With multilevel analysis, I run a lot of data-sets in Mplus, however, I face the following error:
"One or more between-level variables have variation within a cluster for the following clusters. Check your data and format statement"
How can I solve this problem?
Here is also my input for further information:
TITLE: this is an example of a two-level
regression analysis for a continuous
dependent variable with a random slope
and an observed covariate
DATA: FILE name is C:/Users/Saeid/Desktop/saeid.txt;
VARIABLE: NAMES = y x w xm clus;
WITHIN = x;
BETWEEN = w xm;
CLUSTER = clus;
DEFINE: CENTER x (GROUPMEAN);
ANALYSIS: TYPE = TWOLEVEL RANDOM;
MODEL:
%WITHIN%
s | y ON x;
%BETWEEN%
y s ON w xm;
y WITH s;
Dear Saeid Nosrati
These links might be useful, have a look:
1. http://www.statmodel.com/discussion/messages/11/645.html
2. http://www.statmodel.com/discussion/messages/11/7934.html?1567412486
3. http://www.statmodel.com/discussion/messages/9/9651.html?1569709901
4. http://www.statmodel.com/discussion/messages/12/801.html?1477948920
5. http://www.statmodel.com/discussion/messages/11/411.html?1563130630
Kind Regards
Qamar Ul Islam
- Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early Pokémon go days that it was showing only white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.
- Several times the data received has missing values. Data scientists have to treat outliers and missing values properly to increase the accuracy. There are multiple methods to do that – impute mean, median or mode values in case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.
- Finding the right variables or features which will have maximum impact on the outcome is one of the key aspect. This will come from better domain knowledge, visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.
- Ensemble models is combining multiple models to improve the accuracy using bagging, boosting. This assembling can improve the predictive performance more than any single model. Random forests are used many times for assembling.
- Re-validate the model at proper time frequency. It is necessary to score the model with new data every day, every week or month based on changes in the data. If required rebuild the models periodically with different techniques to challenge the model present in the production.
Dear Dr. Qamar Ul Islam and Dipta Voumick
Thank you so much for your responses and recommendations.
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