Apply Biostatistical Methods In Analyzing Health Data

APPLY BIOSTATISTICAL METHODS IN ANALYZING HEALTH DATA

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Learning tasks

• At the end of this session, students are expected to be able to:
• Define data analysis
• Identify data to be analyzed
• Identify methods for data analysis
• Describe data entry process
• Explain data cleaning
• Explain data summarization
• List tools for data analysis
• Analyze health data

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Definitions

Data Analysis

Is the systematic process of inspecting, cleaning, transforming, and modeling raw data to discover useful information, identify patterns, and support decision-making.

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Data to be analyzed

Qualitative Data: Non-numerical data from sources like interviews, open-ended survey questions, and observations Quantitative Data: Numerical data that represents quantities or measurements, such as sales figures, age, or height.

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Methods for data analysis

Data analysis has four main methods or techniques.

Descriptive; Summarizes historical data to show what has happened by looking for causes and correlations.

Predictive; uses historical data and models to forecast what is likely to happen in future.

Diagnostic; Examines data to understand why something happened by looking for causes and correlations.

• Prescriptive; Recommends specific actions to be taken based on the predictions.

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Data entry

This is the manual or automated process of inputting or transferring data from physical or digital sources into an electronic format for an organization.

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Data cleaning

Is the process of finding and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality.

• The goal is to ensure the data is reliable, complete, and consistent before analysis.

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Data summarization

Is the process of reducing large datasets into a more manageable and understandable form.

This helps to highlight key aspects of data, such as patterns and trends without losing critical information.

Techniques for summarization include calculating measures of central tendency(mean, median) and creating visual summaries like charts and graphs.

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Tools and applications for data analysis

• Microsoft excel
• Stata
• R
• SPSS
• Python

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References

Kothar, C.R. (2004). Research Methodology: Methods and Techniques (2nd ed.). India: New Age International (P) Limited 10