Discuss about the Interpreting Statistics in Research Articles.
Research articles involve statistical methods in studies to describe the fundamental features of data. The data findings are analyzed, interpreted, and reported to give meaning to the numbers (Ali & Bhaskar, 2016). Data can either be qualitative or quantitative. This paper tries to identify and interpret statistical data in two health-related research articles. Article one is “Alcohol and other drug use at school leavers’ celebrations” (Lam, Liang, Chikritzhs, & Allsop, 2013); and, article two is “The rising tide of diabetes mellitus in a Chinese population: A population-based household survey on 121,895 persons” (Wong, Leung, Tsang, Lo, & Griffiths, 2013). It describes the hypothesis, variables, statistical methods, and study limitations management.
Lam, Liang, Chikritzhs, and Allsop (2013) conducted a research to determine the rate of “alcohol consumption and other drug use at school leavers’ celebrations” after identifying a gap in research. The research indicates that many adolescents indulge in alcohol and other drugs (AOD) when they attend celebratory events. The study examined drug use patterns, influences, and impacts during the final year of study celebration. As such, the study had two aims which were to: “(i) compare the levels of AOD use at end of school celebration and use at other peer-based social events and (ii) relate the experience of harms experienced at the celebrations to levels of use and engagement in harm-minimization strategies” (Lam et al., 2013).
The level of AOD use and the experience of harm is greater in the second survey than in the first survey.
The article identifies six variables to each hypothesis. The null hypothesis identified three independent variables as follows: gender, place of accommodation, and the mode of survey administration (face to face or online). The dependent variable for the null hypothesis is then intention to attend the event and use AOD. To test the alternative hypothesis, the study identified three independent variables as follows: the average amount of alcohol used consumed by a single participant in just a day during the event; indulgence in other drugs apart from alcohol; and utilization of harm-minimization safety strategies. The dependent variable in this test is the likelihood of negative consequences such as blackout (loss of memory), unprotected casual intercourse, and vomiting, among others.
The sampling method refers to the procedure of selecting a sample population from the general population. The research study employed the stratified random sampling technique to select its study participants. The main advantage of stratified random sampling combines random and stratified sampling to provide the purest form of probability sampling with minimal sampling errors. By stratifying the population the research ensures that data collected is an accurate representation of the population under study, in this case, “young people who intended to, and/or had attended the 2009 school leaver’s celebration on Rottnest Island” (Lam et al. 2013, p, 409). Moreover, stratified random sampling ensures accurate representation of each stratum within the sample; for instance, 50% male and 50% female. Underrepresentation or overrepresentation of one stratum result in representation errors and can affect the accuracy of the research.
Conversely, stratified random sampling is not applicable to every study. Thus, the biggest disadvantage of the method is that the researcher must first identify population members and classify each into a stratum or subpopulation. The first challenge is finding an exhaustive list of the population. However, the procedure might be straightforward in some cases. The other challenge is accurate sorting of each subject into one stratum. Although in the article under review it might have been easy as male, female, graduate and undergraduate are clearly defined strata. In different situations, the sorting process may be more difficult hence stratified random sampling becomes ineffective.
The sample population of Lam et al. (2013) is characterized by teenagers and young adults who wanted to attend, and/or attended the event. According to the descriptive statistics in the article, subjects in the sample were classified as either male or female. Their age was also recorded and their accommodation location. In the first survey, the population size was “541: 56% female; 91% 17 years old and 9% 18 years of age; 87% enrolled in an independent school” (p, 409). The second survey was characterized by 405 participants: “50% female; 94% 17 years of age and 6a5 18 years and over; 92% attended an independent school” (p, 409).
The article applied two inferential statistics to analyze its data: the Wilcoxon signed rank test and the logistics regression analysis. The Wilcoxon ranked test was used because the two samples on AOD use in the last event and on the leavers’ celebration were related and it was important to compare their mean ranks. Logistics regression analysis was used because the study wanted to predict the probability that AOD use at the leavers’ celebration would have negative consequences management.
With all other factors controlled and comparing the population that utilized protective measures with the highest frequencies (the reference group), the odds ratio indicates that: those who employed the safety measures more often was 1.14 implying that their likelihood to report unprotected sex was only 14%; that is, only 21% reported a hangover; 13% likelihood of reporting vomiting; and, only 22% chances of reporting a blackout (Lam et al., 2013).
How representative the sample is of the national population of schoolies has not been examined in totality because of the limitation for inferences of the findings. The Rottnest Island school leavers’ celebration is efficient for a national population. Though this assumption underlies all inferences in the article, it is not fully examined.
Wong, Leung, Tsang, Lo, and Griffiths (2013), “studied the prevalence of diabetes mellitus in China from 2001 to 2008.” The study evaluated the self-reporting trend in diabetes mellitus with reference to sex, age, and income. The factors influencing the prevalence of diabetes were analyzed using binary logistic statistics. The findings associated self-reporting to low income and advanced age. Wong and his colleagues thence concluded that there is a strong correlation between the growing trend in diabetes among the Chinese and population demographics. Though a large sample size was used and reliable sampling technique employed to facilitate the findings, there were several limitations including the number of independent variables used. As such, the article recommends that future studies should include more of the external variables that can potentially influence the prevalence of diabetes in the population. These factors include the body mass index, genes and lifestyle behaviors.
Wong et al. (2013) identified a gap in the recent literature on the scarcity of cross-sectional large-scale research using representative sampling to inform policy development in public health. As such, the aims of the study were “to evaluate the prevalence of self-reported diabetes by territory-wide household surveys representative of the whole Hong Kong population” and to examine “the factors independently associated with diabetes” p, 270. The study was therefore guided by the following hypotheses:
The null hypothesis seeks to answer the question on the current prevalence of diabetes mellitus in Hong Kong in comparison to past prevalence and future estimates. According to Wong and his colleagues, previous data indicated that the prevalence of diabetes in Hong Kong was approximately 12.8%. Moreover, future projections indicated that the prevalence in 2025 would still be approximately 12.8%. Therefore, it was important for the study to determine if there is any change in the prevalence of diabetes between the past and the present survey data.
The alternative hypothesis on the hand seeks to determine the factors that influence the prevalence of diabetes in the dynamic population of Hong Kong. Hong Kong has both the rural and urban areas and “is one of the most rapidly developing economies in Asia” (p, 270). It was, therefore, important to understand the effects of the growing trend of the Chinese Population demographics on the incidence and prevalence of diabetes in China.
The study targeted the Hong Kong population. The study selected a sample population of people who are land-based and resided in non-institutional settings. The sample population excluded people residing in hotels transiently, transience aboard vessels and non-citizen house helpers who live in local households. The information collected on Hong Kong’s health status selected field works between “Jan 2001 – May 2001; May 2002 – July 2002; Nov 2005 – May 2006 and Feb 2008 – May 2008, respectively” (p, 270).
The article reports that binary logistic regression used to determine the outcome of self-reported diabetes. Binary logistics is applied where the dependent variable is dichotomous in natures and a linear regression cannot be used (Binary logistics regression, 2018).Binary logistics regression was used in the article because the relationship being predicted was between independent variables (age, income, and sex) and a binary dependent variable (rate of self-reported diabetes mellitus).
The demographic characteristics of the sample population had changed implying a change in the structure across the years of study. As a result, the study adjusted the prevalence rates of diabetes mellitus according to age and sex. The adjustment of age revealed that the rate of diabetes mellitus reporting increased with age among male adults. The rate in 2001, 2002, 2005, and 2006, was 2.80, 2.87, 3.32 and 4.66%, respectively. The prevalence rates of among female adults after the adjustment were “3.25, 3.37, 3.77, and 4.31 %” and all p
The odds ratio indicates that self–reported incidence of diabetes was nearly 50 % between 2001 and 2008. The percentage increase was greater in the female participants at 69.3% than the male participants reported to be 47.9%. Besides, there were no relative disparities in the observed prevalence between women and men regardless of their age and household income; however, men indicated a more significant relationship between age advancement and diabetes prevalence. Finally, households with lower income showed a higher likelihood of reporting diabetes than households with high-income status.
The self-reporting methodology used in the research limits the reliability of the data obtained since the participants may fail to indicate the correct information concerning their health. Still, studies are yet to justify the validity of self-reporting as a more reliable tool in comparison to a standardized methodology. Furthermore, there were external variables that could have influenced the outcome of diabetes in the population under study, such as body mass index, lifestyle behaviors, and genes. These factors have the potential to influence the outcome of the research. As a result, further studies should consider working with more factors that influence the prevalence of diabetes to determine more variability in the large population of China.
References
Binary Logistics Regression. (2018). Retrieved April 22 , 2018, from Statistics solution: https://www.statisticssolutions.com/binary-logistic-regression/
Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60(9), 662-669.
Lam, T., Liang, W., Chikritzhs, T., & Allsop, S. (2013, August 27). Alcohol and other drug use at school leaver’s celebrations. Jurnal of Public Health, 36(3), 408-416.
Skuza, P. (2013). Intermediate IBM Statistics: Understanding your data (dscriptive statistics, graphs and custom tables). Flinders University.
Wong, M. C., Leung, M. C., Tsang, C. S., Lo, S. V., & Griffiths, S. M. (2013). The rising tide of diabetes mellitus in a Chinese population: a population-based household survey on 121, 985 persons. International journal of public health, 58(2), 269-276.
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