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Using the Best Practices of Causal Data Analysis

causal data analysis

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Causal data analysis is used to determine what happened to a given variable if another happens to be changed. It gives an indication of what can occur should given alterations be introduced. It also provides an understanding of the power a variable may have over another one. This is not just an exercise in statistics or can only be used for scientific purposes or research. Indeed, this form of analysis is starting to make its presence known in the world of Internet advertising.

statistical data analysis schema

Causal Data Analysis and Internet Marketing

Internet advertising is an arena in which clicking on a banner ad sends a person to the landing page where a transaction might be made. The website has to be able to encourage the “conversion” of a visit to a sale, and changes need to be made if necessary. An Internet marketer will tell you that A/B testing is extremely important. Any changes in the keywords or content needs to be evaluated. That is right in line with causal data analysis. The addition or subtraction of a keyword group as a variable, with the existing pages as a control, determines the value of that keyword group to the landing page. The use of best practices causal data analysis, statistical data analysis or predictive data analysis can help set up the A/B testing so that highly reliable results are the consequence. This optimizes the file information that is derived from the A/B testing.

Causal Comparative Method Helps in Educational Research

Of course, there are other uses for causal data analysis and by adhering to the best practices considerable amount of information can be extracted. Educational research is enhanced with the use of causal comparative methodology. It can be something as uncomplicated as comparing the degree of knowledge acquisition from traditional classroom lecture as opposed to education done with mobile devices. The best practices technique looks to see if there is any comparison between the two, and in such an instance the variables are not altered.

The Value Cannot Be Understated

As mentioned earlier, this is more than merely an academic exercise. It is not just Internet advertising that is being impacted by causal data analysis. Businesses requiring greater use of big data and mining information to seek answers. Customer service departments need to know what impact certain techniques have, and testing variables against a control is going to help improve the relationship with consumers. The value to education has always been a reason for using causal data analysis. It is highly unlikely that it will ever be discarded.

The analysis also can develop modeling that can help in the business world as situations are constantly tested. The globalization of the market makes this kind of analysis increasingly more important. Granted, there are some limitations to what causal data analysis can do. There are to be cases where more sophisticated data analysis will be required. Nevertheless, in any number of situations this is a valuable means of studying already collected data to develop what if statements were scenarios where the presence or absence of a given variable could make a difference.

Start using causal comparative method in your research today!