WWC attrition table

P. 13 of the WWC stadards document.

https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_standards_handbook.pdf

 

Overall Attrition Conservative Boundary Liberal Boundary
0 0.057 0.1
0.01 0.058 0.101
0.02 0.059 0.102
0.03 0.059 0.103
0.04 0.06 0.104
0.05 0.061 0.105
0.06 0.062 0.107
0.07 0.063 0.108
0.08 0.063 0.109
0.09 0.063 0.109
0.1 0.063 0.109
0.11 0.062 0.109
0.12 0.062 0.109
0.13 0.061 0.108
0.14 0.06 0.108
0.15 0.059 0.107
0.16 0.059 0.106
0.17 0.058 0.105
0.18 0.057 0.103
0.19 0.055 0.102
0.2 0.054 0.1
0.21 0.053 0.099
0.22 0.052 0.097
0.23 0.051 0.095
0.24 0.049 0.094
0.25 0.048 0.092
0.26 0.047 0.09
0.27 0.045 0.088
0.28 0.044 0.086
0.29 0.043 0.084
0.3 0.041 0.082
0.31 0.04 0.08
0.32 0.038 0.078
0.33 0.036 0.076
0.34 0.035 0.074
0.35 0.033 0.072
0.36 0.032 0.07
0.37 0.031 0.067
0.38 0.029 0.065
0.39 0.028 0.063
0.4 0.026 0.06
0.41 0.025 0.058
0.42 0.023 0.056
0.43 0.021 0.053
0.44 0.02 0.051
0.45 0.018 0.049
0.46 0.016 0.046
0.47 0.015 0.044
0.48 0.013 0.042
0.49 0.012 0.039
0.5 0.01 0.037
0.51 0.009 0.035
0.52 0.007 0.032
0.53 0.006 0.03
0.54 0.004 0.028
0.55 0.003 0.026
0.56 0.002 0.023
0.57 0 0.021
0.58 0.019
0.59 0.016
0.6 0.014
0.61 0.011
0.62 0.009
0.63 0.007
0.64 0.005
0.65 0.003

Statistical joint test of categorical variables when expressed as a series of dummy variables

When I have a group represented in a series of dummy variables (e.g.,  race groups, grade levels, etc.), I want to also know if dummy variables as a meaningful group unit  contribute to the model with statistical significance.  The easiest way to do this is to treat those variables as classification variables.  You will get a joint statistical test in one of the result tables.

proc glimmix ..;

class race grade_level;

….

run;

In my application I almost always use numeric version of variables, i.e., dummy variables (coded as 0 or 1).  I like this approach because I can just use PROC MEANS on them to create a descriptive statistics table.

The question is how I get joint statistical tests when  all of my predictors are numerically coded and thus I can’t rely on the class statement (shown above in the syntax example).

The GLIMMIX syntax below treats race groups and grade levels as numerically coded dummy variables (if YES 1, else 0).

The parameter estimate tables will show coefficients derived for each of the numeric variables; however, I wouldn’t know if race groups as a group matters to the model or grade levels as a system matters to the model.   For example, even when  the coefficient derived for subjects being black is statistically significant, that is only about how black students are different from white students (reference group in this example).  We don’t know if race as a group matters and race groups jointly make a statistically significant contribution to the model.

<Again this can be done easily by using class variables instead (as shown earlier); however, I like using numeric variables in my models.>

Contrast statements will do the trick.

proc glimmix data=usethis namelen=32;
class groupunit;
model Y= treat black hispanic other grade09 grade10 grade11/
solution ddfm=kr dist=&dist link=&link ;
output out=&outcome.gmxout residual=resid;
random intercept /subject=groupunit;
CONTRAST ‘Joint F-Test Race groups ‘ Black 1, Hispanic 1, other 1;
CONTRAST ‘Joint F-Test Grade levels’ grade09 1, grade10 1, grade11 1,

ods output
ParameterEstimates=_3_&outcome.result covparms=_3_&outcome.cov
Contrasts=cont&outcome;
run;

 

Why use METHOD=RSPL for PROC GLIMMIX

The reason for using R (Restricted method) is because the alternative M (Maximum method) can have bias about covariance (level-2 variance in our application) and when the number of group unit is relatively small, so this is a real threat.

 

Negative R-square problem

Quick note:

When we add covariates to the model, we expect variance to reduce, but we can get negative R-squares from between-school variance.  This is because when we adjust for predictors the school averages/intercepts may change for both directions.  The between school variance may reduce or may enlarge as the meaning of school averages/intercept changes.  Can I simulate the case where between-school variance in fact enlarges??  Maybe this happens when pretest is very different by the two groups.

Microsoft Access, Creating forms

When I put subforms into the main form, I need to make sure that the main ID and the sub IDs are all correct.  I need to make this correct in three places.

  • Highlight the form by clicking on it to activate the property sheet.  Find DATA, LINK CHILD FIELDS and specify the correct subID.
  • Also remove the solidline.

f you select multiple buttons by pressing and hold Ctrl key, then right click, you should be able to change any common property of the buttons.