

Using Proportion Data that equals up ... 

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I have three predictor variables that counted how many times an offender used: 1)change talk, 2)was resistant, or 3)responded neutrally during a supervision session with a probation officer. I wanted to determine whether the number of evidencebased practices an officer uses during a supervision session changes as a function of the proportion of time the offender is using change talk vs. resistance vs. responds neutrally. Therefore I have three variables (change talk, resistance, neutral response) and I divided each one by the total number of offender reactions to get a proportion. For example if the offender used change talk 5 times, resistance 2 times, and responded neutrally 10 times, I created three proportions that add up to 1 (e.g., 5/17 = 0.294 would be the proportion I would use to represent the proportion of time used change talk). What would be the best way to use three variables that are proportions and add up to 1 when running a multilevel model analysis. I tried treating the 3 predictors like multinomial data, but Mplus gave me an error message notifying me that they are not even integers. Does anyone have any ideas of other ways I can model the proportion of time an offender used change talk, was resistant, or was neutral during each supervision session? I do not want to have to dichotomize these three variables, because then I lose information. 


You should post this question on a general discussion forum like SEMNET. 


Thank you for your advice. I appreciate it. I apologize if this type of question was not appropriate for this discussion board. 


I do have one more quick question. Can you specify in Mplus that a independent variable is a proportion and ranges from 0 to 1 in a Crossed Random Effects Multilevel Model? The syntax I am currently using looks like this and I just want mplus to know that Change Resist and Neutral are proportions that add up to 1: VARIABLE: NAMES ARE ID Change Resist Neutral NumEBPs; USEVARIABLES ARE ID Time Change Resist Neutral NumEBPs x1 x2 x3; WITHIN=Change Resist Neutral x1 x2 x3; COUNT IS NumEBPs; CLUSTER=ID; ANALYSIS: TYPE=TWOLEVEL RANDOM; ESTIMATOR=MLF MODEL: %WITHIN% NumEBPs ON Change Resist Neutral; Any advice you have would be greatly appreciated. 


In regression, independent variables can be binary or continuous. In all cases, they are treated as continuous. 

Tamara Kang posted on Saturday, March 25, 2017  3:51 pm



Thank you so much for your help! 

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