Similarities Of Univariate & Multivariate Statistical Analysis

Univariate and multivariate represent two approaches to statistical analysis. Univariate involves the analysis of a single variable while multivariate analysis examines two or more variables. Most multivariate analysis involves a dependent variable and multiple independent variables. Most univariate analysis emphasizes description while multivariate methods emphasize hypothesis testing and explanation. Although univariate and multivariate differ in function and complexity, the two methods of statistical analysis share similarities as well.

Although multivariate statistical methods emphasize correlation and explanation rather than description, researchers in business, education and the social sciences can use univariate and multivariate methods for descriptive purposes. Analysts may calculate descriptive measures, such as frequencies, means and standard deviations to summarize a single variable, such as scores on the Scholastic Aptitude Test (SAT), they can deepen this univariate analysis by displaying SAT scores in a cross tabulation that displays mean SAT scores and standard deviations by demographic variables, such as the gender and ethnicity of the students tested.

Although most real-world research examines the impact of multiple independent variables on a dependent variable, many multivariate techniques, such as linear regression, can be used in a univariate manner, examining the effect of a single independent variable on a dependent variable. Some researchers call this bivariate analysis while others call it univariate because of the presence of only one independent variable. Some introductory statistics and econometrics courses introduce students to regression by teaching univariate techniques. For example, a political scientist examining voter participation might study the effect of a single independent variable, such as age, on a person's likelihood to vote. A multivariate approach, meanwhile, would examine not only age, but also income, party affiliation, education, gender, ethnicity and other variables.

If statistical researchers want their analyses to have any impact on decisions and policies, they must present their results in a way that decision makers can understand them. This often means presenting results in written reports that use tables and charts, such as bar graphs, line charts and pie charts. Fortunately, researchers can present the results of univariate and multivariate analyses using these visual techniques. Displaying results in an understandable format is especially important in multivariate analysis because of the greater complexity of these techniques.

Perhaps the greatest similarity between univariate and multivariate statistical techniques is that both are important for understanding and analyzing extensive statistical data. Univariate analysis acts as a precursor to multivariate analysis and that a knowledge of the former is necessary for understanding the latter. Statistical software programs such as SPSS recognize this interdependence, displaying descriptive statistics, such as means and standard deviations, in the results of multivariate techniques, such as regression analysis.