Research Methodologies: Quantitative, Qualitative & Mixed Method

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  • 0:05 Quantitative,…
  • 0:32 Quantitative Research
  • 2:39 Qualitative Research
  • 4:39 Mixed Methods
  • 5:42 Lesson Summary
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Lesson Transcript
Instructor: Devin Kowalczyk

Devin has taught psychology and has a master's degree in clinical forensic psychology. He is working on his PhD.

While there are many ways to conduct an experiment in psychology, there are only so many ways you can describe it. In this lesson, we will discuss the differences, strengths, and weaknesses of the qualitative, quantitative, and mixed methods.

Quantitative, Qualitative, and Mixed Methods

Researchers have many ways of examining and relating their study. Quantitative, qualitative, and mixed measures are all differentiated by the question, 'How is the researcher explaining his or her findings?' If the researcher uses numbers, they are using a quantitative measure; if they use a descriptive style, it is qualitative measure; and if they are somewhere in between, it is a mixed method.

Quantitative Research

Quantitative research uses numbers to test hypotheses and make predictions by using measured amounts, and ultimately describe an event by using figures. By using numbers, the researcher has the opportunity to use advanced and powerful statistical tests to ensure that the results have a statistical relationship and are not just a fluke observation.

When using quantitative research, the researcher must define what they are measuring. The idea here is to look at a specific attribute or variable. This is referred to as an operational definition. By operationalizing what you are looking for, you are only measuring a particular and relevant thing, which restricts your view to what is relevant. For example, if you are only looking at acts of aggression by physically touching someone, you don't count when someone yells at another person.

A strength of quantitative methods is that, by examining numbers, a certain level of bias is removed. It is hard to argue that one kicking a ball, for instance, is not kicking a ball. When a researcher studies a specific variable that is operationally defined, then the results can be applied to larger populations, making the findings generalizable.

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