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In a nutshell, quantitative data is all about numbers and statistics, whereas with qualitative data we’re talking words and meanings. Read on to discover when to use which data research approach and what kind of methods you might consider.
Data that is expressed as a defined amount, quantity or within a specific range is referred to as quantitative data. Quantitative data can be counted or measured, so it is common for the data to be stated with a unit of measurement.
Some examples of quantitative data: kilograms when referring to the weight of something, or metres, kilometres or centimetres in reference to distance. Methods of quantitative research might include experiments or closed-ended question focused surveys.
Qualities or characteristics of findings are described with qualitative data. This variety of information can be gathered using observation techniques, interviews, focus groups or questionnaires, and is often presented in a narrative format. Some examples of qualitative data: video recordings, case studies or interview transcriptions.
Ultimately, you will be best placed using a quantitative approach if you are needing to test to confirm something, such as a theory.
Qualitative research is the way to go if you want to understand characteristics or traits of trends, or to determine the boundaries for larger data sets.
Both data research options enable you to answer different kinds of questions, so the choice you make will largely be driven by what you’re trying to answer or respond to.
There’s also the possibility of taking a mixed approach, again depending on what you’re trying to answer.
Quantitative data collection
Qualitative data collection
Observation: watching within natural setting with no variable control
Publication reviews:Analysis of texts by various authors on the relevant topic areas
Experiments: variables controlled and manipulated to create cause and effect relationships
Ethnography: close observation of behaviours within a predetermined group for an ongoing period of time
Focus groups and surveys: interviewing with a closed question or multiple-choice approach
Focus groups and interviews: discussions within one-to-one and group settings to compile opinions, using open-ended questioning
When it comes to the analysis of data, the methods unsurprisingly alter for each data approach.
As we’re dealing with numbers, statistical analysis is often applied to establish data patterns, with outcomes plotted in graphs or tables.
Generally speaking, you could be looking to discover things such as average scores, reliability of results and how many times a certain answer was provided.
Preparation of the data before it is analysed is incredibly important. The data gathered needs to be validated, any known errors removed, and remaining data coded. This process ensures the best quality data is going to be analysed and provides a more accurate and helpful outcome.
The two most used quantitative data methods for analysis are inferential statistics and descriptive statistics.
As we’re dealing with words, images or video content, qualitative data can be more challenging to analyse.
Examination of any recurring themes within the data is a helpful approach to take, as is exploring the frequency of use of phrases or words. The idea, like quantitative data analysis, is to discover patterns.
Methodologies which could be used include:
Now that we’ve looked at various approaches to gathering data, let’s look at some specific tools.
While we might find ourselves using focus groups or interviews as a technique to collect data, tools such as ‘sentence completion’ or ‘word association’ can provide a wealth of further data to explore. With sentence completion, an individual is given a part-sentence to complete, and the answers provided give us an insight into the views and ideas of that person. Word association performs a similar function, where the individual is asked to share what comes to mind when they read or hear particular words.
When it comes to drilling down into the digits, you might embrace statistical software options such as SPSS, JMP, Stata, SAS, R or MATLAB.
Arm yourself with quality industry-aligned training that teaches you the process of collecting, organising, cleaning, and analysing raw data to identify patterns and draw conclusions.
With study options to suit all levels of ability, Academy Xi has you covered:
Do you have any questions? Our experienced team is here to discuss your training options. Speak to a course advisor today and take the first steps in your Data Analytics journey.