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HomeTech GlossaryData Analysis Techniques: Objectives, Methods and Analysis Procedures

Data Analysis Techniques: Objectives, Methods and Analysis Procedures

A researcher is certainly no stranger to doing data analysis. Before researchers conduct data analysis, they can collect data related to research needs.

Materials for data analysis techniques can be obtained from various sources ranging from interviews with research respondents, journals related to observations, archival documents from various agencies, distributing questionnaires.

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After collecting the data needed for the research, the researcher will then sort or select the data for data analysis. The technique used in the research data analysis process is called the data analysis technique. Data analysis techniques the most important aspect in a research.

The data analysis stage is a stage that cannot be forgotten in the research process. This stage requires that data be collected using various data collection techniques, then processed and presented to help answer the research problems under study.

It can be said that data analysis techniques can only be done when the research points have been met, for example collecting the right data that is tailored to the problems in a particular research.

The process of analyzing the data then uses special techniques to make it easier to draw conclusions from the data set obtained.

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The use of this data analysis technique has various types depending on the type of data that has been collected. In general, data analysis techniques in research are divided into two, namely quantitative data analysis techniques and qualitative data analysis techniques.

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Quantitative data analysis aimed at processing data in the form of numbers.

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While, qualitative data analysis is ideally used by researchers to process non-numerical data. Each researcher can use one type of technique or vice versa, namely combining two or more qualitative data analysis techniques or a mixture of quantitative and qualitative data analysis techniques.

Whatever the data analysis technique used — quantitative and qualitative data analysis techniques — the goal remains the same, namely determining or getting overall conclusions derived from research data that has been collected by researchers. In addition, data analysis techniques aim to describe and explain research data, so that they can be understood by others.

Usually in research, data analysis techniques are quite popular and become the target of researchers in conducting research data analysis both for final projects, theses and theses. What types of data analysis are quite popular in research.

Come on, let’s find out more in data. On the article data analysis technique aims to describe and explain the research data, so that it can be understood by others. Usually in research, data analysis techniques are quite popular and become the target of researchers in conducting research data analysis both for final projects, theses and theses.

What types of data analysis are quite popular in research. Come on, let’s find out more in data friends. On the article data analysis technique aims to describe and explain the research data, so that it can be understood by others.

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This time, we will discuss the definition, techniques, examples and procedures in performing data analysis techniques.

It is possible that data friends also use this analysis in your research. With the hope of being an additional insight and recommendation for you prospective data practitioners, researchers and data enthusiasts.

Surely you are curious, right, make sure you don’t miss the following article, and read carefully, stay tune and keep scrolling on this article guys!

1. Understanding Data Analysis Techniques

Data analysis technique is one way to process data into a variety of information so that the characteristics of the data become easy to understand and also useful for finding solutions to problems.

The problems that are reviewed in data analysis techniques are problems that are indeed a concern in the research carried out. The issues raised must also be in-depth and truly illustrate that this is a problem that must be resolved.

Performing data analysis requires hard work and creativity to find a way out in solving research problems. Each research has different characteristics and views.

It cannot be generalized between one study and another. The techniques used are also different, so the focus of observation must be different.

Please read Job Opportunities in the Field of Data Scientist

2. Purpose of Data Analysis

In general, the purpose of data analysis is to explain data so that it is easier to understand, then a conclusion is made.

A conclusion from data analysis is obtained from a sample which is generally made based on hypothesis testing or conjecture.

As for before conducting data analysis, there are various stages that must be passed, namely data processing first. These stages include: editing or editing, codification or coding on the list of questions, tabulation of data.

After processing the data, the data analysis stage is carried out. This stage aims to examine what data still needs to be searched, what research hypotheses need to be tested, what questions need to be answered, what methods should be used to obtain new information, to what errors must be corrected immediately.

3. Examples of Data Analysis Techniques

Examples of data analysis techniques, one of which is correlation analysis. Correlation analysis is a method to determine whether or not there is a linear relationship between variables.

If there is a relationship, the changes that occur in one of the X variables will result in changes in the other variable (Y).

Correlation is divided into three, namely, simple, partial and multiple correlations. In the correlation coefficient there is the term reliability coefficient.

The reliability coefficient is the square of the correlation coefficient between apparent-score and pure-score. The correlation coefficient between apparent-score and pure-score will always be greater than the reliability coefficient, as long as the reliability coefficient is not equal to 0 or 1.0.

4. Data Analysis Procedure

The steps in writing data analysis techniques, among others:

1. Data Processing

Data that has been collected through the data collection process, then the data must be processed. Data processing aims to make the data simpler, so that all the data that has been collected can be arranged properly and neatly, then the data is analyzed.

In performing data processing, there are stages that must be followed, namely:

a. Editing

Editing, also known as editing, is the activity of checking the entire list of questions returned by research respondents.

b. Coding

Coding is an activity carried out by means of giving symbols or signs in the form of numbers to the respondents’ answers received. Coding can be explained as data categorization.

There are at least three types of encoding:

Open coding is a part of analysis that is specifically related to naming and categorizing a phenomenon through data testing.

Axial coding is a set of procedures for placing data back in new ways by making links between categories, starting with determining the type of category and then continuing with the discovery of relationships between categories or between subcategories.

Selective coding, namely at this stage the researcher is likely to find a large amount of data with many categories and relationships between categories/sub categories and very large variations.

Even on the other hand, the coding stage can be done manually or using qualitative data analysis software such as NVivo, Atlas ti 6.0, HyperRESEARCH 2.8, Max QDA and others.

Please read Data Mining Definition, Functions, Methods and Applications

c. Tabulating

Tabulation can be said to be an activity of compiling and calculating data from the results of coding, which will then be displayed in the form of a list of tables.

2. Data Analysis

If the data processing has been completed, then the next step is to analyze the data. Data analysis aims to reveal what data still needs to be searched, what research hypotheses need to be tested, what questions need to be answered, what methods should be used to obtain new information and what errors must be corrected immediately.

After the data has been collected, it is grouped into two groups, namely quantitative data in the form of numbers and qualitative data which is more expressed in the form of words or symbols.

3. Interpretation of Analysis Results

If the data has been analyzed, the next thing to do is interpret the results of the data analysis. The interpretation of the results of data analysis aims to provide conclusions from the qualitative research that has been done.

So that finally the conclusion is made by comparing the assumptions that have been formulated with the results of the data analysis that has been obtained, so that finally the researchers can draw conclusions whether to accept or reject the assumptions that have been formulated.

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Muhamad Nagib
Muhamad Nagibhttps://istalkingtech.com
Information technology writer who has great interest in Windows, MacOS and Linux-based computers as well as Android and iOS smart devices, therefore this website is dedicated to reviewing all information about these operating systems and devices. Learn more.

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