When it comes to the level of analysis in statistics, there are three different analysis techniques that exist. These are –

**Univariate analysis****Bivariate analysis****Multivariate analysis**

The selection of the data analysis technique is dependent on the number of variables, types of data and focus of the statistical inquiry. The following section describes the three different levels of data analysis –

**Univariate analysis**

Univariate analysis is the most basic form of statistical data analysis technique. When the data contains only one variable and doesn’t deal with a causes or effect relationships then a Univariate analysis technique is used.

Here is one example of Univariate analysis-

In a survey of a class room, the researcher may be looking to count the number of boys and girls. In this instance, the data would simply reflect the number, i.e. a single variable and its quantity as per the below table. The key objective of Univariate analysis is to simply describe the data to find patterns within the data. This is be done by looking into the mean, median, mode, dispersion, variance, range, standard deviation etc.

Univariate analysis is conducted through several ways which are mostly descriptive in nature –

•Frequency Distribution Tables

•Histograms

•Frequency Polygons

•Pie Charts

•Bar Charts

**Bivariate analysis**

Bivariate analysis is slightly more analytical than Univariate analysis. When the data set contains two variables and researchers aim to undertake comparisons between the two data set then Bivariate analysis is the right type of analysis technique.

Here is one simple example of bivariate analysis –

In a survey of a classroom, the researcher may be looking to analysis the ratio of students who scored above 85% corresponding to their genders. In this case, there are two variables – gender = X (independent variable) and result = Y (dependent variable). A Bivariate analysis is will measure the correlations between the two variables.

Bivariate analysis is conducted using –

•Correlation coefficients

•Regression analysis

**Multivariate analysis**

Multivariate analysis is a more complex form of statistical analysis technique and used when there are more than two variables in the data set.

Here is an example of multivariate analysis –

A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week). She wants to investigate the relationship between the three measures of health and eating habits?

In this instance, a multivariate analysis would be required to understand the relationship of each variable with each other.

Commonly used multivariate analysis technique include –

•Factor Analysis

•Cluster Analysis

•Variance Analysis

•Discriminant Analysis

•Multidimensional Scaling

•Principal Component Analysis

Redundancy Analysis