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In research, there are different types of sampling. No matter which sampling type you choose, you must have a strong justification for the sample size and type. In your case, out of a population of several million females, if your sample size is only 16 then i do think it is too less. I advise...

You need to first code the variables as dummy variables and then perform whichever test you want. This is a good question, i will write a detailed blog about it today.

If your aim is to check how age affects gaze behaviour, then AGE is the independent variable and GAZE BEHAVIOUR is the dependent variable. Regression analysis is the best fit for your case. The other 5 conditions (gender, image, ssgroup, condition and faceperc) seem to be control variables. You...

It does not work this way. Sampling is done on the basis of the universe, i.e. proportion of population. If the population of Italy is 60 million, then you must first determine the male: female ratio. For instance, if it is 1000: 800, then your sample size will be 210 males and 175 females (out...

There are many ways to check for a relationship between two variables. CHi square and correlation/ regression will probably work best in your case.

So one variable would be coded value for the different type of surgeries and other variable would be this 10 case classification like head could be group and 1st 10 case can be stated as A while other 10 as B..Now dependent variable would be surgeries and factor of analysis would be group. Based...

Thanks for explaining the problem. I think One-way ANOVA will be appropriate.

Yes, it is possible in SPSS. You need to apply Chi square test.

Pre and post test is a classic situation that needs T TEST. Im surprised no one has suggested it so far... am i missing something?

There is a way to conduct regression using moderating variable. This piece may be useful: https://www.projectguru.in/how-to-work-with-a-moderating-variable-in-the-regression-test-with-spss/

Hey, if the original options ranged from 1-5 originally (1- strongly disagree, 2- disagree, 3- neutral, 4- agree, 5- strongly disagree) then it would have been okay to keep it that way and perform a regression test (H0: The patient's condition affects the surgeon's decision to perform a...

Hi, if you want to check if there is a correlation between 'how fast does someone break down the caffeine molecule' (variable 1) and 'coffee preference' (variable 2), then yes, chi square is applicable, because the scales are different in both the questions. Good luck!

I may be wrong, but i think this is a simple case of the file being absent from the Desktop. Please check. Hope it works!

If all the data is numeric in both the datasets (compliant and non-compliant) then you must apply T-Test (https://www.projectguru.in/how-to-perform-t-test-statistics-in-spss/) If the data is categorical then you must apply chi square test (https://www.projectguru.in/chi-square-test-spss/)

If all the data is numeric in both the datasets (compliant and non-compliant) then you must apply T-Test (https://www.projectguru.in/how-to-perform-t-test-statistics-in-spss/) If the data is categorical then you must apply chi square test (https://www.projectguru.in/chi-square-test-spss/)

a) A normal model does NOT necessarily have only one independent variable, it can have many. b) mediating variable is not present in all statistical models. It is rarely used, depending on the problem. c) in SEM, you define beforehand the hypotheses you are going to test. Accordingly you...

This error occurs when the sample size (number of observations) is too less. It should be at least 250 in my experience to remove this error.

It should be more than 0.5 to be considered effective. However, it’s not always a compulsion as R square just states the proportion of variance. So your conclusion about generalisation is incorrect. Moreover i have provided references in that article in case you doubt my approach.

Im assuming that Drilon wants to test the impact of X and Y (independent variables) on another factor (the dependent variable, lets call it "A"). If so then the values he has obtained in this correlation test (0.280) is too less to proceed with the regression test. The value must be at least...

The correlation is too low. It should be at least 0.5 to proceed with the regression test