If you are reading this, it is simply because you are not aware of the differences between qualitative research data and quantitative research data methods. The two concepts Qualitative and Quantitative Research Data can not be interchanged because they are two entirely different research methods.
What then is the difference you will ask?
This data collection method involves focus group discussions, Record keeping, Case Studies, individual interviews, observations, and participation. It is used to find out trends, thoughts, and opinions while identifying the problems. It provides insights into the problems, helps to develop ideas/hypotheses for qualitative research. This is a research method that approximates and explores. The data mined is non-numerical in nature. This means – it does not include numbers in its definition of traits. In statistics, qualitative data is also known as categorical data – Data that can be arranged in categories based on its attributes, properties or phenomenon. In summary, this method provides an avenue for observers to quantify the world around them.
When you have collected your data, For proper analysis,
Arrange your data systematically – You can either export the data into a spreadsheet or manually type in the data or choose from any of the computer-assisted qualitative data analysis tools.
Organize your data – One of the best ways to organize the data is by going back to your research objectives and then organizing the data based on the questions asked. Arrange your research objective in a table, so it appears visually clear.
Give your data code names – This means categorizing and assigning properties and patterns to the collected data. After assigning names to your data, you can then begin to build on the patterns to gain in-depth insight into the data that will help make informed decisions.
Validate your Data –
There are two sides to validating data:
1. Accuracy of your research design or methods.
2. Reliability, which is the extent to which the methods produce accurate data consistently.
Conclude your analysis. This means presenting your data as a report that can be easily and readily used.
The report should state the method that you, as the researcher, used to conduct the research studies, the positives, and negatives and study limitations. In the report, you should also state the suggestions/inferences of your findings and any related area for future research.
Quantitative Research uses measurable data (Numbers and forms of count) to formulate facts and uncover patterns in research.Quantitative data is used to answer questions such as “How many?”, “How often?”, “How much?”. This data can be verified and can also be conveniently evaluated using mathematical techniques. This is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population. Quantitative data collection methods include various forms of surveys – online surveys, paper surveys or questionnaires, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations sent across to a specific section of a population.
Quantitative data should be analyzed in order to find evidential data that would help in the research process. Using the following methods;
Cross-tabulation: Cross-tabulation is the most widely used quantitative data analysis methods. It is a preferred method since it uses a basic tabular form to draw inferences between different data-sets in the research study. It contains data that is mutually exclusive or have some connection with each other.
Trend analysis: Trend analysis is a statistical analysis method that provides the ability to look at quantitative data that has been collected over a long period of time. This data analysis method helps collect feedback about data changes over time and if aims to understand the change in variables considering one variable remains unchanged.
MaxDiff analysis: The MaxDiff analysis is a quantitative data analysis method that is used to gauge customer preferences for a purchase and what parameters rank higher than the others in this process. In a simplistic form, this method is also called the “best-worst” method. This method is very similar to conjoint analysis but is much easier to implement and can be interchangeably used.
Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative data analysis method that analyzes parameters behind a purchasing decision. This method possesses the ability to collect and analyze advanced metrics which provide an in-depth insight into purchasing decisions as well as the parameters that rank the most important.
TURF analysis: TURF analysis or Total Unduplicated Reach and Frequency Analysis, is a quantitative data analysis methodology that assesses the total market reach of a product or service or a mix of both. This method is used by organizations to understand the frequency and the avenues at which their messaging reaches customers and prospective customers which helps them tweak their go-to-market strategies.
Gap analysis: Gap analysis uses a side-by-side matrix to depict quantitative data that helps measure the difference between expected performance and actual performance. This data analysis helps measure gaps in performance and the things that are required to be done to bridge this gap.
SWOT analysis: SWOT analysis, is a quantitative data analysis methods that assigns numerical values to indicate strength, weaknesses, opportunities and threats of an organization or product or service which in turn provides a holistic picture about competition. This method helps to create effective business strategies.
Text analysis: Text analysis is an advanced statistical method where intelligent tools make sense of and quantify or fashion qualitative and open-ended data into easily understandable data. This method is used when the raw survey data is unstructured but has to be brought into a structure that makes sense.
Key things to note about Qualitative and Quantitative Research Data are:
Associated with numbers
Implemented when data is numerical
Collected data can be statistically analyzed
Examples: Height, Weight, Time, Price, Temperature, etc.
Associated with details
Implemented when data can be segregated into well-defined groups
Collected data can just be observed and not evaluated
Examples: Scents, Appearance, Beauty, Colors, Flavors, etc.