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  1. J

    Can I use Quantile Regression Model for a one year, say 2018 cross-sectional data on students results?

    Hey,Majority papers I have come across on Quantile Regression used either two different time periods (i.e. 1999 and 2000) or more different time periods for estimation purposes.My data is only a year cross-sectional; therefore, I am thinking whether Qreg is still applicable.Please make some...
  2. J

    Is it possible to use Logistic Model?

    I am attempting to investigate 7891 students who recently took an undergraduate entrance exams. I am interested in estimating this econometric model (i.e. Logit/Probit) to investigate factors affecting the result of students who took the exam. The results status has three factors, i.e...
  3. J

    Number of Observations for Robust Statistic

    Well, it depends on the type of data that you're working with. Basically, if you are working with time series obs, then a minimum obs of 24 could be ok, but be careful of the methods, variables size among others. Other data like survey, panel, cross sectional, etc.. are huge sample sizes...
  4. J

    Want to perform Anova for count data with survey weight using stata code

    I think you've to make your comment crystal clear because, ANOVA usage comes with several factors. One of such is, the types of ANOVA methods to adopt for a certain case.
  5. J

    Logistic Regression Stata: ommitted due to collinearity ?? Help

    Collinearity is a data/sample problem; however, if you realize that there seems to be perfect relationship from the regression, I suggest that you try to run auxiliary regressions first and thereafter, re-run the entire equation. This is just a suggestion to test the variables in the regression.
  6. J

    Sampling Method(s)

    Hello, I am undertaking a research project but focusing basically now on my congregation with approximately 300 members. The church has several auxiliaries or ministries. I am interested in studying members of the church needs assessment for community dwellers empowerment and development...
  7. J

    Missing Data Imputation - How to choose the best method?

    Expectation-Maximization Bootstraping
  8. J

    Missing Data Imputation - How to choose the best method?

    The MI approach is a more accurate approach to adapt to fill in missing data. Data missing ness is an everyday thing and a robust algorithm like the EMB can be used with some good packages like R or Stata. To further buttress the previous writer, it depends the data you are using as well.