As we have seen, the scientific process is an iterative one; scientists repeat their experiments, gather new information, test those findings against what is already known, and then incorporate that knowledge into their theories or conclusions. This cycle of experimentation, analysis, and revision is repeated over and over again until the scientist comes to a stable theory or conclusion.
The same thing happens in quantitative research. Starting with a question or hypothesis, researchers perform studies to see if their hypothesis is supported by fact. They use statistics and mathematical equations to analyze their data to determine whether there are significant relationships between two variables.
Then they can either reject or accept the original hypothesis based on these results. Like any good science, it all comes down to testing your hypotheses using the appropriate methodology.
Quantitative research does not always lead directly to qualitative answers, but it gives you very precise numbers that help describe how much impact each variable has on the dependent ones.
A key part of any research is defining your sample population or survey group. What you are looking for in respondents will determine how well your hypothesis works! You want enough participants to give you an adequate representation, but not so many that you get too much data and it becomes difficult to analyze.
In quantitative research, one common way to test your hypotheses is through statistical significance testing. This means calculating whether there was a significant difference between two groups’ average results. For example, if you were doing a study about the effectiveness of exercise on weight loss, then your control (or placebo) group could be people who do not participate in regular workouts, whereas your intervention (experimental) group could be people who did. If the placebo group lost more weight than the experimental group, then your experiment failed!
However, this cannot be done without first having an appropriate sample size. An appropriate sample size is when you have enough responses from both groups such that you can draw meaningful conclusions. For example, if we re-ran our previous experiment with a different outcome, what would happen?
Fortunately, scientific method comes in handy here as well. The systematic process for determining whether a hypothesis is supported by evidence includes using and comparing various strategies to gather empirical information.
The second major part of the quantitative research process is study design. This includes deciding what type of topic or question you want to ask, determining if your questions are appropriate for this topic, and then choosing which approach to take in gathering data.
There are three main types of studies that can be done using quantitative methods. These include surveys, experiments, and observational studies. Each one has their advantages and disadvantages, but all require careful planning.
Surveys ask people to state their opinions on various topics. This does not involve doing an experiment, like testing different treatments on patients, but it is similar to asking patients about symptoms or trying to determine how well someone knows their field by asking them questions. Surveys are very common way to gather information because they are cost-effective and do not need much time or resources to complete.
Experiments use either human participants or materials (like drugs or surgeries) as interventions. If studying the effect of nutrition on health, giving some people a special diet would be an intervention. A control group receives normal food while the experimental group gets a specific diet. Both groups are observed for changes to see whether one group benefits more than the other.
Observational studies look at correlations between two things to try to determine cause and effect. For example, we could observe whether people with heart disease also have high blood pressure or diabetes. By seeing what factors influence one thing, we could begin to understand why the other one happens.
One of the most important concepts in statistics is that of confidence interval. A confidence interval is an area or range within which you can place a proportion or estimate with a degree of certainty. For example, if I asked you how many people like dogs, you would not know unless I gave you a hard number, but a very good guess would be “almost everyone”.
A 95% confidence interval means that we are confident that the true value lies within this range 94 percent of the time. That is, only 5 out of every 100 times will the true value lie outside this range. This seems pretty safe, doesn’t it?
By contrast, when we dont have much information, we cannot put down a precise number or statistic. If I told you that there is a 90% chance that your roommate does not like apples then my statement makes no sense because I just said she did! We simply do not have enough data to come up with such an accurate figure.
The first step in doing quantitative research is deciding what your topic should be. Then, you have to determine if the hypothesis being proposed is true by conducting descriptive or statistical tests.
Descriptive statics are used to describe, analyze, and interpret data through numbers. They include things like calculating average scores for products, figuring out how many people belong to a group, and determining whether there were more positive or negative results from experiment A than B.
By using descriptive stats, we can find patterns in the data and test theories about the data. For example, if there was an increase in sales after advertising campaign X then it proves theory Y about the effectiveness of ad campaign X.
There are several types of descriptive stats that researchers use to evaluate their studies.
A second way to use regression is called multivariable or multiple variable analysis. With this method, you test more than one independent (or predictor) variable at a time against your dependent (outcome) variable.
The reason why people do this is because they want to know how much each individual factor contributes to the outcome. For example, if you wanted to learn what foods are good for you, then knowing how much sodium in food makes you sick would be very important information.
Similarly, with the quantitative research process, understanding how different factors influence success or failure can help you identify which ones are most influential in creating an impact.
This is done through statistical significance- when there is a strong correlation between two things, then adding the second thing will make the first thing significant! For example, if having more fruits and vegetables is associated with healthier diets, then offering additional fruits and veggies to someone may motivate them to eat more healthfully.
The scientific method
This process has been refined over centuries, but it always includes six steps. They are as follows:
Step 1 – Hypothesis
In quantitative research, this is typically a question or assertion that you will test by doing an experiment or survey. For example, your hypothesis for this article article was that eating berries can help prevent weight gain.
Step 2 – Experiment
You must determine what berry types to use, how much of each berry to use, and when to administer them. Using our previous example, you would pick one type of berry at a time, so that there is no confounding influence from other ingredients in the berry. You would also have to do enough experiments to get statistical significance- meaning enough samples that show the effect.
Step 3 – Data collection
Once you have determined which berries work and under what conditions, you need to gather data! This could be done using surveys or interviews, but most often it is done via questionnaire or interview with individuals who have already experimented with the product. These people are called experimental participants, and they must give informed consent before taking part.