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.
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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
This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable.[1] Typically it would be of interest to investigate the possible association between the two variables.[2] The association can be studied via a tabular or graphical display, or via sample statistics which might be used for inference. The method used to investigate the association would depend on the level of measurement of the variable. This association that involves exactly two variables can be termed a bivariate correlation, or bivariate association.
For two quantitative variables (interval or ratio in level of measurement) a scatterplot can be used and a correlation coefficient or regression model can be used to quantify the association.[3] For two qualitative variables (nominal or ordinal in level of measurement) a contingency table can be used to view the data, and a measure of association or a test of independence could be used.[3] If the variables are quantitative, the pairs of values of these two variables are often represented as individual points in a plane using a scatter plot. This is done so that the relationship (if any) between the variables is easily seen.[4] For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs.
In a bivariate correlation, outliers can be incredibly problematic when they involve both extreme scores on both variables. The best way to look for these outliers is to look at the scatterplots and see if any data points stand out between the variables.
Main article: Dependent and independent variables In some instances of bivariate data, it is determined that one variable influences or determines the second variable, and the terms dependent and independent variables are used to distinguish between the two types of variables. In the above example, the length of a person's legs is the independent variable. The stride length is determined by the length of a person's legs, so it is the dependent variable. Having long legs increases stride length, but increasing stride length will not increase the length of your legs.[5] Correlations between the two variables are determined as strong or weak correlations and are rated on a scale of –1 to 1, where 1 is a perfect direct correlation, –1 is a perfect inverse correlation, and 0 is no correlation. In the case of long legs and long strides, there would be a strong direct correlation.[6] In the analysis of bivariate data, one typically either compares summary statistics of each of the variables or uses regression analysis to find the strength and direction of a specific relationship between the variables. If each variable can only take one of a small number of values, such as only "male" or "female", or only "left-handed" or "right-handed", then the joint frequency distribution can be displayed in a contingency table, which can be analyzed for the strength of the relationship between the two variables.
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