When dealing with complex, multi-variable research data, simplifying the analysis can be a daunting task. Here are some strategies to help you simplify the analysis: Data Preparation 1. Clean and preprocess the data 2. Remove irrelevant variables 3. Transform variables Data Visualization 1. Use plots and charts 2. Create heatmaps 3. Use dimensionality reduction techniques Statistical Analysis 1. Apply correlation analysis 2. Use regression analysis 3. Apply clustering analysis Model Simplification 1. Use feature selection techniques 2. Apply model simplification techniques

I’d break it down into key variables, use visualizations, and apply pivot tables or SQL queries to find patterns. Focusing on actionable insights keeps the analysis clear and manageable.

While handling complicated multiple variable data, I prioritise identifying the main variables and breaking the data into smaller parts. If helpful, I organise the data in meaningful groups to identify any patterns. I also like using SPSS and visual representations of data and their relationships to help identify further interactions between the data sets. I personally find graphs and charts to be extremely helpful not just for interpretation but also communicating the data.

When being overwhelmed with complex, multi-variable research data, take a step back and remind yourself of your research question & purpose, as well as objectives. What is it exactly you are trying to find out? This will streamline your process and help identify key variables.

It is important to first "get to know your data." Make sure you are clear on what each variable is assessing, conduct basic descriptive analyses and visualizations to see what the data looks like (e.g., variability, possible outliers). These initial steps will give build foundational intuition of your data set that will be invaluable when navigating more complex analyses. It is also important to have a working hypothesis or visualization that conceptually grounds your analysis. I personally like path diagrams outlining proposed ways that variables are related to each other for factor analysis, SEMs, or multivariate regression. These steps help keep complex analyses on track and prevent becoming overwhelmed by the data set.