PSY 5110 Assignment Summarize and Evaluate a Peer-Reviewed Journal Article
PSY 5110 Assignment Summarize and Evaluate a Peer-Reviewed Journal Article
Week 7 Assignment: Chi-Square Analysis
Data analysis is a key part of any research efforts; hence researchers usually focus on the most appropriate data analysis that can help produce the targeted results and fulfill the study objectives or study aims (Sharma, 2018). The implication is that individuals need to have adequate knowledge of aspects such as the hypothesis, methods, and variables used in the analysis to be in a position to correctly interpret the results and answer the questions such as the reasons behind research choosing a particular research analysis method and statistical tests (Heavey, 2022). Therefore, the purpose of this assignment is to read an assigned article and write a summary of the article by exploring aspects such as background information on the topic, hypothesis, methods, and results. In addition, the paper will seek to address various prompts, such as a description of the variables used, why chi-square was used in data analysis, and if the authors used diverse groups of participants.
Summary of the Research
The article under consideration was authored by Buzi et al.(2014) with the title “Screening for Depression among minority young males attending a family planning clinic.” Therefore, the purpose of this study was to assess depression among the population (young males) attending the family clinic and whether their depression was related to service requests and sociodemographics. The background of this study is that major depressive disorder is among the top chronic conditions, and by the time the article was published, up to 8% of the population between the ages of twelve to seventeen experienced at least a major depressive episode. Males have also been shown to experience depression more often than females. Nonetheless, they seek help less frequently (Buzi et al.,2014). The implication is that such only reduces their chances of accurate diagnosis and treatment hence a need to screen them appropriately.
The main hypothesis of this study was that depression among the young males attending the family planning clinic varied by service requests and sociodemographics. This study used a convenience sample of five hundred and thirty-five Hispanic and African American young males, ages thirteen to twenty-five. While 353 were African American, the remaining 182 were Hispanic. The participants were recruited to take part in the study during their visit to the facility on male-designated days. After a clinician explained the purpose of the study, informed consent was then obtained from the participants (Buzi et al.,2014). Depression was measured using the Center for Epidemiologic Studies Depression Scale, and the sociodemographic data collected included employment status, fatherhood status, marital status, owning health insurance, school status, ethnicity, and age.
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The analysis of the results showed important information regarding depression among young males. Upon using the Center for Epidemiologic Studies Depression Scale (CES-D) to assess depression in this group of patients, the researchers noted that up to 22.2% or 119 patients met the depression criteria. In addition, the depressed patients were more likely to be Hispanic as compared to the non-depressed patients (Buzi et al.,2014). This group was also more likely to request services connected to well-being, physical issues, financial resources, feelings, and relationships. The researchers concluded that the young males impacted by depression were found to have unmet needs and that they were able to show such needs given the opportunity. The researchers also indicated that the family clinics are bests positioned to screen for depression in this group of patients because there has been an increasing trend of increased male patients attending the family planning clinics.
The Variables Used In the Analysis
The researchers used several variables to help them fulfill their objectives. Therefore, this section describes the variable and their levels of measurement. They include ethnicity, employment, requests for service, and depression. Ethnicity is a nominal level and describes whether a participant is African American or Hispanic (Sheshkin, 2020). Employment status is also a nominal level measurement and describes whether a participant was employed or not. Request for service was the other variable, and it described the list of services to assist with various aspects such as employment, exercising, eating well, anger management, relationships, and health screenings. The level of this measurement is nominal. Depression is the level of depression experienced by participants, and the level is ordinal.
The Reason Why the Researchers Used Chi-Square
Chi-Square tests are usually used in determining the difference between expected and observed data and can be applied in determining if it correlates to the categorical variables. The implication is that by using the test, a researcher is in a position to know if the difference between the categorical variables is due to a relationship or occurring by chance. Therefore, the researchers used the Chi-Square tests since they were dealing with categorical variables (nominal and ordinal) (Schober & Vetter, 2019). Indeed each of the described variables is either ordinal or nominal. The researchers also wanted to test the hypothesis that depression among the young males attending the family planning clinic varied by service requests and sociodemographics. Therefore, the best and most proven statistical test in this regard is Chi-Square. The test has also been used to help estimate the size of inconsistency between the actual and expected results. The Chi-Square also uses degrees of freedom in determining if a specific null hypothesis can be rejected depending on the total number of observations made in a particular research (Gaboardi & Rogers, 2018). As such, Chi-Square was the best and most appropriate statistical test that could be used by these researchers.
Whether The Authors Used Diverse Groups
The aim of this study was to assess depression among the population (young males) attending the family clinic and whether their depression was related to service requests and sociodemographics. The researchers majorly targeted Hispanics and African American. Therefore, they used diverse groups. As part of the study, they also examined if race was connected to employment status since these factors have been shown to be directly connected to depression. The researchers did not also focus on a particular age of patients; instead, they focused on young males aged twelve to twenty-five, which also shows a focus or use of diverse groups (Buzi et al.,2014). Indeed, they found that race or ethnicity as a factor was related to employment and depressive symptoms. For example, the researchers found that in terms of depression, African American males were less depressed as compared the Hispanic young males. The results observed for employment were the direct opposite since the researchers noted that African American young males were less likely to be employed as compared to Hispanic young males.
Chi-Square can appropriately be used to help in examining the relationship between categorical variables, which include nominal and ordinal levels of measurement. Therefore, when researchers have variables with these levels of measurement, the Chi-Square statistical test is used. Therefore, the researchers of the analyzed article decided to use Chi-Square since they had such variables. Upon using the Center for Epidemiologic Studies Depression Scale (CES-D) to assess depression in this group of patients, the researchers noted that up to 22.2% or 119 patients met the depression criteria.
Buzi, R. S., Smith, P. B., & Weinman, M. L. (2014). Screening for depression among minority young males attending a family planning clinic. Psychology of Men & Masculinity, 15(1), 116.
Gaboardi, M., & Rogers, R. (2018, July). Local private hypothesis testing: Chi-square tests. In International Conference on Machine Learning (pp. 1626–1635). PMLR. https://proceedings.mlr.press/v80/gaboardi18a.html
Heavey, E. (2022). Statistics for nursing: A practical approach. Jones & Bartlett Learning.
Schober, P., & Vetter, T. R. (2019). Chi-square tests in medical research. Anesthesia & Analgesia, 129(5), 1193.
Sharma, S. (2018). Nursing research and statistics. Elsevier Health Sciences.
Sheskin, D. J. (2020). Handbook of parametric and nonparametric statistical procedures. CRC Press.