Quantitative research relies on statistical data to demonstrate the relationship between different variables. Most quantitative research articles have an introduction, literature review, statement of purpose, methodology, findings, and discussions. The authors should clearly state these sections to enable the reader to understand the article’s main ideas. “Risk behaviors of high school students who report knowing someone who self-harms” is an article that observes the vital requirements of quantitative research; thus, its findings are applicable in clinical practice.
“ Risk behaviors of high school students who report knowing someone who self-harms” uses a quantitative research design. It provides a well-stated purpose and research question at the end of the literature review. The study aimed to determine the link between high school students’ health risk and internet behaviors, and self-harm (Dowdell & Noel, 2020). Quantitative research design has a research question and statement of purpose between the literature review and the methodology (Apuke, 2017). The purpose statement is relevant to counseling practice. It investigates how internet use and risky behaviors such as substance abuse contribute to self-harm among adolescents and guide healthcare practitioners to offer assistance to this population.
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This research’s credibility is evident in the use of current scholarly sources. The authors use academic sources published between 2011 and 2018 (Dowdell & Noel, 2020). Besides, the research incorporated primary sources that provided new insights on the factors associated with self-harm among teenagers. Expert opinions from journals described the prevalence of self-harm in young people from the US and the UK (Dowdell & Noel, 2020). These sources’ findings provided a solid basis for the study; they contained facts, statistics, evidence, and teenagers’ opinions about self-harm.
However, the research lacks a conceptual or theoretical framework. This section is essential in a quantitative research design. It enhances the internal validity of the data (Kivunja, 2018). The theoretical framework would have enabled the authors to substantiate their argument. Additionally, it fosters the transferability of the data analysis’s external validity and generalizability (Kivunja, 2018). Without a theoretical framework, it may be challenging to apply the findings to different contexts. Besides, the theoretical framework enhances the objectivity and reliability of the results. Despite the benefits of a theoretical framework, the authors did not mention or define critical concepts.
Nonetheless, the authors included an explicitly stated research question. They inquired whether there were behavioral risk differences between adolescents who knew someone who had self-harm problems compared to those who did not (Dowdell & Noel, 2020). High school students are the independent variable, while health risk and internet behaviors are the dependent variables. Since it is a descriptive correlational study, the authors expected a strong link between the independent and dependent variables (Apuke, 2017). However, these variables are not aligned with the theoretical framework.
This research followed appropriate procedures to safeguard the study participants’ rights. The researchers investigated the dataset of previous studies subject to the Centers for Disease Control and Prevention’s guidelines (Dowdell & Noel, 2020). They ensured that the last collection data process did not harm the adolescents’ health. Additionally, the Institutional Review Board (IRB) approved the dataset (Dowdell & Noel, 2020). The researchers did not ask students whether they self-harmed due to ethical concerns. This study was safe and observed the ethical and health guidelines.
Furthermore, the research methods fit the research question and purpose. In addition to analyzing previous datasets, the researchers analyzed the high students’ ethnicity and experiences to determine the relationship between self-harm and health risk behaviors (Dowdell & Noel, 2020). Secondary data analysis enabled the research to identify gaps in the previous studies. Explanatory research describes the covariance between the independent and dependent variables (Apuke, 2017). Besides, the number of data collection points was appropriate to the study. The authors minimized biases and threats to internal construct and external validity using natural contexts, such as the school and running frequencies on all the selected variables (Dowdell & Noel, 2020). For this reason, the study’s findings were reliable and informative.
Most importantly, the authors identified and described the population. They focused on students in grades 9 to 12 who provided a yes or no answer to whether they knew a person who tried to harm themselves (Dowdell & Noel, 2020). Although the authors did not mention the students’ school, they highlighted their socio-demographic features, such as race and ethnicity. The authors used the simple random sampling method, which was best suited for the research since it gave students from different backgrounds equal chances to participate. Additionally, the sample size comprising of 54111 high school students was adequate to provide accurate findings (Dowdell & Noel, 2020). However, the authors did not mention whether a power analysis tool was used.
The research relied on precise measurements and structured and validated data-collection items. Although the authors did not provide a clearly-stated conceptual definition, they highlighted the role adolescents play in promoting self-harm behaviors (Dowdell & Noel, 2020). They also defined the concept of self-harm and described its prevalence among adolescence. The operational definition explained the method used to collect data, namely, secondary analysis of the dataset. Besides, the instruments used were valid and reliable. For example, the paper-and-pencil instrument obtained from the Youth Risk Behavior (YRBS) underwent multiple test assessments to enhance its validity (Dowdell & Noel, 2020). Another approach was the use of a telephone survey with close-ended questions. Besides, the authors visited schools to inquire students about self-harm behaviors; they did not ask personal questions to improve the study’s validity and reliability. Since the staff was well-trained, the data provided was not biased.
Moreover, the authors used appropriate data analysis tools. They used Pearson correlations to examine the relationship between independent and dependent variables (Dowdell & Noel, 2020). Pearson’s correlation coefficient measures the statistical association between variables and provides information about the relationship’s magnitude. After establishing the relationship between variables, the authors design a cross-table to determine the significance. They considered a significance value of 0.05 to be minimum (Dowdell & Noel, 2020). Any value higher than 0.05 meant that the authors would accept the alternative hypothesis. Other statistical tools were the chi-square which investigated all the dichotomous variables, and Analysis of Variance (ANOVA) for determining group differences (Dowdell & Noel, 2020). If any of the study groups had differences, the ANOVA would have reported a statistically significant result. Although the authors used vital statistical tools, they did not mention whether the Type I and Type II errors were addressed.
In the results section, the authors provided tables and figures that summarized the correlation between health risk and internet behaviors and self-harm in teenagers. Since the statistical significance value was less than 0.005, the authors ascertained that students with substance abuse disorders and exposure to risky behaviors like bullying and pornographic content were likely to know someone who elicits self-harm behaviors (Dowdell & Noel, 2020). These findings supported the hypothesis since they demonstrated a positive relationship between the independent and dependent variables. The authors noted that students who had not been exposed to risky behaviors were unlikely to know an individual with self-harm problems. Besides, the research has sufficient information that supports the meta-analysis. The authors investigated each risk behavior separately to understand the students’ perspectives. They also supported their findings with accurate and detailed statistical analysis. The clinical practice recommendations were sufficient and reasonable since the authors explained how psychiatric nurses, pediatric nurses, and nurse practitioners could understand adolescents’ experiences by asking questions about their health history and assessing their physical health (Dowdell & Noel, 2020). This research describes how these health practitioners can work with counselors and educators to improve teenagers’ health outcomes. One of the qualities of a compelling study is the mentioning of limitations. The authors highlighted the study’s limitations: emphasis on white students, failure to ask personal questions, and overreliance on self-reported data (Dowdell & Noel, 2020). These limitations will guide further studies that intend to address self-harm among adolescents.
Overall, this article follows the quantitative research design guidelines. It uses adequate statistical tools to establish the relationship between different variables. The purpose statement and research questions are highlighted to guide a reader. The only problem is that the authors relied on self-reported data. However, this problem can be addressed in future studies.
References
Apuke, O. D. (2017). Quantitative research methods: A synopsis approach. Kuwait Chapter of Arabian Journal of Business and Management Review , 33 (5471), 1-8.1 https://doi.org/0.12816/0040336
Dowdell, E.B., & Noel, J. (2020). Risk behaviors of high school students who report knowing someone who self-harms. Issues in Mental Health Nursing , 41 (5), 415-420. https://doi.org/10.1080/01612840.2019.1663568
Kivunja, C. (2018). Distinguishing between theory, theoretical framework, and conceptual framework: A systematic review of lessons from the field. International Journal of Higher Education , 7 (6), 44-53.https://doi.org/10.5430/ijhe.v7n6p44
