Data Analysis biostatistics in epidemiology

Data Analysis biostatistics in epidemiology

1. Data Analysis Biostatistics Presenter: Richard Nkhata Chiza B.Sc. B.Ph., B.Sc. Bchm, Dip SLT, M.Sc. med stats, M.Sc. Mph & PhD-fellow.

2. OBJECTIVES At the end of the session students should be able to: Identify patterns and trends Making prediction Testing hypothesis Comparative analysis Exploratory analysis

3. Con; Data validation and quality Identifying outliers and anomalies Segmentation and profiling Optimizing and decision marking Visualization and communication

4. Definition Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the aim of discovering useful information, informing conclusions, and supporting decision-making. It involves various techniques and methodologies to uncover patterns, trends, correlations, and insights within datasets, ultimately leading to actionable outcomes. Data analysis is integral across various domains including business, science, healthcare, finance, and social sciences, among others, serving as a cornerstone for informed decision-making and problem-solving.

5. Components of data analysis coding Code book construction Editing and coding questionnaires

6. Coding of data Data coding is the process of assigning labels or numerical codes to qualitative data, typically textual information, to facilitate analysis. It's a crucial step in qualitative research where researchers aim to identify themes, patterns, or categories within the data. Here's a general procedure for data coding:

7. Cont.; Familiarization with the Data : Before coding begins, researchers need to thoroughly understand the data by reading through it multiple times. This helps in gaining familiarity with the content and identifying potential patterns or themes. Developing a Coding Scheme : Based on the research objectives and the nature of the data, researchers develop a coding scheme or framework. This scheme outlines the categories, themes, or concepts that will be used to organize the data. It can be developed deductively (based on existing theory) or inductively (emerging from the data itself).

8. Cont.; Initial Coding: In this phase, researchers start assigning codes to segments of the data based on the coding scheme. This can involve highlighting or tagging specific phrases, sentences, or paragraphs that relate to the identified categories or themes. Iterative Coding : As coding progresses, researchers continuously refine and expand the coding scheme. New categories or themes may emerge, requiring adjustments to the coding framework. This iterative process continues until saturation is reached, meaning no new categories or themes are emerging from the data.

9. cont. Peer Review : To ensure the reliability and validity of the coding process, researchers may involve multiple coders or conduct peer reviews. This involves independent coding of the same data by different researchers, followed by discussions to resolve discrepancies and ensure consistency in coding. Data Organization : Once coding is complete, the coded data is organized for analysis. This can involve compiling coded segments into a spreadsheet or software program for further exploration. Data Analysis : With the coded data organized, researchers can begin analyzing the patterns, relationships, and insights within the data. This often involves aggregating data across codes, comparing between different groups or contexts, and interpreting the findings in relation to the research questions.

10. Cont.; Reporting: Finally, the results of the analysis are reported in research publications or presentations. This typically includes a description of the coding process, the identified themes or categories, and the insights derived from the analysis.

11. Code book construction Define Research Objectives : Clearly articulate the research questions or objectives that the codebook will address. Understanding the purpose of the research will guide the development of the codebook. Literature Review : Review relevant literature to identify existing frameworks, theories, or previous coding schemes related to your research topic. This can provide insights into potential codes to include in your codebook. Code Generation : Generate an initial list of codes based on the research objectives and literature review. These codes should represent the key concepts, themes, or variables that you intend to analyze in your data.

12. Cont.; Code Definition: Define each code in detail to ensure clarity and consistency in interpretation. Each code should have a clear definition that describes what it represents and includes examples or criteria for application. Code Organization : Organize the codes into a structured format within the codebook. This might involve grouping related codes together or creating hierarchies if the codes have different levels of abstraction. Codebook Formatting : Design the layout and format of the codebook for ease of use. Consider including a table of contents, index, or glossary to facilitate navigation and reference.

13. Cont.; Pilot Testing : Pilot test the codebook with a small sample of data to assess its effectiveness and identify any areas for improvement. This can help refine the code definitions and ensure that the coding process is clear and consistent. Revision : Revise the codebook based on feedback from the pilot test and any additional insights gained. Make adjustments to code definitions, organization, or formatting as needed to improve clarity and usability. Training : Train coders or researchers on how to use the codebook effectively. Provide guidance on applying the codes consistently and resolving any coding ambiguities that may arise.

14. Cont.; Finalization: Finalize the codebook once it has been thoroughly tested and refined. Ensure that all codes are clearly defined, organized logically, and formatted appropriately for use in data analysis. Documentation: Document the development process of the codebook, including any revisions or updates made along the way. This documentation can help maintain consistency and provide transparency in the coding process. Implementation: Implement the codebook in the actual data analysis phase of the research project. Apply the codes systematically to the dataset according to the guidelines outlined in the codebook.

15. Editing and coding of questionnaire Review the Initial Draft : Begin by reviewing the initial draft of the questionnaire. Understand its purpose, target audience, and the information it aims to gather. Clarify Objectives : Ensure that the objectives of the questionnaire are clearly defined. This helps in framing appropriate questions and selecting relevant response options. Structural Organization : Organize the questionnaire logically, with related questions grouped together. This makes it easier for respondents to navigate and understand.

16. Cont.; Question Wording : Review each question for clarity, simplicity, and relevance. Avoid ambiguous language, jargon, leading questions, and double-barreled questions (questions that ask about multiple things at once). Response Options : Ensure that response options are exhaustive and mutually exclusive. Include options that cover all possible responses without overlapping. Coding : Assign numerical codes to each response option. This coding simplifies data entry and analysis. For example, if a question asks for age groups, you might assign codes like 1 for 18-25, 2 for 26-35, and so on.

17. Cont.; Formatting and Layout : Pay attention to the visual presentation of the questionnaire. Use consistent formatting for fonts, sizes, and styles. Ensure that the layout is clean and easy to read. Pilot Testing : Before finalizing the questionnaire, conduct a pilot test with a small sample of respondents. This helps identify any confusing or unclear questions and allows for necessary revisions. Final Review : After incorporating feedback from the pilot test, conduct a final review of the questionnaire. Ensure that all changes have been properly implemented and that the questionnaire meets its objectives

18. Cont.; Validation : If necessary, validate the questionnaire through statistical techniques such as factor analysis or reliability testing to ensure its effectiveness in measuring the intended constructs. Translation (if applicable) : If the questionnaire will be administered in multiple languages, ensure accurate translation and cultural adaptation to maintain consistency and validity across versions.

19. Cont.; Documentation : Document the questionnaire development process, including any revisions made, rationale behind question choices, and validation results. This documentation helps maintain transparency and facilitates future revisions or replications. Finalization : Once everything is in order, finalize the questionnaire for distribution or implementation.

20. End of presentation Thank End