In addition to reviewing hard numbers (quantitative) and experiences (qualitative), the Mixed Method combines them both. This approach offers extremely valuable insights about what could be going wrong in a company.

Mixed Method Data

Combining Quantitative and Qualitative

Sometimes, it is necessary to look at data that tells us about both quantity (how much) and quality (to what degree).

This “mixed method evaluation” can be sequential or concurrent, and it is useful for exploring the meaning behind the data.

Mixed Method measurements use:

discrete items that can be counted (days of the week), and

continuous events (like temperature) along with

images, narratives, experiential sensations, and feedback.

Purpose of Mixed Method Data

It is designed to:

• Triangulate root causes
• Balance the research methods
• Develop risk intelligent strategies
• Initiate a transition plan
• Decide which services and product lines to expand

For example, a graph of customer turnover data might show that a significant number of customers left during one week in particular. But this graph will not show the events that occurred that week, so you won’t be able to make a risk intelligent decision about how to correct this problem… unless you have narrative or feedback data to corroborate and add dimension to this data point.

You could gather this qualitative type of information by interviewing staff, asking customers why they left, placing yourself in a first-person situation that mimics the conditions, and finding additional insights about the circumstances that may have contributed to the events of that week.

Forms of Analyzing Mixed Method Data

There are several ways to look at the data using a mixed method approach. These include:

• reasons for staff and customer turnover (which we just discussed)
• the degree of success of a marketing campaign
• what type of appreciation for achievement your employees may find most effective
• which communication methods to use with stakeholders
• ways to increase staff retention and engagement

Tools for Gathering Mixed Method Data

The Mixed Method uses a variety of tools from both Quantitative and Qualitative methods, including:

• online ratings
• staff and customer satisfaction scores
• staff and turnover data

…and many others.

Illustration Using Mixed Method Data

In our example above, a company could ask:

“Why are customers leaving?”

With a Mixed Method, we could re-state this as:

“How can we adjust our internal processes to meet customers’ needs and add to their experience?

Why We Use the Mixed Method

When we review data from several sources, it allows us to analyze a variety of things that would otherwise be very difficult to understand using just numbers or quality data alone. By applying these two methods together, we can perceive:

• Customer attrition rates compared to customer feedback
• Customer attrition and satisfaction rates compared to employee turnover and satisfaction rates
• Frequency of purchases compared to customer complaints
• Frequency of the use of feedback mechanisms, compared to communication styles and personality type (introverted customers are less likely to be assertive or offer verbal suggestions; extroverts are less likely to spend time on an anonymous survey)
• Which communication methods do Ideal Customers prefer: phone calls, e-mails, in-person visits, or direct mail?
• What are the main reasons customers are leaving, both from directly asking and from other data sources?

Applying Data to Make Risk Intelligent Decisions

Once you collect and analyze your company’s data using the above categories (Quantitative, Qualitative, and Mixed Method), you can use it to develop information. Data is simply raw, unorganized, randomly collected facts and figures. In order to transform data into useful information, we need to see the data in context and draw conclusions.

The process of acquiring good information is easily hampered. Make sure your collection methods are healthy and that your conclusions are:

• Transparent (communication is compromised by dishonesty or indirectness)
• Congruent (new information allows you to realign your beliefs and philosophy)
• Detached (impartial to a final conclusion and open to the possibility of bias)

 

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I hope you have enjoyed this deep-dive into three different categories of data collection: Quantitative (measurable numbers), Qualitative (emotions & experiences), and Mixed Method (countable data along with images, narratives, or sensations).

Be sure to read my other posts:

How to Understand the Quantitative and Qualitative Data in Your Business

Interpreting the Quantitative Data (Numbers) in Your Business

Interpreting the Qualitative Data (Experiences and Emotions) in Your Business

 

 

Be sure to read 20 Excellent Places to Look for Strategic Risk in a Company: Quantitative, where I share 20 places to look for risk using quantitative data in a company.

 

Interested in discussing your company’s challenges with staff turnover? Find out more here.

 

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Grace LaConte is a business consultant, writer, workplace equity strategist, and the founder of LaConte Consulting. Her risk management tools are used around the globe, and she has successfully reversed toxic work environments for clients in the healthcare and non-profit fields. Grace specializes in lactation law compliance & policy development, reducing staff turnover after maternity leave, and creating a participatory work culture.

Find more at laconteconsulting.com, or connect with her on Instagram and Twitter @lacontestrategy.