How does a market researcher know when to use a qualitative approach and when to use a quantitative approach to a study? Is one approach better than the other?
A choice between research methods rests fundamentally on a set of decisions about the questions a researcher wants to answer and the practicality of gathering the kind of data that will answer those questions. The first step is to look for an obvious fit.
Although there are a number of soft differences between the two types of methods, there is one very important distinction. Quantitative research is deductive and hinges on the presence of a hypothesis, which is identified before research begins. Qualitative research is inductive and does not require a hypothesis to start the research process.
Let's take a closer look at this important difference, and dig a bit deeper into three key terms that help define quantitative and qualitative research.
- Deductive research
- Inductive research
Quantitative Research Confirms
Quantitative research looks at the general case and moves toward the specific. This deductive approach to research considers a potential cause of something and hopes to verify its effect. Since the phrase cause and effect is part of nearly every child's history of parental lectures, we are all familiar with the concept. In research, cause and effect are all about the strength of the relationship. If a very strong relationship exists between two variables, the cause and effect relationship may be said to be highly probable or highly likely. There is still room to say that the effect does not occur as a result of the cause, but this is considered not very probable.
The following is an example of a deductive market research approach that seeks to measure differences in online purchasing behavior and use of website shopping carts:
The purchasing behavior of internet shoppers who regularly place items in their online shopping cart but do not complete many purchases differs from the purchasing behavior of internet shoppers who do not use the cart to hold items they never buy.
Internet shoppers who habitually place items in their online shopping carts but do not complete the purchases are 75% more likely to return to the same websites and complete a purchase within 7 days.
Retaining the online shopping cart contents for 10 days when a consumer leaves a website before completing a purchase is good business and means a high probability of future purchases by that consumer on the visited website.
Hypothesis - A Tentative Assumption
A hypothesis is a tentative assumption in the form of a statement or a question that a research effort is designed to answer. In quantitative research, there are two hypothesis statements. One hypothesis is called the null hypothesis, or Ho. A researcher does not expect the null hypothesis to be true. At the conclusion of the research process, the researcher will analyze the data collected, and then will either accept or reject the null hypothesis. Researchers refer to the process of confirming a hypothesis -- the assumption -- as testing the hypothesis.
The second hypothesis is called the alternative hypothesis, or Ha. The researcher assumes the alternative hypothesis is true. Rejecting the null hypothesis suggests that the alternative hypothesis may be true -- that is, the chance that there is an error in the data that would make the alternative hypothesis not true is acceptably small, by scientific standards. Hypothesis testing in quantitative research is never absolute.
For a study about online purchasing behavior, one example of a null hypothesis could be:
Ho = Internet shoppers who place items in the cart before leaving the website are no more likely to return and complete a purchase than internet shoppers who do not place items in their cart but also return to the website.
An example of a corresponding alternative hypothesis could be:
Ha = Internet shoppers who leave a website before purchasing items they have placed in their cart are more likely to complete a purchase on the same website in the near future.
Qualitative Research Explores
Qualitative research begins with the specific and moves toward the general. The data collecting process in qualitative research is personal, field-based, and iterative or circular. As data are collected and organized during analysis, patterns emerge. These data patterns can lead a researcher to pursue different questions or concepts, like rolling a snowball downhill.
Throughout the data collecting process, researchers typically record their thoughts and impressions about the emerging data patterns. Qualitative researchers gather data about their research in several different ways or from many different sources. This expanded view of relevant data is called triangulation and is a very important way of ensuring that data can be verified. When the data set is considered large enough or deep enough, the researcher will interpret the data.
The example below suggests several ways that a qualitative researcher might triangulate data and move the research project from specific data to general themes, and ultimately to a research conclusion or finding.
Specific Consumer Interviews
Consumers convey the reasons why they leave items in online shopping carts and why they do not complete online purchases.
Specific Website User Observations
Researchers observe consumers engaged in online shopping who report what they are thinking as they shop.
General Researcher Field Notes
Researchers record ideas that emerge during the data collection process, such as Online shoppers tend to treat shopping carts like the dressing rooms in actual shops, where leaving items behind is just part of the shopping experience.
Online shoppers engage in window shopping as evidenced by the practice of leaving items in the online shopping cart; this consumer behavior contributes to feelings of familiarity with an online store which tends to draw the consumer back to make purchases at more opportune times.