A Guide to Data Analysis Methods

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information. But with so many techniques available—from simple averages to complex machine learning—it is hard to know where to start.

This guide provides a high-level overview of the entire data analysis landscape. If you have a specific dataset and don’t know what to do with it, our data analysis consulting service can provide a roadmap.

Qualitative vs. Quantitative Analysis

The first step is to identify your data type.

Quantitative Analysis

Deals with numbers and measurable forms. It uses statistical and mathematical models.

• Examples: Survey ratings (1-5), sales revenue, height, temperature.
• Goal: To test hypotheses, look for cause and effect, and make predictions.

Qualitative Analysis

Deals with non-numerical data like text, video, or audio. It relies on interpretation.

• Examples: Interview transcripts, open-ended survey responses, social media posts.
• Goal: To understand concepts, thoughts, or experiences.

The 4 Levels of Data Analytics

As analysis becomes more complex, it adds more value.

1. Descriptive Analysis (What happened?)

Summarizes past data to understand what has already occurred. Uses mean, median, mode, and basic charts.

2. Diagnostic Analysis (Why did it happen?)

Digs deeper to find the root cause of the outcome. Uses drill-down, data discovery, and correlations.

3. Predictive Analysis (What will happen?)

Uses statistical models and machine learning to forecast future outcomes. Techniques include regression analysis and time series forecasting.

4. Prescriptive Analysis (What should we do?)

Recommends specific actions to achieve a desired outcome. Uses optimization and simulation algorithms.

For a business perspective on these levels, Harvard Business School Online provides excellent case studies.

Common Statistical Techniques

Once you know your goal, you can choose a specific method.

For Comparison: T-Tests and ANOVA

Used to compare the means of two or more groups to see if they are significantly different. See our T-Test guide.

For Relationships: Regression

Used to determine the strength and character of the relationship between a dependent variable and one or more independent variables. See our Linear Regression guide.

For Grouping: Cluster Analysis

Used to group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. See our Cluster Analysis guide.

For Data Reduction: Factor Analysis

Used to reduce a large number of variables into fewer numbers of factors. See our Factor Analysis guide.

Tools of the Trade

We support analysis in all major software packages:

• SPSS: Standard for social sciences. See our SPSS guide.
• R / RStudio: Powerful for statistical computing and graphics.
• Python: Best for machine learning and automation.
• Excel: Great for basic descriptive statistics and pivot tables.

Get Help with Your Analysis

Choosing the wrong method can invalidate your entire study. Our team of data scientists can help you select the right technique, run the analysis, and interpret the results correctly.