The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies. Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent nor can generalizations be made to a wider context than the one studied with any confidence.
The time required for data collection, analysis and interpretation are lengthy. Analysis of qualitative data is difficult and expert knowledge of an area is necessary to try to interpret qualitative data, and great care must be taken when doing so, for example, if looking for symptoms of mental illness.
Because of close researcher involvement, the researcher gains an insider's view of the field. This allows the researcher to find issues that are often missed such as subtleties and complexities by the scientific, more positivistic inquiries. Qualitative descriptions can play the important role of suggesting possible relationships, causes, effects and dynamic processes. Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports in order to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.
Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest. Research is used to test a theory and ultimately support or reject it.
Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires can produce both quantitative information. For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories e.
Experimental methods limit the possible ways in which a research participant can react to and express appropriate social behavior. Findings are therefore likely to be context-bound and simply a reflection of the assumptions which the researcher brings to the investigation. Statistics help us turn quantitative data into useful information to help with decision making.
We can use statistics to summarise our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential. Descriptive statistics help us to summarise our data whereas inferential statistics are used to identify statistically significant differences between groups of data such as intervention and control groups in a randomised control study.
Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions may have for those participants Carr, Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation Black, Variability of data quantity: Large sample sizes are needed for more accurate analysis.
Small scale quantitative studies may be less reliable because of the low quantity of data Denscombe, This also affects the ability to generalize study findings to wider populations. Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.
Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective, and rational Carr, ; Denscombe, Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved Antonius, Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
McLeod, S. Qualitative vs. Quantitative data will give you measurements to confirm each problem or opportunity and understand it. You can probably already measure several things with quantitative research, such as attendance rate, overall satisfaction, quality of speakers, value of information given, etc. All these questions can be given in a closed-ended and measurable way. But you also may want to provide a few open-ended, qualitative research questions to find out what you may have overlooked.
You could use questions like:. If you discover any common themes through these qualitative questions, you can decide to research them more in depth, make changes to your next event, and make sure to add quantitative questions about these topics after the next conference. Next time, your survey might ask quantitative questions like how satisfied people were with the location, or let respondents choose from a list of potential sites they would prefer.
A good way of recognizing when you want to switch from one method to the other is to look at your open-ended questions and ask yourself why you are using them. For example:. Relative to our competitors, do you think our ice cream prices are:. This kind of question will give your survey respondents clarity and in turn it will provide you with consistent data that is easy to analyze.
There are many methods you can use to conduct qualitative research that will get you richly detailed information on your topic of interest. However, this open-ended method of research does not always lend itself to bringing you the most accurate results to big questions.
And analyzing the results is hard because people will use different words and phrases to describe their points of view, and may not even talk about the same things if they find space to roam with their responses. Using quantitative questions helps you get more questions in your survey and more responses out of it. Even word responses in closed-ended questionnaires can be assigned numerical values that you can later convert into indicators and graphs.
This means that the overall quality of the data is better. Remember that the most accurate data leads you to the best possible decisions.
Our customer satisfaction survey template includes some good examples of how qualitative and quantitative questions can work together to provide you a complete view of how your business is doing.
How long have you been a customer of our company? How likely are you to purchase any of our products again? The following is another example from our employee engagement survey. When you make a mistake, how often does your supervisor respond constructively? Now that you know the definition of qualitative and quantitative data and the differences between these two research methods, you can better understand how to use them together.
You can put them to work for you in your next project with one of our survey templates written by experts. Check out our library of expert-designed survey templates. Products Surveys. Specialized products. View all products. Survey Types. People Powered Data for business. Solutions for teams.
Explore more survey types. Curiosity at Work. Help Center. Log in Sign up. The difference between quantitative vs. For example: face-to-face interviews, telephone interviews, remote interviews. Qualitative data is not countable. You can turn qualitative data into structured quantitative data through analysis methods like. Quantitative data can help to give you more confidence about a trend, and allow you to derive numerical facts. From here, you count all of the vehicles on a particular road, and conclude that 60 percent of vehicles are cars, 30 percent are trucks, and the rest are motorbikes.
This would be a quantitative information. If you then landed on the ground and interviewed some motorbike riders about their thoughts on truck drivers, the notes or recording of those interviews would be qualitative data.
You can turn qualitative data into quantitative data, and vice versa. They often blur, and you can represent the same data set in both ways.
In its raw form, this would be considered qualitative data. By doing this, you would have turned some unstructured qualitative data into a structured, countable insight. Because quantitative data is based on numbers, some form of mathematical analysis will be required. The methods range from simple maths like calculating means and medians, to more advanced statistical analysis like calculating the statistical significance of your results.
Because of its unstructured and somewhat ambiguous nature, analyzing qualitative data involves a more interpretive style of analysis. There are many tools that help with the analysis of qualitative data, Dovetail being one of them.
Coding your data with tags and conducting a thematic analysis. After you tag all your data, you can analyze the frequency of certain types of responses and identify patterns and themes.
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