When you think of research, you probably think of hitting the book (or web), grabbing a bunch of facts, and taking some notes. Unfortunately, this is a good way to pull false information and ruin a report or study.
Professional circle (and savvy individuals) instead use either qualitative or quantitative studies to compile the most accurate information possible. It’s important to understand these methods, as they can not only save a grade, but could lead to a promotion when you make a suggestion and back it up with the proper research.
Qualitative vs Quantitative
It’s a good idea to first understand the difference between quantitative and qualitative research. Qualitative tends to use a narrow pool of subjects for more precise information. Longitudinal studies (observing a test group over a long period of time) is an important form of qualitative research.
Quantitative research may be a little less accurate for certain types of research, but it provides more data at a faster (and cheaper) rate. Surveys are perhaps the most common form of quantitative research. In business circles, quantitative research is a key factor in following trends for better financial investment decisions.
See Also: Different Types of Educational Research
Four Types of Quantitative Research
Now that we have an idea of what quantitative research is, let’s take a moment to look at some different kinds of quantitative research is and how they might suit your needs in work, school, or everyday life.
1. Causal-Comparative Research
In causal-comparative research, the goal is to discover a cause and effect relationship between two variables (one dependent and one independent). The researcher has no control over the independent variable and thus any relationship between the two variables may be suggestive at best.
This method of research is based on Mill’s Methods of Agreement and Disagreement (two of the five methods introduced by philosopher John Stuart Mill).
The idea of agreement is that when a single factor is present in all groups and those suffer the same effect, that factor is the cause. Disagreement follows the opposite logic, where the only exception to a factor is directly related to the only exception in the results, that factor is the cause. The University of Hong Kong illustrates Mill’s Methods beautifully.
Statistical analysis is a vital part of causal-comparative research, and creates a more precise conclusion. A good example of causal-comparative research was performed by S. Weigman in 2005 regarding racism awareness in graduate counselling students in regards to the number of credit hours and whether a specific course was taken.
2. Correlational Research
As the name implies, this form of research takes two or more variables and examines how they correlate with one another. Two or more groups are used to observe the variables and their effects without interfering with those variables. The goal of correlative research is to find out how certain variables may predict other variables and their potential relationships.
It’s important to note that correlational relationships do not equate to causal relationships (i.e. cause and effect). In a correlational relationship, A may cause B or B may cause A, but the two may also be caused by a totally different variable without having any effect on each other. In causal relationships, manipulating A directly causes or contributes to a change in B.
An example of a correlated study was performed by W.H. Decker in 1987 and measured the correlation between a manager’s perceived sense of humor and the positive effects on employees’ job satisfaction. Note that humor is subjective, thus it could not be proven as entirely causal (some employees might not react to the same joke in the same way or even at all).
3. Experimental Research
This method is essentially the scientific method you learned in school. It takes one or more hypotheses and test them to reach a true/false/inconclusive result. It often uses probability in its final results.
One of the most basic (and famous) examples of experimental research was on whether a rock and feather would hit the ground at the same time when dropped at the same time from the same height. While it’s easy to conclude the rock would fall first, understanding why this occurs is important.
Here on Earth, the rock will hit first, but in a vacuum, the rock and paper will actually hit at the same time. Why this happens (and how the conclusion was achieved) is described nicely by the University of Illinois at Urbana-Champaign’s Department of Physics.
4. Survey Research
Perhaps the most common form of quantitative research, surveys use one or more groups as the test pool. The individuals in these groups must be picked randomly unless a specific factor is involved (for example, when the survey is related to smoking habits, you’ll want smokers to respond).
To keep surveys as random as possible, data is usually collected in a public place where passersby are asked to fill out a form if they qualify.
As mentioned, surveys quite often use questionnaire forms to collect data. These forms may use yes/no answers or ratings to gain more precise results. Interviews are also a common tool, although sampling polls are also effective in specific situations (such as feedback for customer service quality).
The results are often very precise and the broader the pool of respondents used, the more accurate the results will likely be.
Surveys are almost always presented in percentages. The results are sometimes skewed slightly because the research was taken from a small portion of the population, but are able to show trends in thinking and behavior with surprising accuracy when performed properly.
Two famous examples of survey research are US election polls and the Kinsey Report (a periodical report on human sexuality released by the Kinsey Institute).
How to Perform Quantitative Research
As with all forms of research, there is an organized series of steps involved in quantitative research that are easy to learn and master but cannot be skipped at any point. To perform your research, simply follow the steps in order.
We’ll pretend to be a small family clothing business so you can see these steps in action.
Step 1: Define the Problem & Choose a Method (AKA The Question)
This step breaks down to asking a question and giving what you think will be the answer, then picking a way to test that conclusion. What are you trying to discover from doing this research? Come up with a hypothesis (initial theory) about what the results might be and how they apply to your question.
Once you know the question and have a theory, decide which quantitative method best suits your needs. Make a list of the materials, subjects, and other factors you’ll need to perform the research.
These notes will help form a comprehensive report that backs your results. Be sure to use all of the tools at your disposal.
We want to add to our upcoming summer line but will halter tops or sports bras be more popular this year? The weather is supposed to be hot, so we think looser halter tops might fare better.
Conversely, sports bras may prove more popular if local women intend to be more active. To find out which type of top would be better, we decided to perform a survey at the local university and mall.
The survey will cover 50 female individuals from each and chart age range, preference, and have a field for comments.
Step 2: Gather the Data
Once you’ve gathered your tools, perform the research and record all relevant results. In the event you have multiple test groups, be sure to keep the results separate for comparison purposes later.
As quantitative research is mathematically-based, the majority of data should be based on numbers. Make note of any unexpected variables that arise, as these could skew the result or require further testing to account for.
Our survey teams quickly completed research at both locations and have compiled the survey papers into separate marked folders. These were then turned over to my husband (the marketing expert) to examine. The teams both reported seeing trends that will help give a better picture, although they also had a lot of comments that may affect the final results.
Step 3: Analysis and Conclusion
In the final step, the data is analyzed and compared to the original hypothesis. This is almost always a mathematical process which turns the data into probability or percentile results. Some variables may be listed separately as grounds for future research. The final result is then used to reach a conclusion.
33 of the college girls chose halter tops, and of those 24 mentioned their boyfriends as a deciding factor. 14 quoted morning jogs or aerobics/yoga as the reason for preferring sports bras. 3 were undecided. 49 of these were in the 18-24 age range with one professor participating.
The mall group was more diverse, with 27 preferring halter tops. 18 of those quoted fashion as the deciding factor, including emo and punk styles. 18 chose sports bras, with 14 giving some form of exercise as the reason.
The other four claimed general comfort as the deciding factor. 5 were undecided. The age demographics came to 23 in the 13-18 (teen) range, 9 in the 18-24 range, and 18 in the 24-40 range.
After looking at the data, we found 60% of women planned to wear halter tops this summer. Another 33% wanted sports bras. Because of the close split, we should aim to produce halter tops this year and, if early summer profits permit, make a smaller quantity of sports bras by mid-summer.