What is Quantitative Research?
Quantitative research means studies where the inputs and outputs include measurements of measurable factors in the research.
There are different Characteristics of Quantitative Research which will be discussed later in this article.
While qualitative research has dominated much marketing, some disciplines such as Personal finance extensively use quantitative research methodologies.
The reason for this is that business professionals working in these fields often need to convey complex numbers or, perhaps, more importantly, understand and compare them with other data points (for example, a portfolio manager needs to be able to analyze several companies’ financial statements and compare their performance).
For this post, we will concentrate on the characteristics that would be common to most quantitative explanations. It would help if you considered characteristics before using “quantitative” as a descriptor for your topic.
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Here are the nine characteristics of quantitative research.
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Characteristics of Quantitative Research
1. It includes Statistical Tests
Quantitative research almost always includes statistical tests to determine the strength of relationships. These tests include linear regression, one-way ANOVA, and multiple regressions. While this is a requirement of all quantitative research, some researchers consider the statistical tests a given and focus on other characteristics.
2. Includes Mathematics
Experiments often include at least one mathematical formula to calculate the success rate in an experiment involving random sampling from different-sized populations.
The “formula” may look like Y = abX + b, which shows that if X increases by 1 unit, Y would increase by b units. In addition, there are times when research includes graphs with information represented as curves or formulas such as P=1/3C+1/6L+4/9P 2 where P is pressure and C, L, and P 2 are constants. While a graph alone might not have any math
3. It is based on Large Sample Sizes
Although there are many examples of small sample size studies in marketing (such as qualitative or field studies), most quantitative analysis tools uses large sample sizes to generalize the results to larger populations. If this were not the case, you would have to collect data for every person in the population of interest, and this is just not feasible.
The large sample sizes are possible because there is often no need to generalize downward to individuals; instead, the results are generalized up to larger populations based on industry, location, or demographic characteristics.
4. Is Based on Hypothesis Testing
This characteristic builds off of characteristics 1 and 2 – since quantitative research includes statistical tests (characteristic 1), it also involves hypothesis testing.
For example, in an experiment, if you want to determine if a change in advertising spending has any effect on sales, then you may hypothesize that “a 10% increase in the ad will cause sales to increase by 5%”.
The hypothesis would be tested using statistical methods. As another example, you may hypothesize that “Compared to other industries, the financial services industry has a higher profit margin.” This statement would be confirmed or denied based on characteristics 1 and 2 above.
5. Includes Quantitative Inputs and Outputs
While qualitative research only includes the characteristics of the participants in a study, quantitative explanations include characteristics of both independent and dependent variables.
This often leads to experimental designs that include control groups or randomized samples because these reduce the possibility that those being studied would impact results.
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6. Uses Large Amounts of Data for Inference
To determine whether there is statistical significance, researchers separate their sample data into small segments called cells to calculate probabilities.
For example, suppose your experiment involved separating your random sample into males and females. In that case, you could use percentages from each cell to determine whether any differences between males and females were statistically significant (i.e., unlikely to be a chance occurrence). Because of characteristics 1 and 2 above, you would need many cells to have enough data for the statistics.
7. Uses Randomized Controlled Experiments
As mentioned above, while there are many examples of small sample size qualitative studies in Characteristics of Qualitative Research, quantitative research relies on randomized controlled experiments so the results can be generalized to larger populations.
The characteristics assigned to each type of qualitative design were based on characteristics 1-4 from this list – if researchers begin with large sample sizes and randomly divide them into groups, they have characteristics 1-3 covered. In addition, characteristics four will often include an experiment that tests a hypothesis. For example: If your stated hypothesis is “advertising campaigns that point out a’s
8. It includes Full Population Representation
Most quantitative research samples represent the entire population characteristics instead of just specific sub-populations within a total population characteristic (for example, all-male/female characteristics).
However, this does not mean that all characteristics will be included in the study – the age range is often excluded because it does not apply to products such as auto insurance or medications.
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9. It includes the characteristics of Systematic Study
A systematic study is a key to quantitative research because, after all, characteristics 1-4 allow this type of research to be systematic – not just any study can proceed using these characteristics. Systematic studies are those which use a process involving steps or stages to accomplish an objective.
This may be as simple as making several measurements over time or repeatedly replacing parts until the desired result occurs without variation (i.e., Quality Assurance). Quantitative researchers typically rely on data analysis software to help them determine if their results are statistically significant. It is often possible to identify outliers before performing further analysis by running tests for normal distribution.
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