Hello everyone!
I need your help with some work I am doing.
Some context first:
I am writing a dissertation for my master. The topic is about perceived trust in Smart Home technology. I launched a survey with a closed ended questions for demographic data, and likert scale that asks 8 Questions on a scale of 1 to 5. I gathered 159 responses in total.
The 8 Questions in ther likert scale are actually 4 different dependent variables. Q1/Q2 make dependent variable1, Q3/Q4 dependent variable 2 etc.
Since it's a likert scale the data is not an interval, so what I did is that I took the sum of Q1 and Q2 and divided it by 2, which gave me a mean. This mean is one of the 4 dependent variables. I did this an additional 3 times for the other 3.
The idea is to test each one of these dependent variables and see if they can be predicted with the independent variables (and control variables) that I have ( age, gender, educational attainment, household size and income).
For that I read that a multiple linear regression would be enough. So I started reading about that method and I saw that there were some assumptions that needed to be met before I could use that method. For normality (3 of the 4dependent variables were normally distributed, but the last one had was not quite normally distributed. Secondly, it seems that testing the the four variables for linearity resulted in all of them not being linear.
Now I need to start the analysis part of my dissertation but I have no clue wich method I should use since the assumptions of the multiple linear regression are not met.
I know about non-parametric tests, but I can't find anything non-parametric alternative for the multiple linear regression.
If you need more info about the variables etc let me know, I will provide them!
Thanks for your help and time.
Multiple linear regression (MLR) is a commonly used statistical technique for modeling the relationship between a dependent variable and several independent variables. However, MLR has certain assumptions that must be met for the results to be valid. When these assumptions are violated, the results of the analysis can be biased or misleading.
Here are some common assumptions of MLR:
If one or more of these assumptions are violated, there are several things you can do:
It is important to note that there is no one-size-fits-all solution for dealing with violated assumptions in MLR. The approach you take will depend on the violation's specific nature and your dataset's characteristics. It is important to carefully examine the results of your analysis and to use good judgment in interpreting them.
Hello Abdelkarim,
As noted in Ma'Mon Abu Hammad 's response, the normality requirement applies to the residuals from the fitted model, not to the set of scores for one (or more) of your ersatz DV values.
1. However, if you are concerned about whether scores are genuinely interval strength, you could try ordinal regression (though this is sometimes perceived as more challenging to explain).
2. If you are concerned about linearity, normality or the like, you might consider using robust regression methods.
Good luck with your work.
Thank you both Ma'Mon Abu Hammad and David Morse for your help. I will consider your recommendations.
You can use alternative techniques such as artificial neural networks ANNs it doing the same jobs of regression analysis and it doesn't need any assumption
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