Choosing the correct statistical analysis is crucial for obtaining meaningful and valid results in a research study. Here are some steps to guide you in selecting the appropriate statistical analysis:

1. Define Your Research Question:
   • Clearly articulate your research question or hypothesis. The nature of your question will influence the type of statistical analysis needed. Questions generally fall into one of three types: descriptive, correlational/predictive and cause/effect or experimental.
2. Understand Your Data:
   • Examine the characteristics of your data, including the type of variables (categorical or continuous), distributional properties, and sample size. This understanding helps in choosing methods that are suitable for your data.
3. Identify the Study Design:
   • Determine the design of your study (e.g., cross-sectional, longitudinal, experimental). Different study designs may require specific statistical methods.
4. Consider Variable Types:
   • Identify the types of variables involved in your analysis (e.g., independent variables, dependent variables). This helps determine whether you need a univariate or multivariate analysis.
5. Check Assumptions:
   • Be aware of the assumptions associated with different statistical methods. Ensure that your data meet these assumptions, or choose methods robust to violations when possible.
6. Choose Between Parametric and Nonparametric Methods:
   • Parametric methods assume a specific distribution (e.g., normal distribution), while nonparametric methods make fewer assumptions. Choose between them based on the distributional properties of your data.
7. Consider the Number of Groups:
   • If comparing multiple groups, determine whether you are conducting pairwise comparisons or overall group differences. This will guide you in choosing between ANOVA, t-tests, or nonparametric alternatives.
8. Time-Dependent Analyses:
   • For longitudinal or time-dependent data, consider repeated measures analyses or survival analyses, depending on the nature of the outcome.
9. Account for Covariates:
   • Decide whether covariates need to be included in the analysis to control for potential confounding factors. This may involve regression analysis or analysis of covariance (ANCOVA).
10. Explore Relationships:
    • If examining relationships between variables, consider correlation analysis for continuous variables or contingency tables for categorical variables.
11. Statistical Power:
    • Consider the statistical power of your analysis. Ensure that your sample size is adequate to detect meaningful effects if they exist.
12. Consult Statistical Resources:
    • Refer to statistical textbooks, online resources, or consult with a statistician for guidance. There are also software tools that can assist in selecting appropriate analyses based on your study design and data characteristics. It can help to find a published study that uses a similar design to your study; examine the statistical methods they used and you can apply the same techniques based upon the previous considerations above.
13. Pilot Studies:
    • If possible, conduct pilot studies to assess the feasibility of your chosen statistical methods and make adjustments if necessary.

Remember that the choice of statistical analysis should align with the specific details of your study, and careful consideration of these factors will contribute to the validity and reliability of your research findings. If in doubt, seeking the advice of a statistician is always a good practice.

A more detailed explanation of this process can be viewed here: https://youtu.be/DcS6_I63PHs?si=3lQslOct8C7Rb7n_