What is happening here? What makes the difference? Is your finding accurate? The answer is obvious NO. Your research population is residents from both city A and B, and you found that income is not a significant predictor variable of the money spent on designer products. If you were a researcher, you collect samples from both city A and B. There is no significant correlation anymore. Now, consider the correlation between the income and the money spent on designer products. Instead, they hire personal guards or install high-tech security system at home. In city B, people with higher income do not really buy designer products to exhibit their socioeconomic status. Therefore, designer shops are not quite as popular as they are in the miracle town. Everyone in the city is afraid of being robbed. You live in a city B town where the crime rate has been skyrocketing in the past decade. Let’s assume that beta is 20, and p-value is. Now, it is reasonable to assume there is a positive significant correlation between the income and the amount of money spent on designer products. In the city, people with higher income tend to buy designer products to exhibit their socioeconomic status. There are many designer shops including Gucci, Louis Vuitton, Montblanc, etc. Everyone in the city has abundant resources. You live in a city A or a literally utopian city with extremely low crime rate. Let’s consider the following hypothetical scenarios. In that case, W is said to be a moderator of X’s effect on Y, or that W and X interact in their influence on Y (220).” Now, let’s translate it into English. Regression with a moderator is an advance regression model to test for interaction, but what is a moderator in the first place?Ī moderator was defined in Hayes (2017), “The effect of X on some variable Y is moderated by W if its size, sign, or strength depends on or can be predicted by W. The mathematics behind various regression models can be messy however, the main objective of this book is to how to apply it. The linear regression modeling as illustrated in chapter 3 has different variations, just like ANOVA.
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