STATISTICS PROJECT
STATISTICS PROJECT
Statistics project
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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
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Male
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Female
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Total participants
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15
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15
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“Negative Post”
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6
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12
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“Positive Post”
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9
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3
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Age Median
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17
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15
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Age Mean
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17
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16
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Data Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4183915/
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male
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female
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15
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15
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Negative
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6
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12
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Positive
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9
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3
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Positive percentage
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30%
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10%
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Negative Percentage
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20%
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40%
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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.
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Factor
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Rates in % for Female
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Rates in % for Male
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Interactions between social and biological vulnerability
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80%
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15%
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Gender roles
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96%
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5%
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Gender- based violence’s
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80%
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15%
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Health seeking behavior
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40%
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60%
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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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