I have a scenario where many people are using a software and while utilizing they are assigned values between 0 to 1 based on their good or bad usage. e.g. good use would provide value to "usage" as 0.99 and "mistakes performed" parameter would be having value as 0.01.
I have such 15 parameters in the data which directly or indirectly quantifies the usage of software. I need to score all the people using this software using any mathematical / Statistical / machine learning model.
How should i build a relationship among 15 variables without a dependent variable and score everyone on the scale of 1 to 100?
How to get this equation built using only data without dependent variable?
Any discussion or reply is appreciated! Thanks in advance!
You can, I reckon, employ hypothesised inter-covariances/-correlations amongst the variables, either in SPSS or Amos.
Thanks,
A
See examples of the inter-correlation matrix:
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Hi,
What I understood from your question is that: you have 15 variables and you need to use them to construct one index to score software usage. To do that statistically, the best you can try factor analysis using spss.
Hello Mayur,
I'm going to guess that you want to know whether what I am going to call 'Rating' (ranging from 0 to 1) depends on 15 explanatory variables, which you wish to combine into a single variable. Your15 explanatory variables may well be correlated. If so, there is much to be said for using a multivariate technique (such as Principle Components or Factor Analysis) to reduce your 15 variables to a single PC or Factor as your explanatory variable.
Your response *appears to me* to be a continuous variable bounded at zero and one. If so the gold standard is regression analysis (Rating ~ Factor) with a beta distribution for your error structure. However, the statistical package you are using may not include this.
I can suggest 2 "kluges" for the problem posed by your response variable. But first, you need to confirm that your response variable is indeed a number that ranges from zero to one. If not, provide more details.
~David S
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