Depressive Symptoms in Patients: Archival Data Analysis

Posted on | by Laureen
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The option chosen for theproject
My topic of choice is Archival Data Analysis.
Archival data refers to data that already exists in libraries, reports, or other data sources. It exists in other files written by another person or organization. Archival data is initially collected for research purposes and kept for legal requirements or used as reference or records within an entity. This type of data is usually fixed and not subject to change. The study will focus on mental health, particularly depression, by identifying the prevalence of its symptoms during the 1990s to learn how this phenomenon has changed over time. The researchers will collect data from government publications, healthcare journals, and records from various accredited health writers.
Theresearch question and/or hypothesis
RQ: How was the prevalence of depressive symptoms for patients in the 1990s, and how has the situation changed?
HO: The prevalence of depressive symptoms in patients does not show a difference between the 1990s and the current situation.
The methodology that you plan to use to obtain your data
The study will use various data collection methods to gather data from existing archive sources. First, it will use document and records review as the main data collection method to facilitate the entire study. A significant part of the data will come from existing written records and reports regarding depression and other mental health aspects and their prevalence during the 1990s. The method will provide a wide range of information that will enhance conclusions for the study. Because this study is based on existing data, there will be no need for primary data. However, secondary data will be necessary through reviewing data from other sources conducted during the 1990s regarding depressive symptoms prevalence. Besides, the study will use government publications regarding mental health symptoms prevalence to identify relevant information regarding this study topic as per the government perspective. These publications will provide reliable data for the study. In the same case, historical information will be a significant part of the study because the topic is based on historical events regarding mental health, particularly depression. In the same case, healthcare journals will provide substantial data regarding this topic, which will help determine how the phenomenon has changed over time.
Type of graphical representation to use to acquire theresults
The study will use pie charts (circle graphs) to presents the results obtained from this study. Pie charts are a geographical tool used to summarize a set of categorical data depending on the category in which each item falls. It shows the different aspects of a study and how they appear to relate to each other. I also show their effect on the overall topic or the dependent variable of the research. It involves dividing a circle into segments that represent part of the whole issue in discussion. The amounts in each component of the pie chart will appear in percentage to show the intensity in which each aspect affects the studys outcome. All elements contribute to a total of 100% for the overall effect. The method is necessary for the study because it will show the prevalence of depression symptoms over various years during the 1990s to determine how the phenomenon has changed over time and whether there is a likelihood of experiencing further changes.
What types of challenges do you anticipate that you will encounter along the way
The study might face several challenges that might affect data collection and the overall results. First, there might be difficulties accessing data sources, such as government reports and other organizational reports, due to access restrictions. Besides, some sources might require specialized access, such as the need to pay for such services. In the same case, financial constraints might pose another significant challenge for this process, limiting adequate data access. Failure to obtain sufficient data might lead to wrong conclusions or providing insights that do not reflect reality.
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