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| 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 evidence-based 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 multi-level 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. |
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| You should post this question on a general discussion forum like SEMNET. |
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| Thank you for your advice. I appreciate it. I apologize if this type of question was not appropriate for this discussion board. |
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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. |
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| In regression, independent variables can be binary or continuous. In all cases, they are treated as continuous. |
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| Tamara Kang posted on Saturday, March 25, 2017 - 3:51 pm
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| Thank you so much for your help! |
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