n. a method of searching for the cause or causes of certain effects. Because the causal factor needs to be identified, the researcher will have to obtain data or use inferences. When data cannot be obtained through experimentation, the causal inference must be dependable and justifiable.
Causal Analysis seeks to identify and understand the reasons why things are as they are and hence enabling focus of change activity. Root causes. The basic principle of causal analysis is to find causes that you can treat rather than treating symptoms (which, as all doctors know, seldom effects a lasting cure).
Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.
In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
Inferential analysis uses statistical tests to see whether a pattern we observe is due to chance or due to the program or intervention effects. Research often uses inferential analysis to determine if there is a relationship between an intervention and an outcome as well as the strength of that relationship.