Hypotheses and Independent/Dependent Variables

The following are the two hypotheses:

1. Is the use of alcohol among the school leavers high or low today?
2. Null hypothesis

              The use of alcohol is on a downward   trend in school leaver’s party

1. Alternative hypothesis

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.

• Males’ Average School leavers’ in the event day
• Female’ Average School leavers’ in the event day 

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:

• Amount of alcohol  consumed  for the all  event days
• Daily average consumption of  alcohol in the event days

1. What is the level of the use of other drugs apart from alcohol among the school leavers, in their final party?
2. Null hypothesis

The use of drugs such as other drugs like amphetamine, caffeine, and cannabis has been the same.

1. Alternative hypothesis

 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:

• Number of  males and  females  using other drugs apart from alcohol  
• Average daily use of other drugs like amphetamine, caffeine, cannabis, and ecstasy

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:

• Use of other drugs in school other than alcohol leavers’ celebration
• Use of drugs in leavers’ day 1, day 2, day3

 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

• The stratified sample gives more accurate result compared to a simple random sample of the same size.
• With the stratified sample, sufficient sample points can be obtained to help in independent and separate of a subgroup in the sample.
• Due to fact that it provides precision, a stratified sample requires a smaller sample, which reduces research cost.
• Ensure representation of all groups from the population in the sample.

Shortcomings of stratified sampling

• Most of the analyses are computational more than in a simple random sample, and a lot of work needs to be done.
• Stratified random sample involves a lot of work compared to a simple random sample.

 The following are the demographic characteristics of the people in the sample:

• The sample is 56% female and 44% male.
• The  sample comprises of majority  young  persons who  are of   17 years and the others are 18 years olds
• The samples involved of persons exitin

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.

Advantages and disadvantages of stratified random sampling

 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 average age of persons was 38.2 years
• The population  contains both males ad female   Hong Kong  inhabitants
• The sample  includes adults aged between 15 years and  above
• The sample represents a population of wealthy inhabitants of Hong Kong.

  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.

Demographic Characteristics of the Sample

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

1. There was no information from the population subgroups to ascertain the soundness of self-reported diabetes data.

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.

1. The omission of other factors that is related to diabetes and is the main cause of  diabetes

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.

1. There no room to explore the correlation between diabetes and other risk factors in the large China Population, as regression analyses can only explain 20% of the variance of diabetes prevalence.

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.