Data Presentation Technique -Choosing Data Analysis and 3 Data Presentation Techniques

Data Presentation Technique -Choosing Data Analysis and 3 Data Presentation Techniques

Most researchers are concerned about their choice of data analysis and data presentation techniques/approaches. There are various methods or forms of data analysis and presentation techniques that are available to a researcher.

Depending on the data to be analyzed and the technique of its presentation, a researcher can employ one or more of these in his research. One should bear in mind that data analysis and presentation are key components of a research study and as such the integrity of the data must be ensured.

What is data analysis?

Data analysis can be viewed as the process by which a researcher collects, organize and describe raw data to arrive at a reasonable conclusion. The methods of data analysis are a collection of techniques that may be used to analyze data. Basically, there are two main methods of data analysis:

• Qualitative analysis
• Quantitative analysis

Qualitative data analysis deals with analyzing data by observing and interpreting data and themes and explaining how they help to answer the research questions.

Quantitative analysis on the other hand deals with analyzing data using numbers. Unlike qualitative data analysis, the quantitative analysis of data depends on the use of statistical tools like SPSS to analyze and interpret data.

There are two main methods of Data Analysis:

Qualitative – This method focuses on answering “wh” inquiries. Qualitative tools such as surveys, standard outcomes, and more are used to answer each of these questions. Texts and narratives are frequently used in this type of study, which may also involve audio and video representations.

Quantitative – This type of study is usually quantified in numbers. The data here are presented in terms of measurement scales and can be further manipulated statistically.

What is data presentation and presentation technique?

Consider how burdensome statistical data without a clear presentation will be. One of the most significant components of statistics is data presentation. The data is presented in a way that allows users to properly explore and interpret the numbers.

Arranging two or more data sets visually in an organized manner that is easily accessible by users or researchers defines data presentation. It involves the use of tables, charts, graphs, frequency distribution, etc. to interpret data. The ways through which data is shown or presented are known as data presentation techniques.

Choosing the method of data analysis

The choice of data analysis method depends on the type of data that is being used in the study. For this reason, we shall look at the choices for each data type, that is, qualitative and quantitative data.

The following are the method of data analysis for qualitative data

Content Analysis

Rather than statistics and numbers, content analysis is the act of evaluating and analyzing textual content. The statistical methods would be worthless because the qualitative material does not contain any quantitative data. Analysts of the content, on the other hand, should be unbiased and devoid of personal prejudices.

Analysis of the Discourse

Discourse analysis examines how individuals communicate with one another. Its goal is to determine the social environment in which the responder and researcher communicate and interact. In discourse analysis, you examine the subject’s daily activities and apply them in your study.

Analysis of Narrative

Narrative analysis is a sort of analysis in which you review and verify data from a variety of sources, such as field surveys, interviews, or focus group observation. The goal of narrative analysis is to examine people’s perspectives, encounters, and tales in order to find answers to your research questions.

Grounded Theory

Grounded theory is also a type of qualitative data analysis where you create and give an explanation to the data. Here the researcher doesn’t use statistical tools to analyze the data. However, you use inductive reasoning to study and analyze the data and think of the possible

For quantitative data, the method of data analysis includes;

Average

The average is the dataset’s centre point or number. Mean, median, and mode are the three methods for calculating and determining the average. Whatever method you use to calculate the average, the outcome will give you the answer. If you determine the average, though, you may smooth out your data and make conclusions from it.

Percentage

A percentage is a figure or a ratio that offers you a fraction of a hundred. A percentage is expressed as per cent, per cent. For example, you may say 65 per cent or. 65 is the same as 65, and it’s a fraction.

Frequency

The terms frequency and mode are interchangeable. It refers to the number of times a number appears in the dataset. Frequency is, in reality, a standard for calculating the average or mode in a collection.

Range

A range is a sort of quantitative data analysis in which the difference or gap between the lowest and greatest value is determined. The range specifies how much or how much data is contained inside the set. The range is a crucial metric or benchmark. In the face of changing conditions, businesses commonly employ a range to aid decision-making.

Regression

Regression is a strategy for studying the connection between two or more dependent and independent variables in quantitative data analysis. This study will assist you in determining the most important aspect and how it influences others. Regression analysis is used by businesses and enterprises to forecast occurrences.

Correlation

Correlation is a quantitative method for determining the strength of a link between two numerical variables. If there is a significant association between two variables, the correlation will be greater. It won’t be otherwise.

Variance Analysis

Variance analysis is a type where you analyze the difference between actual and planned numbers. When you add up all the variances, then the final summary would provide you the performance of the company in a certain time period.

How do you make a choice of data analysis?

Now it’s up to you to decide which data analysis approach to use. Because there are several data analysis methodologies and tools to choose from.

The data analysis tool you choose is determined by the sort of data you have and the goals you want to achieve with it. For quantitative data, you should use the above-mentioned quantitative technique. Otherwise, the qualitative technique should be used.

Choosing presentation technique.

The choice of presentation technique is very important for your research studies. Findings should be presented such that the users of the research can easily identify and have an understanding of the presentation. There are different techniques of data presentation.

The choice of technique depends on what the researcher intends to achieve in the research. Here are some data presentation techniques a researcher can choose from;

• Presentation in tabular form
• Presentation by text
• Presentation using diagrams

Presentation in a tabular form

As the name implies, tabular presentation of data involves using tables. The choice of using tables helps the researcher to avoid the complexity involved in using charts. Data is displayed in rows and columns in this technique.

Presentation by text

Presentation by text is the basic method of presenting data. Almost all researcher uses this method of data presentation. It involves logically explaining the result using text.

One disadvantage of using this method is that it takes time for readers to read through the entire text to make meaning of it. A summary of the analyzed data can help in this situation.

Presentation using diagrams

With such a short length of time, this type of data display and analysis approach may convey a lot. Data is shown as charts, graphs, pictograms, and other visual representations using this approach.

Summary -Data Presentation Technique

After analyzing numerous data analysis methods, we’ve come to the conclusion that different data kinds necessitate distinct data analysis approaches. If you pick a technique incorrectly, your study will be tainted and the results will be incorrect. As a result, it’s critical to pick the correct data analysis approach carefully.