Qualitative vs. Quantitative: Difference, Similarity and Examples

When analyzing or compiling data it is important to know whether the data is qualitative or quantitative. The key difference is that qualitative data is descriptive, where quantitative data is numerical. 

Before exploring the differences between the two types of data, it’s necessary to define the two properly, so we’ll be able to know which type of data is in use at any given time.

Qualitative vs Quantitative Data: Definitions

Qualitative data is data which is measured by its value, rather than a numerical figure. For instance, qualities such as appearance and color are qualitative values. It is the quality of the data that is recorded.

Quantitative data, on the other hand, is data which is measured in numerical form, such as percentages and statistics, or the numerical dimensions of an item. 

Qualitative vs Quantitative: Pros and Cons 

It is important to know when to use qualitative or quantitative values, particularly when examining the difference between qualitative and quantitative research.

Qualitative Pros and Cons

Pros

  • A greater depth of information
  • More descriptive information
  • Qualifiers such as “Very” can be used to further describe information.

Cons

  • Can lead to bias
  • Certain qualitative data can be subjective

Quantitative Pros and Cons

Pros

  • Allows for examination of specific hypotheses
  • Data can be simplified
  • Data can be generalized

Cons

  • Does not allow for details (such as taste)

Qualitative versus Quantitative: Similarities

There are some similarities in the difference between qualitative and quantitative research. Whichever kind of data you are looking to compile, you are going to require raw data. This could be in the form of a collection of objects (for, perhaps, calculating the range of size — for quantitative data—or color—for qualitative data—of fruit), or in the form of surveys completed by participants. These are only two examples, but they demonstrate the fact that whether you are analyzing qualitative or quantitative data, you will require something to measure.

This leads to a second similarity. Both qualitative and quantitative forms are used as a way to collect, measure, and compare data. 

Finally, both forms have the same end: to produce a set of data which can then be easily analyzed. 

Qualitative vs Quantitative: Differences and Examples

Let’s examine in some more detail the differences between qualitative and quantitative data. This will be key in deciding which form is most useful for you to use for any given information. 

Qualitative vs. Quantitative Research

Quantitative research, as we’ve previously established, will give you figures, where qualitative research will give you facts, or statements. Quantitative research is used to sort through data in order to come to statistics that can be analyzed or implemented. These statistics can be simplified (10 in 100 can be simplified to 1 in 10; the same simplification cannot be implemented with qualitative data). 

Qualitative vs. Quantitative Data

Quantitative data is objective: there can be no bias in measurable statistics. Qualitative data can be subjective. That is, if an object is described as “large”, this is subjective. One person’s idea of “large” may be different to another person’s. Furthermore, qualitative data can sometimes necessitate context. In terms of the largest buildings in the United States, large has far different connotations than, for example, when used to describe the size of fruit. 

Quantitative data does not have these concerns. If the height of a building or a piece of fruit is measured numerically, there’s no subjectivity in, say, a number of inches. 7 inches is 7 inches and, assuming the measurement was precise, cannot be argued with.

Quantitative data, therefore, is best implemented for anything which can be objectively measured. Examples can include commute time, a person’s height, a number of hours worked, and so on. Research on these will result in hard numerical data.

Qualitative data, on the other hand, is better for subjective analysis. Although examples given are fairly simplistic qualitative research is still useful. For instance, data collected from a focus group will usually be qualitative. 

Example of Quantitative Research & Data

Let’s take the example of a focus group discussing a new restaurant. Management is interested in ensuring their food is being positively received by customers, but also that they are making a profit and that their food is consistent. 

Let’s take consistency. Quantitative research will be most useful here. Management could weigh every portion of lasagne. For customer satisfaction, for product control, and for cost-efficiency, they would want each portion of lasagne to weigh the same. This research will result in numerical, objective data, let’s say 200g. This research is useful for management, because if the weight of portions of lasagne range from 150g to 250g, they will know they are not being consistent enough in their portions. Nonetheless, the data is objective. 

Example of Qualitative Research & Data

Now let’s imagine they host a focus group for customer satisfaction. Several customers might say that they feel there is not enough meat in the lasagne. This is subjective: one customer’s idea of too little meat might be another customer’s perfect amount. The customer’s complaint cannot be objectively measured, and it can be argued with. This is still important for the management to know, however, because if a large number of customers have the same complaint they will know that this is something that they have to work on for high customer satisfaction.

Comparison Chart

To be precise: quantitative research results in data that can be quantified; qualitative research results in data that expresses the qualities.

QualitativeQuantitative
SubjectiveObjective
Focus groupsMeasurements/surveys
QualitiesNumerical figures
Qualitative vs. Quantitative Comparison Chart

Comparison Video

Conclusion

To conclude, both quantitative and qualitative research have their uses, but it is important to know which one to implement at any given time. It is also important to know which has been used when analyzing the data, so as not to fall into the error of disagreeing with objective fact, or taking subjective suggestions as infallible fact.

Image Courtesy:

  • Photo by Louis Reed on Unsplash
  • Photo by Carlos Muza on Unsplash

Author & Researcher @ Difference 101 Master in Philosophy & Data Analysis, Sorbonne Université (Graduated 2011) Lived in New York City

Difference 101
Logo