I want to know more about simulation data associated with psychology in the R program
Hello!
If you want 100 cases on a scale ranging from 1 to 7, the simplest way I know is this:
floor(runif(100, min=1, max=7+1))
You'll get uniform data. If you need data based on a different distribution you may user another random function.
You may also want to take a look at the discussion at stackoverflow about generating correlated data (see attached link).
Regards,
Jan
http://stackoverflow.com/questions/10535235/generate-correlated-random-numbers-from-binomial-distributions-in-r
thanks jan. Your answer helps me a lot. An additional question. I can put that data into a matrix. I try to simulate data as people responding to a psychological test.
best regards
Well, people respond different to psychological tests depending on the content. Is it a personality test, a performance test or a current state test?
Hi. Alexander. It is for personality test or attitudes. I've seen some studies where the researcher changes the number of response categories. For example,
https://www.researchgate.net/publication/236979916_Effect_of_the_Number_of_Response_Categories_on_the_Reliability_and_Validity_of_Rating_Scales
If you have an idea to do this in r, I'll be very grateful.
Best regards
Article Effect of the Number of Response Categories on the Reliabili...
Hello!
Lozano et al (2008) leave it rather unclear what they actually did. They claim they used normally distributed data. However, questionnaire data creates discrete (not continuous) responses. I think you have two options:
You could either use the link to stackoverflow I gave you. There you'll find code that uses the binomial distribution. The binomial distribution could be considered the discrete sister of the normal distribution (i.e. because if you increase the number of trials it converges to a normal distribution).
Another possibility would be to use "rnorm(n, mean, sd)" to create the vectors and then reduce the numbers into bins. However, there is the question on how wide the bins are supposed to be. You could probably use the hist function to do the binning.
these ways are not correct. because each item of a Likert are dependent. however, these random data are independent.
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