Passenger Willingness to fly on airline flights based on the gender of the pilot
Paper details:
When reading the final paper example, a few items are not perfectly written:
The first is the RQ. We will write RQs as such: what is the effect of the IV on the DV? You therefore need to fill in the blanks with your topic.
What is the effect of______on ______?
What is the effect of blank on blank?
Secondly, when writing your hypothesis, you need to pick a direction. Which category will be higher? Also, for our class, we will only be focusing on one (not multiple) hypothesis, e.g., willingness to fly will be higher for Indian pilots compared to American pilots
Since we are comparing the means of passenger willingness to fly between male and female pilots, American and Indian pilots, or young and old pilots, we are going to be using a data analysis calculation called an independent samples t-Test. Please read up about t-Tests in the note and slides. We cannot conduct a correlation analysis since the independent variable is dichotomous (meaning two options only), such as male pilots and female pilots. A correlation requires a continuous independent variable. For example, if we wanted to correlate willingness to fly with flight hours of the pilot, flight hours could be any numerical value between one and infinity and therefore is a continuous variable. We are using male and female, American and Indian, and young and old, so what we have is called a categorical variable. This means we cannot use a correlation. Therefore, when answering the question of what design we are using, correlational design is the wrong answer. We are using an experimental design size. We have two groups, and we are trying to determine whether one group has a higher willingness to fly score than the other group. We are conducting a cause- effect experiment and not a correlation.