When analyzing data, you’ll need to analyze any possible relationships among variables. Let’s say our research topic was about students who live on campus and their on-campus dining habits.
The first component you’ll need to identify is whether or not a relationship exists 🤷♂️. Is there a relation between students who live on campus and their on-campus dining habits? This is referred to as the presence.
To do that, you’ll need to look at the p value. We know from prior analysis that .05 is the p value that will help us determine a statistical significance. Here, though, a p value of .05 or less means that there is a relationship between the two variables. If you’re p value is higher than .05, there is not a relationship between the variables. Alternatively, we can say that there is (or isn’t) a presence or that a relationship does (or doesn’t) exist.
The next factor to determine is the direction or pattern of the relationship. Relationships can be positive or negative 📈 📉. If the relationship is positive, both increase together. If the relationship is negative, one increases as the other decreases.
The last factor is the strength of the relationship between the variables 💪. This will further help our analysis because if there is a positive or negative relationship, it’s helpful to know how strong that relationship is. If it’s a weak relationship, the variables at-hand don’t strongly influence the other. If there’s a strong relationship, the variables do strongly influence each other.
These three factors are critical when analyzing data and they can futher support your results. Remember, a lot of work goes into conducting marketing research, so it’s important that we conclude as much as possible from our data set to provide the client with the strongest feedback/recommendation.
Do you already conduct this type of analysis when you’re conducting marketing research? If not, let me know why in the comments. If you do, let me know how this process has helped solidify your findings and final recommendations.
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