09-01-2015
kirkwanderson
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09-10-2013
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Subject Views Posted 7226 10-01-2013 03:35 PM 7226 09-25-2013 02:47 PM 8029 09-10-2013 02:49 PM -
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- Got a Like for PROC SURVEYREG questions. 07-16-2019 12:43 PM
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- Posted Re: PROC SURVEYREG questions on Statistical Procedures. 10-01-2013 03:35 PM
- Posted Re: PROC SURVEYREG questions on Statistical Procedures. 09-25-2013 02:47 PM
- Posted PROC SURVEYREG questions on Statistical Procedures. 09-10-2013 02:49 PM
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Subject Likes Posted 2 09-10-2013 02:49 PM
10-01-2013
03:35 PM
I wasn't very clear about the variables. The Y variable is a reading achievement score, which is continuous. These scores are left-skewed, which probably explains the left-skewed residuals. Frankly, the graphs don't look terrible. The outliers are not extreme. The formal hypothesis tests reject the null hypothesis of normality, though. The first X variable is reading interest/competence, which is somewhat discrete. The values in the first column contains the reading interest/competence scores, and the second column shows the frequencies. As discrete as it is, it would not be appropriate to create dummy variables or treat it as a CLASS variable. 1 194 1.25 306 1.33 9 1.5 561 1.67 17 1.75 737 2 1028 2.25 1061 2.33 23 2.5 1215 2.67 28 2.75 914 3 971 3.25 727 3.33 19 3.5 596 3.67 14 3.75 420 4 405 The other 4 X variables are essentially continuous. The data is from the ECLS-K study, if you happen to be familiar with it.
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09-25-2013
02:47 PM
Thank you, that is helpful. Following your advice using COLLIN, I do not have any collinearity issues. However, I still have left-skewed residuals and a downward sloping residual plot. The residual plot slopes downward for the full model with 5 independent variables. I was not able to blame any particular X variable, as running simple regression models with each X on its own produces a downward sloping residual plot. Could you, or anyone reading this, explain further the sentence "A linear trend of the residuals against an independent variable indicates that the model does not account for a linear effect in that independent variable" ? I have read this elsewhere as well, but don't know how to make my model account for any missing linear effects.
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09-10-2013
02:49 PM
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Hello, I am using PROC SURVEYREG for the first time. I believe I have the WEIGHT, CLUSTER, and STRATA statements/variables figured out correctly, but am struggling with a couple of things: It seems that SURVEYREG has limited capability compared to, say, PROC REG. It appears that I cannot get VIF values. Is this the case? If so, how should I assess collinearity without a great deal of effort? Regarding residual diagnostics, I have a couple of issues. One is left-skewed residuals, and the other is a downward sloping trend in the residuals vs. predicted values plot. FYI, the Y data is left-skewed and some of the X variables are discrete, although certainly ordinal. I have tried several transformations of Y to no avail. I am most concerned with the residual plot. The only guidance I can find for this issue assumes that my data were collected over time, but this is not the case. Does it have anything to do with how the SURVEYREG procedure accounts for the sampling scheme? Thanks, Kirk proc surveyreg data=edat.ECLSKclean; model C7R4RSCL = C7SDQRDC C7SDQINT C7LOCUS C7CONCPT W8SESL / anova adjrsq clparm deff inverse xpx; cluster C67CSTR; strata C7TCWPSU / list; weight C7CW0; output out=edat.ECLSKresids predicted=fitted residual=resids; run; proc univariate data=edat.ECLSKresids plots; var resids; histogram resids / normal; probplot resids / normal(mu=est sigma=est); run; proc gplot data=edat.ECLSKresids; plot resids*fitted; run;
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