The Critical Differences: Qualitative vs. Quantitative Analysis

The Critical Differences: Qualitative vs. Quantitative Analysis

Picture of Adam Ramshaw
Adam Ramshaw
Adam Ramshaw has been helping companies to improve their Net Promoter® and Customer Feedback systems for more than 15 years. He is on a mission to stamp out ineffective processes and bad surveys.
Get the Customer Feedback Data Analyis Spreadsheet

It’s Tuesday afternoon and you’ve just been handed a stack of customer feedback forms with a wealth of information and data to analyse before Friday. You look at the forms; there are numbers, scales, and paragraphs upon paragraphs of commentary.

You pause for a moment to breathe. Where do you begin? What can you even do with all that information?

Which one is better, Qualitative Analysis or Quantitative Analysis?  Which will provide the best results? This post reviews where to use each approach for the best outcomes.

I’ve packaged the examples in this blog post in an Excel Spreadsheet you can  download and use:Download Now

What is Qualitative Analysis

Qualitative analysis is data analysis where you are relying on a subjective review of the data, i.e., where you have data that cannot be objectively assessed and you must use your human judgement.

Examples of Qualitative analysis

Analysing Customer Feedback on Service Interaction:

  • Data Collection: Conducting focus groups or individual interviews to gather insights on customer experiences.
  • Analysis: Theme analysis of open-ended responses to identify common sentiments and suggestions.

Evaluating Responses to a New Product Feature:

  • Data Collection: Collecting user feedback from forums, interviews, or feedback forms.
  • Analysis: Analysing qualitative feedback to understand user behaviours and preferences.

What is Quantitative Analysis

Quantitative analysis is data analysis based on some objectively verifiable fact about the data. The most familiar versions are statistics and charts. These two approaches deal with the objective data.

Examples of Quantitative analysis

Survey Analysis on Service Satisfaction:

  • Data Collection: Distributing surveys with structured questions to collect measurable data.
  • Analysis: Calculating average satisfaction ratings, identifying trends, and comparing different customer segments.

Usage Metrics of a New Product Feature:

  • Data Collection: Tracking usage metrics through analytics platforms.
  • Analysis: Analyzing usage data to understand adoption rates and gather actionable insights.

Where To Use Quantitative Vs Qualitative Analysis

Qualitative analysis is used to explore and understand a topics and ideas, whereas quantitative analysis is used to confirm conclusions about topics and ideas.

Generally, you will start you process with qualitative analysis and questions. From there you create quantitative survey questions and analyse them to confirm your understanding.

Let’s say you want to discover reasons why customers disliked your service, how to improve a product or why one area of your business is better than another, that’s when Qualitative Analysis becomes the more suitable selection for you.

It provides an exploration into individuals’ feelings; uncovering the motivations, desires, behaviour and needs. However, the accuracy/validity of the findings may vary due to the subjective nature in which the information and data is analysed and interpreted.

Below is an example of a question which provides Qualitative data:

A Survey Question that Generates Qualitative Data

Once you have an understanding of the subject area you can create quantitative questions analyse them for confirmation.

Quantitative analysis enables you to interpret the data statistically and make mathematical judgements about it.

This is what you need if you are:

  • Trying to identify patterns over a period of time;
  • Deciding how effective a certain variable is; or
  • Determining the satisfaction levels of a particular service, etc.

This approach is useful because it can provide you with objective information to understand what has occurred or what will occur.

Below is an example of a customer survey question which generates quantitative data:

A Survey Question That Generates Quantitative Data

You might think this would be qualitative data because it asks about a subjective topic but the data it generates consists of numbers, which are analysed quantitatively.

Benefits and Limitations

Qualitative Analysis

  • Benefits: Enables deeper understanding, uncovering motivations and behaviours.
  • Limitations: Subjective nature may affect the accuracy and validity of findings.

Quantitative Analysis

  • Benefits: Provides objective data, allowing for clear conclusions and generalizations.
  • Limitations: May overlook underlying factors that aren’t easily measurable.

Validity and Reliability

Quantitative analysis is all about hard objective data i.e., non-anecdotal information. As Quantitative Analysis is a ‘numbers game’, the validity and reliability of the data gathered from the customer feedback or survey is much more concrete.

This is not to say all Quantitative analysis is reliable and valid; the validity and reliability depend on the instruments used, what it’s measuring, and the skill of the analyst in statistical techniques.

Qualitative data is great to explore your respondents’ perception and to uncover hidden reasons why your customers are happy or unhappy with your products. Whatever the reason may be, you need to remind yourself of what you’re trying to achieve with the data that you have.

How Much Data Do You Need?

The notion of the ‘bigger the better’ applies to Quantitative analysis as, generally, the larger the sample size, the better the insights you are able to obtain from the data.

Let’s say for example, you would like to rate your organisation’s professionalism on a Likert Scale of 1-7. You will be able to gain a more reliable perspective of your organisation’s professionalism with a larger population.

A larger sample size enables you to be more confident on how the overall population feels.

Putting It All Together

In the customer feedback process you want to have both Qualitative and Quantitative Analysis because they complement each other.

For instance, in your survey you will want to have a range of questions for quantitative analysis to tell you what you are doing well and what you are doing poorly:

How you would use Quantitative Questions

You should also have some questions for qualitative analysis to allow you to understand how to improve:

How you would use Quantitative Questions

Qualitative Vs Quantitative Analysis FAQ

What is an example of qualitative analysis?

Examples of qualitative analysis include tagging text responses in a customer feedback survey, writing movie reviews, and assessing the management potential in a company.

What is an example of quantitative analysis?

Calculating the mean, median and mode of response data when analysing a customer feedback survey is an example of quantitative analysis

How combining Qualitative and Quantitative Analysis benefits your business?

Combining Qualitative and Quantitative Analysis in your business operations can provide a more holistic understanding of issues at hand. Qualitative Analysis dives deep into the ‘why’ and ‘how’ of user behaviour, providing insights into human emotions and motivations, while Quantitative Analysis deals with hard data, offering statistical evidence and trends. By employing both methods, you can not only understand the underlying reasons behind certain phenomena but also quantify these insights, making it easier to communicate findings and make informed decisions.

How can I ensure the accuracy of Qualitative Analysis given its subjective nature?

Ensuring the accuracy in Qualitative Analysis can be a bit challenging due to its inherent subjective nature. However, there are several strategies to mitigate bias and enhance accuracy:

Triangulation: Employ multiple data collection methods and sources to validate your findings.

Peer Review: Have other experts in your field review your analysis process and findings.

Transparency: Be open about the methods and techniques used in your analysis. Clearly document your steps and be honest about the limitations of your findings.

Use of Software: Utilize qualitative data analysis software to help in coding and categorizing the data, which can help in reducing human error.

Training and Experience: Ensure that the analysts have proper training and experience in qualitative analysis to carry out the process effectively and accurately.

By incorporating these strategies, you can improve the accuracy and reliability of your qualitative analysis, making the insights more valuable for your business.

I’ve packaged the examples in this blog post in an Excel Spreadsheet you can  download and use:Download Now

Originally Posted 12 November 2012, Last Updated 5 October 2023