Univariate, Bivariate and Multivariate Analysis

One thing to look at in quantitative data analysis is to understand the level of your analysis, and this is determined by the number of variables you have in your research. When it comes to the level of analysis in statistics, there are three different analysis techniques that exist, These are ;

1. Univariate analysis
2. Bivariate analysis
3. Multivariate analysis

Let's explain each of these levels with examples

Univariate Analysis

Univariate analysis is the most basic form of the statistical data analysis technique. When the data contains only one variable and doesn't deal with a cause or effect relationship, then a univariate analysis technique is used.

Example of Univariate Analysis;

In a survey of a classroom, 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. This is being done by looking into the mean, median, mode, dispersion, variance, range, standard deviation etc.

Statistical Techniques to Conduct Univariate Analysis

Univariate analysis is conducted in several ways, which are mostly descriptive in nature.

1. Frequency Distribution Tables
2. Histograms
3. Frequency Polygons
4. Pie Charts
5. 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 a comparison between the two data sets, then Bivariate analysis is the right type of analysis technique.

Example of Bivariate Analysis

In a survey of a classroom, the researcher may be looking to analyse the ratio of two 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 will measure the correlations between the two variables.

Statistical Techniques to Conduct Bivariate Analysis

Bivariate analysis is conducted using;

1. Correlation coefficients
2. Regression analysis

Multivariate Analysis

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

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 consume 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 the other.

Statistical Techniques to Conduct Multivariate Analysis

Commonly used multivariate analysis techniques include;

1. Factor Analysis
2. Cluster Analysis
3. Variance Analysis
4. Discriminant Analysis
5. Multidimensional Scaling
6. Principal Component Analysis
7. Redundancy Analysis