Data Analysis: Definition, Types, Models, Up to Procedures

Data analysis is a skill that must be owned by a data practitioner. The data analysis process requires good critical thinking and problem solving. This skill is needed to determine the correct data analysis method. The use of appropriate analytical methods has a significant impact on the results of the analysis. If you choose the wrong data analysis method, you may not get the desired results, which of course takes time and effort.

But before that, you need to know about data analysis. This article covers the data analysis in more detail. Let's scroll the writing below!

Definition of Data Analyst

Data analysis is the process of processing data for the purpose of finding useful information that can be used as a basis for making decisions to solve a problem. This analysis process includes grouping data based on its characteristics, cleaning data, transforming data, creating data models to find important information from the data.

Remember that data that has gone through this process must be presented in an attractive and easy-to-understand form, often in the form of a chart or graph. The use of technology today affects most of our activities. This technology is of course related to data that will continue to grow all the time.

If data is allowed to accumulate, it will only be wasted. Although data can be processed and used to obtain useful information. Therefore, data analysis is a very important step in data processing. When it comes to data analysis, many different methods or techniques can be used.

Types of Data Analysis

In general, data analysis techniques are divided into two categories, namely data analysis techniques for qualitative and quantitative research. Here's an explanation.

A. Qualitative Data Analysis Techniques

Qualitative data analysis is the analysis of data obtained from the data collection process, starting with literature review, participation, and interviews. The qualitative analysis technique of the research data is described below.

• Content analysis

Generally, data is collected or generated by researchers who record or compile textual material transcripts such as images or sound.

• Discourse analysis

Discourse analysis usually examines speech, language, writing, conversation, and so on.

• Narrative analysis

The purpose of narrative analysis is to analyze a collection of descriptions of events and phenomena, such as biographies.

B. Quantitative Data Analysis Techniques

This technique is a data management technique that is numerical and statistical. The following are data analysis techniques for quantitative research.

• Descriptive statistics

Descriptive statistics are data analyzes that use techniques to describe or describe data as such, for example count the number or date of occupation.

• Inferential statistics

Inferential statistical techniques are carried out by making inferences based on data derived from research variables to be generalized. The goal of this technique is to determine whether the data is representative of the population for a group of people.

Data Analysis Method Model

A. Inductive Model

The inductive model is a data analysis method that is processed from facts (data) into theory. This is done to avoid manipulation of research data so that it is based on knowledge which is then adapted to theory.

B. Deductive Model

Deductive data analysis is data analysis which is the opposite of inductive, namely the process is obtained from theory to facts (research data).

Data Analysis Procedures

A. Data Processing

Data processing occurs when all data is collected and selected according to the focus of the research question. Processing of data processing includes the following:

• Editing

Editing or editing is the earliest step in verifying data for research materials.

• Coding

Coding or coding is the second step after data inspection, which labels data with certain symbols or signs for analytical materials.

• Tabulation

Tabulation is a compilation or presentation of information according to a research problem.

B. Data Analysis

The second step is data analysis, where data simplification, classification and easy interpretation are carried out. Data collected in quantitative research is then arranged with statistical figures and qualitative data with symbols or words.

C. Interpretation of Analysis Results

The final step of the data analysis technique is the interpretation of the analysis results. This is done to interpret the information collected, processed and presented as a conclusion. To draw conclusions, one must compare the hypothesis with the research found, whether it makes sense or not, and so on.

Thus information about data analysis along with explanations. Hope it is useful!