Classification of Descriptive Analysis You May Don’t Know Before

Descriptive analysis plays an important role in statistically analyzing data. However, many do not know which form of descriptive analysis you should use. It is because they don’t have enough information on the descriptive analysis and its various classifications. This affects the quality of their overall analysis in the research. This article will give details on the classification of descriptive analysis that you may not know before so that you can use this analysis in the right way.

Descriptive Analysis:

A descriptive analysis summarizes or describes something quantitatively from a set of information. In this analysis, you use and analyze the statistics. You convert raw data into a descriptive form so that it will be easy to interpret and understand. Simply put, you try to manipulate order and rearrange data in this analysis to give more insightful information about the given data. This way, you can show or describe a data set in a more constructive way that leads to patterns that can fulfil the data conditions. This type of analysis is very important in the statistical analysis of data. It provides you with a meaningful conclusion describing the distribution of data. It can enable you to check the variables’ similarities and identify typos and outliers. This way, you will become more ready to do further analysis.

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Classification of Descriptive Analysis:

Measures of Frequency:

Many times descriptive analysis is used to measure the frequency. Knowing how often a certain response or event can occur is important. For this, we measure the frequency of that event or response like a percentage or count. For instance, consider research that uses a survey to ask five hundred participants about their a clothing brand. Getting a list of five hundred will be very difficult to accommodate and consume. However, you can make data easy to access and analyze if you measure how many times people select that particular clothing as their favourite. This will make things easy for you to do further analysis.

Measure Of Central Tendency:

Another type of descriptive analysis is used to find out the average or central tendency. Three types of averages are used to check central tendency which are mode, median and means. For example, try to consider a survey in which you are trying to get responses from more than 500 people. In this, it will not be easy to analyze the response of every participant as it will take a lot of your time. In this case, you can choose to measure the central tendency to make your analysis easier. You can also hire the best coursework writing service in this regard.

Measures Of Dispersion:

Another classification of this analysis is related to the measurement of dispersion. Understanding how the data is dispersed all over the range is often vital. To explain this, take the average weight from the sample of two participants. If both people have a weight of 60 kilos, then the average weight will also be 60. But, if the weight of one person is 70 kilos and the other one is 50 kilos, then the average weight will still be 60kg. This way, you can measure such distribution in data by using distribution measures.

Measures Of Position:

In the descriptive analysis, you also identify the position of a single response or value by relating it to others. In this type of analysis, measures like quartiles and percentiles are very beneficial. This will give you a relation of a single event to other variables. Other than this, if you have data on various variables, you use this data to do multivariate or bivariate analysis to study the relationship between the,

Bivariate Analysis:

In this type of analysis, you study the variability and frequency of two variables together. You check if the analysis has some pattern or how they vary from each other. Moreover, you measure the central tendency of these 2 variables and then compare them with one another before doing any further statistical analysis. This type of analysis helps to determine if there is some kind of statistical connection exists between the 2 variables, and then you will check the level of connection between them.

Multivariate Analysis:

This is just like the bivariate analysis. But instead of two variables, it is done by studying more than two variables. Following are the two methods for the bivariate analysis:

Contingency Table:

In this method, you make a table for your variables. Each cell has a combination of 2 variables. On the vertical axis, you write all the independent variables. On the horizontal axis, you write down the dependent variable. You need to check and study each cell to determine how the dependent and independent variables are connected to each other. This type of method can be used to summarise the relationship among several relationships. A contingency table is a particular form of distribution table that shows the connection between variables all at the same time.

Scatter Plots:

In this method, you make a chart that helps you study the relationship between the three or two variables.  It is a visual representation to show the strength of the relationship. In the scatter plots, you plot one variable on the horizontal axis and the one on the vertical axis. Then each data point is shown by a point in the scatter chat. This way, you can have a graph or chart to study various variables’ relationships. This is the best to visually represent complex relationships in an easy way.


Descriptive analysis is a famous type of statistical data analysis used in research. It is usually used before doing predictive or diagnostic analysis. It is because it aims to summarize and describe the collected data. This, this type of analysis uses many methods such as position, dispersion, central tendency and frequency. How you do this analysis will depend on what you seek to find out. However, the usual steps involve collecting, cleaning and analysing data in this analysis.