Multiple imputation
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Multiple Imputation. Multiple Regression. Input From SPSS. *** Mult-Imput_M-Reg.sas ***; PROC IMPORT OUT= WORK.IntroQuest DATAFILE = " C:\Users\Vati\Documents\StatData\IntroQ\IntroQ .sav " DBMS=SPSS REPLACE ; Run; Use the Import Wizard to bring the data into SAS.

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Multiple Imputation

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Multiple imputation

Multiple Imputation

Multiple Regression


Input from spss

Input From SPSS

*** Mult-Imput_M-Reg.sas ***;

PROCIMPORT OUT= WORK.IntroQuest

DATAFILE= "C:\Users\Vati\Documents\StatData\IntroQ\IntroQ.sav"

DBMS=SPSS REPLACE; Run;

  • Use the Import Wizard to bring the data into SAS.


Check for missing data

Check For Missing Data

procmeans n nmiss; run;


Check correlates of missingness with satm

Check Correlates of Missingness With SATM

* p < .05


Oh crap

Oh Crap !

  • We have a lot of missing data on SATM

  • Missingness on SATM is associated with statophobia and year.

  • It is not missing completely at random.

  • Need to employ multiple imputation.


Create five imputations

Create Five Imputations

  • ProcMI seed=69301 out=MIdata; var gender ideal nucoph SATM year; run;


Patterns of missingness

Patterns of Missingness

  • Most frequent pattern of missing data is missing on SATM only

.


Means by pattern of missingness

Means By Pattern of Missingness

.


Estimated means variances

Estimated Means & Variances


Analyze the imputed data

Analyze the Imputed Data

ProcRegoutest = MRbyImputcovout;

Model Statoph = gender ideal nucoph SATM year / stb; By _Imputation_; run;

ProcMIAnalyze; modeleffects intercept gender ideal nucoph SATM year; run;

  • See the complete output here: XYZZY

  • In every imputation, Gender, SATM, and Year have significant effects.


Proc mianalyze output

ProcMIAnalyze Output

  • Pools the results from the five imputations.

  • The variance in the scores is partitioned between that among imputations and that within imputations.

  • Ideally, little of the variance is due to differences among imputations.


Variance among within imputations

Variance Among/Within Imputations


Multiple imputation

  • “Relative Increase in Variance” is the increase in variance due to having missing data imputed (relative to the condition where no data are missing). Low is good.

  • “Fraction of Missing Information,” is an index of how much more precise the parameter estimate would have been if there had been no missing data. Low is good.


Multiple imputation

  • “Relative efficiency” tells you how much poweryou have for the number of imputations you have employed relative to what you would have if you used an uncountably large number of imputations.

  • High is good.


Riv fmi re

RIV, FMI, & RE


Conclusions

Conclusions

  • Women report greater fear of the stats course than do men.

  • Reported Math Aptitude is inversely correlated with fear of stats.


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