The First Step in Revealing the Perceptions of Justice Among Turkish Adolescent Athletes: Adapting the Justice in Sport Scale

The First Step in Revealing the Perceptions of Justice Among Turkish Adolescent Athletes: Adapting the Justice in Sport Scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The First Step in Revealing the Perceptions of Justice Among Turkish Adolescent Athletes: Adapting the Justice in Sport Scale MELEK KURŞUNEL, ÖZNUR AKPINAR, ASLI ECE KOÇAK, RECEP MEHMET GÖRÜNÜ, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9023785/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Athletes’ perceptions of justice toward their coaches’ behaviours play a critical role in athlete satisfaction, team cohesion, and performance outcomes. However, no validated instrument exists to measure athletic justice perceptions among Turkish athletes. Aim This study aimed to adapt and validate the Turkish version of the Athletic Justice Scale developed by Kim et al. ( 2024 ). Methods The scale comprises 12 items across four subscales (distributive, procedural, informational, and interpersonal justice) measured on a 7-point Likert scale. Participants included 717 licensed adolescent athletes (402 males, 315 females; aged 8–19 years) from various sports in Türkiye. Following translation and back-translation procedures, content validity was established through expert panel evaluation (S-CVI/Ave = 1.00). Confirmatory factor analysis (CFA) was conducted, and measurement invariance was tested across gender, sport type, and coach gender. Results The four-factor model demonstrated acceptable fit indices (χ² = 167.47, df = 48, CFI = .981, TLI = .973, RMSEA = .059, SRMR = .040). All factor loadings were significant and ranged from .69 to .96. Internal consistency was acceptable to excellent across subscales (Cronbach's α = .739-.909; composite reliability = .742-.911). Full scalar measurement invariance was achieved across all demographic groups. Conclusion The Turkish version of the Athletic Justice Scale is a valid and reliable instrument for assessing adolescent athletes’ perceptions of justice toward their coaches’ behaviours. This tool enables researchers and practitioners to examine fairness perceptions within Turkish sport contexts. Athletic justice scale adaptation coach-athlete relationship confirmatory factor analysis Turkish athletes Figures Figure 1 INTRODUCTION The concept of justice has long been recognized as fundamental to understanding workplace attitudes and behaviours. Greenberg ( 1987 ) defined organizational justice as employees’ perceptions of fairness within their organizations, and this concept has since become one of the most extensively studied topics in organizational behaviour research. The theoretical foundation of organizational justice evolved considerably over the decades. Early work focused primarily on distributive justice, which refers to the perceived fairness of outcome distributions based on equity principles (Adams, 1965 ; Deutsch, 1975 ). Leventhal ( 1980 ) subsequently expanded the framework by introducing procedural justice, which concerns the fairness of processes used to determine outcomes. Later, Bies and Moag ( 1986 ) proposed the concept of interactional justice, focusing on the interpersonal treatment people receive during the implementation of procedures. Colquitt’s ( 2001 ) influential work further refined this framework by distinguishing between informational justice and interpersonal justice. Informational justice refers to the adequacy and truthfulness of explanations provided for decisions, while interpersonal justice concerns the degree of dignity and respect shown by authority figures. This four-dimensional model, comprising distributive, procedural, informational, and interpersonal justice, has received substantial empirical support and is now widely adopted in organizational research. The importance of justice perceptions extends beyond theoretical interest. A substantial body of evidence demonstrates that when employees perceive fairness in their workplace, they exhibit greater job satisfaction, stronger organizational commitment, enhanced performance, and more frequent organizational citizenship behaviours (Cohen-Charash & Spector, 2001 ; Colquitt et al., 2013 ). Conversely, perceptions of injustice have been linked to counterproductive work behaviours, increased turnover intentions, and diminished psychological well-being (Colquitt et al., 2013 ). These findings underscore the practical significance of understanding and fostering justice perceptions in organizational settings. Within sport settings, the coach-athlete relationship presents a unique and particularly important context for examining justice perceptions. Unlike typical workplace relationships, the coach-athlete dynamic is characterized by an intensive and often emotionally charged interaction where coaches wield considerable authority over multiple aspects of athletes’ sporting lives. Coaches make consequential decisions about playing time allocation, position assignments, team selection, training intensity, tactical approaches, and performance feedback. All these decisions directly and visibly affect athletes’ experiences, development, and career trajectories. The public nature of many coaching decisions, made apparent during competitions and visible to teammates, opponents, and spectators alike, amplifies the salience of fairness concerns in ways that distinguish sport from most other organizational contexts. Research has increasingly demonstrated the significance of justice perceptions in sport. De Backer et al. (2011) found that perceived justice and need support from coaches predicted team identification and cohesion among elite volleyball and handball players in Belgium and Norway. Subsequent work by the same research group revealed that athletes who viewed their coaches as fair reported greater satisfaction and self-rated progression (De Backer et al., 2021). In a study of elite women’s team sport athletes, De Backer et al. (2015) concluded that the motivational climate created by coaches, including perceptions of fairness, significantly impacted the optimal functioning of sports teams. Furthermore, Grant et al. ( 2009 ) demonstrated that perceived procedural injustice was associated with negative emotional responses such as anger, frustration, and resentment, which can undermine athletic performance and team harmony. Collectively, these findings suggest that understanding how athletes perceive coach fairness has substantial practical implications for optimizing the sport environment, enhancing athlete well-being, and improving team performance. Despite the theoretical and practical importance of justice perceptions in sport, measuring this construct has proven challenging. Most studies examining fairness in athletic contexts have relied on organizational justice scales originally developed for business settings, such as Colquitt’s ( 2001 ) widely used measure. However, several scholars have argued that the sport environment differs from traditional workplaces in important ways that warrant context-specific measurement approaches (Fletcher & Wagstaff, 2009 ; Mahony et al., 2009 ). Athletic performance is publicly evaluated under competitive pressure, success and failure are often immediately visible to others, and the physical and emotional demands of training and competition create unique stressors. Moreover, the coach-athlete relationship involves dynamics such as physical proximity, emotional intensity, and developmental influence that distinguish it from typical supervisor-employee relationships. These distinctive features suggest that justice perceptions in sport may manifest differently than in conventional organizational settings. Recognizing this limitation in the existing literature, Kim et al. ( 2024 ) recently developed the Athletic Justice Scale specifically for sport contexts. Drawing on both deductive approaches through reviewing 16 existing organizational justice scales and inductive methods through conducting semi-structured interviews with 22 elite athletes, these researchers generated items that capture fairness perceptions relevant to the unique culture and environment of sport. The resulting 12-item instrument assesses athletes’ perceptions across four dimensions. Distributive justice includes three items measuring perceived fairness of role assignments and rewards. Procedural justice contains three items assessing freedom from external pressures on coaching decisions. Informational justice comprises three items evaluating timeliness and clarity of coach communication. Interpersonal justice consists of three items measuring respectful and dignified treatment. The scale demonstrated acceptable psychometric properties across two samples of elite athletes from Saudi Arabia ( n = 279 for initial scale development; n = 503 for validation), with Cronbach’s alpha values ranging from .677 to .823 and confirmatory factor analysis supporting the four-factor structure. Research on justice in Turkish sport contexts has begun to emerge but remains limited in scope. Studies have examined organizational justice perceptions among sport administrators and employees at Provincial Directorates of Youth and Sports (Tapşın et al., 2024 ), social justice considerations in sport investment policies (Kasapoğlu & Öçal, 2021 ), and wage justice perceptions among sport organization employees (Dal & Donuk, 2016 ). Related work has investigated coaches’ ethical attitudes (Horzum, 2024 ), moral decision-making processes among football coaches (Temel et al., 2021 ), and athletes’ perceptions of abusive supervision in coach-athlete relationships (Bülbül, 2019 ). However, despite growing interest in fairness-related constructs, no validated instrument currently exists to assess Turkish athletes’ justice perceptions specifically regarding their coaches’ behaviours. This represents a notable gap in the literature, particularly given the central role that coaches play in Turkish sport culture and athlete development. The need for a culturally adapted measure is further underscored by research demonstrating that cultural factors shape how individuals interpret and respond to fairness-related situations. Türkiye is characterized by relatively high-power distance and a collectivistic orientation (Hofstede, 2001 ), and these cultural dimensions may influence athlete-coach dynamics and justice perceptions. In high power distance cultures, hierarchical relationships are more accepted, and subordinates may have different expectations regarding authority figures’ decision-making processes. Similarly, collectivistic values emphasizing group harmony and relational obligations may affect how athletes evaluate the fairness of coaches’ interpersonal behaviours. These cultural considerations suggest that simply translating an existing scale may be insufficient. Rather, systematic adaptation and validation procedures are necessary to ensure that the instrument functions appropriately within the Turkish context. The present study therefore aimed to adapt the Athletic Justice Scale (Kim et al., 2024 ) for use with Turkish athletes and to comprehensively examine its psychometric properties. Specifically, we sought to establish the linguistic equivalence and content validity of the Turkish version through systematic translation and expert evaluation procedures, examine the factor structure of the adapted scale using both exploratory and confirmatory factor analytic approaches, assess the internal consistency reliability of the scale and its subscales, evaluate convergent and discriminant validity evidence, and test measurement invariance across key demographic variables including gender, sport type, and coach gender. To maximize statistical power and obtain stable parameter estimates, primary analyses were conducted on the full sample, followed by split-sample cross-validation to assess replicability. A validated Turkish version of the Athletic Justice Scale would provide researchers with a much-needed tool to investigate justice dynamics in Turkish sport settings, enable cross-cultural comparisons with findings from other nations, and potentially inform evidence-based interventions aimed at enhancing coach-athlete relationships and optimizing the athletic experience for Turkish adolescent. METHOD Study Design This cross-sectional study aimed to adapt and validate the Turkish version of the Athletic Justice Scale (Kim et al., 2024 ). The adaptation process followed the guidelines proposed by Beaton et al. ( 2000 ) and the International Test Commission (2017), encompassing translation, back-translation, expert review, and comprehensive psychometric evaluation. Both exploratory and confirmatory factor analytic approaches were employed to examine the factor structure, as recommended for cross-cultural scale adaptation studies (Worthington & Whittaker, 2006 ). Language Validity The scale was adapted into Turkish using the translation-back translation procedure described by Brislin ( 1970 ). Seven bilingual translators independently translated the original English items into Turkish. These translations were reviewed by the research team, and the most suitable expressions were selected to form a preliminary Turkish version. Two different translators, who had no prior exposure to the original scale, then translated this Turkish version back into English. Comparison of the back-translated version with the original revealed minor discrepancies in wording, which were resolved through discussion among the research team. Content validity was evaluated by a panel of 14 experts. Half of the panel consisted of language specialists, while the other half were academics in sports sciences. Each expert independently rated the relevance of every item on a 4-point scale ranging from “not relevant” to “highly relevant.” The panel also met to discuss the items and suggest revisions where needed. Item-level content validity was quantified using the Content Validity Index (Davis, 1992 ) and Content Validity Ratio (Lawshe, 1975 ). The results of the expert evaluations are presented in Table 1 . Table 1 Expert panel evaluation results for content validity (N = 14) Item Quite Relevant Highly Relevant I-CVI CVR 1 5 9 1.00 1.00 2 2 12 1.00 1.00 3 7 7 1.00 1.00 4 (R) 3 11 1.00 1.00 5 (R) 2 12 1.00 1.00 6 (R) 0 14 1.00 1.00 7 1 13 1.00 1.00 8 1 13 1.00 1.00 9 2 12 1.00 1.00 10 0 14 1.00 1.00 11 1 13 1.00 1.00 12 2 12 1.00 1.00 S-CVI/Ave 1.00 Note. I-CVI = (Quite Relevant + Highly Relevant) / N (Davis, 1992 ). CVR = ( N e − N /2) / ( N /2), where N e = number of experts rating “Quite Relevant” or “Highly Relevant” (Lawshe, 1975 ) All items received ratings of either “quite relevant” or “highly relevant” from every expert, yielding I-CVI and CVR values of 1.00 for each item. The scale-level content validity index averaged across items was also 1.00. These values exceed the recommended cutoffs of .78 for I-CVI and .51 for CVR when 14 experts are involved (Lawshe, 1975 ; Polit & Beck, 2006 ), indicating strong agreement on content validity. The expert panel noted that three procedural justice items (Items 4, 5, and 6) describe negative coaching behaviours, such as being influenced by administrators, political considerations, or parental pressure. Because higher scores on these items reflect lower perceived justice, they require reverse scoring during analysis. The panel agreed to retain this reverse-coded format in the Turkish version, consistent with the original scale. Finally, based on expert feedback, the scale title was adapted from “Athletic Justice Scale” to “Sporda Adalet Ölçeği” to better fit Turkish sports terminology. The Sample of the Research Sample size recommendations for confirmatory factor analysis vary in the literature. Comrey and Lee ( 1992 ) suggested that 300 participants constitute a “good” sample size, 500 “very good,” and 1,000 “excellent.” Kline ( 2016 ) recommended a minimum of 200 participants for structural equation modelling studies. The present study included 717 licensed adolescent athletes actively training at facilities affiliated with the Karaman Provincial Directorate of Youth and Sports in Türkiye. Participants were involved in various individual and team sports. Demographic characteristics of the sample are summarized in Table 2 . Table 2 Demographic characteristics of participants (N = 717) Variable Category n % Gender Male 402 56.1 Female 315 43.9 Age Group U12 (8–11 years) 24 3.3 U16 (12–15 years) 594 82.8 U20 (16–19 years) 99 13.8 Sport Type Individual Sports 267 37.2 Team Sports 450 62.8 Coach Gender Male 250 34.9 Female 467 65.1 Note. Age categories follow the national sport federation classification system. Many participants (82.8%) were in the U16 category The sample consisted of 402 male (56.1%) and 315 female (43.9%) athletes. Most participants were in the U16 age category covering ages 12 to 15 years ( n = 594, 82.8%), followed by U20 covering ages 16 to 19 years ( n = 99, 13.8%) and U12 covering ages 8 to 11 years ( n = 24, 3.3%). Regarding sport type, 450 athletes (62.8%) participated in team sports, while 267 (37.2%) were involved in individual sports. In terms of coach gender, 467 athletes (65.1%) trained under female coaches, whereas 250 (34.9%) trained under male coaches. The mean training experience was 2.80 years ( SD = 2.09), and athletes had been working with their current coach for an average of 2.44 years ( SD = 1.85). Data Collection Process This research was approved by the Karamanoğlu Mehmetbey University Social Sciences Scientific Research and Publication Ethics Committee (Decision No: 191, Date: 21 May 2024). Prior to the study, permission to use and adapt the scale was obtained via email from the original authors. Data collection took place between June 1 and September 1, 2024, at training facilities affiliated with the Karaman Provincial Directorate of Youth and Sports. The questionnaire was administered in person by trained research assistants who visited athletes during their regular training sessions. Before participation, athletes were informed about the purpose of the study, the voluntary nature of participation, and the confidentiality of their responses. Written informed consent was obtained from all participants. For athletes under 18 years of age, parental consent was also obtained in accordance with ethical guidelines. No incentives were provided for participation. The questionnaire took approximately 10 minutes to complete. Instruments Two instruments were used for data collection: a demographic information form and the Turkish version of the Athletic Justice Scale. The Demographic Form Included questions about gender, age group, sport type, years of training experience, coach gender, and duration of working with the current coach. The Athletic Justice Scale; Originally developed by Kim et al. ( 2024 ), assesses athletes’ perceptions of fairness regarding their coaches’ behaviours. The scale contains 12 items distributed across four subscales with three items each. Distributive justice items assess perceived fairness of role assignments within the team. Procedural justice items measure the extent to which coaching decisions are free from external pressures. Informational justice items evaluate the timeliness and clarity of coach communication. Interpersonal justice items assess whether the coach treats athletes with respect and dignity. All items are rated on a 7-point Likert scale from 1 (Strongly Disagree) to 7 (Strongly Agree). In the original validation study conducted with Saudi Arabian athletes, Cronbach’s alpha coefficients ranged from .677 to .823 across subscales (Kim et al., 2024 ). Data Analysis Statistical analyses were performed using R version 4.4.2 (R Core Team, 2024 ). The psych package (Revelle, 2024 ) was used for exploratory factor analysis and reliability estimation, while confirmatory factor analysis and measurement invariance testing were conducted using the lavaan (Rosseel, 2012 ) and semTools packages (Jorgensen et al., 2024 ). Given that the data showed some deviation from normality, the robust maximum likelihood estimator was applied throughout confirmatory analyses (Satorra & Bentler, 1994 ). The dataset was first examined for missing values, outliers, and distributional properties. Skewness and kurtosis values were evaluated to assess univariate normality, with values within the range of ± 2 considered acceptable (George & Mallery, 2010 ). Common method bias was checked using Harman’s single-factor test, where the first factor accounting for less than 50% of total variance indicates no serious concern (Podsakoff et al., 2003 ). Item quality was assessed through corrected item-total correlations for each subscale, with values above .30 indicating adequate discrimination (Tabachnick & Fidell, 2013 ). Independent samples t -tests comparing the upper and lower 27% of respondents were also conducted (Kelley, 1939 ), with Cohen’s d calculated to quantify effect sizes. Prior to factor analysis, data suitability was examined using the Kaiser-Meyer-Olkin measure and Bartlett’s test of sphericity. Exploratory factor analysis was conducted on the polychoric correlation matrix using minimum residual extraction with oblimin rotation, which allows factors to correlate consistent with theoretical expectations. The number of factors was determined by jointly considering the Kaiser criterion, parallel analysis (Horn, 1965 ), and the scree plot. Five competing confirmatory models were then tested: a single-factor model, a three-factor model combining interpersonal and informational justice based on exploratory results, the original four-factor model (Kim et al., 2024 ), a second-order model with a general justice factor, and a bifactor model. Model fit was evaluated using chi-square, CFI, TLI, RMSEA with 90% confidence intervals, and SRMR. Following Hu and Bentler ( 1999 ), CFI and TLI values of .95 or above, RMSEA of .06 or below, and SRMR of .08 or below indicated good fit, while CFI and TLI above .90 and RMSEA below .08 indicated acceptable fit. Nested models were compared using the Satorra-Bentler scaled chi-square difference test (Satorra & Bentler, 2001 ) and changes in CFI. Convergent validity was evaluated through composite reliability and average variance extracted, with values of .70 and .50, respectively, considered adequate (Fornell & Larcker, 1981 ; Hair et al., 2019 ). Following Malhotra and Dash ( 2011 ), AVE slightly below .50 was deemed acceptable when composite reliability exceeded .60. Discriminant validity was assessed using the Fornell-Larcker criterion, which requires the square root of AVE for each factor to exceed its correlations with other factors, and the Heterotrait-Monotrait ratio, with values below .85 indicating good and below .90 acceptable discriminant validity (Henseler et al., 2015 ). Internal consistency was estimated using Cronbach’s alpha, McDonald’s omega, and composite reliability, with values of .70, .80, and .90 representing acceptable, good, and excellent reliability, respectively (Nunnally & Bernstein, 1994 ). Measurement invariance was tested using multi-group confirmatory factor analysis across gender, sport type, and coach gender at configural, metric, and scalar levels (Vandenberg & Lance, 2000 ). Following Chen ( 2007 ), decreases in CFI of .010 or less and increases in RMSEA of .015 or less between nested models indicated invariance. After establishing scalar invariance, latent mean comparisons were conducted with the reference group mean fixed at zero (Hancock, 1997 ), and effect sizes were reported as Cohen’s d . The primary EFA and CFA analyses were conducted on the full sample ( N = 717) to maximize statistical power and obtain stable parameter estimates. To address potential concerns about conducting EFA and CFA on the same sample, a supplementary split-sample cross-validation was performed: the dataset was randomly divided into two subsamples ( n = 358 and n = 359), with EFA conducted on the first and CFA on the second (Worthington & Whittaker, 2006 ). RESULTS Preliminary Analyses Before conducting the main analyses, the dataset was examined for missing values, outliers, and distributional properties. No missing data were found. Skewness and kurtosis coefficients for all items were within the ± 2 range suggested by George and Mallery ( 2010 ), supporting univariate normality. Harman’s single-factor test showed that the first factor explained 49.65% of the variance, which is below the 50% threshold, suggesting that common method bias was not a major concern (Podsakoff et al., 2003 ). Descriptive statistics and item analysis results are presented in Table 3 . Table 3 Descriptive statistics and item analysis results Item Dim M SD Skewness Kurtosis Corrected r t d p A1 DJ 5.09 2.02 -0.83 -0.58 .75 20.17 2.03 < .001 A2 DJ 5.14 1.90 -0.83 -0.46 .81 21.70 2.18 < .001 A3 DJ 4.93 1.80 -0.59 -0.53 .66 19.77 1.99 < .001 A4 PJ 3.35 2.09 0.37 -1.10 .56 7.82 0.79 < .001 A5 PJ 3.61 2.03 0.15 -1.09 .57 9.88 0.99 < .001 A6 PJ 3.22 2.11 0.45 -1.11 .56 6.47 0.65 < .001 A7 IFJ 5.31 2.05 -0.96 -0.40 .74 18.64 1.87 < .001 A8 IFJ 5.60 2.05 -1.30 0.25 .85 20.78 2.09 < .001 A9 IFJ 5.74 2.06 -1.46 0.59 .82 19.19 1.93 < .001 A10 ITJ 5.76 2.05 -1.52 0.75 .85 20.09 2.02 < .001 A11 ITJ 5.64 2.03 -1.34 0.35 .86 20.91 2.10 < .001 A12 ITJ 5.46 2.11 -1.16 -0.14 .75 20.71 2.08 < .001 Note : Corrected r = corrected item-total correlation by subscale; DJ = Distributive Justice; PJ = Procedural Justice; IFJ = Informational Justice; ITJ = Interpersonal Justice; d = Cohen’s d . The 27% cut-off point based on total scale scores was used for upper-lower group comparisons (lower group n = 201, upper group n = 195). All t -tests were significant at p < .001 Item means ranged from 3.22 to 5.76. Procedural justice items showed lower means because they are negatively worded and reflect external pressures on coaching decisions. Informational and interpersonal justice items had the highest means, indicating that athletes generally viewed their coaches as communicative and respectful. Corrected item-total correlations ranged from .56 to .86, all exceeding the .30 threshold (Tabachnick & Fidell, 2013 ). Upper-lower 27% group comparisons revealed significant differences for all items ( p < .001), with large effect sizes for distributive ( d = 1.99–2.18), informational ( d = 1.87–2.09), and interpersonal justice items ( d = 2.02–2.10). Procedural justice items showed smaller but adequate effect sizes ( d = 0.65–0.99). Construct Validity Exploratory factor analysis. The Kaiser-Meyer-Olkin measure was .90, indicating excellent sampling adequacy (Kaiser, 1974 ), and Bartlett’s test of sphericity was significant (χ² = 6203.73, df = 66, p < .001). The Kaiser criterion, parallel analysis, and scree plot all indicated a three-factor structure. Using minimum residual extraction with oblimin rotation on the polychoric correlation matrix, the three-factor solution explained 75.73% of the total variance. Distributive justice items (A1-A3) and procedural justice items (A4-A6) loaded onto separate factors, while informational justice (A7-A9) and interpersonal justice items (A10-A12) combined under a single factor. This pattern is consistent with earlier conceptualizations of interactional justice (Bies & Moag, 1986 ; Greenberg, 1993 ). However, since the original scale has a four-factor structure, the final decision was based on competing confirmatory models (Worthington & Whittaker, 2006 ). Detailed EFA results are provided in Appendix 1 . Confirmatory factor analysis. Five models were tested to assess construct validity: a single-factor model, a three-factor model combining interpersonal and informational justice consistent with EFA results, a four-factor model matching the original structure, a second-order model with four first-order factors loading onto a general justice factor, and a bifactor model. All analyses used robust maximum likelihood estimation. Fit indices are presented in Table 4 . Table 4 Fit indices of competitive CFA models Model χ² df χ²/ df CFI TLI RMSEA SRMR 1 Factor 1262.59 54 23.38 .805 .761 .177 .098 3 Factor 204.71 51 4.01 .975 .968 .065 .045 4 Factor 167.47 48 3.49 .981 .973 .059 .040 Second-order 92.30 50 1.85 .986 .981 .049 .045 Bifactor 44.60 36 1.24 .997 .995 .026 .018 Note : χ² = Chi-square; df = degrees of freedom; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual The single-factor model showed poor fit (CFI = .805, TLI = .761, RMSEA = .177), confirming that the data cannot be explained by a unidimensional structure. The three-factor model demonstrated acceptable fit (CFI = .975, TLI = .968, RMSEA = .065, SRMR = .045), though RMSEA exceeded the .06 threshold for good fit. The four-factor model showed the best fit among standard models (CFI = .981, TLI = .973, RMSEA = .059, SRMR = .040), meeting all recommended criteria (Hu & Bentler, 1999 ). The chi-square difference test comparing the four-factor and three-factor models was significant (Δχ² = 37.24, Δ df = 3, p < .001), supporting the four-factor structure. Although ΔCFI was below .01 (Cheung & Rensvold, 2002 ), the four-factor model was retained based on the significant chi-square difference, lower AIC and BIC values, and theoretical consistency with the original scale (Kline, 2016 ). Two hierarchical models were also examined due to the high correlation between interpersonal and informational justice. The second-order model showed acceptable fit (CFI = .986, TLI = .981, RMSEA = .049), but the loading of procedural justice onto the higher-order factor was not significant ( β = − .08, p = .260), indicating theoretical unsuitability. The bifactor model yielded excellent fit indices (CFI = .997, TLI = .995, RMSEA = .026) but produced negative variance estimates for interpersonal (ψ = − .018) and informational justice factors (ψ = − .342). These Heywood cases indicate over-parameterization, rendering the bifactor model inappropriate (Chen et al., 2001 ). Model comparison results are presented in Table 5 . Table 5 Model comparison summary Comparison Δχ² Δ df p ΔCFI ΔRMSEA 3F vs 1F 1057.88 3 < .001 .170 .112 4F vs 1F 1095.12 6 < .001 .176 .118 4F vs 3F 37.24 3 < .001 .006 .006 Note : Δχ² = Chi-square difference; Δ df = difference in degrees of freedom; ΔCFI = difference in CFI; ΔRMSEA = difference in RMSEA As shown in Table 5 , the four-factor model fits significantly better than both the single-factor and three-factor models. The standardized factor loadings and inter-factor correlations are presented in Table 6. Table 6 Four-factor model: Standardized factor loadings and factor correlations Panel A: Factor loadings Dimension Item λ SE z p R² Distributive Justice A1 .86 .02 43.00 < .001 .73 A2 .91 .02 45.00 < .001 .82 A3 .71 .03 23.67 < .001 .50 Procedural Justice A4 .71 .03 23.67 < .001 .50 A5 .70 .03 23.33 < .001 .49 A6 .69 .03 23.00 < .001 .47 Informational Justice A7 .76 .02 38.00 < .001 .58 A8 .89 .02 44.50 < .001 .80 A9 .94 .01 94.00 < .001 .88 Interpersonal Justice A10 .96 .01 96.00 < .001 .91 A11 .90 .02 45.00 < .001 .81 A12 .77 .02 38.50 < .001 .59 Panel B : Factor correlations DJ DJ PJ IFJ ITJ 1 PJ − .01 1 IFJ .67*** − .12* 1 ITJ .68*** − .11* .97*** 1 Note : λ = Standardized factor loading; SE = Standard error; R² = Explained variance; DJ = Distributive Justice; PJ = Procedural Justice; IFJ = Informational Justice; ITJ = Interpersonal Justice. * p < .05, ** p < .01, *** p < .001 All items loaded significantly onto their respective factors ( p < .001), with loadings ranging from .69 to .96, well above the .50 threshold (Hair et al., 2019 ). The highest loadings were observed for interpersonal justice (A10: λ = .96) and informational justice (A9: λ = .94), while procedural justice items showed the lowest loadings (A6: λ = .69). Explained variance (R²) ranged from .47 to .91. Distributive justice showed moderate positive correlations with informational ( r = .67) and interpersonal justice ( r = .68). Procedural justice displayed weak negative correlations with other dimensions ( r = − .01 to − .12) due to the reverse-coded nature of these items. The correlation between informational and interpersonal justice was very high ( r = .97), which raises questions about discriminant validity between these dimensions. This high correlation is consistent with the theoretical debate in organizational justice literature regarding whether these constructs represent distinct dimensions or a unified interactional justice factor (Bies & Moag, 1986 ; Colquitt, 2001 ). Although the three-factor model combining these dimensions showed acceptable fit, it was statistically inferior to the four-factor model. The second-order model was rejected because procedural justice did not load significantly onto the higher-order factor, and the bifactor model produced inadmissible estimates. Therefore, the four-factor structure was retained, consistent with the original scale. Standardized parameter estimates are illustrated in Fig. 1 . Figure 1 illustrates the measurement model with standardized estimates. The visual representation clearly shows the distinct clustering of items within their respective factors and highlights the notably high covariance between informational and interpersonal justice. Validity and Reliability Following validation of the four-factor model, convergent validity, discriminant validity, and reliability were assessed. Results are presented in Table 7 . Table 7 Validity and reliability indicators Dimension λ range CR AVE √AVE α ω Distributive Justice (DJ) .71-.91 .866 .685 .827 .861 .866 Procedural Justice (PJ) .69-.71 .742 .490 .700 .739 .742 Informational Justice (IFJ) .76-.94 .900 .751 .867 .901 .900 Interpersonal Justice (ITJ) .77-.96 .911 .775 .880 .909 .911 Overall Scale .69-.96 — — — .858 — Note : λ = Standardized factor loading range; CR = Composite Reliability; AVE = Average Variance Extracted; α = Cronbach’s alpha; ω = McDonald’s omega. Criteria: CR ≥ .70, AVE ≥ .50, α and ω ≥ .70 acceptable, ≥ .80 good, ≥ .90 excellent (Fornell & Larcker, 1981 ; Nunnally & Bernstein, 1994 ) Convergent validity. Composite reliability (CR) and average variance extracted (AVE) values were examined. Distributive justice (CR = .866, AVE = .685), informational justice (CR = .900, AVE = .751), and interpersonal justice (CR = .911, AVE = .775) met the recommended thresholds of CR ≥ .70 and AVE ≥ .50 (Fornell & Larcker, 1981 ). The procedural justice dimension showed adequate CR (.742) but AVE slightly below the threshold (.490). However, given that all factor loadings exceeded .50 and CR exceeded .70, convergent validity was considered acceptable for this dimension (Malhotra & Dash, 2011 ). Reliability. Informational justice (α = .901, ω = .900) and interpersonal justice (α = .909, ω = .911) demonstrated excellent internal consistency (Nunnally & Bernstein, 1994 ). Distributive justice showed good reliability (α = .861, ω = .866), while procedural justice was acceptable (α = .739, ω = .742). The overall scale reliability was also good (α = .858). These values are comparable to or higher than those reported for the original scale (α = .601-.834; Kim et al., 2024 ). Discriminant validity. Discriminant validity was assessed using the Fornell-Larcker criterion and HTMT ratio. Results are presented in Table 8. Table 8 Discriminant validity: Fornell-Larcker matrix and HTMT ratios Panel A: Fornell-Larcker matrix (Diagonal = √AVE) DJ DJ PJ ITJ IFJ .827 PJ − .010 .700 ITJ .670 − .120 .867 IFJ .680 − .110 .970 .880 Panel B : HTMT ratios Dimension Pair HTMT DJ – PJ .104 DJ – ITJ .678 DJ – IFJ .685 PJ – ITJ .121 PJ – IFJ .125 ITJ – IFJ .956 Note : Panel A diagonal values (bold) represent √AVE. Below-diagonal values are factor correlations. Panel B criteria: HTMT < .85 (strict) or maximum r = .680) and procedural justice (√AVE = .700 > maximum r = .120). However, the correlation between informational and interpersonal justice (.970) exceeded the √AVE values for both dimensions (.867 and .880), failing the Fornell-Larcker criterion. The HTMT ratio between these dimensions (.956) also exceeded recommended thresholds (Henseler et al., 2015 ). This finding aligns with the theoretical view that informational and interpersonal justice may represent a unified interactional justice construct (Bies & Moag, 1986 ). Nevertheless, the four-factor structure was retained based on several considerations: the four-factor model showed significantly better fit than the three-factor model (Δχ² = 37.24, p .90), and measurement invariance was achieved across demographic groups. These findings suggest that despite the high correlation, Turkish athletes can distinguish between how coaches communicate information and how they treat athletes interpersonally. The high correlation between informational and interpersonal justice ( r = .97) and the HTMT value exceeding recommended thresholds (.956) indicate that discriminant validity between these two dimensions was not fully established. This finding is consistent with theoretical debates in the organizational justice literature regarding whether these constructions are empirically distinct (Bies & Moag, 1986 ; Colquitt, 2001 ). Researchers using this scale should be aware of this limitation and may consider combining these dimensions into a single “interactional justice” factor depending on their research objectives. Measurement Invariance Measurement invariance was tested using multi-group confirmatory factor analysis across gender, sport type, and coach gender. Configural, metric, and scalar invariance were examined sequentially, with ΔCFI ≤ .01 and ΔRMSEA ≤ .015 as criteria (Chen, 2007 ). Results are presented in Table 9 . Table 9 Measurement invariance results Group Variable Model CFI RMSEA ΔCFI ΔRMSEA Gender (Male n = 402; Female n = 315) Configural .980 .060 — — Metric .978 .061 .002 .001 Scalar .975 .062 .003 .001 Sport Type (Team n = 450; Individual n = 267) Configural .977 .064 — — Metric .974 .065 .003 .001 Scalar .971 .066 .003 .001 Coach Gender (Male n = 250; Female n = 467) Configural .977 .064 — — Metric .973 .066 .004 .002 Scalar .970 .067 .003 .001 Note : ΔCFI ≤ .01 and ΔRMSEA ≤ .015 criteria were used (Chen, 2007 ; Cheung & Rensvold, 2002 ) Full scalar invariance was achieved across all three grouping variables. For gender comparisons (male n = 402; female n = 315), ΔCFI ranged from .002 to .003 and ΔRMSEA was .001. Similar results were obtained for sport type (team n = 450; individual n = 267) and coach gender (male n = 249; female n = 467), with all ΔCFI and ΔRMSEA values well below critical thresholds. These findings indicate that the four-factor model functions equivalently across different demographic groups, supporting the generalizability of the scale. Achievement of scalar invariance also permits meaningful latent mean comparisons between groups (Vandenberg & Lance, 2000 ). Latent mean comparisons. Following scalar invariance, latent mean comparisons were conducted with the reference group mean fixed at zero (Hancock, 1997 ). Results are presented in Table 10 . Table 10 Latent mean comparisons Group Variable Dimension β SE z p Cohen’s d Gender (Ref: Female) Distributive Justice − .11 .08 -1.40 .162 .10 Procedural Justice .07 .07 0.98 .330 .07 Informational Justice − .18 .07 -2.41 .016* .18 Interpersonal Justice − .20 .08 -2.65 .008** .20 Sport Type (Ref: Individual) Distributive Justice − .06 .08 -0.81 .416 .06 Procedural Justice − .00 .07 -0.05 .961 .00 Informational Justice − .16 .08 -2.03 .043* .15 Interpersonal Justice − .16 .08 -2.12 .035* .16 Coach Gender (Ref: Female Coach) Distributive Justice − .16 .08 -2.01 .045* .15 Procedural Justice − .11 .08 -1.40 .161 .10 Informational Justice − .12 .08 -1.57 .117 .12 Interpersonal Justice − .21 .08 -2.66 .008** .20 Note : β = Standardized latent mean difference; SE = Standard error. Negative values indicate that the comparison group has a lower mean than the reference group. * p < .05, ** p < .01 Regarding gender, male athletes reported significantly lower informational justice ( β = − .18, p = .016, d = .18) and interpersonal justice ( β = − .20, p = .008, d = .20) compared to female athletes. For sport type, team sport athletes perceived lower informational justice ( β = − .16, p = .043, d = .15) and interpersonal justice ( β = − .16, p = .035, d = .16) than individual sport athletes, possibly reflecting the limited capacity for individualized attention in team settings. Regarding coach gender, athletes with male coaches reported lower distributive justice ( β = − .16, p = .045, d = .15) and interpersonal justice ( β = − .21, p = .008, d = .20) compared to those with female coaches. Across all comparisons, interpersonal justice showed consistent group differences, while informational justice varied by gender and sport type, and distributive justice by coach gender. Effect sizes were small ( d = .15-.20), indicating that the scale can detect meaningful variations between groups. Cross-validation. To address the limitation of conducting EFA and CFA on the same sample, the dataset was randomly split into two subsamples (Worthington & Whittaker, 2006 ). EFA on the first subsample ( n = 358) revealed a three-factor structure consistent with full sample findings (KMO = .89; variance explained = 73.35%). CFA on the second subsample ( n = 359) confirmed that the four-factor model fit well (CFI = .985, TLI = .979, RMSEA = .053, SRMR = .043) and outperformed the three-factor model (ΔCFI = .008). These results support the replicability of the four-factor structure. DISCUSSION This study set out to adapt the Athletic Justice Scale (Kim et al., 2024 ) for Turkish adolescent athletes and evaluate whether it works adequately in this new context. The short answer is that it does, though the process revealed some complexities worth discussing. Perhaps the most striking finding was the very high correlation between informational and interpersonal justice ( r = .97). This was not entirely unexpected. Bies and Moag ( 1986 ) originally proposed these as a single construct called interactional justice, and it was only later that Colquitt ( 2001 ) made the case for treating them separately. Our exploratory analysis sided with Bies and Moag, consistently suggesting three factors. Yet when we tested competing models, the four-factor version fit better statistically, and the items behaved as they should, loading onto their intended factors. Therefore, we kept the four-factor structure, but the question of whether Turkish athletes truly distinguish between how coaches communicate and how they treat them remains open. Why might these dimensions be so closely linked in our sample? One possibility is cultural. Türkiye is characterized by relatively high-power distance and collectivistic values according to cross-cultural research (Hofstede, 2001 ; Kabasakal & Bodur, 2002 ). In the GLOBE study, Türkiye scored high on in-group collectivism and power distance practices, which may influence how athletes perceive and evaluate authority figures such as coaches. In such contexts, people may view authority figures more holistically. When a coach explains a decision respectfully, athletes might not mentally separate the explanation from the respectful manner. The message and the delivery blend together. This is speculation, of course, and would need to be tested by comparing Turkish athletes with those from different cultural backgrounds. However, it offers one plausible explanation for why our correlation was higher than what Kim et al. ( 2023 ) reported in Saudi Arabia ( r = .77). The procedural justice dimension behaved somewhat differently from the others. Its reliability was acceptable but lower, its AVE fell just under the threshold, and it showed near-zero correlations with other dimensions. Looking at the items helps explain this. They ask about external pressures on coaches from administrators, politics, and parents. These are conceptually different from the other dimensions, which focus on what coaches themselves do. Athletes seem to recognize this distinction. Whether external pressure constitutes a form of injustice that belongs on the same scale as direct coach behaviours is a reasonable question for future research to address. Comparing our results to the original scale, the Turkish version performed at least as well and, in some cases, better. Distributive justice reliability jumped from .677 in the Saudi sample to .861 in ours. The factor structure replicated, and the scale showed measurement invariance across gender, sport type, and coach gender. This last point matters because it means the scale measures the same thing regardless of who is responding, which is essential for any tool intended for diverse populations. The group comparisons yielded some expected patterns. Male athletes and team sport athletes reported lower informational and interpersonal justice perceptions. For team athletes, this probably reflects simple math. A coach with twenty athletes cannot give each one the same attention as a coach working with five. The gender finding is consistent with broader literature suggesting female athletes often rate their coaches more favourably, though why this occurs is still debated. Athletes with male coaches reported lower distributive and interpersonal justice, which fits with research showing female leaders tend toward more participative styles. None of these effects were large, but the fact that the scale detected them suggests it has adequate sensitivity. Practical Implications What does this mean for practice? Coaches could use the scale to get honest feedback about how athletes perceive their fairness. Low scores on particular dimensions would point to specific areas for improvement. Sport organizations might incorporate justice assessments into coach development programs. The finding that team sport athletes feel less fairly treated in terms of communication suggests coaches of larger groups need to be intentional about individual attention, even brief check-ins can matter. And while male coaches should not interpret these findings as criticism, they might consider whether their communication style could be perceived as less personal or transparent. Limitations This study has several limitations. First, data were collected from a single province (Karaman), limiting generalizability to the broader Turkish athlete population. Second, the sample included only adolescent athletes aged 8–19 years; adults and elite athletes might respond differently. Third, criterion validity was not examined, so we do not yet know whether scale scores predict outcomes such as satisfaction, commitment, or dropout. Fourth, data was collected with coaches present nearby, which could have influenced responses despite assurances of confidentiality. Fifth, discriminant validity between informational and interpersonal justice was not fully established, with the correlation between these dimensions ( r = .97) and HTMT ratio (.956) exceeding recommended thresholds. Although the four-factor model showed superior fit and was retained for theoretical consistency, researchers should interpret these dimensions with caution, and a three-factor model combining them into interactional justice may be a viable alternative. Finally, procedural justice showed lower reliability ( α = .739) and near-zero correlations with other dimensions, likely reflecting that these items assess external pressures rather than direct coach behaviours. Future Research Several directions seem promising. Testing the scale with adult and elite athletes would establish whether it generalizes beyond adolescents. Examining criterion validity by linking justice perceptions to athlete outcomes would strengthen the case for using the scale in applied settings. Cross-cultural comparisons could clarify whether the high correlation between informational and interpersonal justice is specific to Turkish culture or appears elsewhere too. Longitudinal studies could track how justice perceptions change over a season and whether changes predict important outcomes. And given the issues with procedural justice items, future work might consider whether alternative items focusing on coach decision-making processes rather than external pressures would work better. Conclusion To sum up, the Turkish version of the Athletic Justice Scale is a valid and reliable tool for measuring how adolescent athletes perceive fairness in their coaches’ behaviours. The scale demonstrated acceptable to excellent psychometric properties, with the four-factor structure holding across gender, sport type, and coach gender groups. However, some limitations should be noted: discriminant validity between informational and interpersonal justice was not fully established, suggesting that a three-factor model may be a viable alternative for some research purposes, and procedural justice operates somewhat differently from other dimensions due to its focus on external pressures rather than direct coach behaviours. Despite these limitations, the scale showed adequate reliability, measurement invariance across demographic groups, and sensitivity to detect meaningful group differences. For researchers interested in coach-athlete relationships in Türkiye, this scale provides a foundation for investigating justice dynamics in sport settings. For practitioners and coaches seeking to enhance athlete experiences, it offers a practical tool for obtaining feedback on perceived fairness. Declarations Conflict of Interest: The authors declare that they have no conflict of interest. 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A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods , 3 (1), 4–70. https://doi.org/10.1177/109442810031002 Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist , 34 (6), 806–838. https://doi.org/10.1177/0011000006288127 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9023785","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623475390,"identity":"12be2149-29a2-45d9-822f-467edc45f97c","order_by":0,"name":"MELEK KURŞUNEL","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFAC5gbGhgNQ9oOKBDAtgV8LI5KWhDNwLQZEaklsI0KLbntj48MZZw7ny8/IPfggcV5atMEB5oO3eRj+5OPSYnbmYLPhhhuHLTfcyEs2SNyWk7vhAFuyNQ+DgWUDLi03EtskH3w4bGAgkWMmkbitAqiFx0waqAWny8zuP2z/CdIiPyPH/EfiHJAW/m/4tdxgbGMEOsyA4UaOGUNiA8hhPGz4tZxJbJaccSbdwODMG2OJhGNpuTMPsxlbzjEwxq3l+OGDH3uOWRvIt+cYfvhQk5zbd7z54Y03FXL4IgYdMIMIUjSMglEwCkbBKMAAACgjX6de9yrEAAAAAElFTkSuQmCC","orcid":"","institution":"Karamanoğlu Mehmetbey University","correspondingAuthor":true,"prefix":"","firstName":"MELEK","middleName":"","lastName":"KURŞUNEL","suffix":""},{"id":623475391,"identity":"d468f14d-f409-4186-8b36-c847699b93ef","order_by":1,"name":"ÖZNUR AKPINAR","email":"","orcid":"","institution":"Karamanoğlu Mehmetbey University","correspondingAuthor":false,"prefix":"","firstName":"ÖZNUR","middleName":"","lastName":"AKPINAR","suffix":""},{"id":623475392,"identity":"764b40e0-4197-433c-870d-7ae4a2c9db9d","order_by":2,"name":"ASLI ECE KOÇAK","email":"","orcid":"","institution":"Duzce University","correspondingAuthor":false,"prefix":"","firstName":"ASLI","middleName":"ECE","lastName":"KOÇAK","suffix":""},{"id":623475393,"identity":"eed38e9f-17cc-453d-b451-8efd49974598","order_by":3,"name":"RECEP MEHMET GÖRÜNÜ","email":"","orcid":"","institution":"Duzce University","correspondingAuthor":false,"prefix":"","firstName":"RECEP","middleName":"MEHMET","lastName":"GÖRÜNÜ","suffix":""},{"id":623475394,"identity":"0b629ed0-89a0-4b17-a80e-5b68a2355702","order_by":4,"name":"NAZLI YANAR TUNÇEL","email":"","orcid":"","institution":"Karamanoğlu Mehmetbey University","correspondingAuthor":false,"prefix":"","firstName":"NAZLI","middleName":"YANAR","lastName":"TUNÇEL","suffix":""},{"id":623475395,"identity":"769939b8-0c1e-41f8-b4d4-576e45027931","order_by":5,"name":"HANDE BABA KAYA","email":"","orcid":"","institution":"Duzce University","correspondingAuthor":false,"prefix":"","firstName":"HANDE","middleName":"BABA","lastName":"KAYA","suffix":""},{"id":623475396,"identity":"dfba359c-fe4d-4735-aa8c-cc7af7026aa7","order_by":6,"name":"SELAHATTİN AKPINAR","email":"","orcid":"","institution":"Duzce University","correspondingAuthor":false,"prefix":"","firstName":"SELAHATTİN","middleName":"","lastName":"AKPINAR","suffix":""},{"id":623475397,"identity":"906ba3fe-5092-4b74-adf8-8b68dd6d829b","order_by":7,"name":"ŞEREF YİĞİT AKPINAR","email":"","orcid":"","institution":"Ministry of National Education","correspondingAuthor":false,"prefix":"","firstName":"ŞEREF","middleName":"YİĞİT","lastName":"AKPINAR","suffix":""}],"badges":[],"createdAt":"2026-03-03 21:23:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9023785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9023785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107356402,"identity":"37667e30-0479-47dc-8758-ec4a7d6a6b65","added_by":"auto","created_at":"2026-04-20 16:58:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e.\u003c/strong\u003eStandardized parameter estimates for the four-factor CFA model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e \u003c/em\u003eDA = Distributive Justice; PA = Procedural Justice; KA = Informational Justice; BA = Interpersonal Justice. Single-headed arrows represent standardized factor loadings, double-headed curved arrows represent factor correlations, and values to the right of observed variables represent error variances\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9023785/v1/f68a4860be869af73d0536b0.png"},{"id":107488431,"identity":"ca6a84ef-e658-45c9-a6a0-fbae831125e9","added_by":"auto","created_at":"2026-04-22 02:44:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1225091,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9023785/v1/eaa3061e-446a-46db-a094-9e1de40fb1c7.pdf"},{"id":107356401,"identity":"b84fa892-63b1-4498-b013-3df193d418a3","added_by":"auto","created_at":"2026-04-20 16:58:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":66765,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023785/v1/9228520c7967be50bd92b69c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The First Step in Revealing the Perceptions of Justice Among Turkish Adolescent Athletes: Adapting the Justice in Sport Scale","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe concept of justice has long been recognized as fundamental to understanding workplace attitudes and behaviours. Greenberg (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) defined organizational justice as employees\u0026rsquo; perceptions of fairness within their organizations, and this concept has since become one of the most extensively studied topics in organizational behaviour research. The theoretical foundation of organizational justice evolved considerably over the decades. Early work focused primarily on distributive justice, which refers to the perceived fairness of outcome distributions based on equity principles (Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Deutsch, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). Leventhal (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) subsequently expanded the framework by introducing procedural justice, which concerns the fairness of processes used to determine outcomes. Later, Bies and Moag (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) proposed the concept of interactional justice, focusing on the interpersonal treatment people receive during the implementation of procedures. Colquitt\u0026rsquo;s (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) influential work further refined this framework by distinguishing between informational justice and interpersonal justice. Informational justice refers to the adequacy and truthfulness of explanations provided for decisions, while interpersonal justice concerns the degree of dignity and respect shown by authority figures. This four-dimensional model, comprising distributive, procedural, informational, and interpersonal justice, has received substantial empirical support and is now widely adopted in organizational research.\u003c/p\u003e \u003cp\u003eThe importance of justice perceptions extends beyond theoretical interest. A substantial body of evidence demonstrates that when employees perceive fairness in their workplace, they exhibit greater job satisfaction, stronger organizational commitment, enhanced performance, and more frequent organizational citizenship behaviours (Cohen-Charash \u0026amp; Spector, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Colquitt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Conversely, perceptions of injustice have been linked to counterproductive work behaviours, increased turnover intentions, and diminished psychological well-being (Colquitt et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These findings underscore the practical significance of understanding and fostering justice perceptions in organizational settings.\u003c/p\u003e \u003cp\u003eWithin sport settings, the coach-athlete relationship presents a unique and particularly important context for examining justice perceptions. Unlike typical workplace relationships, the coach-athlete dynamic is characterized by an intensive and often emotionally charged interaction where coaches wield considerable authority over multiple aspects of athletes\u0026rsquo; sporting lives. Coaches make consequential decisions about playing time allocation, position assignments, team selection, training intensity, tactical approaches, and performance feedback. All these decisions directly and visibly affect athletes\u0026rsquo; experiences, development, and career trajectories. The public nature of many coaching decisions, made apparent during competitions and visible to teammates, opponents, and spectators alike, amplifies the salience of fairness concerns in ways that distinguish sport from most other organizational contexts.\u003c/p\u003e \u003cp\u003eResearch has increasingly demonstrated the significance of justice perceptions in sport. De Backer et al. (2011) found that perceived justice and need support from coaches predicted team identification and cohesion among elite volleyball and handball players in Belgium and Norway. Subsequent work by the same research group revealed that athletes who viewed their coaches as fair reported greater satisfaction and self-rated progression (De Backer et al., 2021). In a study of elite women\u0026rsquo;s team sport athletes, De Backer et al. (2015) concluded that the motivational climate created by coaches, including perceptions of fairness, significantly impacted the optimal functioning of sports teams. Furthermore, Grant et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) demonstrated that perceived procedural injustice was associated with negative emotional responses such as anger, frustration, and resentment, which can undermine athletic performance and team harmony. Collectively, these findings suggest that understanding how athletes perceive coach fairness has substantial practical implications for optimizing the sport environment, enhancing athlete well-being, and improving team performance.\u003c/p\u003e \u003cp\u003eDespite the theoretical and practical importance of justice perceptions in sport, measuring this construct has proven challenging. Most studies examining fairness in athletic contexts have relied on organizational justice scales originally developed for business settings, such as Colquitt\u0026rsquo;s (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) widely used measure. However, several scholars have argued that the sport environment differs from traditional workplaces in important ways that warrant context-specific measurement approaches (Fletcher \u0026amp; Wagstaff, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mahony et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Athletic performance is publicly evaluated under competitive pressure, success and failure are often immediately visible to others, and the physical and emotional demands of training and competition create unique stressors. Moreover, the coach-athlete relationship involves dynamics such as physical proximity, emotional intensity, and developmental influence that distinguish it from typical supervisor-employee relationships. These distinctive features suggest that justice perceptions in sport may manifest differently than in conventional organizational settings.\u003c/p\u003e \u003cp\u003eRecognizing this limitation in the existing literature, Kim et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) recently developed the Athletic Justice Scale specifically for sport contexts. Drawing on both deductive approaches through reviewing 16 existing organizational justice scales and inductive methods through conducting semi-structured interviews with 22 elite athletes, these researchers generated items that capture fairness perceptions relevant to the unique culture and environment of sport. The resulting 12-item instrument assesses athletes\u0026rsquo; perceptions across four dimensions. Distributive justice includes three items measuring perceived fairness of role assignments and rewards. Procedural justice contains three items assessing freedom from external pressures on coaching decisions. Informational justice comprises three items evaluating timeliness and clarity of coach communication. Interpersonal justice consists of three items measuring respectful and dignified treatment. The scale demonstrated acceptable psychometric properties across two samples of elite athletes from Saudi Arabia (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;279 for initial scale development; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;503 for validation), with Cronbach\u0026rsquo;s alpha values ranging from .677 to .823 and confirmatory factor analysis supporting the four-factor structure.\u003c/p\u003e \u003cp\u003eResearch on justice in Turkish sport contexts has begun to emerge but remains limited in scope. Studies have examined organizational justice perceptions among sport administrators and employees at Provincial Directorates of Youth and Sports (Tapşın et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), social justice considerations in sport investment policies (Kasapoğlu \u0026amp; \u0026Ouml;\u0026ccedil;al, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and wage justice perceptions among sport organization employees (Dal \u0026amp; Donuk, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Related work has investigated coaches\u0026rsquo; ethical attitudes (Horzum, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), moral decision-making processes among football coaches (Temel et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and athletes\u0026rsquo; perceptions of abusive supervision in coach-athlete relationships (B\u0026uuml;lb\u0026uuml;l, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, despite growing interest in fairness-related constructs, no validated instrument currently exists to assess Turkish athletes\u0026rsquo; justice perceptions specifically regarding their coaches\u0026rsquo; behaviours. This represents a notable gap in the literature, particularly given the central role that coaches play in Turkish sport culture and athlete development.\u003c/p\u003e \u003cp\u003eThe need for a culturally adapted measure is further underscored by research demonstrating that cultural factors shape how individuals interpret and respond to fairness-related situations. T\u0026uuml;rkiye is characterized by relatively high-power distance and a collectivistic orientation (Hofstede, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and these cultural dimensions may influence athlete-coach dynamics and justice perceptions. In high power distance cultures, hierarchical relationships are more accepted, and subordinates may have different expectations regarding authority figures\u0026rsquo; decision-making processes. Similarly, collectivistic values emphasizing group harmony and relational obligations may affect how athletes evaluate the fairness of coaches\u0026rsquo; interpersonal behaviours. These cultural considerations suggest that simply translating an existing scale may be insufficient. Rather, systematic adaptation and validation procedures are necessary to ensure that the instrument functions appropriately within the Turkish context.\u003c/p\u003e \u003cp\u003eThe present study therefore aimed to adapt the Athletic Justice Scale (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for use with Turkish athletes and to comprehensively examine its psychometric properties. Specifically, we sought to establish the linguistic equivalence and content validity of the Turkish version through systematic translation and expert evaluation procedures, examine the factor structure of the adapted scale using both exploratory and confirmatory factor analytic approaches, assess the internal consistency reliability of the scale and its subscales, evaluate convergent and discriminant validity evidence, and test measurement invariance across key demographic variables including gender, sport type, and coach gender. To maximize statistical power and obtain stable parameter estimates, primary analyses were conducted on the full sample, followed by split-sample cross-validation to assess replicability. A validated Turkish version of the Athletic Justice Scale would provide researchers with a much-needed tool to investigate justice dynamics in Turkish sport settings, enable cross-cultural comparisons with findings from other nations, and potentially inform evidence-based interventions aimed at enhancing coach-athlete relationships and optimizing the athletic experience for Turkish adolescent.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study aimed to adapt and validate the Turkish version of the Athletic Justice Scale (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The adaptation process followed the guidelines proposed by Beaton et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and the International Test Commission (2017), encompassing translation, back-translation, expert review, and comprehensive psychometric evaluation. Both exploratory and confirmatory factor analytic approaches were employed to examine the factor structure, as recommended for cross-cultural scale adaptation studies (Worthington \u0026amp; Whittaker, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLanguage Validity\u003c/h3\u003e\n\u003cp\u003eThe scale was adapted into Turkish using the translation-back translation procedure described by Brislin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). Seven bilingual translators independently translated the original English items into Turkish. These translations were reviewed by the research team, and the most suitable expressions were selected to form a preliminary Turkish version. Two different translators, who had no prior exposure to the original scale, then translated this Turkish version back into English. Comparison of the back-translated version with the original revealed minor discrepancies in wording, which were resolved through discussion among the research team.\u003c/p\u003e \u003cp\u003eContent validity was evaluated by a panel of 14 experts. Half of the panel consisted of language specialists, while the other half were academics in sports sciences. Each expert independently rated the relevance of every item on a 4-point scale ranging from \u0026ldquo;not relevant\u0026rdquo; to \u0026ldquo;highly relevant.\u0026rdquo; The panel also met to discuss the items and suggest revisions where needed. Item-level content validity was quantified using the Content Validity Index (Davis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and Content Validity Ratio (Lawshe, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The results of the expert evaluations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eExpert panel evaluation results for content validity (N\u0026thinsp;=\u0026thinsp;14)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuite Relevant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighly Relevant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-CVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCVR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS-CVI/Ave\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote.\u003c/b\u003e I-CVI = (Quite Relevant\u0026thinsp;+\u0026thinsp;Highly Relevant) / \u003cem\u003eN\u003c/em\u003e (Davis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). CVR = (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e \u0026minus; \u003cem\u003eN\u003c/em\u003e/2) / (\u003cem\u003eN\u003c/em\u003e/2), where \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e = number of experts rating \u0026ldquo;Quite Relevant\u0026rdquo; or \u0026ldquo;Highly Relevant\u0026rdquo; (Lawshe, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1975\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll items received ratings of either \u0026ldquo;quite relevant\u0026rdquo; or \u0026ldquo;highly relevant\u0026rdquo; from every expert, yielding I-CVI and CVR values of 1.00 for each item. The scale-level content validity index averaged across items was also 1.00. These values exceed the recommended cutoffs of .78 for I-CVI and .51 for CVR when 14 experts are involved (Lawshe, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Polit \u0026amp; Beck, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), indicating strong agreement on content validity.\u003c/p\u003e \u003cp\u003eThe expert panel noted that three procedural justice items (Items 4, 5, and 6) describe negative coaching behaviours, such as being influenced by administrators, political considerations, or parental pressure. Because higher scores on these items reflect lower perceived justice, they require reverse scoring during analysis. The panel agreed to retain this reverse-coded format in the Turkish version, consistent with the original scale. Finally, based on expert feedback, the scale title was adapted from \u0026ldquo;Athletic Justice Scale\u0026rdquo; to \u0026ldquo;Sporda Adalet \u0026Ouml;l\u0026ccedil;eği\u0026rdquo; to better fit Turkish sports terminology.\u003c/p\u003e\n\u003ch3\u003eThe Sample of the Research\u003c/h3\u003e\n\u003cp\u003eSample size recommendations for confirmatory factor analysis vary in the literature. Comrey and Lee (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) suggested that 300 participants constitute a \u0026ldquo;good\u0026rdquo; sample size, 500 \u0026ldquo;very good,\u0026rdquo; and 1,000 \u0026ldquo;excellent.\u0026rdquo; Kline (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) recommended a minimum of 200 participants for structural equation modelling studies. The present study included 717 licensed adolescent athletes actively training at facilities affiliated with the Karaman Provincial Directorate of Youth and Sports in T\u0026uuml;rkiye. Participants were involved in various individual and team sports. Demographic characteristics of the sample are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;717)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU12 (8\u0026ndash;11 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU16 (12\u0026ndash;15 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU20 (16\u0026ndash;19 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSport Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual Sports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeam Sports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoach Gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote.\u003c/b\u003e Age categories follow the national sport federation classification system. Many participants (82.8%) were in the U16 category\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sample consisted of 402 male (56.1%) and 315 female (43.9%) athletes. Most participants were in the U16 age category covering ages 12 to 15 years (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;594, 82.8%), followed by U20 covering ages 16 to 19 years (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;99, 13.8%) and U12 covering ages 8 to 11 years (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24, 3.3%). Regarding sport type, 450 athletes (62.8%) participated in team sports, while 267 (37.2%) were involved in individual sports. In terms of coach gender, 467 athletes (65.1%) trained under female coaches, whereas 250 (34.9%) trained under male coaches. The mean training experience was 2.80 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.09), and athletes had been working with their current coach for an average of 2.44 years (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.85).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Process\u003c/h2\u003e \u003cp\u003e This research was approved by the Karamanoğlu Mehmetbey University Social Sciences Scientific Research and Publication Ethics Committee (Decision No: 191, Date: 21 May 2024). Prior to the study, permission to use and adapt the scale was obtained via email from the original authors. Data collection took place between June 1 and September 1, 2024, at training facilities affiliated with the Karaman Provincial Directorate of Youth and Sports.\u003c/p\u003e \u003cp\u003eThe questionnaire was administered in person by trained research assistants who visited athletes during their regular training sessions. Before participation, athletes were informed about the purpose of the study, the voluntary nature of participation, and the confidentiality of their responses. Written informed consent was obtained from all participants. For athletes under 18 years of age, parental consent was also obtained in accordance with ethical guidelines. No incentives were provided for participation. The questionnaire took approximately 10 minutes to complete.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eTwo instruments were used for data collection: a demographic information form and the Turkish version of the Athletic Justice Scale.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThe Demographic Form\u003c/strong\u003e \u003cp\u003eIncluded questions about gender, age group, sport type, years of training experience, coach gender, and duration of working with the current coach.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Athletic Justice Scale;\u003c/em\u003e Originally developed by Kim et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), assesses athletes\u0026rsquo; perceptions of fairness regarding their coaches\u0026rsquo; behaviours. The scale contains 12 items distributed across four subscales with three items each. Distributive justice items assess perceived fairness of role assignments within the team. Procedural justice items measure the extent to which coaching decisions are free from external pressures. Informational justice items evaluate the timeliness and clarity of coach communication. Interpersonal justice items assess whether the coach treats athletes with respect and dignity. All items are rated on a 7-point Likert scale from 1 (Strongly Disagree) to 7 (Strongly Agree). In the original validation study conducted with Saudi Arabian athletes, Cronbach\u0026rsquo;s alpha coefficients ranged from .677 to .823 across subscales (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R \u003cem\u003eversion 4.4.2\u003c/em\u003e (R Core Team, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The \u003cem\u003epsych\u003c/em\u003e package (Revelle, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used for exploratory factor analysis and reliability estimation, while confirmatory factor analysis and measurement invariance testing were conducted using the \u003cem\u003elavaan\u003c/em\u003e (Rosseel, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and \u003cem\u003esemTools\u003c/em\u003e packages (Jorgensen et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given that the data showed some deviation from normality, the robust maximum likelihood estimator was applied throughout confirmatory analyses (Satorra \u0026amp; Bentler, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dataset was first examined for missing values, outliers, and distributional properties. Skewness and kurtosis values were evaluated to assess univariate normality, with values within the range of \u0026plusmn;\u0026thinsp;2 considered acceptable (George \u0026amp; Mallery, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Common method bias was checked using Harman\u0026rsquo;s single-factor test, where the first factor accounting for less than 50% of total variance indicates no serious concern (Podsakoff et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Item quality was assessed through corrected item-total correlations for each subscale, with values above .30 indicating adequate discrimination (Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Independent samples \u003cem\u003et\u003c/em\u003e-tests comparing the upper and lower 27% of respondents were also conducted (Kelley, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1939\u003c/span\u003e), with Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e calculated to quantify effect sizes.\u003c/p\u003e \u003cp\u003ePrior to factor analysis, data suitability was examined using the Kaiser-Meyer-Olkin measure and Bartlett\u0026rsquo;s test of sphericity. Exploratory factor analysis was conducted on the polychoric correlation matrix using minimum residual extraction with oblimin rotation, which allows factors to correlate consistent with theoretical expectations. The number of factors was determined by jointly considering the Kaiser criterion, parallel analysis (Horn, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1965\u003c/span\u003e), and the scree plot. Five competing confirmatory models were then tested: a single-factor model, a three-factor model combining interpersonal and informational justice based on exploratory results, the original four-factor model (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a second-order model with a general justice factor, and a bifactor model. Model fit was evaluated using chi-square, CFI, TLI, RMSEA with 90% confidence intervals, and SRMR. Following Hu and Bentler (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), CFI and TLI values of .95 or above, RMSEA of .06 or below, and SRMR of .08 or below indicated good fit, while CFI and TLI above .90 and RMSEA below .08 indicated acceptable fit. Nested models were compared using the Satorra-Bentler scaled chi-square difference test (Satorra \u0026amp; Bentler, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and changes in CFI.\u003c/p\u003e \u003cp\u003eConvergent validity was evaluated through composite reliability and average variance extracted, with values of .70 and .50, respectively, considered adequate (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Hair et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Following Malhotra and Dash (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), AVE slightly below .50 was deemed acceptable when composite reliability exceeded .60. Discriminant validity was assessed using the Fornell-Larcker criterion, which requires the square root of AVE for each factor to exceed its correlations with other factors, and the Heterotrait-Monotrait ratio, with values below .85 indicating good and below .90 acceptable discriminant validity (Henseler et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Internal consistency was estimated using Cronbach\u0026rsquo;s alpha, McDonald\u0026rsquo;s omega, and composite reliability, with values of .70, .80, and .90 representing acceptable, good, and excellent reliability, respectively (Nunnally \u0026amp; Bernstein, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeasurement invariance was tested using multi-group confirmatory factor analysis across gender, sport type, and coach gender at configural, metric, and scalar levels (Vandenberg \u0026amp; Lance, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Following Chen (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), decreases in CFI of .010 or less and increases in RMSEA of .015 or less between nested models indicated invariance. After establishing scalar invariance, latent mean comparisons were conducted with the reference group mean fixed at zero (Hancock, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and effect sizes were reported as Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e. The primary EFA and CFA analyses were conducted on the full sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;717) to maximize statistical power and obtain stable parameter estimates. To address potential concerns about conducting EFA and CFA on the same sample, a supplementary split-sample cross-validation was performed: the dataset was randomly divided into two subsamples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;358 and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;359), with EFA conducted on the first and CFA on the second (Worthington \u0026amp; Whittaker, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e\n \u003cp\u003eBefore conducting the main analyses, the dataset was examined for missing values, outliers, and distributional properties. No missing data were found. Skewness and kurtosis coefficients for all items were within the \u0026plusmn;\u0026thinsp;2 range suggested by George and Mallery (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), supporting univariate normality. Harman\u0026rsquo;s single-factor test showed that the first factor explained 49.65% of the variance, which is below the 50% threshold, suggesting that common method bias was not a major concern (Podsakoff et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). Descriptive statistics and item analysis results are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eDescriptive statistics and item analysis results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDim\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrected \u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e19.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e20.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Corrected \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;corrected item-total correlation by subscale; DJ\u0026thinsp;=\u0026thinsp;Distributive Justice; PJ\u0026thinsp;=\u0026thinsp;Procedural Justice; IFJ\u0026thinsp;=\u0026thinsp;Informational Justice; ITJ\u0026thinsp;=\u0026thinsp;Interpersonal Justice; \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e. The 27% cut-off point based on total scale scores was used for upper-lower group comparisons (lower group \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;201, upper group \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;195). All \u003cem\u003et\u003c/em\u003e-tests were significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eItem means ranged from 3.22 to 5.76. Procedural justice items showed lower means because they are negatively worded and reflect external pressures on coaching decisions. Informational and interpersonal justice items had the highest means, indicating that athletes generally viewed their coaches as communicative and respectful. Corrected item-total correlations ranged from .56 to .86, all exceeding the .30 threshold (Tabachnick \u0026amp; Fidell, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Upper-lower 27% group comparisons revealed significant differences for all items (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), with large effect sizes for distributive (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.99\u0026ndash;2.18), informational (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.87\u0026ndash;2.09), and interpersonal justice items (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.02\u0026ndash;2.10). Procedural justice items showed smaller but adequate effect sizes (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65\u0026ndash;0.99).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruct Validity\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eExploratory factor analysis.\u003c/strong\u003e The Kaiser-Meyer-Olkin measure was .90, indicating excellent sampling adequacy (Kaiser, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e), and Bartlett\u0026rsquo;s test of sphericity was significant (\u0026chi;\u0026sup2; = 6203.73, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). The Kaiser criterion, parallel analysis, and scree plot all indicated a three-factor structure. Using minimum residual extraction with oblimin rotation on the polychoric correlation matrix, the three-factor solution explained 75.73% of the total variance. Distributive justice items (A1-A3) and procedural justice items (A4-A6) loaded onto separate factors, while informational justice (A7-A9) and interpersonal justice items (A10-A12) combined under a single factor. This pattern is consistent with earlier conceptualizations of interactional justice (Bies \u0026amp; Moag, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Greenberg, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). However, since the original scale has a four-factor structure, the final decision was based on competing confirmatory models (Worthington \u0026amp; Whittaker, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Detailed EFA results are provided in \u003cem\u003eAppendix 1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis.\u003c/strong\u003e Five models were tested to assess construct validity: a single-factor model, a three-factor model combining interpersonal and informational justice consistent with EFA results, a four-factor model matching the original structure, a second-order model with four first-order factors loading onto a general justice factor, and a bifactor model. All analyses used robust maximum likelihood estimation. Fit indices are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eFit indices of competitive CFA models\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;/\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1262.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e204.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e167.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond-order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e92.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBifactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e44.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u0026chi;\u0026sup2; = Chi-square; \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;degrees of freedom; CFI\u0026thinsp;=\u0026thinsp;Comparative Fit Index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis Index; RMSEA\u0026thinsp;=\u0026thinsp;Root Mean Square Error of Approximation; SRMR\u0026thinsp;=\u0026thinsp;Standardized Root Mean Square Residual\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe single-factor model showed poor fit (CFI = .805, TLI = .761, RMSEA = .177), confirming that the data cannot be explained by a unidimensional structure. The three-factor model demonstrated acceptable fit (CFI = .975, TLI = .968, RMSEA = .065, SRMR = .045), though RMSEA exceeded the .06 threshold for good fit. The four-factor model showed the best fit among standard models (CFI = .981, TLI = .973, RMSEA = .059, SRMR = .040), meeting all recommended criteria (Hu \u0026amp; Bentler, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). The chi-square difference test comparing the four-factor and three-factor models was significant (\u0026Delta;\u0026chi;\u0026sup2; = 37.24, \u0026Delta;\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), supporting the four-factor structure. Although \u0026Delta;CFI was below .01 (Cheung \u0026amp; Rensvold, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), the four-factor model was retained based on the significant chi-square difference, lower AIC and BIC values, and theoretical consistency with the original scale (Kline, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTwo hierarchical models were also examined due to the high correlation between interpersonal and informational justice. The second-order model showed acceptable fit (CFI = .986, TLI = .981, RMSEA = .049), but the loading of procedural justice onto the higher-order factor was not significant (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.08, \u003cem\u003ep\u003c/em\u003e = .260), indicating theoretical unsuitability. The bifactor model yielded excellent fit indices (CFI = .997, TLI = .995, RMSEA = .026) but produced negative variance estimates for interpersonal (\u0026psi; = \u0026minus;\u0026thinsp;.018) and informational justice factors (\u0026psi; = \u0026minus;\u0026thinsp;.342). These Heywood cases indicate over-parameterization, rendering the bifactor model inappropriate (Chen et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). Model comparison results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eModel comparison summary\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;RMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3F vs 1F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1057.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4F vs 1F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1095.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4F vs 3F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e37.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u0026Delta;\u0026chi;\u0026sup2; = Chi-square difference; \u0026Delta;\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;difference in degrees of freedom; \u0026Delta;CFI\u0026thinsp;=\u0026thinsp;difference in CFI; \u0026Delta;RMSEA\u0026thinsp;=\u0026thinsp;difference in RMSEA\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the four-factor model fits significantly better than both the single-factor and three-factor models. The standardized factor loadings and inter-factor correlations are presented in Table\u0026nbsp;6.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;6 Four-factor model: Standardized factor loadings and factor correlations\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel A:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFactor loadings\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lambda;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistributive Justice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcedural Justice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformational Justice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e38.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e94.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpersonal Justice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e96.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e45.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e38.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e: \u003cem\u003eFactor correlations\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.97***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u0026lambda;\u0026thinsp;=\u0026thinsp;Standardized factor loading; SE\u0026thinsp;=\u0026thinsp;Standard error; R\u0026sup2; = Explained variance; DJ\u0026thinsp;=\u0026thinsp;Distributive Justice; PJ\u0026thinsp;=\u0026thinsp;Procedural Justice; IFJ\u0026thinsp;=\u0026thinsp;Informational Justice; ITJ\u0026thinsp;=\u0026thinsp;Interpersonal Justice. *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAll items loaded significantly onto their respective factors (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001), with loadings ranging from .69 to .96, well above the .50 threshold (Hair et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The highest loadings were observed for interpersonal justice (A10: \u0026lambda;\u0026thinsp;=\u0026thinsp;.96) and informational justice (A9: \u0026lambda;\u0026thinsp;=\u0026thinsp;.94), while procedural justice items showed the lowest loadings (A6: \u0026lambda;\u0026thinsp;=\u0026thinsp;.69). Explained variance (R\u0026sup2;) ranged from .47 to .91.\u003c/p\u003e\n \u003cp\u003eDistributive justice showed moderate positive correlations with informational (\u003cem\u003er\u003c/em\u003e = .67) and interpersonal justice (\u003cem\u003er\u003c/em\u003e = .68). Procedural justice displayed weak negative correlations with other dimensions (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.01 to \u0026minus;\u0026thinsp;.12) due to the reverse-coded nature of these items. The correlation between informational and interpersonal justice was very high (\u003cem\u003er\u003c/em\u003e = .97), which raises questions about discriminant validity between these dimensions. This high correlation is consistent with the theoretical debate in organizational justice literature regarding whether these constructs represent distinct dimensions or a unified interactional justice factor (Bies \u0026amp; Moag, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Colquitt, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). Although the three-factor model combining these dimensions showed acceptable fit, it was statistically inferior to the four-factor model. The second-order model was rejected because procedural justice did not load significantly onto the higher-order factor, and the bifactor model produced inadmissible estimates. Therefore, the four-factor structure was retained, consistent with the original scale. Standardized parameter estimates are illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the measurement model with standardized estimates. The visual representation clearly shows the distinct clustering of items within their respective factors and highlights the notably high covariance between informational and interpersonal justice.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eValidity and Reliability\u003c/h2\u003e\n \u003cp\u003eFollowing validation of the four-factor model, convergent validity, discriminant validity, and reliability were assessed. Results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eValidity and reliability indicators\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026lambda; range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026radic;AVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026omega;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistributive Justice (DJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e.71-.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcedural Justice (PJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e.69-.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformational Justice (IFJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e.76-.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Justice (ITJ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e.77-.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\"−\"\u003e\n \u003cp\u003e.69-.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u0026lambda;\u0026thinsp;=\u0026thinsp;Standardized factor loading range; CR\u0026thinsp;=\u0026thinsp;Composite Reliability; AVE\u0026thinsp;=\u0026thinsp;Average Variance Extracted; \u0026alpha;\u0026thinsp;=\u0026thinsp;Cronbach\u0026rsquo;s alpha; \u0026omega;\u0026thinsp;=\u0026thinsp;McDonald\u0026rsquo;s omega. Criteria: CR \u0026ge; .70, AVE \u0026ge; .50, \u0026alpha; and \u0026omega;\u0026thinsp;\u0026ge;\u0026thinsp;.70 acceptable, \u0026ge; .80 good, \u0026ge; .90 excellent (Fornell \u0026amp; Larcker, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e; Nunnally \u0026amp; Bernstein, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eConvergent validity.\u003c/strong\u003e Composite reliability (CR) and average variance extracted (AVE) values were examined. Distributive justice (CR = .866, AVE = .685), informational justice (CR = .900, AVE = .751), and interpersonal justice (CR = .911, AVE = .775) met the recommended thresholds of CR \u0026ge; .70 and AVE \u0026ge; .50 (Fornell \u0026amp; Larcker, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). The procedural justice dimension showed adequate CR (.742) but AVE slightly below the threshold (.490). However, given that all factor loadings exceeded .50 and CR exceeded .70, convergent validity was considered acceptable for this dimension (Malhotra \u0026amp; Dash, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReliability.\u003c/strong\u003e Informational justice (\u0026alpha;\u0026thinsp;=\u0026thinsp;.901, \u0026omega;\u0026thinsp;=\u0026thinsp;.900) and interpersonal justice (\u0026alpha;\u0026thinsp;=\u0026thinsp;.909, \u0026omega;\u0026thinsp;=\u0026thinsp;.911) demonstrated excellent internal consistency (Nunnally \u0026amp; Bernstein, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). Distributive justice showed good reliability (\u0026alpha;\u0026thinsp;=\u0026thinsp;.861, \u0026omega;\u0026thinsp;=\u0026thinsp;.866), while procedural justice was acceptable (\u0026alpha;\u0026thinsp;=\u0026thinsp;.739, \u0026omega;\u0026thinsp;=\u0026thinsp;.742). The overall scale reliability was also good (\u0026alpha;\u0026thinsp;=\u0026thinsp;.858). These values are comparable to or higher than those reported for the original scale (\u0026alpha;\u0026thinsp;=\u0026thinsp;.601-.834; Kim et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDiscriminant validity.\u003c/strong\u003e Discriminant validity was assessed using the Fornell-Larcker criterion and HTMT ratio. Results are presented in Table\u0026nbsp;8.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;8 Discriminant validity: Fornell-Larcker matrix and HTMT ratios\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePanel A:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFornell-Larcker matrix (Diagonal = \u0026radic;AVE)\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tabc\" style=\"width: 171px;\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"height: 70px; width: 16px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 36px;\" align=\"left\"\u003e\n \u003cp\u003eDJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 36.8171px;\" align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 26.1829px;\" align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 27px;\" align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003cth style=\"height: 35px; width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.827\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"height: 35px; width: 36.8171px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"height: 35px; width: 26.1829px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"height: 35px; width: 27px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 16px;\" align=\"left\"\u003e\n \u003cp\u003ePJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36.8171px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.700\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 26.1829px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 27px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 16px;\" align=\"left\"\u003e\n \u003cp\u003eITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36.8171px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 26.1829px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.867\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 27px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr style=\"height: 35px;\"\u003e\n \u003ctd style=\"height: 35px; width: 16px;\" align=\"left\"\u003e\n \u003cp\u003eIFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36px;\" align=\"left\"\u003e\n \u003cp\u003e.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 36.8171px;\" align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 26.1829px;\" align=\"left\"\u003e\n \u003cp\u003e.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"height: 35px; width: 27px;\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e.880\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e: \u003cem\u003eHTMT ratios\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension Pair\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHTMT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ \u0026ndash; PJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ \u0026ndash; ITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDJ \u0026ndash; IFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ \u0026ndash; ITJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePJ \u0026ndash; IFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITJ \u0026ndash; IFJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Panel A diagonal values (bold) represent \u0026radic;AVE. Below-diagonal values are factor correlations. Panel B criteria: HTMT \u0026lt; .85 (strict) or \u0026lt; .90 (lenient) (Henseler et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eDiscriminant validity was established for distributive justice (\u0026radic;AVE = .827\u0026thinsp;\u0026gt;\u0026thinsp;maximum \u003cem\u003er\u003c/em\u003e = .680) and procedural justice (\u0026radic;AVE = .700\u0026thinsp;\u0026gt;\u0026thinsp;maximum \u003cem\u003er\u003c/em\u003e = .120). However, the correlation between informational and interpersonal justice (.970) exceeded the \u0026radic;AVE values for both dimensions (.867 and .880), failing the Fornell-Larcker criterion. The HTMT ratio between these dimensions (.956) also exceeded recommended thresholds (Henseler et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThis finding aligns with the theoretical view that informational and interpersonal justice may represent a unified interactional justice construct (Bies \u0026amp; Moag, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e). Nevertheless, the four-factor structure was retained based on several considerations: the four-factor model showed significantly better fit than the three-factor model (\u0026Delta;\u0026chi;\u0026sup2; = 37.24, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), items loaded strongly onto their respective factors (IFJ: \u0026lambda;\u0026thinsp;=\u0026thinsp;.76-.94; ITJ: \u0026lambda;\u0026thinsp;=\u0026thinsp;.77-.96), both dimensions demonstrated excellent internal consistency (\u0026alpha;\u0026thinsp;\u0026gt;\u0026thinsp;.90), and measurement invariance was achieved across demographic groups. These findings suggest that despite the high correlation, Turkish athletes can distinguish between how coaches communicate information and how they treat athletes interpersonally.\u003c/p\u003e\n \u003cp\u003eThe high correlation between informational and interpersonal justice (\u003cem\u003er\u003c/em\u003e = .97) and the HTMT value exceeding recommended thresholds (.956) indicate that discriminant validity between these two dimensions was not fully established. This finding is consistent with theoretical debates in the organizational justice literature regarding whether these constructions are empirically distinct (Bies \u0026amp; Moag, \u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e; Colquitt, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). Researchers using this scale should be aware of this limitation and may consider combining these dimensions into a single \u0026ldquo;interactional justice\u0026rdquo; factor depending on their research objectives.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurement Invariance\u003c/h2\u003e\n \u003cp\u003eMeasurement invariance was tested using multi-group confirmatory factor analysis across gender, sport type, and coach gender. Configural, metric, and scalar invariance were examined sequentially, with \u0026Delta;CFI \u0026le; .01 and \u0026Delta;RMSEA \u0026le; .015 as criteria (Chen, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMeasurement invariance results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;RMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Male \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;402; Female \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfigural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScalar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSport Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Team \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;450; Individual \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfigural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScalar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoach Gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Male \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;250; Female \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfigural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScalar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u0026Delta;CFI \u0026le; .01 and \u0026Delta;RMSEA \u0026le; .015 criteria were used (Chen, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheung \u0026amp; Rensvold, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFull scalar invariance was achieved across all three grouping variables. For gender comparisons (male \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;402; female \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;315), \u0026Delta;CFI ranged from .002 to .003 and \u0026Delta;RMSEA was .001. Similar results were obtained for sport type (team \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;450; individual \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;267) and coach gender (male \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;249; female \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;467), with all \u0026Delta;CFI and \u0026Delta;RMSEA values well below critical thresholds. These findings indicate that the four-factor model functions equivalently across different demographic groups, supporting the generalizability of the scale. Achievement of scalar invariance also permits meaningful latent mean comparisons between groups (Vandenberg \u0026amp; Lance, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLatent mean comparisons.\u003c/strong\u003e Following scalar invariance, latent mean comparisons were conducted with the reference group mean fixed at zero (Hancock, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). Results are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eLatent mean comparisons\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup Variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDimension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ref: Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistributive Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcedural Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformational Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.016*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSport Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ref: Individual)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistributive Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcedural Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformational Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoach Gender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(Ref: Female Coach)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistributive Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.045*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcedural Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformational Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterpersonal Justice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\"\u003e\n \u003cp\u003e-2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.008**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standardized latent mean difference; SE\u0026thinsp;=\u0026thinsp;Standard error. Negative values indicate that the comparison group has a lower mean than the reference group. *\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eRegarding gender, male athletes reported significantly lower informational justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e = .016, \u003cem\u003ed\u003c/em\u003e = .18) and interpersonal justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.20, \u003cem\u003ep\u003c/em\u003e = .008, \u003cem\u003ed\u003c/em\u003e = .20) compared to female athletes. For sport type, team sport athletes perceived lower informational justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e = .043, \u003cem\u003ed\u003c/em\u003e = .15) and interpersonal justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e = .035, \u003cem\u003ed\u003c/em\u003e = .16) than individual sport athletes, possibly reflecting the limited capacity for individualized attention in team settings. Regarding coach gender, athletes with male coaches reported lower distributive justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e = .045, \u003cem\u003ed\u003c/em\u003e = .15) and interpersonal justice (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.21, \u003cem\u003ep\u003c/em\u003e = .008, \u003cem\u003ed\u003c/em\u003e = .20) compared to those with female coaches.\u003c/p\u003e\n \u003cp\u003eAcross all comparisons, interpersonal justice showed consistent group differences, while informational justice varied by gender and sport type, and distributive justice by coach gender. Effect sizes were small (\u003cem\u003ed\u003c/em\u003e = .15-.20), indicating that the scale can detect meaningful variations between groups.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCross-validation.\u003c/strong\u003e To address the limitation of conducting EFA and CFA on the same sample, the dataset was randomly split into two subsamples (Worthington \u0026amp; Whittaker, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). EFA on the first subsample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;358) revealed a three-factor structure consistent with full sample findings (KMO = .89; variance explained\u0026thinsp;=\u0026thinsp;73.35%). CFA on the second subsample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;359) confirmed that the four-factor model fit well (CFI = .985, TLI = .979, RMSEA = .053, SRMR = .043) and outperformed the three-factor model (\u0026Delta;CFI = .008). These results support the replicability of the four-factor structure.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study set out to adapt the Athletic Justice Scale (Kim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for Turkish adolescent athletes and evaluate whether it works adequately in this new context. The short answer is that it does, though the process revealed some complexities worth discussing.\u003c/p\u003e \u003cp\u003ePerhaps the most striking finding was the very high correlation between informational and interpersonal justice (\u003cem\u003er\u003c/em\u003e = .97). This was not entirely unexpected. Bies and Moag (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) originally proposed these as a single construct called interactional justice, and it was only later that Colquitt (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) made the case for treating them separately. Our exploratory analysis sided with Bies and Moag, consistently suggesting three factors. Yet when we tested competing models, the four-factor version fit better statistically, and the items behaved as they should, loading onto their intended factors. Therefore, we kept the four-factor structure, but the question of whether Turkish athletes truly distinguish between how coaches communicate and how they treat them remains open.\u003c/p\u003e \u003cp\u003eWhy might these dimensions be so closely linked in our sample? One possibility is cultural. T\u0026uuml;rkiye is characterized by relatively high-power distance and collectivistic values according to cross-cultural research (Hofstede, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kabasakal \u0026amp; Bodur, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In the GLOBE study, T\u0026uuml;rkiye scored high on in-group collectivism and power distance practices, which may influence how athletes perceive and evaluate authority figures such as coaches. In such contexts, people may view authority figures more holistically. When a coach explains a decision respectfully, athletes might not mentally separate the explanation from the respectful manner. The message and the delivery blend together. This is speculation, of course, and would need to be tested by comparing Turkish athletes with those from different cultural backgrounds. However, it offers one plausible explanation for why our correlation was higher than what Kim et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported in Saudi Arabia (\u003cem\u003er\u003c/em\u003e = .77).\u003c/p\u003e \u003cp\u003eThe procedural justice dimension behaved somewhat differently from the others. Its reliability was acceptable but lower, its AVE fell just under the threshold, and it showed near-zero correlations with other dimensions. Looking at the items helps explain this. They ask about external pressures on coaches from administrators, politics, and parents. These are conceptually different from the other dimensions, which focus on what coaches themselves do. Athletes seem to recognize this distinction. Whether external pressure constitutes a form of injustice that belongs on the same scale as direct coach behaviours is a reasonable question for future research to address.\u003c/p\u003e \u003cp\u003eComparing our results to the original scale, the Turkish version performed at least as well and, in some cases, better. Distributive justice reliability jumped from .677 in the Saudi sample to .861 in ours. The factor structure replicated, and the scale showed measurement invariance across gender, sport type, and coach gender. This last point matters because it means the scale measures the same thing regardless of who is responding, which is essential for any tool intended for diverse populations.\u003c/p\u003e \u003cp\u003eThe group comparisons yielded some expected patterns. Male athletes and team sport athletes reported lower informational and interpersonal justice perceptions. For team athletes, this probably reflects simple math. A coach with twenty athletes cannot give each one the same attention as a coach working with five. The gender finding is consistent with broader literature suggesting female athletes often rate their coaches more favourably, though why this occurs is still debated. Athletes with male coaches reported lower distributive and interpersonal justice, which fits with research showing female leaders tend toward more participative styles. None of these effects were large, but the fact that the scale detected them suggests it has adequate sensitivity.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eWhat does this mean for practice? Coaches could use the scale to get honest feedback about how athletes perceive their fairness. Low scores on particular dimensions would point to specific areas for improvement. Sport organizations might incorporate justice assessments into coach development programs. The finding that team sport athletes feel less fairly treated in terms of communication suggests coaches of larger groups need to be intentional about individual attention, even brief check-ins can matter. And while male coaches should not interpret these findings as criticism, they might consider whether their communication style could be perceived as less personal or transparent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, data were collected from a single province (Karaman), limiting generalizability to the broader Turkish athlete population. Second, the sample included only adolescent athletes aged 8\u0026ndash;19 years; adults and elite athletes might respond differently. Third, criterion validity was not examined, so we do not yet know whether scale scores predict outcomes such as satisfaction, commitment, or dropout. Fourth, data was collected with coaches present nearby, which could have influenced responses despite assurances of confidentiality. Fifth, discriminant validity between informational and interpersonal justice was not fully established, with the correlation between these dimensions (\u003cem\u003er\u003c/em\u003e = .97) and HTMT ratio (.956) exceeding recommended thresholds. Although the four-factor model showed superior fit and was retained for theoretical consistency, researchers should interpret these dimensions with caution, and a three-factor model combining them into interactional justice may be a viable alternative. Finally, procedural justice showed lower reliability (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.739) and near-zero correlations with other dimensions, likely reflecting that these items assess external pressures rather than direct coach behaviours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eSeveral directions seem promising. Testing the scale with adult and elite athletes would establish whether it generalizes beyond adolescents. Examining criterion validity by linking justice perceptions to athlete outcomes would strengthen the case for using the scale in applied settings. Cross-cultural comparisons could clarify whether the high correlation between informational and interpersonal justice is specific to Turkish culture or appears elsewhere too. Longitudinal studies could track how justice perceptions change over a season and whether changes predict important outcomes. And given the issues with procedural justice items, future work might consider whether alternative items focusing on coach decision-making processes rather than external pressures would work better.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, the Turkish version of the Athletic Justice Scale is a valid and reliable tool for measuring how adolescent athletes perceive fairness in their coaches\u0026rsquo; behaviours. The scale demonstrated acceptable to excellent psychometric properties, with the four-factor structure holding across gender, sport type, and coach gender groups. However, some limitations should be noted: discriminant validity between informational and interpersonal justice was not fully established, suggesting that a three-factor model may be a viable alternative for some research purposes, and procedural justice operates somewhat differently from other dimensions due to its focus on external pressures rather than direct coach behaviours. Despite these limitations, the scale showed adequate reliability, measurement invariance across demographic groups, and sensitivity to detect meaningful group differences. For researchers interested in coach-athlete relationships in T\u0026uuml;rkiye, this scale provides a foundation for investigating justice dynamics in sport settings. For practitioners and coaches seeking to enhance athlete experiences, it offers a practical tool for obtaining feedback on perceived fairness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of Interest:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors received no specific funds for the present study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: [MK, \u0026Ouml;A, AEK] ; Methodology: [MK, AEK, SA]; Formal analysis and investigation: [AEK, RMG]; Writing - original draft preparation: [HBK, RMG, NYT, ŞYA, SA]; Writing - review and editing: [MK, \u0026Ouml;A, AEK, RMG, NYT, SA, HBK, ŞYA]; Resources: [MK, \u0026Ouml;A,NYT]; Supervision: [SA]\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the authors, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, J. 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Scale development research: A content analysis and recommendations for best practices. \u003cem\u003eThe Counseling Psychologist\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(6), 806\u0026ndash;838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0011000006288127\u003c/span\u003e\u003cspan address=\"10.1177/0011000006288127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Athletic justice, scale adaptation, coach-athlete relationship, confirmatory factor analysis, Turkish athletes","lastPublishedDoi":"10.21203/rs.3.rs-9023785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9023785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAthletes\u0026rsquo; perceptions of justice toward their coaches\u0026rsquo; behaviours play a critical role in athlete satisfaction, team cohesion, and performance outcomes. However, no validated instrument exists to measure athletic justice perceptions among Turkish athletes.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThis study aimed to adapt and validate the Turkish version of the Athletic Justice Scale developed by Kim et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe scale comprises 12 items across four subscales (distributive, procedural, informational, and interpersonal justice) measured on a 7-point Likert scale. Participants included 717 licensed adolescent athletes (402 males, 315 females; aged 8\u0026ndash;19 years) from various sports in T\u0026uuml;rkiye. Following translation and back-translation procedures, content validity was established through expert panel evaluation (S-CVI/Ave\u0026thinsp;=\u0026thinsp;1.00). Confirmatory factor analysis (CFA) was conducted, and measurement invariance was tested across gender, sport type, and coach gender.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe four-factor model demonstrated acceptable fit indices (χ\u0026sup2; = 167.47, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48, CFI = .981, TLI = .973, RMSEA = .059, SRMR = .040). All factor loadings were significant and ranged from .69 to .96. Internal consistency was acceptable to excellent across subscales (Cronbach's α\u0026thinsp;=\u0026thinsp;.739-.909; composite reliability = .742-.911). Full scalar measurement invariance was achieved across all demographic groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Turkish version of the Athletic Justice Scale is a valid and reliable instrument for assessing adolescent athletes\u0026rsquo; perceptions of justice toward their coaches\u0026rsquo; behaviours. This tool enables researchers and practitioners to examine fairness perceptions within Turkish sport contexts.\u003c/p\u003e","manuscriptTitle":"The First Step in Revealing the Perceptions of Justice Among Turkish Adolescent Athletes: Adapting the Justice in Sport Scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 16:58:36","doi":"10.21203/rs.3.rs-9023785/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f06802b5-4df9-4e28-9085-e99242953c1d","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:58:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 16:58:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9023785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9023785","identity":"rs-9023785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}