Archive

Volume 7, Number 4 / August issue 2021
Mehmet Bayansalduz, Abdurrahman Kepoglu, Sabri Can Metin
Child protection scale in sports for coaches
Abstract

This study aims to develop the “Child Protection Scale in Sports (CPSS)”, which determines the knowledge level of trainers about the protection of child athletes. The sample of the study consisted of 225 trainers working in different sports branches in sports clubs in various regions of Turkey. To determine the child protection knowledge level of the trainers, the CPSS consisting of thirty-two (32) items was created. The CPSS was prepared by taking the opinions of the experts on the subject. First of all, when the results of the item analysis of the CPSS were examined, it was decided to remove these items from the CPSS because the 23rd, 25th, 26th, 27th, 28th, 30th, 31st, and 32nd items had a value below 0.30. When the item-total correlation values related to the CPSS were examined, it was decided to remove items 30 and 31 from the CPSS, since items 30 and 31 were not statistically significant. According to this result, after item extraction, the item-total correlation values of 24 items in the CPSS were found to be between 0.871 and 0.286. When the item scores were examined, it was determined that there was consistency between the items and that all items were statistically significant at the 99% confidence level (p<0.01). To ensure the structural validity of the CPSS and to obtain a functional dimensioning by determining the factor loads of the items in the CPSS (Buyukozturk, 2011), exploratory factor analysis was performed with principal component analysis and varimax axis rotation method (Gurbuz & Sahin, 2018). Before factor analysis, the suitability of the data for factor analysis was analyzed with Kaiser Mayer Olkin (KMO) and Bartlett sphericity tests. The KMO value for the CPSS, which consists of 24 items, was 0.777; and the Bartlett test result was χ2= 2045, 086; P=0.000 (p<0.01). If the KMO is higher than 0.60 and the Barlett test is significant (p<0.01), it indicates that the sample size is suitable for factor analysis. According to the data obtained from the factor analysis, three factors with an eigenvalue above 1.00 explain 70,589% of the variance in CPSS scores. To decide how many factors the CPSS will consist of, the scree plot was examined. After the evaluation of the scree plot, it was decided to have 3 (three) subscales of the CPSS. According to the second-factor analysis performed after the factor number was determined, the 2nd item, which is the overlapping item, in which the difference between the two factors in terms of factor load value was less than 0.10, was removed from the CPSS. After the items that did not meet these values were removed from the analysis, factor analysis was performed for the third time. As a result of the exploratory factor analysis, item factor loads varied between 0.952 and 0.539. The 23-item CPSS was gathered under 3 factors and it was determined that these 3 factors explained 70,589% of the total variance. Cronbach Alpha value was used to calculate the internal consistency reliability of the factors. Since the Cronbach's Alpha value is over 0.70, it can be said that the internal consistency reliability between the items of the CPSS is quite high. To validate the model, real study data were created by collecting data from the participants again. As a result of the confirmatory factor analysis performed on 225 samples, it was not necessary to remove any item from the scale since there was no item with standardized load values below 0.30. While performing the confirmatory factor analysis of the CPSS, the sub-dimensions of the scale were named as factor1= Physical and Sexual Abuse in Sports, factor2=Taking Precautions for Physical and Sexual Abuse in Sports, factor3= Presence of Child Protection Program in Sports. It was determined that the amount of relationship between factor1 and factor2 sub-dimensions was 6.49, 7.80 between factor2 and factor3, and 13.09 between factor1 and factor3, and these relationships were statistically significant at 99% confidence level (p< 0.01). It can be said that the covariance matrix (Comparative Fit Index - CFI) value estimated by the model, which was found to be 0.93 in the research findings, showed a good fit. It was determined that the 27% lower and upper slice values used in the decision of the discrimination of the items related to CPSS were statistically significant at the 99% confidence level for all items (p<0.01). Again, in the research findings, the normed fit index (NFI) value of our model was obtained as 0.91, and it can be said that this value shows a good fit. In addition, the value of the non-normed fit index (NNFI) was found to be 0.93 in the research findings, which indicates a good fit. As a result of the confirmatory factor analysis, the items confirmed the relevant factors at 99% confidence level (p<0.01; p=0.000) and the fit indices were among the good fit values; and the fit of the model was found to be at an acceptable fit value (X2/df=784, 67/223≤5) Considering these results, it can be said that the model of the CPSS is an acceptable and usable scale.
Keywords: Child Protection Scale in Sports (CPSS), knowledge level, sports, coaches

Cite this article:
Mehmet Bayansalduz, Abdurrahman Kepoglu, Sabri Can Metin. Child protection scale in sports for coaches. Acta Scientiae et Intellectus, 7(4)2021, 61-79.


REFERENCES

  1. Bayansalduz, M. (2014). An Investigation into the statetrait anger expression level of taekwondo students attending high school. Anthropologist, 18(3),921-926.
  2. Bentler P.M. and Bonett D.G. (1980). Significancet ests and goodness of fit in the analysis of covariance structures. Psychol Bulletin, 88(3):588.
  3. Bollen, K. (1989). Structural Equations with Latent Variables. A Wiley– Interscience Publication, USA, P.514.
  4. Brackenridge, C., Tess K., Daniel, R. (2012). Sport, Children’s Rights And Violence Prevention: A Sourcebook On Global Issues And Local Programmes. Brunel University, London.
  5. Brackenridge C., Bringer J.D., Cockburn C., Nutt G., Pitchford A., Russell K., & Pawlaczek Z. (2004). The Football Association’s Child Protection in Football Research Project 2002-2006:Rationale, design and first year results, Managing Leisure, 9;1:30-46. DOI: 10.1080/1360671042000182943
  6. Buyukozturk, S. (2011). Manual of data analysis for social sciences. Pegem Academy Publication, Ankara.
  7. Can A. (2014). Quantitative data analysis in the SPSS scientific research process, Pegem Academy Publication, Ankara.
  8. Colakoglu, O.M., Buyukeksi, C. (2014). Evaluation of Factors Effecting Exploratory Factor Analysis Process. Karaelmas Journal of EducationalSciences, 2:58-64.
  9. Etta (2010). Child Protection Policy And Guidelines. Issue 2.
  10. Fan, X., Tohmpson B,. Wang, L. (1999). Effects of Sample Size, Estimation Methods, and Model Specification on Structural Equation Modeling Fit Indexes. Struct Equ Modeling, 6(1):56-83.
  11. Gurbuz, S., and Sahin, F. (2018). Research Methods in Social Sciences, 5th Ed., Seckin Publication, Ankara.
  12. Hooper, D., Coughlan, J., and Mullen, MR. (2008). Equation modelling: Guide lines for determining model fit. EJBRM, 6(1):53-60.
  13. Hu, L.T., Bentler, P.M. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychol Methods, 3(4):424-453.
  14. Hu, L.T., Bentler, P.M. (1999). Cutoff Criteria for Fit Indexes in covariance structure analysis: conventional. Criteriaversus New Alternatives, Structural Equation Modeling, 6(1):1-55.
  15. Kerr, G., Stirling A. (2008). Child Protection in Sport: Implications of an Athlete-Centered Philosophy. Quest, 60: 307-323.
  16. Kline, R.B. (2005). Principle and practice of structural equation modelling, 2nd Ed., New York: The Guilford Press.
  17. Koc, B. (2007). Osmanli islahhanelerinin islevlerine iliskin bazi gorusler, Gaziantep University. Sosyal Bilimler Enstitusu Dergisi, 6 (2).
  18. Koyuncu, I., Kilic, A.F. (2019) The use exploratory and confirmatory factor analyses: A document analysis. Educ Sci, 2019; 44(198):361-388.
  19. MacCallum, R.C., Browne, M.W., Sugwara, H.M. (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods, 1(2):130-149.
  20. Malkin K., Johnston L., Brackenridge, C. (2000). A critical evaluation of training needs for child protection in Uk Sport. Managing Leisure, 5, 151–160.
  21. Musa, M., Bayansalduz, M., Kepoglu, A. Yanar, S., Sunal, Y. (2021). An investigation of the empathy levels of team athletes during the competition in terms of certain variables. Acta Scientiae et Intellectus. 7(4).
  22. Oral, R., Can, D., Kaplan, S., Ates, N., Cetin, G., Miral, S., Hanci, H., Ersahin, Y., Tepeli, N., Bulgu, A. G., Tiras, B. (2001). Child abuse in Turkey: an experience in overcomingdenialand a description of 50 cases. Child Abuse & Neglect, 25: 279-290.
  23. Salim, M. (2011). Child Protection Policies and Social Services and Child Protection Agency in Turkey from Past to Present. Unpublished Master Thesis. Suleyman Demirel University. Social Sciences Institute. Isparta. S.85.
  24. Steiger, J.H. (1990). Structural model evaluation and modification. Multivariate Behav Res, 25:214-12.
  25. Tabachnick, B.G., Fidell, L.S. (2012). Using multi variate statistics, 6th Ed., Boston: Allyand Bacon.
  26. Unicef (2010). Protecting Children From Violence In Sport. A ReviewWith A Focus On Industrialized Countries, Unicef.
  27. World Health Organization (WHO). (1999). Child Protection in Sport Policy, Sport And Child&Youth Development Thematic Working Group Definition of Child Abuse.
  28. Zeren, C.,Yengil E., Celikel, A., Arik A., Arslan, M. (2012). The frequency of childhood abuse among university students. Journal of Dicle Medicine, 39 (4): 536-41.
  29. Zwick, W.R., Velicer, W.F. (1986). Comparison of five rules for determining the number of component storetain. Psychol Bull, 99(3):432.