- Null and Alternative Hypothesis
Hypothesis 1: Comparing the levels of AUO utilization at end of school celebrations and utilization of AOD at different peer-based social events.
Null Hypothesis
Ho: The levels of AOD use or consumption at end of school celebrations are not similar or close to the level of AOD consumption or use at other peer-based social events.
Alternative Hypothesis
H1: The levels of AOD consumption or use at the end of school celebrations are similar or close to that of AOD consumption in other peer-based social events.
Dependent variables: alcohol consumption, drug consumption
Independent Variables: social events and leavers celebration
Hypothesis 2: Relating the experience of damage or harms experienced during the celebrations to the levels of engagement and use in harm minimization strategies
Null Hypothesis
Ho: The is no relation between the experience of harms experienced during the celebrations to the level of engagement and use in harm minimizing strategies
Alternative Hypothesis
H1: There is a relation between the experience of harm experienced during the celebrations to the levels of engagement and use in harm minimizing strategies.
Dependent variables: hangover, non-protected sex, vomiting
Independent variables: alcohol use, safety strategy, drug consumption
- Sampling Method
The study used purposive sampling to select the respondents of the study. The authors report that respondents had attended or intended to participate in the school leaver’s party or celebration. This makes it vivid that the researchers intentionally selected respondents base on their ability to elucidate a particular phenomenon, concept, and theme.
Advantages of Purposive Sampling methods
v It is among the most cost and time-effective sampling techniques
v It is considered the sole appropriate method in case there are a limited number of primary data sources who could contribute to a study
v It is effective in the exploration of anthropological occurrences where the discovery of meaning could advantage the intuitive approach (Daniel, 2011).
Disadvantages of Purposive Sampling methods
v It is vulnerable to judgmental errors by the researcher
v It has low-reliability levels and high bias levels
v It is characterized by the inability to generalize the research findings
- Demographic Characteristics
In the first sample survey of the youth who had intentions of attending the celebration event had a total number of 541, with 56% females who 91 % were age 17, 9% aged 18 years, 87% of the female respondents were enrolled in a school that was independent. The post-celebration survey sample was made up of 405 respondents with 505 females who 94% were 17 years of age, 6% were 18 years, and more than 925 female respondents were enrolled from an independent school (Wong et al., 2013). The total population of young people celebrating in the Island was 1466; 375 of this population participated in the pre-celebration survey and 28% in the post-celebration survey.
- Inferential Statistics used in Analyzing Data
Inferential statistics refer to techniques or methods that enhance the utilization of samples in making generalizations regarding the populations in which the samples had been derived. Wilcoxon sign ran evaluations were used to enhance the comparison of AOD utilization between the initial social event and the average day at the school leavers celebrations. The study also used logistic regression to assess the effects of six aspects on the probability of the reported experience of the negative adverse outcomes. The six variables entailed in every model were: a mean quantity of alcohol utilized on one day at the social event, other drug use apart from alcohol, utilization of alcohol-associated safety strategies as assessed via the (PBSS) Protective Behavioral Strategies Survey (Wong et al., 2013). The PBSS refers to a psychometrically validated behaviors list that minimizes the use of alcohol and associated acute harm, gender, the modality of survey administration, and accommodation location. The major use of the inferential statistics in this study was to establish inference from the provided data to more general conditions. The aim of inferential statistics is to reach conclusions extending beyond the present or immediate data.
- interpretation of Ratios
According to table four in the article, respondents who indulged in unprotected sex ratios differed with the level of safety measures used. Notably, respondents who used the least safety strategies were 10.92 times more likely to indulge in unprotected sex compared to those who use greater safety strategies. As shown in table 3 the reference group h refers to the respondents who scored between 66 and 84 in the use of safety strategies which is considered to be the least safe. The ration provided in the 4th table shows that individuals in this reference group are more likely to indulge in unprotected sex compared to those who scored 14-46. It is vivid that individuals who make use of safety measures during events or school leaving celebrations are less likely to indulge in unprotected sex compared to those who do not use safety measures during such social events. Nonetheless, the study also shows that unprotected sex is also a negative consequence irrespective of using safety strategies.
- Representativeness of the Sample Survey
A representative sample refers to one that accurately reflects or represents a population. It should be unbiased of how or what the population should be. There are various ways to evaluate a sample’s representativeness like socioeconomic, profession, education, personality, or status. Factors used to evaluate effectiveness are dependent on the objective of the scope of the research and the available information regarding the population.
In the presented study, the sample used represents the population-based on its recruitment and sampling procedures. With the objective of the study is comparing the level of AOD in school leaving parties and AOD use in social events, the selection of respondents aged between 17 and 20 reduces any errors. Additionally, by using the purposive sampling technique, the study minimizes errors that could arise in the case of respondents who did not attend such parties were respondents in the study. By reducing the error, the sample significantly represents the population. Additionally, existing literature on the topic aligned or concurred with the conclusions and results derived from the study showings its effectiveness in representing the young population behavior in school leaving parties and the varying levels of AOD consumption in school leaving parties.
The rising tide of diabetes mellitus in a Chinese population: a population-based household survey on 121,895 persons
Aims/Hypothesis of the study The aim of the study was evaluating the prevalence of self-reported diabetes through territory-broad surveys representative of the entire Hong Kong population as well as examined factors that \are independently related to diabetes
Ho: There is no increased prevalence of self-reported diabetes in Hong Kong.
H1: There is an increase in the prevalence of self-reported diabetes in Hong Kong.
- Demographic Characteristics
The study used initial surveys that collected information on the Hong Kong population’s health status. The household surveys entailed all land-based population of the city, Hong Kong, who made up the residents but in a non-institutional setting. The surveys excluded individuals residing in inboard vessels, foreign domestic helpers, and hotel transients. The coverage of household surveys was about 96% if the entire Hong Kong residents. The respondents of the surveys were mainly from built-up regions like new towns, developed areas, and urban areas. Therefore, it is vivid that the sample population used by the study was the Hong Kong residents irrespective of their age, race, sex, and gender. This is evident since the Health and government surveys tend to focus on the entire memb4ers of a population. Nonetheless, a distinctive aspect is that the study sample population was only urban and dwelled in Hong Kong.
- Inferential Statistics
The study used compared trends in surveys from 2001, 2002, 2005, and 2008, with respect to sex, age, and income like a socioeconomic status proxy measure. The study utilized sex-adjusted and age-adjusted prevalence rates considering alterations in demographics in the population across time. The study utilized a binary logistic regression model using self-reported diabetes as an outcome variable. To examine heterogeneity in the relation between self-reported diabetes and increased age among women versus men, the researchers stratified all the subjects with respect to sex and equivalent regression analysis was run differently for females and groups (Lam et al., 2014). The inferential statistics used in the study aided in understanding the prevalence of self-reported diabetes in Hong Kong in both men and women. It also enhances the comparison between the prevalence of diabetes based age.
- Outcome after adjusted age and sex
The age-adjusted diabetes prevalence among adult males was 2.80% in 2001, 2.87% in 2002, 3.32% in 2005, and 4.66% in 2008. In females it was 3.25% in 2001, 3.37% in 2002, 3.77% in 2005 and 4.31% in 2008 (Lam et al., 2014). Notably, the adjusted prevalence in the groups exhibited a drastic rise with an increase in age. The study shows that there a significant increase in self-reporting in adults over 75 years. Women had higher self-reporting rates compared to women.
- Interpretation of Odd ratio
After the analysis of diabetes rates and patient demographics, older age-adjusted odds ratios were 32.2, 95% CI 20.6–50.4, p\0.001 for individuals between 40 and 65 years and 120.1, 95% CI 76.6–188.3, p\0.001 for respondents older than 65 years and lower-income adjusted odds ration were 2.19, 95% CI 1.66–2.88, p\0.001. These rations show that individuals who are aging are more likely to self-report on diabetes irrespective of their sex. Illustratively, people over 65 years old had a 765 likelihood of self-reporting on diabetes compared to the 50% of those aged between 45 and 65 years.
Interestingly, the study analysis shows that individuals with low incomes are more like to self-report on diabetes compared to individuals with high incomes. Males with an income of less than 10000 are 8.74% rate of self-reporting diabetes compared to males who ear above 50, 000 who have 1.85%. Based on this ratio, it is evident that ‘the major people at risk of suffering from diabetes are older people and people with a lower income. Evidently, the high number of self-reports arise from people with low incomes compared to those with high incomes (Lam et al., 2014). Notably, individuals with high income manage to easily access health care compared to those with low income. Additionally, old age could be a major risk factor for old people due to decreased immunity. However, there are other studies that have linked high-income earners with high risks of suffering from diabetes due to their lifestyles which could be risk factors for diabetes.
- impact do the limitations
The first limitation in the study is the researchers relied on information that was self-reported to establish the prevalence of diabetes and other studies in China stated that three in every four Chinese people patients of diabetes go undiagnosed. Therefore, by relying on self-reports, the study could have collected the wrong information which increases the error of the findings hence leading to a wrong conclusion. Self-reports from patients could be inaccurate and wrong hence would lead to the wrong conclusion. As a result, it would be hard to trust the findings of a study that made use of self-report in data collection.
Additionally, the factor that the study did not consider about 70% of other variables that could impact diabetes prevalence reduces the reliability and validity of the findings. The neglect of factors like family history, mass index, and lifestyle factors would lead to a biased conclusion that would be misleading. As a result, to increase the reliability and credibility of the study, there is a need to ensure that future research will focus on other variables apart from age, sex, and income and also ensure that data collected and analyzed is not based on self-reports.