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The Greatest Data Analysis Techniques Ever Used

What Are the Best Data Analysis Techniques?

Surprisingly enough many researchers quickly realize that there is not a single data analysis tool that you can just throw your results into and have the “answers” delivered to you at the press of a button. Most methods of data analysis in qualitative research and quantitative research require a huge amount of thought and planning on the part of the researcher and will often be designed specifically for the research that is being done. With so many methods of data analysis the trick is how to select and use the right methods at the right time.

statistical data analysis schema

Defining Different Methods of Data Analysis

Choosing the right data analysis techniques is something that is often overlooked within most books and training courses which tend to jump straight from data collection into the various tools that are available for your use. Many students are left wondering how to go about the process of analyzing their data so as to be able to draw conclusions from their research. The following framework for data analysis is what many suggest;

  • Define the required data; use your thesis and some careful thought to define exactly what data you will need to actual gather if you want to prove your thesis as well as the conditions the data will need to be collected under
  • Collect the data; collect the data according to your needs; usually through surveys, interviews and even direct measurement or observation
  • Initial data processing; usually in the form of simple tabulation within a spreadsheet
  • Cleaning of the data; more often than not your data will suffer from missing information as well as other problems that will need to be sorted out before you continue
  • Initial data analysis; usually through the use of simple graphical techniques and the use of well known measures such as the median or mean
  • Modeling; using the data you have to create a relationship between the different data sets in the form of formulas. Typically will involve regression analysis. Inferential statistics

The above process is typically iterative in nature and you will often find yourself going back a few steps each time to refine what you are doing.

An Alternative Framework for Data Analysis Techniques

Steve Baty suggests an alternative framework for data analysis that is very well thought out and worthy of mention. The basics of the suggested process are as follows:

  • Deconstruction; the rearrangement of the data into its component pieces
  • Manipulation; reorganizing and moving your data around without making any changes to the data that you have collected
  • Transformation; similar to manipulation but the processing of the data into another form can alter the actual data that you have collected
  • Summation; collecting together similar observations so you can see their effect
  • Aggregation; draw together data from multiple sources in much the same way as you would with summation; this often provides a much higher level view of what is going on
  • Generalization; this is the start of creating rules for how the data sets relate to one another
  • Abstraction; this involves removing the specifics from the data to enable the general characteristics of the data to be seen
  • Synthesis; pulling together all of the ideas and concepts to create something fundamentally new

This is a similar framework to the first that was shown but shows a much better way of looking at the actual qualitative or quantitative data analysis techniques and how they fit together. Of course you may still find yourself having to develop a framework that will better fit your research field and data so as to ensure that you use the right data analysis techniques to arrive at the results that you are seeking.

Learn what are the methods of data analysis and how to use them efficiently today!