Introduction
While research is frequently employed to determine relationships between phenomena, it can also be used for descriptive purposes. Descriptive designs are defined by their aim of describing rather than inferring, and while they are often qualitative, quantitative studies can also qualify (Houser, 2016; Polit & Beck, 2017). Quantitative descriptive research can offer an opportunity for discussing and evaluating descriptive methodologies and statistics.
The present paper will summarize the key design points of descriptive studies that were reported in three recent articles from peer-reviewed journals. Specifically, the works by Grant et al. (2016), Reynolds, Pietrzak, El-Gabalawy, Mackenzie, and Sareen (2015), and Stein et al. (2017) will be considered. The paper will focus on the descriptive nature of the studies, as well as the descriptive statistics used in each of them, and will make conclusions about their strengths, weaknesses, and similarities. The analysis demonstrates that the three articles were comparable in their methods due to their common goal of describing phenomena.
Article 1
The study by Grant et al. (2016) focuses on the topic of drug use disorder in the United States (US), and it attempts to describe this phenomenon by investigating its prevalence. The National Epidemiologic Survey on Alcohol and Related Conditions–III became the source of the study’s data. It took place in the US between April 2012 and June 2013, and the analysis of its data was finalized in 2015. With the total response rate of about 60%, the final sample included 36,309 adults, and the data collection method consisted of in-person interviews.
The authors of the article checked for drug use disorder in the respondents, including lifetime and twelve-month incidence. Grant et al. (2016) employed percentages to report their findings; according to the article, the lifetime prevalence of drug use disorder in the US amounts to 9.9% with 3.9% twelve-month incidence. Similarly, descriptive statistics (means and percentages) were used to provide some information about the participants’ demographics, which were also put into tables.
The latter form of data representation was employed to also demonstrate the prevalence of the disorder in people of different sex, ethnicity, age, and levels of education and income. Also, the authors traced the presence of other health conditions among the people exhibiting drug use disorder. Finally, the information about the prevalence of treatment for the disorder was also presented in the same way.
It should be noted that the article did not use descriptive statistics only; inferential tests were also employed to determine the associations between the studied disorder and various demographic factors or other health conditions. While not descriptive, this approach to data analysis did not change the general aim of the article, which consisted of describing drug use disorder in the US. Thus, the authors also found that the studied disorder had high comorbidity in the US and was more likely to be encountered in younger people from low-income households and with lower-level education. Also, belonging to particular ethnicities (white and Native American) was associated with the condition. This information is a significant contribution to the description of the phenomenon.
The large sample of the study, which the authors described as nationally representative, as well as its rigorous methodology and detailed questionnaire, can be considered the strengths of the design. However, the authors also reported significant limitations. Specifically, the survey did not include certain groups of the US population (especially incarcerated individuals) and could fail to represent other ones, even those that could be particularly susceptible to the studied condition (for example, people from the military).
In addition, as a self-report study that used individual interviews, the survey may have underestimated the disorder’s prevalence due to people with the condition choosing to avoid participating or reporting correct information. Still, the article successfully reviews the available data that characterizes the phenomenon with the help of descriptive and inferential statistics.
Article 2
The article by Reynolds et al. (2015) is dedicated to the study of psychiatric disorders; specifically, it was meant to assess their prevalence in the US among the older population. The authors employed the National Epidemiologic Survey on Alcohol and Related Conditions–II, which engaged 12,312 older people in face-to-face interviews. The survey took place between 2004 and 2005, which limited the number of disorders included in it. However, the sample was deemed nationally representative, even with respect to different populations, especially those of particular ethnicities, different levels of income, and marital status.
The article employed descriptive statistics and tables, as well as one graph, to report the numbers and percentages that could summarize the findings. The results suggested that among older Americans, 4.5% experienced a mood disorder, 7.9% had an anxiety disorder, and 16.8% had a personality disorder. Also, 6.6% reported having a substance use disorder. The authors specifically focused on past-year incidents. In addition, Reynolds et al. (2015) used inferential tests (chi-square) to determine the factors that were associated with the disorders. They found that among older people, women were more likely to have a mood or anxiety condition, and men were more prone to personality or substance abuse disorders. More specific disorders and their prevalence, as well as the association of the disorders with marital status, income, education, and ethnicity, were also discussed.
Aside from the above-described survey limitations, this study employed its relatively old version, which did not use the latest diagnostic manual. However, the findings did present a significant contribution due to its ability to describe in notable detail the phenomenon of common psychiatric disorders among older people with separate categories for people of different age (from young older people to old older people). The chosen descriptive statistics were efficient in summarizing the data, and so were the methods of presentation, including graphs and tables.
Article 3
Stein et al. (2017) studied the findings of the World Mental Health Survey Initiative with the aim of describing the phenomenon of social anxiety disorder. Due to the specifics of the survey, the article was able to focus on the cross-national epidemiology. In other words, it attempted to describe the prevalence and risk factors for the disorder in all the countries that participated in the initiative.
The latter involved 142,405 people from the high-, middle-, and low-income counties of the African, both American, Eastern Mediterranean, Western European, and Western Pacific regions. Each country managed its own survey, which could result in very slight differences in the methodology and certain disparities in response rates (from 45% to 97%) that Stein et al. (2017) describe and take into account. The data collection method consisted of face-to-face interviews.
The data about social anxiety disorder, related impairment, and treatment were used by the authors and analyzed through cross-tabulation and chi-square tests. As a result, both descriptive and inferential statistics were employed, and the former reported the data while the latter helped to discover the association between the disorder and various risk factors. The authors used tables with percentages (as well as inferential tests’ results) for most of the data, but the age of onset was displayed with a graph. The findings demonstrated that the lifetime prevalence of the disorder amounts to 4% across the world, and it falls to 2.4% and 1.3% for twelve-month and 30-day periods. Also, early onset was consistently shown by the survey results, and higher-income countries were more likely to demonstrate a higher prevalence.
Survey limitations, especially self-reporting and representativeness, remain an issue for this article. In addition, not all surveys from all countries were identical; five countries did not involve people who were older than 65, and Australia did not engage the people who were older than 85. The different response rates could also have affected the findings, which is pointed out by the authors. However, the article presents a very comprehensive overview of a particular disorder and describes its global prevalence with the help of a reliable source of information and well-developed and described methodology. The use of descriptive statistics with relevant commentary and illustration (tables and graphs) helped to report the data, and the inferential tests provided the initial information about the factors that might be associated with the disorder.
Conclusion
The three articles had a similar aim of describing the prevalence of a phenomenon, and they employed descriptive statistics to that end. While inferential tests were applied as well, from the perspective of describing their phenomena, the articles mostly used percentages and means. Tables and graphs with commentary were the primary methods of representing the findings. The studies were comparable, especially in having strong, well-developed methodologies and reliable sources of data, which enabled them to describe the phenomena of interest.
References
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Houser, J. (2016). Nursing research: Reading, using and creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.
Polit, D.F., & Beck, C.T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott, Williams & Wilkins.
Reynolds, K., Pietrzak, R., El-Gabalawy, R., Mackenzie, C., & Sareen, J. (2015). Prevalence of psychiatric disorders in U.S. older adults: Findings from a nationally representative survey. World Psychiatry, 14(1), 74-81. Web.
Stein, D. J., Lim, C. C., Roest, A. M., Jonge, P., Aguilar-Gaxiola, S., Al-Hamzawi, A.,… Scott, K. M. (2017). The cross-national epidemiology of social anxiety disorder: Data from the World Mental Health Survey Initiative. BMC Medicine, 15(143), 1-21. Web.