Predicting Eating Attitude Test Scores With Children's Depression Inventory - Research Paper

Introduction

To understand why Altun (2018) utilized correlation and bivariate regression analysis in the study it is essential to examine the independent and dependent variables that were used to address the phenomenon of interest (problem statement) of the study and their levels of measurement.

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In one of the linear regression analyses conducted in this study, Altun (2018) sought to determine if the Children's Depression Inventory (CDI) score predicts the Eating Attitude Test (EAT) scores. The independent variable was the CDI score while the dependent variable was EAT score. In this case, Altun (2018) used linear regression because both of these variables were measured at the continuous level of measurement. Participants' CDI scores were assessed using Children's Depression Inventory (CDI) consisting of 27 items measured on a 3-point scale scored from 0 to 2 (Babore, Trumello, Candelori, Paciello, & Cerniglia, 2016; Bang, Park, & Kim, 2015). Consequently, participants score on the CDI scale ranges from 0 to 54 (Kim et al., 2014; Olorunju, Akpa, & Afolabi, 2018; Vries et al., 2019). Because of this, the CDI score is measured at the continuous level of measurement, making the CDI score an appropriate independent variable for linear regression analysis.

The EAT score was also measured at a continuous level of measurement. This is because, the Eating Attitude Test (EAT) comprised of 40 items assessed on a 6-point Likert scale (Demirci, Demirci, Akpinar, Demirdas, & Atay, 2015; Koleoso, Akanni, & James, 2018; Kumcagiz, 2017; Marco & Tormo-Irun, 2018). Consequently, the EAT scale generates numerical scores measured at the interval level of measurement, thus suitable for linear regression analysis.

I also firmly believe that Alton's (2018) use of correlation analysis was informed by the fact that the independent and dependent variables were measured at the continuous level of measurement. For example, in one of the correlation analyses, Alton (2018) sought to examine whether EAT scores were correlated with depression scores as assessed using CDI. Because both the EAT scores and the depression scores were measured at the continuous level of measurement, Alton's (2018) use of Pearson correlation analysis to examine the relationship between the two variables was justified.

Reasons why Linear Regression Analysis and Regression Analysis were Appropriate

I think that Alton's (2018) use of both linear regression analysis and correlation analysis were appropriate. One of the reasons why both of these data analysis techniques were appropriate is because both the dependent and independent variables that were examined (EAT scores and CDI scores) were measured at the continuous level of measurement. Another reason why I support Alton's (2018) use of correlation analysis in this study is that it enabled the researcher to establish a linear relationship between the variables. It can also show the direction of the relationship between the variables as well as the strength of the relationship. Lastly, I believe that the researcher's use of linear regression analysis was justified because it can help to determine if EAT scores predict CDI scores.

Display of Data

Alton (2018) displayed data in tables. The display of data in the tables was done in a way that allowed a novice reader to understand the findings of the study. For instance, Table 3 (Alton, 2018, p. 4) shows the correlation between variables of interest to the study. The table is self-explanatory because it has a clear and succinct heading.

Determining if the Results of the Study Can Stand Alone

The display of data in the tables was done in a way that allowed a novice reader to understand the findings of the study. For instance, Table 3 (Alton, 2018, p. 4) shows the correlation between variables of interest to the study. The table is self-explanatory because it has a concise title and heading that summarizes what is contained in the table. Specifically, in Table 3 (correlational analysis table), the table's title summarizes everything presented in the table. In this case, the clinical variables (independent variables) included Trait Anxiety Inventory (STAI-T), State Anxiety Inventory (STAI-S), Obsessive- Compulsive Disorder (OCD), Maudsley Obsessive-Compulsive Inventory (MOCI), CDI, and BMI. These independent variables and their abbreviations were displayed in the table and at the bottom of the table. Additionally, in the table's title, it is correctly indicated that the dependent variable of the study is to EAT scores.

Lastly, the correlation coefficients given in Table 3 makes the reader to easily understand the relationship between the independent and the dependent variables as well as the strength of the relationship between each of the two variables. It is visible that the strongest positive correlation between clinical variables and EAT scores in the experimental group was found between CDI and EAT (r = 0.511). On the other hand, the weakest positive correlation in the control group was found between STAI-T and EAT. Conversely, the strongest negative correlation can be seen between BMI and EAT (in control, group) while the weakest negative relationship between two variables in the control group can be seen between CDI and CDI.

References

Altun, H. (2018). Association of eating attitude with anxiety and depression levels in children and adolescents with obsessive-compulsive disorder. Psychiatry and Clinical Psychopharmacology, 0(0), 1-7. https://doi.org/10.1080/24750573.2018.1449182

Babore, A., Trumello, C., Candelori, C., Paciello, M., & Cerniglia, L. (2016). Depressive symptoms, self-esteem and perceived parent-child relationship in early adolescence. Frontiers in Psychology, 29(1), 1-7. https://doi.org/10.3389/fpsyg.2016.00982

Bang, Y. R., Park, J. H., & Kim, S. H. (2015). Cut-off scores of the children's depression inventory for screening and rating severity in Korean adolescents. Psychiatry Investigation, 12(1), 23. https://doi.org/10.4306/pi.2015.12.1.23

Demirci, K., Demirci, S., Akpinar, A., Demirdas, A., & Atay, I. M. (2015). Evaluation of eating attitude in patients with migraine. Noro Psikiyatri Arsivi, 52(4), 367. https://doi.org/10.5152/npa.2015.9997

Kim, M. H., Mazenga, A. C., Devandra, A., Ahmed, S., Kazembe, P. N., Yu, X., ... Sharp, C. (2014). Prevalence of depression and validation of the beck depression inventory-ii and the children's depression inventory-short amongst HIV-positive adolescents in Malawi. Journal of the International AIDS Society, 17(1), 18965. https://doi.org/10.7448/IAS.17.1.18965

Koleoso, O. N., Akanni, O. O., & James, J. O. (2018). Body image objectification and disordered eating attitudes among secondary school students of South-West Nigeria. International Journal of School Health, 5(2). https://doi.org/10.5812/intjsh.66891

Kumcagiz, H. (2017). The relationship between quality of life and eating attitudes in turkish high school students. World Journal of Education, 7(6), 57. https://doi.org/10.5430/wje.v7n6p57

Marco, J. H., & Tormo-Irun, M. P. (2018). Cyber victimization is associated with eating disorder psychopathology in adolescents. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00987

Olorunju, S. B., Akpa, O. M., & Afolabi, R. F. (2018). Modelling the factor structure of the Child Depression Inventory in a population of apparently healthy adolescents in Nigeria. PLOS ONE, 13(3), e0193699. https://doi.org/10.1371/journal.pone.0193699

Vries, J. E. de, Dekker, C., Bastiaenen, C. H. G., Goossens, M. E. J. B., Engelbert, R. H. H., & Verbunt, J. A. M. C. F. (2019). The Dutch version of the self-report Child Activity and Limitations Interview in adolescents with chronic pain. Disability and Rehabilitation, 41(7), 833-839. https://doi.org/10.1080/09638288.2017.1407969