Data Analysis Technique: Definition, Types & Examples

A data analysis technique is a set of procedures used to understand, clarify, and gather knowledge from data.

In today's digital age, you must use the right techniques to understand patterns, trends, and important insights in data.

Depending on the type of data being analyzed and its intended use, you can apply a variety of data analysis techniques, including qualitative and quantitative methods.

In the following article, we'll learn about data analysis techniques, including their functions, types, stages, and examples. 

What Is Data Analysis Technique?

In the context of business and technology, you may have heard the terms "data analysis" and "data analysis technique" numerous times. However, are you truly familiar with both of them?

Generally speaking, data analysis is the process of collecting, organizing, and cleaning data to generate information.

On the other hand, a data analysis technique is a set of methods for handling, analyzing, and interpreting data. 

In this phase, you will look through the information to find relationships and patterns that have significance.

Data Analysis's Functions

The following are some of the data analysis functions that can provide significant benefits for your business's operation and success:

• Provides solutions to business problems.
• It becomes a solid foundation for making business strategy decisions.
• Evaluate the quality of products and services.
• Serve as a guide for business operations.
• Serve as the foundation for establishing future business goals.

Types of Data Analysis Technique

Based on the goal and characteristics of the data being analyzed, you can use a variety of data analysis approaches. Here are a few types that are frequently used:

1. Data Analysis Technique in Qualitative Research

The data analysis technique in qualitative research is non-numerical and focuses on the conceptual analysis of information that produces an understanding of the subject. Several kinds of techniques for qualitative data analysis are listed below:

• Discourse Analysis: Delivers a method for understanding the contextual meaning of a language. This implies that you will focus on the respondents' understanding of the research topic.
• Narrative Analysis: Describes the stories collected through various methods such as interviews, surveys, and observations.
• Grounded Theory Analysis: This creates a theory that is directly sourced from the data gathered, not from a theory that already exists.

2. Data Analysis Technique in Quantitative Research

Unlike the qualitative approach, quantitative data analysis involves numerically measuring information and analyzing it with mathematical and statistical methods.

Here are two kinds of data analysis techniques in quantitative research that are frequently used:

1. Descriptive Analysis

A method for comprehending, summarizing, and spotting patterns in a sample of numerical data. Several techniques that are frequently used in descriptive analysis are:

• Median: Locating the center of numerical data.
• Mean: Obtaining the average of the values in a data set
• Mode: Identifying the value that is most frequently seen.
• Frequency: Indicates how often a specific value appears.
• Range: Presents a dataset's values from lowest to highest.
• Percentage: Refers to the proportion of values or groups within a larger group of respondents.

2. Inferential Analysis

Attempts to forecast the outcomes of descriptive statistical analysis, particularly when interpreting outcomes, predicting the outcomes of groups, evaluating hypotheses, and demonstrating connections between several variables. These are two techniques that are frequently used:

• Regression analysis: This is used to predict the connection between two variables. You can understand how one variable impacts another by using this method.
• Hypothesis analysis: This means the evaluation of the hypotheses based on data.

Stages of Qualitative Data Analysis Technique

Several key steps in the qualitative data analysis technique help researchers understand, organize, and analyze the collected data. The steps are listed below:

1. Understanding the Data

Since most qualitative research focuses on data in the form of words, the first step in analyzing qualitative data is acknowledging this. 

To fully grasp the context of the data, you must understand and read it several times.

2. Reviewing the Research Objectives

During this process, you need to make sure that the data collected is relevant and responds to the research inquiries that have been developed.

If the data falls short of the research objectives, additional information must be gathered to fill the gap.

3. Data Reduction

Data reduction is a method for creating precise information and assisting decision-making by simplifying, classifying, and eliminating unimportant data.

Because of the large volume and complexity of data, this step is vital in determining which data is relevant to the final goal of your analysis.

4. Developing a Framework

In this stage, relevant concepts, behaviors, or phrases in the data are identified and given particular codes. 

For example, you're able to code for socioeconomic status, gender, age, or respondent responses.

5. Identifying Patterns

After the coding procedure has finished, you can start identifying the primary concepts in the data, finding the most common patterns, and determining which areas require additional research.

Stages in Quantitative Data Analysis Technique

The stages of quantitative data analysis are a set of systematic steps for processing, analyzing, and explaining numerical data. Here is a breakdown of the phases:

1. Data Validation

The process of data validation seeks to verify that the technique has been followed in the collection of data. The process of data validation consists of four primary phases, which include:

• Fraud: Ensuring that every participant in the survey is an actual participant and that the information is not submitted with false intent.
• Screening: The process of making sure the participants selected fulfill the requirements set out in the research.
• Procedure: Verifying that the guidelines set forth during data collection are being followed.
• Completeness: Confirm that the respondent has been asked every question by the researcher.

2. Data Editing

The step of data editing involves confirming any errors in the data collected.

Errors frequently occur during the data collection process, such as respondents answering questions incorrectly or missing particular ones. These circumstances may lead to inaccurate data.

3. Data Coding

Data coding is the process of categorizing the collected information and giving it values. For a more methodical analysis of the data, this procedure typically uses a structured table.

For instance, there are 500 respondents with different levels of education. To make data analysis simpler, data coding is required.

An Example of Data Analysis

In today's business world, data analysis techniques play a role in making the right decisions.

One example of data analysis is used to understand the relationship between social media marketing spending and a business's profit.

In this circumstance, regression analysis will be needed. Regression analysis is a technique for investigating and analyzing the relationship between an independent variable (social media marketing spending) and a dependent variable (sales revenue).

The dependent variable represents the factor that you want to project or boost, and the independent variable refers to the variable that impacts the dependent variable. 

So, with the help of regression analysis, you can draw:

• Positive correlation: This shows that the higher the spending on social media marketing, the greater the profit from sales. If so, the business may want to consider raising its investment in social media marketing to boost sales.
• Negative correlation: It suggests that spending on social media marketing has no major impact on business sales. If this is the case, the business might think about reducing this cost or eliminating it to use more effective marketing techniques.

That covers everything there is to know about data analysis techniques, including their types, stages, applications, and examples. 

Through data analysis, businesses can improve their business strategy and obtain a better understanding of how things work.

Furthermore, you can optimize your business plan by promoting it both organically and digitally. 

This is where SEO Services by cmlabs can help you with digital marketing by boosting company visibility through organic search engine optimization.

With the correct SEO plan, your company can expand organically without using paid advertising. Get a special offer right away by scheduling a meeting with our Marketing Team!