Rancakmedia.com – Let's get to know the meaning of data analysis techniques so that you can understand them in detail, please refer to the article below that we have provided.
Data analysis techniques are ways of transforming data into certain types of information. When doing research, you need to evaluate the data you have to make it easier for consumers to understand. In addition, this data analysis is also important so that you get answers to the various research challenges that are being worked on.
Perhaps you are not currently well versed with different research methodologies or approaches to data analysis. Data analysis is one of the important steps in research. Data analysis was carried out of course to obtain an explanation of the research that had been carried out.
As time goes on and we do different work, we are more likely to come across some of these research words. In fact, you may even be personally involved in research afterward. For now, it can't hurt to learn the basics of data analysis.
Understand the Definition of Data Analysis Techniques
Data analysis is a technique of processing data and information into the research process. Then the data will be used as research findings or fresh information. Data analysis is needed to find out whether the information collected is correct, which will make the next steps easier.
Expert Advice on Data Analysis Techniques
The definition of data analysis techniques according to some experts, as follows:
Lexy J. Moleong
In his opinion, technique analysis data is an activity to check originating from research instruments, such as documents, notes, recordings, and so on in a study.
Data analysis is an activity related to testing in research sections, which aims to find certain patterns.
Data analysis is an activity to process and present data to answer the problem being studied.
Data analysis technique is a systematic process of finding and compiling data obtained from documentation, interviews, etc. into a category. Arranging in this case is sorting out what is important and also making conclusions.
Purpose of Data Analysis Techniques
The purposes of data analysis include:
- Decompose data so that it is easy to understand.
- Obtain information from research that will be used to draw conclusions according to the data collected.
- Understanding how someone interprets a phenomenon.
- Get an explanation of a particular event.
- Get the reason why someone did a certain action.
Various Data Analysis Techniques
The data analysis approach can be classified as either quantitative or qualitative based on the research findings. Here's the explanation:
Quantitative Data Analysis Techniques
Quantitative data is data that is numerical in nature or can be calculated because it is in the form of statistical numbers. Quantitative information is collected through the use of survey data, such as questionnaires. As a result of the data being in numerical form, mathematical or statistical models are also used in analytical techniques. There are two kinds of quantitative analysis techniques:
Descriptive analysis was carried out by studying past data track records to obtain a research result. This imaging technique is often used when large amounts of research data, such as data from a census, are being studied.
Inferential analysis technique is a way of processing data using statistical tools. Usually, calculations using statistical techniques are performed using SPSS software to obtain findings. When further research is carried out in the future, the calculation results will be used as a starting point.
Qualitative Data Analysis Techniques
Qualitative data is the antithesis of quantitative data. So, the data collected is in the form of non-numeric data, or data that is not numbers. Qualitative data were obtained through interviews and observations so that the data obtained is more descriptive of a phenomenon.
There are various strategies that can be used to process qualitative data, including:
Discourse analysis can be done by observing how individuals communicate with each other in technical settings. Discourse analysis seeks to find out the patterns that exist in a communication activity.
In narrative analysis, it's common to look at things like plot points and character motivations to find out what happened in a story. This method of narrative analysis can be used both inside and outside the organization as part of the work.
Content analysis is a qualitative analysis technique that focuses on studying data in a specific context. Specific settings in this scenario, such as the culture within the group or person, Transcripts, recordings, and videotapes, among other sources, are used to construct this content analysis.
Model Analysis Techniques
When it comes to data analysis, there are two main models to consider:
The inductive model is a data processing procedure that is carried out in stages, starting with obtaining facts and then modifying them to suit the theory that has been decided.
To avoid possible data tampering, it is necessary to have reliable facts. Examination of social problems is closely related to the inductive model.
One of the limitations of inductive data is that searching for data may occur frequently because it requires data that really fits the hypothesis.
The deductive model is data analysis carried out from the theoretical stage to the next fact identification stage. In other words, this approach is the antithesis of the inductive model.
Data Analyzing Techniques
There are various actions that can be performed to process data. The steps to follow include:
Collecting important data is certainly one of the tasks that must be done. The next stage in the research cannot be completed and no results can be obtained if no data is collected.
Editing is the process of ensuring that all data collected conforms to the study's definition of the problem.
Coding or coding is the process of converting raw data into meaningful symbols or signs that can be used in further analysis. Coding serves two purposes: to speed up the next step and to make the current step easier.
Input is the step of collecting and presenting the data that has been collected according to the research objectives. In other words, this tabulation stage is the stage of inputting data that has been recorded into a table.
Analyzing the data comes next, after the processing has been completed. This analysis was carried out to clearly understand the data. These data were analyzed according to research techniques, both qualitative and quantitative in the analysis.
Statistical figures are used to represent quantitative information that is not written down but is given in a different way, such as through conversation.
The final analysis is determining how to make sense of the data collected. Whatever the results of the data, both statistically and qualitatively, the data provided is in the form of a concluding sentence that can be understood by the reader. It goes without saying that the data must be interpreted based on the data of the previous analysis.
Things to Look For in the Selection of Data Analysis Techniques
When choosing a data analysis technique, keep these four points in mind:
Understanding the characteristics of research
One of the most important aspects of any research project is having a clear picture of exactly what the problem at hand is. By understanding these features, we can predict how the analytical technique will be selected.
Knowing the Data Obtained
To select an analytical technique, the researcher must first determine how the data will be collected.
In order for the analysis technique to focus, you also need to know what kind of people are in the population.
Number of Variables
The choice of data analysis methodology must also pay attention to the number of variables used in the study. Because of the many variables can also affect the analysis technique. For example, two variables will vary more than two variables.
Data analysis is a technique of processing data and information into the research process. The purpose of data analysis, among others, is to describe the data so that it is easy to understand.
The data analysis approach can be classified as either quantitative or qualitative. Quantitative data is data that is numerical in nature or can be calculated because it is in the form of statistical numbers.
Thus the article about getting to know the meaning of data analysis techniques, I hope the above article can be useful and useful for all of you.