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# Introduction to Univariate, Bivariate, and Multivariate Analysis

When it comes to the level of analysis in statistics, there are three different data analysis techniques that exist:

**Univariate:** When there is just one variable in the data and no mention of causes, effects, or causal links. For instance, the researcher may want to count the number of males and girls in a school when conducting a survey. The data, in this case, would just show a number, i.e., a variable and amount. The main goal of univariate data is to describe the data using mean, median, variance, mode, dispersion, range, standard deviation, etc. to identify patterns within the data. Univariate Data can be analyzed with the help of the following:

Frequency Distribution Tables

Histograms

Frequency Polygons

Pie Charts

Bar Charts

**Bivariate:** When the dataset contains two variables and researchers aim to undertake a comparison between the two datasets then the bivariate analysis is the right technique. For instance, in a survey of a classroom, the researcher may be looking to analyze the ratio of students who scored above 85% corresponding to their genders. In this case, there are two variables: gender (independent variable) and marks (dependant variable). **A bivariate analysis will measure the correlation between the two variables.** Bivariate data can be analyzed with the help of the following:

Correlation Coefficients

Regression Analysis

**Multivariate:** When there are more than two variables in the dataset, a more complicated statistical analysis method called multivariate analysis is utilized. For instance, a doctor has gathered information on weight, blood pressure, and cholesterol. Additionally, she has gathered information about the individuals’ dietary habits. She wants to look at how eating habits and the three health metrics are related. In this case, the multivariate analysis would be necessary to comprehend how one variable related to the other. Multivariate data can be analyzed with the help of the following:

Factor Analysis

Cluster Analysis

Variance Analysis

Discriminant Analysis

Multidimensional Scaling

Principal Component Analysis

Redundancy Analysis