The following are the two hypotheses:
The use of alcohol is on a downward trend in school leaver’s party
The use of alcohol in leaver party is on upward trend today.
Independent variables
In every study/ research there are factors that influence the effect of the other variables These variables are known as independent variables, for example, in this study by Lam et.al. (2014) level of consumption of alcohol among the leavers relies on the number of leavers who attended the party. In this case, the number of leavers in the party is an independent variable.
Below is a list of the independent variable for the hypothetical statements.
Dependent Variables
These are factors or variables whose effects are influenced by the independent variables, (Heeringa, West & Berglund, 2017). for example, the level of alcohol consumption in Australia among the school leaver in a shed off parties will largely depend on the number of leavers in these parties. In this case, the level of alcohol consumption is leaver party is the dependent variable.
These result variables that depend on the estimates of the independent variables. Coming up next are the variables for the hypothetical statement:
The use of drugs such as other drugs like amphetamine, caffeine, and cannabis has been the same.
The use of drugs such as other drugs like amphetamine, caffeine, and cannabis among the school leavers as they celebrate their final party has been grown by a great number.
Independent Variables
These are predictor variables that can be altered in a study to observe the behaviors or effects of the dependent variable (Francis, 2004). The variables for the hypotheses are recorded below:
Dependent Variables
These are outcome variables that depend on the value of the independent variables (Francis, 2004). The variables to the hypotheses are stated below:
The sampling technique that has been employed in this research is stratified random sampling. The sample was drawn from a population that contained two strata, female and male, 56% and 44% respectively of the sample. According to Cochran (2007), the advantages and shortcomings of the stratified sample are:
Advantages of stratified sampling
Shortcomings of stratified sampling
The following are the demographic characteristics of the people in the sample:
Inferential data that were used in the study are listed below together with their reasons
Estimates of alcohol use in Victoria Queensland, 69 and 76% of a person, consume 5 drinks or more on a typical school day.
The reported estimates of daily consumption of alcohol by Spring Break, which are ranges between 10 drinks and 18 drinks I both genders.
Need for inferential statistics
Inferential data intended to match with the study’s outcome of the study to work out their significance and irresponsibleness. They are used as benchmarks for the study. Researchers are able to identify their work with other research work outcomes.
The odds ratio for partaking in unprotected sex is 10.92, this implies that there’s a major relationship between partaking in unprotected sex and safety strategy. This additionally implies that those that participate in safety strategy were 10.9 times probable to have engaged in unprotected sex.
The sample is representative, as it includes all the members of the target population. Both genders are considered. Results of the study agree with the outcomes of the past studies, that there’s amplified use of alcohol and other drugs in leavers’ celebrations.
The research investigates the prevalence rate of self-reported diabetes and examines factors that are independently correlated with the diabetic ailment.
The main statistical inference made in this study is that the diabetes prevalence in China is 1.3%. This is a report by International Collaborative Study of Cardiovascular Disease in Asia; from their Chinese study dated 2001.
Need For inferential statistics
They will be used as benchmarks for the study. The researchers will be able to identify their work with the outcome of other research work.
The table below shows the summary of the age-adjusted prevalence rates among male and females adults in selected years between 2001 and 2008.
|
Year |
Prevalence Rate |
|
|
Males |
Females |
|
|
2001 |
2.8% |
3.25% |
|
2002 |
2.87% |
3.37% |
|
2005 |
3.32% |
3.77% |
|
2008 |
4.66% |
4.32%. |
From the table above, it’s clear that the trend of adjusted prevalence of self-reported diabetes is gradually rising across the years from 2001 to 2008 in the age groups of below 75 years.
Below are the interpretation of the odd ratios for the sample, subgroups of the sample (males and females) and comparison of the odd ratios of the subgroups.
Sample
Paying attention to age old aged (above 65 years) persons are at greatest risk of diabetes compared to other age groups. It has the highest adjusted odd ratio of 120.1. The next age group that is a higher risk is 20-65, with an adjusted odd ratio of 32.2. The age group between 0 and 39 years are not connected with diabetes, as their adjusted odd ratio of 1.0, which is an indicator of no correlation. This suggests that people aged 65 years and above are at greater risk of diabetes than middle- aged and young persons.
Regarding the gender, the adjusted odd ratio of both males and females are approximated to be 1, hence there’s no significant association between sex and the prevalence of diabetes
Paying attention to monthly household income range, people who earn below 9, 999 Hong Kong dollars are significantly correlated to diabetes. Their adjusted odd ratio is 2.19, the highest among all the income range brackets. The next are persons who earn between 10,000 and 24,999 Hong Kong Dollars, with an adjusted odd ratio of 1.58. Those who earn above 50,000 Hong Kong dollars are not significantly correlated with diabetes, as their odd ratio is 1.0, which is an indicator of no correlation. These suggest that low income earners are at the greater risk of diabetes than high income earners.
Sample sub-groups
Males
Considering the age of persons, aged males are likely to suffer from diabetes compared to middle aged and young males. Males above of 65 years have adjusted odd ratio of 141.08, which is greater with an adjusted odd ratio of middle aged (45.43), and young males(1). The young male is not associated with diabetes at all, their level of correlation due to their adjusted odd ratio of 1.
If the level of household income is considered, males working for below 9, 999 Hong Kong dollars likely of suffering from diabetes those male whose get more than 9, 999 Hong Kong dollars. Their adjusted odd ratio is 2.28, the highest among all the income range brackets. The males who make above 50,000 Hong Kong dollars have low chances of suffering from diabetes, this has been revealed by adjusted odd ratio is 1.0, which is an indicator of no correlation.
Females
Considering the age, an elderly female is likely to suffer from diabetes compared to the middle aged and young females. Females above of 65 years have adjusted the odd ratio of 105.45, which is greater with an adjusted odd ratio of middle aged(23.49), and young females (1). The young female is not associated with diabetes at all, their level of correlation due to their adjusted odd ratio of 1.
If the level of household income is considered, a female working for below 9, 999 Hong Kong dollars a month is likely of suffering from diabetes that a female whose get more than 9, 999 Hong Kong dollars. Their adjusted odd ratio is 2.28, the highest among all the income range brackets. The female who make above 50,000 Hong Kong dollars have low chances of suffering from diabetes, this has been revealed by adjusted odd ratio is 1.0, which is an indicator of no correlation
Comparison of Males and Females Subgroups
If age were to be put into considerations, aged males are more likely to suffer from diabetes compared to aged females. The adjusted odd ratio of aged males is higher than that of aged females. Moreover, young persons aged between 0 and 39 years from both genders are not correlated with diabetes, their adjusted odd ratio is 1, which is an indicator of no correlation.
In relation to monthly household income, males who get below 9, 999 Hong Kong are at greater risk of diabetes than females who get within the same income bracket. The adjusted odd ratio of males is 2.28, which is greater than that of females, 2.14. People who earn high, above 50,000 Hong Kong dollars from both genders are not correlated with diabetes; they both have adjusted the odd ratio of 1, which is an indicator of no correlation.
Below are the limitations of the study and their effect/impacts
Effect
Failure to look for information that can validate the self-reported data makes the result of the study to be subject of bias. As a result, the outcome of the research may be deemed to be unreliable.
Effect
This shows that the result doesn’t show clearly, what are the key factors that influence the prevalence of diabetes. Failure of the study to account for all potential factor of diabetes makes its results not be reliable and do ann’t larger image of the most causes of polygenic disorder and due to factors among the Chinese population.
Effect
Due to undeniable fact that regression analyses solely justify twentieth of the variable of the prevalence of the polygenic disorder, the models obtained won’t be considerably important for the predictions of the prevalence of diabetes in a massive Chinese population. This suggests that any projection done by model won’t show the prevalence of diabetes in China as the whole due undeniable fact that some risk factors are disregarded.
References
Cochran, W. G. (2007). Sampling techniques. John Wiley & Sons.
Francis, A. (2004). Business mathematics and statistics. Cengage Learning EMEA.
Heeringa, S. G., West, B. T., & Berglund, P. A. (2017). Applied survey data analysis. Chapman and Hall/CRC.
Lam, T., Liang, W., Chikritzhs, T., & Allsop, S. (2014). Journal of Public Health, 36(3), 408-416.
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