The History of ADHD Treatment: Drug Addiction Disorders

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Table of Contents

Introduction
Research Design
Sampling
Data Measurement
Data Collection
Data Analysis
Ethical Considerations
Conclusion
References

Introduction

The suggested project would use a three-step procedure for analyzing quantitative data. The initial step would be to gather data from the current research corpus to assess the history of ADHD treatment techniques among individuals with drug addiction disorders. The second phase would consist of identifying individuals with a history of drug misuse and ADHD using an online survey. Therefore, the gathered data would be classified by year, treatment type, and gender to better comprehend the statistical distribution of the prevalence of drug addiction. Following a T-test and Mann-Whitney U test, the third stage would find statistically significant differences between the two groups.

Research Design

As the research topic is based on statistical results, a quantitative research strategy would be used to acquire actionable insights. Numbers provide a more objective view for making important judgments on the treatment method for ADHD. In this instance, quantitative research methodologies are required to improve healthcare. When making judgments regarding future therapy, insights gleaned from complicated numerical data and analysis are useful. This experimental study establishes the link between a situation’s cause and effect. It is a causal design in which the effect of the independent variable on the dependent variable is observed (Leppink, 2019). It is an effective approach to research since it adds to the solution of a problem.

Sampling

Before the data analysis using the Mann-Whitney U test, all available data from both groups would be evaluated to generate a simple random sample in which every member of the population has an equal chance of being picked owing to the small number of participants that permits more precision. For the T-test, cluster sampling would be used, which entails separating the population into subgroups with comparable features (Usman, 2016). This sampling technique was selected to maximize the data’s precision based on the available dataset values.

Data Measurement

In this study, the nominal scale will be utilized to designate the two groups and categorize them by age and gender. The nominal scale is a measuring scale that is used for identification (Suparji et al., 2019). Sometimes referred to as a categorical scale, it provides numbers to qualities for identification purposes (Suparji et al., 2019). However, these numbers are not qualitative and serve solely as labels. Given the nature of the research issue, nominal scale is the most appropriate method since it focuses on percentages or frequency counts and can be studied visually using bar charts and pie charts.

Data Collection

As noted earlier, data would be gathered from existing medical history databases and internet surveys (using Google forms) to assemble a big dataset. Using existing data points, frequently from disparate data sources, to produce new data by a transformation, such as an arithmetic formula or aggregate, is the process of deriving data (Imam et al., 2020). The nature of the research implies the utilization of both primary and secondary data.

Data Analysis

The T-test, also known as the t-statistic or t-distribution, is a prominent statistical test used to compare the means of two groups or the difference between the mean of one group and a standard value. This statistical instrument would determine whether the differences are statistically significant (Usman, 2016). When the dependent variable is ordinal or continuous but not normally distributed, the Mann-Whitney U test is used to assess the differences between two independent groups.

Ethical Considerations

The research’s ethical issues would include voluntary involvement, informed consent, anonymity, the potential for damage, and the transmission of findings. IRBs are governed by three ethical concepts crucial to protecting human subjects while examining research: respect for individuals, beneficence, and justice. These principles serve as guidelines for this study. Participants in the research survey have the option to opt-in or out of the study at any time. Prior to agreeing or declining to participate, participants would have access to the study’s objective, advantages, risks, and financing via a Google form.

Participants will be required to supply age, gender, drug usage and ADHD history information. However, no data that may be used to identify the participants would be gathered, maintaining the anonymity of each participant. Similarly, the acquired database data will be restricted to this subset. There is no risk of danger, and participants will suffer no physical, social, psychological, or any other sort of harm. This study must be written in good faith, without plagiarism or scientific misconduct, and all findings must be correctly represented. Therefore, any material gained from other sources would be appropriately attributed to the article.

Conclusion

The t-test presupposes random samples, a continuous dependent variable, an independent categorical variable, a normally distributed dependent variable, and equal variances across groups. Test limitations include sensitivity to sample sizes, less resistance to violations of the equal variance and normality assumptions when sample sizes are uneven, and improved performance with larger sample numbers. Simultaneously, the power of Mann-Whitney U tests is lower than that of parametric testing. This implies that these tests are less likely to detect a difference between two groups if one exists (Laerd Statistics, 2018). This should make intuitive sense since there is always a cost for ignorance of the distribution, which often makes things more difficult to estimate.

References

Imam, R., Ferron, L., & Jariwala, A. S. (2020). A review of the data collection methods used at higher education makerspaces. IJAMM.

Leppink, J. (2019). Statistical methods for experimental research in education and psychology. Cham: Springer.

Mann-Whitney U test using SPSS statistics. Laerd Statistics. (2018). Web.

Suparji, S., Nugroho, H. S., & Martiningsih, W. (2019). Tips for distinguishing nominal and ordinal scale data. Aloha International Journal of Multidisciplinary Advancement (AIJMU), 1(6), 133. Web.

Usman, U. (2016). On consistency and limitation of independent T-test kolmogorov smirnov test and Mann Whitney U Test. IOSR Journal of Mathematics, 12(04), 22–27. Web.

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