFI=0.99 and RMSEA=0.08	First order three-factor model
Yang & Jowett, (2012)	1363	Belgium Britain China Greece Spain Sweden, and United States of America	602 males and 761 females Mage from 18.24 ±0.91 to 23.07 ±5.27	All α> 0.70 Multi-group CFA CFI=0.94; TLI=0.92 and RMSEA=0.03	Three-first-order factor model across the seven countries

Additional File 2

Table A2. Western-Arabic version of the CART-Q

	Commitment
	I feel close to my coach
	I feel committed to my coach
	I feel that my sport career is promising with my coach
	Closeness
	I like my coach
	I trust my coach
	I respect my coach
	I feel appreciation for the sacrifices my coach has experienced in order to improve my performance
	Complementarity
	When I am coached by my coach, I feel at ease
	When I am coached by my coach, I feel responsive to his/her efforts
	When I am coached by my coach, I am ready to do my best
	When I am coached by my coach, I adopt a friendly stance