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Quantitative and Qualitative Mixed Methods
Examining qualitative data with quantitative techniques helps to identify or validate patterns or themes. Key Concept: Using mixed methods is a deliberate design decision. You use it when you don't trust the data from any single method.
9 Replies
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Most common applied Qualitative Methods:
Individual interviews
Group discussions
Focus groups
Behavioral observations -
If your purpose is to explain, measure, and/or prove a link between two different
things (e.g. diet and obesity), quantitative data would probably be more appropriate.
For example, quantitative data topics might be:
● A company’s profitability
● A comparison of primary school children’s reading marks and family
background
● How rates of secondary infection in a hospital ward change in winter
● How many newspaper articles mention immigration in a given period
● The frequency of particular personality types e.g. introversion
If your purpose is to explore, illustrate, and/or give rich and detailed information
about particular instances, you are probably going to prefer qualitative data.
Qualitative data topics might be:
● Consumer perceptions of a company or brand
● Parents’ feelings and habits about reading to their children
● Nurses’ knowledge and opinions of infection prevention protocols
● How newspaper articles describe and represent immigrants
● How introverts think of themselves -
Quantitative data are used when a researcher is trying to quantify a problem, or address the "what" or "how many" aspects of a research question. It is data that can either be counted or compared on a numeric scale. For example, it could be the number of first year students at Macalester, or the ratings on a scale of 1-4 of the quality of food served at Cafe Mac. This data are usually gathered using instruments, such as a questionnaire which includes a ratings scale or a thermometer to collect weather data. Statistical analysis software, such as SPSS, is often used to analyze quantitative data.
qualitative
Qualitative data describes qualities or characteristics. It is collected using questionnaires, interviews, or observation, and frequently appears in narrative form. For example, it could be notes taken during a focus group on the quality of the food at Cafe Mac, or responses from an open-ended questionnaire. Qualitative data may be difficult to precisely measure and analyze. The data may be in the form of descriptive words that can be examined for patterns or meaning, sometimes through the use of coding. Coding allows the researcher to categorize qualitative data to identify themes that correspond with the research questions and to perform quantitative analysis.
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Combine qualitative and quantitative data
Using a combination of qualitative and quantitative data can improve an evaluation by ensuring that the limitations of one type of data are balanced by the strengths of another. This will ensure that understanding is improved by integrating different ways of knowing. Most evaluations will collect both quantitative data (numbers) and qualitative data (text, images), however it is important to plan in advance how these will be combined.
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When data are gathered for qualitative and quantitative combination
Parallel Data Gathering: gathering qualitative and quantitative data at the same time.
Sequential Data Gathering (Sequencing): gathering one type of data first and then using this to inform the collection of the other type of data. -
When data are combined
Component design: collecting data independently and then combining at the end for interpretation and conclusions.
Integrated design: combining different options during the conduct of the evaluation to provide more insightful understandings. -
Purpose of combining data:
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Enriching: using qualitative work to identify issues or obtain information on variables not obtained by quantitative surveys.
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Examining: generating hypotheses from qualitative work to be tested through the quantitative approach.
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Explaining: using qualitative data to understand unanticipated results from quantitative data.
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Triangulation (Confirming/reinforcing; Rejecting): verifying or rejecting results from quantitative data using qualitative data (or vice versa)
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Well, that's all I have til now.
