Before jumping into data analysis, it is essential to follow some important steps to ensure the accuracy and reliability of your results. Here are some steps to follow before starting data analysis:
1. Review your research question(s): Make sure you are clear about the research question you want to answer. This will help you identify the outcome you need to process and the variables you need to measure.
2. Choose appropriate analysis techniques: Select appropriate statistical techniques to analyze the data based on the research question and the type of data you have.
3. Organize data: Transfer the data from your database (or sources in case you analyse secondary data) and organize it in a structured format.
4. Clean and preprocess the data: Clean and preprocess the data to remove any errors or inconsistencies. This may include removing duplicates, correcting errors, and handling missing data. Aim data cleaning is to make sure the data is complete and accurate.
5. Validate data: Check the data for outliers, inconsistencies, and other errors. This will help ensure the accuracy of your results.
6. Explore the data: Before analyzing the data, explore it to gain insights into the distribution of variables, relationships between variables, and any patterns or trends in the data.
7. Choose appropriate analysis techniques: Select appropriate statistical techniques to analyze the data based on the research question and the type of data you have.
8. Conduct sensitivity analysis – in case you are doing modelling: Test the sensitivity of the results to changes in assumptions, parameters, and methods.
By following these steps, you can ensure that your data analysis is accurate and reliable, and you can draw meaningful conclusion from your data.