Required Resources

Read/review the following resources for this activity:

Textbook: Chapter 8
Lesson
Minimum of 1 scholarly source
In your reference for this assignment, be sure to include both your text/class materials AND your outside reading(s).

Confidence Intervals

In everyday terms, a confidence interval is the range of values around a sample statistic (such as mean or proportion) within which clinicians can expect to get the same results if they repeat the study protocol or intervention, including measuring the same outcomes the same ways. As you ask yourself, "Will I get the same results if I use this research?", you must address the precision of study findings, which is determined by the Confidence Interval. If the CI around the sample statistic is narrow, you can be confident you will get close to the same results if you implement the same research in your practice.

Consider the following example. Suppose that you did a systematic review of studies on the effect of tai chi exercise on sleep quality, and you found that tai chi affected sleep quality in older people. If, according to your study, you found the lower boundary of the CI to be .49, the study statistic to be 0.87, and the upper boundary to be 1.25, this would mean that each end limit is 0.38 from the sample statistic, which is a relatively narrow CI.

(UB + LB)/2 = Statistic [(1.25 + .49)/2 = .87]

Keep in mind that a mean difference of 0 indicates there is no difference; this CI does not contain 0. Therefore, the sample statistic is statistically significant and unlikely to occur by chance.

Because this was a systematic review, and tai chi exercise has been established from the studies you assessed as helping people sleep, based on the sample statistics and the CI, clinicians could now use your study and confidently include tai chi exercises among possible recommendations for patients who have difficulty sleeping.

Now you can apply your knowledge of CIs to create your own studies and make wise decisions about whether to base your patient care on a particular research finding.

Initial Post Instructions

Thinking of the many variables tracked by hospitals and doctors’ offices, confidence intervals could be created for population parameters (such as means or proportions) that were calculated from many of them. Choose a topic of study that is tracked (or that you would like to see tracked) from your place of work. Discuss the variable and parameter (mean or proportion) you chose, and explain why you would use these to create an interval that captures the true value of the parameter of patients with 95% confidence.

Consider the following:

How would changing the confidence interval to 90% or 99% affect the study? Which of these values (90%, 95%, or 99%) would best suit the confidence level according to the type of study chosen? How might the study findings be presented to those in charge in an attempt to affect change at the workplace?


 

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STATISTICS PROJECT

STATISTICS PROJECT

Statistics project

Student name:

Affiliated Institution:

Course:

Instructor name:

Month, day, year:

Project Outline and Purposes

Mental health and mental disorders are fundamental issues in public health that accommodate significant domains for effective redress, management and issues related to gender articulations. According to the author, gender does not only affect disorder rates but also risks, timing, diagnosis, treatment and adjustment of mental disorder (Wilhelm, 2014). Significantly, address the factored gender, accommodating qualitative and quantitative data for effective explanation on how gender impacts mental disorder as a an onset in male and female is underpinning in statistics projects and practical implications, providing effective data for the public health to management mental disorder as a disease. In an attempt to provide qualitative and quantitative evidence on the differences in mental disorder onset among male and female participants, this statistics project accommodate quantitative data collection by involving thirty individuals , after puberty state( 15 male and 15 female participants) to identify the number of participants with earlier stages of mental disorder onset. Additionally, the project accommodate factors determining the mental disorder onset on male and female within a general population to provide effective qualitative and quantitative data analysis , interpretation and conclusions. In the project, there is significant survey of Facebook post as data mining platform on 30 participants to identify the onset of mental disorders. Specifically, the study accommodates “negative post” with implications on mental disorder such as depression among 30 participants quantitative dataset. Similarly, the study has accommodated literature review to id identify actors that creates such factors that determines the mental disorder onset rates on male and female participants.

Quantitative Dataset

Male

Female

Total participants

15

15

“Negative Post”

6

12

“Positive Post”

9

3

Age Median

17

15

Age Mean

17

16

Data Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183915/

male

female

15

15

Negative

6

12

Positive

9

3

Positive percentage

30%

10%

Negative Percentage

20%

40%

Quantitative Data Interpretations

In the dataset, there are 30 participants, suing Facebook as a source of data with 15 male and 15 female participants. Notably, there are 6 out of 15 male with “negative post” on Facebook, and 9 out of 15 with “positive post” on Facebook, determining mental disorder after puberty among male and female with 40% and 60% negativity and positivity , respectively , demonstrating mental disorders among male participants. Similarly, there are 12 out of 15 male participants with “negative post” on Facebook , while 3 out of 15 with “positive post” on Facebook, giving a negativity and positivity percentages as 80% and 20% respectively. Consequently, in the total observation, there is 20% “negative post” among male participants while, 40% of female participants involved “negative post”. However, the dataset analysis demonstrated that 30% of male in the total population have “positive post” while, 10% of female in the total population posit a “positive post” on Facebook. Additionally the median age for the female participants is 15 years against 17 years of female, while Age mean for female participants is 16 years against male participants with mean of ages as 17. In the dataset, there is significant articulation on data dispersion, distribution and ordinal measurements, accommodating significant articulation on the quantitative data on mental disorders as diseases among the adolescents. Therefore, a comprehensive articulation on the data analysis, based on quantitative data for consideration and conclusion on the findings is necessary for this statistics project.

Conclusion

According to the quantitative data, there are more female with “negative post” on Facebook than male participants with “negative post”. There is 20% “negative post” among male participants while, 40% of female participants involved “negative post”. Similarly, there are more male participants with “positive post” than female participants with “positive post” on Facebook, accommodating, 30% of male in the total population have “positive post” while, 10% of female in the total population posit a “positive post” on Facebook. Thus, accommodating the quantitative data analysis , female mental disorder rates are higher than rates in male and, the onset of mental disorders is earlier in female than in male participants in this statistics project.

Qualitative Data

In supporting quantitative data, there are significant qualitative data that supports differences in gender and mental disorders. Specifically, factors including interactions between social and biological vulnerability, gender roles, gender- based violence, health seeking behavior are fundamental contributors to differences in gender mental health. Therefore, the project accommodated significant description on the qualitative data for explains attribution of mental health in male and female participants in general.

Factor

Rates in % for Female

Rates in % for Male

Interactions between social and biological vulnerability

80%

15%

Gender roles

96%

5%

Gender- based violence’s

80%

15%

Health seeking behavior

40%

60%

According to the data, interactions between social and biological vulnerability, gender roles, gender- based violence, health seeking behavior are fundamental contributors to differences in gender mental health (WHO 2002). Similarly, the qualitative data provide significant explanation on how male and female differences in mental disorder onset and contributing factors plays significant role in the differences. Admittedly, accommodating quantitative and qualitative from the project and statistics analysis, dataset and data analysis offers effective information to manage mental disorder as a public health issues among female and male, accommodating gender differences.

References

Pantic, I. (2014). Online Social Networking and Mental Health. Cyberpsychology, Behavior, and Social Networking, 17(10): 652–657

Wilhelm, A. (2014). Gender and Mental Health. Australian & New Zealand Journal of Psychiatry, Vol. 48(7) 603–605

World Health Organization (2002). Gender and Mental Health. https://www.who.int/gender/other_health/genderMH.pdf


 

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