What is Descriptive Analysis? Types, Techniques , Advantages

Descriptive analysis involves in the constructive description, display, or summarization of data points so that patterns that satisfy all of the data's requirements may show up. Descriptive analysis is one of the main forms of data analysis and is frequently utilized for its capacity to produce understandable insights from otherwise uninterrupted data.

Types of Descriptive Analysis

The four categories of descriptive analysis include position, dispersion or variation, central tendency, and frequency measurements. These techniques work best when applied to a single variable at a time.

1. Measures of Frequency

Understanding how frequently a particular occurrence or reaction is expected to occur is crucial for descriptive analysis. Measuring frequency is mostly used to create counts or percentages. Take a study, for instance, in which 800 participants are asked what flavor cake they like. It would be challenging to go through a list of 800 responds, but by counting the number of times a certain flavor was chosen, the data can be much more easily accessed.

2. Measures of Central Tendency

Descriptive analysis also requires the determination of the Central Tendency, or average response, which is determined by utilizing three averages: mean, median, and mode. Take a survey, for instance, where 500 people's weights are recorded. The mean average would be an extremely helpful descriptive measure in this situation.

3. Measures of Dispersion

Sometimes, it is important to know how data is divided across a range. To further explain this, let's look at the mean weight of a pair of individuals. The average weight will be 60 kg if both people weigh 60 kg. The average weight is still 60 kg even if one person weighs 50 kg and the other 70 kg. This type of distribution may be measured using dispersion metrics such as range or standard deviation.

4. Measures of Position

Lastly, determining how one event or answer compares to others can be a part of descriptive analysis. This is the application of metrics such as quartiles and percentiles.

In addition, you may use bivariate or multivariate descriptive statistics to examine whether there are any correlations between the variables if you have gathered data on many variables. Bivariate analysis looks at two variables' frequency and variability at the same time to determine whether they exhibit any patterns or variations in tandem. Prior to performing any statistical analyses, you may also compare and assess the central tendency of the two variables.

Similar to bivariate analysis, multivariate analysis examines more than two variables. The two techniques for bivariate analysis are as follows.

1. Contingency table: Each cell in a contingency table reflects the two variables connected. Typically, a dependent variable is counted along the horizontal axis, while an independent variable is listed along the vertical axis. To see how the two variables—the independent and dependent variables—relate to one another, you must read "across" the table.

2. Scatter plots: A scatter plot is a type of chart that shows the correlation between two or three variables. It is a graphic representation of a relationship's strength. Plotting one variable along the x-axis and another along the y-axis is how a scatter plot is made. In the graphic, each data point is represented by a point.

Techniques for Descriptive Analysis

1. Building tables of quantiles and means, using dispersion techniques like variance or standard deviation, and creating "crosstabs," or cross-tabulations, which may be used to test a variety of different hypotheses, are examples of descriptive techniques. These theories frequently draw attention to variations within subgroups.

2. Specific descriptive methodologies are employed in the study of measures such as inequality, discrimination, and segregation. Decomposition techniques or audit investigations are used to quantify discrimination. Although further segregation based on the kind of outcomes or disparity need not be inherently good or bad, it is sometimes seen as a sign of unfair social processes; knowledge of these processes requires precise assessment of the many stages throughout time and location.

3. When significant variations between subgroups are displayed, a table of means by subgroup is typically utilized, leading to inference and conclusion making. For instance, we have a natural tendency to infer explanations for patterns that comply when we observe a discrepancy in earnings.

4. The cell proportions, or proportions of components having unique values for each of the two accessible variables, are intended to be displayed using a crosstab or two-way tabulation. For instance, we might calculate the percentage of people who are both high school graduates and in receipt of food or financial aid; that is, a crosstab of education and help receipt has to be created.

Advantages of Descriptive Analysis

1. One major benefit of descriptive analysis is the high degree of impartiality and neutrality of the researchers. Researchers must exercise extra caution since descriptive analysis reveals many aspects of the retrieved data, and if the data deviates from the patterns, significant data dumping may result.

2. Compared to other quantitative techniques, descriptive analysis is thought to be more comprehensive and to paint a wider picture of a phenomena or occurrence. To do a descriptive investigation, it can employ one or more variables, or any combination of variables.

3. This kind of analysis is seen to be a superior way to gather data that depicts relationships as real and represents the world as it is. Because all of the patterns in this analysis are based on study into the actual behavior of the data, it is extremely realistic and relatable to mankind.

4. It is thought to be helpful in locating variables and novel theories that may be investigated further through inferential and experimental research. It is seen as beneficial as there is very little room for error because the trends are extracted directly from the data attributes.

5. The ability to use both quantitative and qualitative data to uncover the characteristics of the population is provided by this kind of study.