Qualitative vs Quantitative Data:15 Differences & Similarities
By busayo.longe
| Last updated:
Jan 12
|
12 min read
Share
Research and statistics are two important things that are not mutually exclusive as they go hand in hand in most cases. The role of statistics in research is to function as a tool in designing research, analysing data and drawing conclusions from there.
On the other hand, the basis of statistics is data, making most research studies result in large volumes of data. This data is measured, collected and reported, and analysed (making it information), whereupon it can be visualised using graphs, images or other analysis tools
In this article, we will be discussing data, a very important aspect of statistics and research. We will be touching on its meaning, types and working with them in research and statistics.
Data is a group of raw facts or information collected for research, reference or analysis. They are individual units of information that has been transformed into an efficient form, for easy movement and/or processing.
The plural of the word Datum, which describes a single quantity or quality of an object or phenomenon. It is applicable in different fields of research, business and statistics.
In the case of data analysis, we define it as the process of inspecting, editing, transforming and modelling data to discover useful information, informing conclusion and supporting decision-making. An important part of performing data analysis is knowing the different types of data we have.
There are two types of data, namely; quantitative and qualitative data;
Quantitative data is the type of data whose value is measured in the form of numbers or counts, with a unique numerical value associated with each data set. Also known as Numerical data, this data type describes numeric variables.
It has various uses in research and most especially statistics because of its compatibility with most statistical analysis methods. There are different methods of analysing quantitative data depending on its type.
Quantitative data is divided into two types, namely; discrete data and continuous data. Continuous data is then further divided into interval data and ratio data.
Qualitative data is the type of data that describes information. Its is a descriptive statistical data type, making it a data that is expressed with groups and categories rather than numbers.
It is also known as categorical data. This data type is relevant to a large extent in research with limited use in statistics due to its incompatibility with most statistical methods.
Qualitative data is divided into two categories, namely; nominal data and ordinal data. Nominal data names or define variables while ordinal data scales them.
Quantitative data is a group of quantifiable information that can be used for mathematical computations and statistical analysis which informs real-life decisions while qualitative data is a group of data that describes information.
Quantitative data is a combination of numeric values which depict relevant information. Qualitative data, on the other hand, uses descriptive approach towards expressing information.
Quantitative data is also known as numerical data while qualitative data is also known as categorical data. This is because quantitative data are measured in the form of numbers or counts.for qualitative data, they are grouped into categories.
Quantitative data are of two types namely; discrete data and continuous data. Continuous data is further divided into interval data and ratio data.
Qualitative data, on the other hand, is also divided into two types, namely; nominal data and ordinal data. However, ordinal data is classified as quantitative in some cases.
Some examples of quantitative data include Likert scale, interval sale etc. The Likert scale is a commonly used example of ordinal data and is of different types — 5 point to 7-point Likert scale.
Some qualitative data examples include name, gender, phone number etc. This data can be collected through open-ended questions, multiple-choice or closed open-ended questions.
The characteristics of quantitative data include the following; it takes the numeric value with numeric properties, it has a standardised order scale, it is visualised using scatter plots, and dot plot, etc.
Qualitative data, on the other hand, may take numeric values but without numeric properties, does not have a standardised order scale And is visualised using a bar chart and pie chart.
Quantitative data analysis is grouped into two, namely; descriptive and inferential statistics. The methods include measures of central tendency, turf analysis, text analysis, conjoint analysis, trend analysis, etc.
Quantitative data analysis methods are however straightforward, where only mean and median analysis can be performed. In some cases, ordinal data analysis use univariate statistics, bivariate statistics, regression analysis etc. which are close substitutes to calculating some mean and standard deviation analysis.
During the collection of qualitative data, researchers use tools like surveys, interviews, focus groups and observations, while Qualitative data is usually collected through surveys and interviews in a few cases. For example, when calculating the average height of students in a class, the students may be interviewed on what their height is instead of measuring the heights again.
Quantitative data is collected through closed-ended methods while qualitative data uses open-ended questions, multiple-choice questions, closed-ended and closed open-ended approach. This gives qualitative data a broader collection mode.
Quantitative data is mostly used to carry out statistical calculations involving the use of arithmetic operations. Calculating the CGPA of a student, for example, will require finding the average of all grades.
Quantitative data, on the other hand, deals with descriptive information without adding or performing any operation with it. It is mainly used to collect personal information.
Quantitative data is compatible with most statistical analysis methods and as such is mostly used by researchers. Qualitative data, on the other hand, is only compatible with median and mode, making it have restricted applications.
Although, in some cases, alternative tests are carried out on ordinal data. For example, we use univariate statistics, bivariate statistics, regression analysis etc. as alternatives.
Although very applicable in most statistical analysis, its standardised environment may limit the proper investigation. Quantitative research is strictly based on the researcher’s point of view, thus limiting freedom of expression on the respondent’s end.
This is not the case for qualitative research. Nominal data captures human emotions to an extent through open-ended questions. This may, however, cause the researcher to deal with irrelevant data.
Qualitative question example
In which of the following interval does your height fall in centimetres?
This is an interval data example.
Quantitative question example 2
Kindly enter your National identification number below.
_____
This is a nominal data example.
Quantitative Data Examples
Qualitative Data Examples
Quantitative data is compatible with most statistical methods, but qualitative data isn’t. This may pose issues for researchers when performing data analysis.
This is part of the reason why researchers prefer using quantitative data for research.
Quantitative data collection methods are more user-friendly compared to that of qualitative data. Although open-ended questions may give the researchers much-needed information, it may get stressful for respondents.
Respondents like spending as little time as possible filling out surveys, and when it takes time, they may abandon it.
Both quantitative and qualitative data has an order or scale to it. That is while ordinal data is sometimes classified under quantitative data. Qualitative data do not, however, have a standardised scale.
Quantitative and qualitative data are both used for research and statistical analysis. Although, through different approaches, they can both be used for the same thing. Consider two organisations investigating the purchasing power of its target audience through the method below.
Organisation A
What is your monthly income? ____
Organisation B
In which interval does your monthly income fall?
The first is a qualitative data collection example while the second is a quantitative data collection example.
Both quantitative data and qualitative data takes a numeric value. Qualitative data takes numeric values like phone number, postal code, national identification number, etc. The difference, however, is that arithmetic operations cannot be performed on qualitative data.
Both qualitative and quantitative data can be collected through surveys/questionnaires and interviews. Although through different approaches, they use similar tools.
The different types of data have their usefulness and advantages over the other. These advantages are why they are chosen over the other in some cases depending on the purpose of data collection. Here are some cases where quantitative data should be chosen over qualitative data.
Quantitative data is more suitable for scientific research due to its compatibility with most statistical analysis methods. It also has numerical properties which allow for the performance of arithmetic operations on it.
Quantitative research has a standardised procedure to it. Hence, it is easy to replicate past research, build on it and even edit research procedures.
Large data sets are best analysed using quantitative data. This is why some researchers turn qualitative data into quantitative data before analysis.
It is called the quantification of qualitative data. This way, they don’t have to be sweeping through a large string of texts for analysis.
Due to its standard procedure of analysis, it is the most suitable data type for laboratory analysis.
Research that involve sensitive data is best processed using quantitative data. This helps eliminate cases of bias due to familiarity or leaking sensitive information.
Although not compatible with most statistical analysis methods, qualitative data is preferable in certain cases. It is mostly preferred when collecting data for real-life research processes. Here are some cases where qualitative data should be chosen over quantitative data.
The main purpose of customer experience research is to know how customers feel about an organisation’s service and get information on what they can do to improve their service. Therefore, to achieve this, organisations need to assess human feelings and emotions. This is something that can only be done with qualitative data.
Especially with this ever-changing workplace culture, recruiters are now more interested in the applicant’s attitude, emotional intelligence, etc. than the skills they have to offer. For them to properly assess these traits, qualitative data about the applicant should be collected through an interview.
Organisations perform competitive analysis to assess their competition’s popularity and what they did to gain such popularity. Quantitative data do not give detailed information about this unlike how qualitative does.
Many web-based companies ask personal questions like, “What is your pet’s name?” or “What is your mother’s maiden name?” as a means of extra security on user’s account. Numbers are usually hard to memorise, which is why some people to find it difficult to memorise their phone number to date. Personal questions (qualitative data) like this is hard to forget and therefore better for security questions.
Dating websites collect personal Information (usually nominal data) of users to properly match them with their type.
Formplus as a data collection tool was built with the notion that proper data collection is the first step towards efficient and reliable research. Therefore, the makers of Formplus form builder software have added necessary features to help you collect your data.
Quantitative and qualitative data is best collected with Formplus because it not only helps you collect proper data but also arrange them for analysis. You no longer have to deal with data that is difficult to read when performing data validation process.
Each data is properly matched to the corresponding variables, making it easy to identify missing or inconsistent data.
To collect qualitative data using Formplus builder, follow these steps:
Step 2: Start Creating Forms: Formplus gives you a 21-day free trial to test all features and start collecting quantitative data from online surveys. Pricing plan starts after trial expiration at $20 monthly, with reasonable discounts for Education and Non-Governmental Organizations.
We will be creating a sample qualitative data collection form that inputs name (nominal data) and happiness level (ordinal data) of a respondent.
We will be creating a sample quantitative data collection form that inputs the courses offered by a student and their score, then output their average score.
The Add Calculations tab lets you perform arithmetic operations on numerical data.
Qualitative and quantitative data do have their key differences and similarities, and understanding them is very important as it helps in choosing the best data type to work with. It also helps in proper identification, so as not to miscategorise data.
These two data types also have their unique advantages over the other, which is why researchers use a particular data type for research and use the other for another research. However, quantitative data remains the more popular data type when compared to qualitative data.
As we have done in this article, understanding data types are the first step towards proper usage.
Connect to Formplus, Get Started Now - It's Free!
You may also like:
Ultimate guide on Primary Data, it’s meaning, collection methods, examples, advantages and disadvantage.
Everything you need to know about data interpretation, visualization techniques and analysis. I.e Charts, Graphs, Tables and question examples.
Simple guide on secondary and primary data differences on examples, types, collection tools, advantages, disadvantages, sources etc.
qualitative data definitions, types; in ordinal and nominal data, examples, data analysis, interpretations, advantages and disadvantages
