The pros and cons of univariate analysis
Univariate analysis is a type of quantitative analysis. It is performed using just one variable. For example, if the variable age is the subject of the research, then the analyst would look into how many people fell into a particular age group. The key method for this sort of analysis is the average, from which standard deviation and variance figures are obtained. There are pros and cons to univariate analysis, compared to using other types of analysis.
Univariate analysis is more reliable as only one variable is used. The other data used in a univariate model is already known, and therefore the results of the model are often more accurate and give reliable predictions in certain situations. Compared to multivariate models, which have one or more unknowns and may or may not produce accurate results.
Given that a univariate analysis model only uses one unknown, or variable, it is a simpler model than bivariate or multivariate models. This means it is easier to build, test and understand than other models. It will also run faster than a more complicated multivariate analysis.
A univariate model is a strong descriptive method. Analysts can change one variable each time the model is run to obtain results that show “what if” scenarios. For example, changing the variables from age to income can show different results which describe what happens when one factor changes within the model.
A univariate model is less comprehensive compared to multivariate models. In the real world, there is often more than just one factor at play and a univariate model is unable to take this into account due to its inherent limitations.
Does not take into account relationships
As only one variable can be changed at a time, univariate models are unable to show relationships between different factors. Correlations or inversions cannot be modelled using a univariate model.
In summary, there are different advantages and disadvantages to using a univariate analysis. If time or cost are the main driving factors, and the subject has a direct or simple inverse relationship with the other types of information in the model, a univariate is the best choice; if a comprehensive, detailed analysis is required, it would be more prudent to use a multivariate type of analysis model as it will provide relationship information as well as quantitative, statistical data.