Try coding scheme to first of all arrange the information of all the extant literature in an order. For this refer table A1 in following paper: Mondal, J. and Chakrabarti, S., 2019. Emerging phenomena of the branded app: a systematic literature review, strategies, and future research directions. Journal of Interactive Advertising, 19(2), pp.148-167.

Next, you can do thematic analysis to draw out themes from it and showcase relationship between these drawn out themes in the form of conceptual framework (ideally based upon some existing theory in your area). For this check out following paper co-authored by me: Singh, H. and Chakrabarti, S., 2020. Defining the relationship between consumers and retailers through user-generated content: insights from the research literature. International Journal of Retail & Distribution Management.

These are the main definitions and elements that you should have in mind for the elaboration of an analysis plan.

According to Thomas Bush, from diagnostic to predictive, there are many different types of data analysis. Perhaps the most straightforward of them is descriptive analysis, also known as descriptive analytics or descriptive statistics, which uses statistical techniques to describe or summarize a set of data. As one of the major types of data analysis, descriptive analysis is popular for its ability to generate accessible insights from otherwise uninterpreted data. Unlike other types of data analysis, descriptive analysis does not attempt to make predictions about the future. Instead, it draws insights solely from past data by manipulating in ways that make it more meaningful.

Descriptive analysis is all about trying to describe or summarize data. Although it doesn’t make predictions about the future, it can still be precious in business environments. This is chiefly because descriptive analysis makes it easier to consume data, making it easier for analysts to act on.

Another benefit of descriptive analysis is that it can help to filter out less meaningful data. This is because the statistical techniques used within this type of analysis usually focus on data patterns and not the outliers.

According to CampusLabs.com, descriptive analysis can be categorized as one of four types. They are measures of frequency, central tendency, dispersion or variation, and position.

Measures of Frequency

In the descriptive analysis, it’s essential to know how frequently a certain event or response occurs. This is the purpose of measures of frequency, like a count or percent.

Measures of Central Tendency

It’s also worth knowing the central (or average) event or response in the descriptive analysis. Common measures of central tendency include the three averages — mean, median, and mode.

Measures of Dispersion

Sometimes, it may be worth knowing how data is distributed across a range. In order to measure this kind of distribution, measures of dispersion like range or standard deviation can be employed.

Measures of Position

Finally, descriptive analysis can involve identifying the position of one event or response to others. This is where measures like percentiles and quartiles can be used.

Like many types of data analysis, descriptive analysis can be quite open-ended. In other words, it’s up to you what you want to look for in your analysis. With that said, the process of descriptive analysis usually consists of the same few steps.

1. Collect data. The first step in any data analysis is to collect the data. This can be done in various ways, but surveys and good old fashioned measurements are often used.

2. Clean data. Another important step in descriptive and other types of data analysis is to clean the data. This is because data may be formatted in inaccessible ways, which will make it difficult to manipulate with statistics. Cleaning data may involve changing its textual format, categorizing it, and/or removing outliers.

3. Apply methods. Finally, descriptive analysis involves applying the chosen statistical methods to draw the desired conclusions. What methods you choose will depend on the data you are dealing with and what you are looking to determine. If in doubt, review the four types of descriptive analysis methods explained above.

Descriptive analysis is often used when reviewing any past or present data. This is because raw data is difficult to consume and interpret, while descriptive analysis metrics are much more focused.

Descriptive analysis can also be conducted as the precursor to diagnostic or predictive analysis, providing insights into what has happened in the past before attempting to explain why it happened or predicting what will happen in the future.

Descriptive analysis is a popular type of data analysis. It’s often conducted before diagnostic or predictive analysis, as it simply aims to describe and summarize past data.

To do so, the descriptive analysis uses a variety of statistical techniques, including measures of frequency, central tendency, dispersion, and position. How exactly you conduct descriptive analysis will depend on what you are looking to find out, but the steps usually involve collecting, cleaning, and finally analyzing data.

In any case, this business analysis process is invaluable when working with data