Data Preparation for Analysis

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Table of Contents

Data Exploration
Data Analysis
Data Representation
Data Interpretation
Data Validation
References

Qualitative data for the review of secondary sources will be prepared through a selection of relevant literary works, which would correspond to the research question – whether the proposed instrument can increase the use of pharmacogenetic testing as a routine practice in the treatment of the bipolar disorder. In addition, exploring the prevalence of the disorder and the current trends in its treatment, will underpin the topical nature of the research question (Carvalho et al., 2020). Risk factors and underlying issues related to bipolar disorder have been an area of intense interest in the medical community, meaning that there is a considerable amount of literature (Rowland & Marwaha, 2018). As for the next quantitative stage, raw data will be collected through direct clinical observations of pharmacogenetic testing frequency. It will be collected directly by the researcher in a specific clinical setting to ensure the validity of future findings.

Data Exploration

During the qualitative stage, the selected literary sources will be subjected to a rigorous review by the researcher. The main themes behind the works will be categorized and viewed in the form of the general classification, reflecting the main themes pertinent to the research question. One of the main concerns, for example, the importance of early interventions for bipolar disorders, discussed by Vieta et al. (2018). The point of this stage is to discern the fundamental literary basis for the quantitative part of the research, during which empirical data will be obtained in a clinical setting (Cooper et al., 2019). Qualitative reviews of secondary data are common in today’s academic environment, remaining a widely recognized practice (Ruggiano & Perry, 2019). The purpose of the quantitative study is to determine whether the proposed tool can actually be effective in terms of increasing the use of pharmacogenetic testing as a routine practice in the treatment of the bipolar disorder. The data will be attributed a numerical value for each stage, pre- and post-experiment, to facilitate its analysis during the next step.

Data Analysis

For the qualitative part, the data will be analyzed on the basis of the devised thematic classification. The process will enable a convenient exploration of the findings, which will then be reviewed in the general context of the study. At the same time, the quantitative review will feature two sets of data, the first one representing the frequency of pharmacogenetic testing in the treatment of bipolar disorder before the intervention and the second one reflecting the situation post-experiment. This way, the potential changes and correlations will be easily noticed and studied in the course of the analysis.

Data Representation

During the qualitative stage, secondary sources will be analyzed and categorized by specific relevant themes. Subsequently, the representation aspect will consist of the synthesis of the findings, devising a common conclusion for the secondary data obtained at this stage and applied to the current research framework. Next, at the quantitative stage, it appears instrumental to arrange the findings inferred from two data sets in a convenient form (Lima et al., 2021). Accordingly, graph-based representation is a feasible and appropriate method in this case.

Data Interpretation

For the qualitative part, it is also related to the synthesis of findings, during which the secondary data will be adapted to the framework of the present study. In the case of quantitative analysis, the representation of data will ensure the credibility and reliability of its interpretation. Having constructed accurate frequency charts, it will be possible to conduct the correct interpretation.

Data Validation

The validity of data is the ultimate goal of any research, as analyses should be accurate, evidence-based, and reproducible. The validation of results of the overall research will be enabled by considering all relevant aspects of the issues, including those, which come from secondary sources. However, validity is rarely self-contained, and the research question comprises a rather broad area of expertise. Accordingly, the process of findings validation is not limited to the envisioned work, as it will continue afterward in the form of subsequent studies.

References

Carvalho, A. F., Firth, J., & Vieta, E. (2020). Bipolar disorder.New England Journal of Medicine, 383, 58–66. Web.

Cooper, H., Hedges, L. V., & Valentine, J. C. (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation.

Lima, E., Hyde, R., & Green, M. (2021). Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques.Scientific Reports, 11. Web.

Lima, E., Hyde, R., & Green, M. (2021). Model selection for inferential models with high dimensional data: synthesis and graphical representation of multiple techniques. Scientific Reports, 11. Web.

Rowland, T. A., & Marwaha, S. (2018). Epidemiology and risk factors for bipolar disorder.Therapeutic Advances in Psychopharmacology, 8(9), 251–269. Web.

Ruggiano, N., & Perry, T. E. (2019). Conducting secondary analysis of qualitative data: Should we, can we, and how?Qualitative Social Work, 18(1), 81–97. Web.

Vieta, E., Salagre, E., Grande, I., Carvalho, A. F., Fernandes, B. S., Berk, M., Birmaher, B., Tohen, M., & Suppes, T. (2018). Early intervention in bipolar disorder. The American Journal of Psychiatry, 175(5), 411–426. Web.

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Data Preparation for Analysis

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