Missing Data Appendix B: Sample Syntax of Analyses for Illustration




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Missing Data


Appendix B: Sample Syntax of Analyses for Illustration

The following three sections of this appendix provide sample syntax for performing mean substitution using SPSS, MI with SAS, and FIML with Mplus. All use the illustrative data set described in the text and available from the journal website. This data set is a tab-delimited text file title “illustration.txt” that contains 60 rows to represent the 60 cases and 10 columns representing 10 variables: ID number (from 1 to 60), group membership (0 or 1), the covariate, 7 variables representing the dependent variable under (a) no missing values; (b) MCAR with 10, 20, or 50% missing; and MAR with 10, 20, or 50% missing. Missing values are denoted by the value of 999 in this text file. The dataset does not contain variable labels in the first row; instead, the syntax files specify the variable names and order.



Mean Substitution using SPSS (not recommended)

Mean substitution, which is an approach we do not recommend, can be performed in all programs. We next provide SPSS syntax to demonstrate these analyses with the illustrative data set.

* The following opens the data file

* assuming it is placed directly in the C drive .

GET DATA

/TYPE=TXT

/FILE='C:\illustration.txt'

/DELCASE=LINE

/DELIMITERS="\t"

/ARRANGEMENT=DELIMITED

/FIRSTCASE=1

/IMPORTCASE=ALL

/VARIABLES=

ID F2.0


Group F1.0

Covariat F18.16

DV0Miss F18.16

DV10MCAR F18.16

DV20MCAR F18.16

DV50MCAR F17.16

DV10MAR F18.16

DV20MAR F18.16

DV50MAR F18.16.

CACHE.


EXECUTE.

DATASET NAME DataSet1 WINDOW=FRONT.

* The next lines recode the missing code 999 into SPSS system missing values .

RECODE DV10MCAR DV20MCAR DV50MCAR DV10MAR DV20MAR DV50MAR (999=SYSMIS).

EXECUTE .
* Analysis with 0% missing .

* The dependent variable (DV0Miss) is regressed onto “Group” .

* The remaining syntax specifies SPSS defaults for regression, which are reasonable in this analysis (including listwise deletion, which is irrelevant in this situation of no missing data) .

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DV0Miss

/METHOD=ENTER Group .


* Analysis with 10% MCAR using Mean Substitution .

* Here, the dependent variable is “DV10MCAR”, which is the same variable as used previously, but with 10% of cases randomly deleted (i.e., missing) .

* Note that the line “/MISSING MEANSUBSTITUTION” specifies mean substitution as the method of managing missing data .

DATASET ACTIVATE DataSet1.

REGRESSION

/MISSING MEANSUBSTITUTION

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DV10MCAR

/METHOD=ENTER Group.
* Analysis with 20% MCAR using Mean Substitution .

DATASET ACTIVATE DataSet1.

REGRESSION

/MISSING MEANSUBSTITUTION

/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DV20MCAR

/METHOD=ENTER Group.
To conserve space, we do not show the syntax for the remaining four analyses, noting that these involve simply inserting DV50MCAR , DV10MAR , DV20MAR , or DV50MAR as the ‘dependent’ (variable).

MI using SAS

MI is an approach greatly preferred over mean substitution. SPSS does not have the ability to directly perform MI. We illustrate these analyses using SAS syntax as shown next:

***The following lines read in data and recode 999 as missing***;

PROC IMPORT OUT= WORK.Illustration

DATAFILE= "C:\illustration.txt"

DBMS=TAB REPLACE;

GETNAMES=NO;

DATAROW=1;

RUN;

DATA illustration; set illustration;



ID = Var1;

Group = Var2;

Covariat = Var3;

DV0Miss = Var4;

IF (Var5 < 999) THEN DV10MCAR = Var5;

IF (Var6 < 999) THEN DV20MCAR = Var6;

IF (Var7 < 999) THEN DV50MCAR = Var7;

IF (Var8 < 999) THEN DV10MAR = Var8;

IF (Var9 < 999) THEN DV20MAR = Var9;

IF (Var10 < 999) THEN DV50MAR = Var10;

RUN;
****Analysis with 0% missing****;

* The dependent variable (DV0Miss) is regressed onto “Group”;

PROC REG DATA=illustration;

MODEL DV0Miss = group;

RUN;

****Multiple Imputation for 10% MCAR****;



* Here, the dependent variable is “DV10MCAR”, which has 10% of cases randomly deleted (i.e., missing);

**** The first command creates 10 imputed data sets ***;

PROC MI DATA=illustration OUT=MCAR10 NIMPUTE=10 SEED=1211981;

VAR group covariat DV10MCAR;

RUN;

**** This second command performs 10 regression analyses for the 10 imputed data sets ***;



PROC REG DATA=MCAR10 outest=a COVOUT;

MODEL DV10MCAR = group;

BY _IMPUTATION_;

RUN;


**** This third command combines results of the 10 regression analyses to estimate appropriate standard errors for the regression coefficients intercept and group ***;

PROC MIANALYZE DATA=a;

MODELEFFECTS INTERCEPT group;

RUN;
****Multiple Imputation for 20% MCAR****;

PROC MI DATA=illustration OUT=MCAR20 NIMPUTE=10 SEED=1211981;

VAR group covariat DV20MCAR;

RUN;

PROC REG DATA=MCAR20 outest=a COVOUT;



MODEL DV20MCAR = group;

BY _IMPUTATION_;

RUN;

PROC MIANALYZE DATA=a;



MODELEFFECTS INTERCEPT group;

RUN;
To conserve space, we do not show the syntax for the remaining four analyses, noting that these involve substituting DV50MCAR , DV10MAR , DV20MAR , or DV50MAR as the new file name (out-file of Proc MI and data-file of Proc REG) and dependent variable in the ‘model’ command of the Proc REG.



FIML using Mplus

FIML is a model-based approach that is also greatly preferred over mean substitution. The FIML approach to missing data management is most commonly implemented in structural equation modeling or multilevel modeling software. We illustrate these analyses using MPlus syntax as shown next (note that two warnings appear in the output for this syntax, one input warning noting that ‘Type=Missing’ is now the default and the other regarding the standard errors; both of these warnings can be ignored) :



Syntax for no missing data:

TITLE: 0% Missingness;

DATA: FILE IS "c:\illustration.txt";

VARIABLE: NAMES ARE ID Group Covariat DV0Miss

DV10MCAR DV20MCAR DV50MCAR

DV10MAR DV20MAR DV50MAR;

USEVARIABLES group DV0Miss covariat;

MISSING = all (999);

ANALYSIS: TYPE IS MISSING;

ESTIMATOR IS ML;

ITERATIONS = 1000;

CONVERGENCE = 0.00005;

COVERAGE = 0.10;

MODEL: DV0Miss on group;

group with covariat;

DV0Miss with covariat;

OUTPUT: tech1 tech3
Syntax for 10% MCAR:

TITLE: FIML 10% MCAR;

DATA: FILE IS "c:\illustration.txt";

VARIABLE: NAMES ARE ID Group Covariat DV0Miss

DV10MCAR DV20MCAR DV50MCAR

DV10MAR DV20MAR DV50MAR;

USEVARIABLES group DV10MCAR covariat;

MISSING = all (999);

ANALYSIS: TYPE IS MISSING;

ESTIMATOR IS ML;

ITERATIONS = 1000;

CONVERGENCE = 0.00005;

COVERAGE = 0.10;

MODEL: DV10MCAR on group;

group with covariat;

DV10MCAR with covariat;

OUTPUT: tech1 tech3

Again, to conserve space, we do not show the syntax for the remaining five analyses. To perform these analyses, one simply replaces DV10MCAR within the syntax above with DV20MCAR, DV50MCAR, DV10MAR, DV20MAR, or DV50MAR.

Illustration.txt

1 0 -.850867829681874 -.154493593047363 -.154493593047363 -.154493593047363 -.154493593047363 -.154493593047363 -.154493593047363 -.154493593047363

2 0 -.211056217578008 -1.29827630533749 -1.29827630533749 -1.29827630533749 999 -1.29827630533749 -1.29827630533749 999

3 0 -1.58158061928887 -1.84729308697254 -1.84729308697254 999 999 999 999 999

4 0 -.892182736398273 -.749896078284945 -.749896078284945 -.749896078284945 -.749896078284945 -.749896078284945 -.749896078284945 999

5 0 -.0154904264600894 -.0350404123315361 -.0350404123315361 -.0350404123315361 999 -.0350404123315361 -.0350404123315361 999

6 0 -.617416180956403 -.791173756467051 -.791173756467051 -.791173756467051 -.791173756467051 -.791173756467051 -.791173756467051 999

7 0 -.160947342612139 -1.39305601560536 -1.39305601560536 -1.39305601560536 -1.39305601560536 -1.39305601560536 -1.39305601560536 999

8 0 .0822082173324673 -.592680564158753 -.592680564158753 -.592680564158753 -.592680564158753 -.592680564158753 -.592680564158753 -.592680564158753

9 0 -2.84579505283808 -.928260768294282 -.928260768294282 -.928260768294282 999 999 999 999

10 0 -.750327378280308 -.566730518398268 -.566730518398268 -.566730518398268 -.566730518398268 999 999 999

11 0 -.192208289689706 1.6010839421218 1.6010839421218 999 999 1.6010839421218 1.6010839421218 1.6010839421218

12 0 -.29929998834839 -.706578484159677 -.706578484159677 -.706578484159677 999 -.706578484159677 -.706578484159677 -.706578484159677

13 0 .397103644817022 -.854409356857959 -.854409356857959 -.854409356857959 -.854409356857959 -.854409356857959 -.854409356857959 -.854409356857959

14 0 -1.86598842125329 -2.01778396402153 -2.01778396402153 -2.01778396402153 -2.01778396402153 -2.01778396402153 -2.01778396402153 999

15 0 -.563836006435321 .336646818210308 .336646818210308 .336646818210308 .336646818210308 .336646818210308 999 999

16 0 -.147133183225006 1.09641236468901 1.09641236468901 1.09641236468901 1.09641236468901 1.09641236468901 1.09641236468901 1.09641236468901

17 0 -.0355818140701889 -.0997428904192106 999 999 999 -.0997428904192106 -.0997428904192106 -.0997428904192106

18 0 .887897514907359 -.182639887782136 -.182639887782136 -.182639887782136 999 -.182639887782136 -.182639887782136 -.182639887782136

19 0 -1.48840552487275 -.539959146486662 -.539959146486662 -.539959146486662 999 999 999 999

20 0 .108675987681218 -.437665939005987 -.437665939005987 999 999 -.437665939005987 -.437665939005987 -.437665939005987

21 0 -.20275634025207 -1.38987700220506 999 999 999 -1.38987700220506 -1.38987700220506 -1.38987700220506

22 0 -.376034169822592 -.0396592797147315 -.0396592797147315 -.0396592797147315 999 999 999 999

23 0 1.00077829138927 -.358278452919041 999 999 999 -.358278452919041 -.358278452919041 -.358278452919041

24 0 -.542027337979399 .0171460549100705 .0171460549100705 .0171460549100705 .0171460549100705 .0171460549100705 .0171460549100705 .0171460549100705

25 0 -.849060312346486 -.549660453094282 -.549660453094282 -.549660453094282 999 -.549660453094282 -.549660453094282 -.549660453094282

26 0 -1.80705390136401 -.289094061603656 -.289094061603656 -.289094061603656 -.289094061603656 -.289094061603656 999 999

27 0 -.101662079471109 -.276314150394167 -.276314150394167 999 999 -.276314150394167 -.276314150394167 999

28 0 -3.2620755881702 -1.39658693937835 -1.39658693937835 -1.39658693937835 -1.39658693937835 -1.39658693937835 999 999

29 0 -.0330451498263781 .24681560739095 .24681560739095 .24681560739095 .24681560739095 999 999 999

30 0 .0396122058845316 -.680536147388183 999 999 999 -.680536147388183 999 999

31 1 1.05826453711523 .99917526641664 .99917526641664 .99917526641664 .99917526641664 .99917526641664 .99917526641664 .99917526641664

32 1 -.181826632905936 .29456458296444 .29456458296444 .29456458296444 .29456458296444 .29456458296444 .29456458296444 999

33 1 .325298620152614 -.124115873877004 -.124115873877004 -.124115873877004 -.124115873877004 -.124115873877004 -.124115873877004 -.124115873877004

34 1 -.554650029752322 -.622863667018844 -.622863667018844 -.622863667018844 999 -.622863667018844 -.622863667018844 -.622863667018844

35 1 -.654212127213383 -1.01541423045684 -1.01541423045684 -1.01541423045684 999 -1.01541423045684 999 999

36 1 -1.1177906863268 .209256887497134 .209256887497134 .209256887497134 999 .209256887497134 .209256887497134 999

37 1 -.181923486491488 -.717691998996674 -.717691998996674 -.717691998996674 -.717691998996674 -.717691998996674 -.717691998996674 -.717691998996674

38 1 .0570406365471001 .156146647451192 999 999 999 .156146647451192 .156146647451192 999

39 1 .664147230154368 -.288229623773199 -.288229623773199 -.288229623773199 -.288229623773199 -.288229623773199 -.288229623773199 999

40 1 1.20738016510671 .251229411715677 .251229411715677 .251229411715677 .251229411715677 .251229411715677 .251229411715677 .251229411715677

41 1 1.49167062362998 1.82453435527453 1.82453435527453 1.82453435527453 1.82453435527453 1.82453435527453 1.82453435527453 1.82453435527453

42 1 .10443799659144 -.505317323754116 -.505317323754116 -.505317323754116 999 -.505317323754116 -.505317323754116 999

43 1 -.549409315402947 .688840627679144 .688840627679144 .688840627679144 999 .688840627679144 .688840627679144 .688840627679144

44 1 1.2355847811718 .488225613985325 .488225613985325 .488225613985325 .488225613985325 .488225613985325 .488225613985325 .488225613985325

45 1 1.4159448165691 .102824753225284 .102824753225284 .102824753225284 999 .102824753225284 .102824753225284 .102824753225284

46 1 -.829901220499762 -.884557475454705 -.884557475454705 999 999 -.884557475454705 -.884557475454705 999

47 1 1.16593272486838 2.28809182337569 2.28809182337569 2.28809182337569 2.28809182337569 2.28809182337569 2.28809182337569 2.28809182337569

48 1 .43362804115754 .446226340180492 .446226340180492 .446226340180492 999 .446226340180492 .446226340180492 999

49 1 .206497558025471 .641067713273713 .641067713273713 .641067713273713 .641067713273713 .641067713273713 .641067713273713 999

50 1 .847932701494698 1.25236793850088 1.25236793850088 1.25236793850088 1.25236793850088 1.25236793850088 1.25236793850088 1.25236793850088

51 1 1.84759808931025 1.69199874491296 1.69199874491296 1.69199874491296 1.69199874491296 1.69199874491296 1.69199874491296 1.69199874491296

52 1 -.7760562984868 -.390328025772538 -.390328025772538 -.390328025772538 -.390328025772538 -.390328025772538 999 999

53 1 1.83621118386067 1.06028971435592 1.06028971435592 1.06028971435592 999 1.06028971435592 1.06028971435592 1.06028971435592

54 1 1.63096618805731 1.72387743255306 1.72387743255306 1.72387743255306 999 1.72387743255306 1.72387743255306 1.72387743255306

55 1 2.43408888774903 .821243682071921 .821243682071921 .821243682071921 .821243682071921 .821243682071921 .821243682071921 .821243682071921

56 1 1.05397592438395 .507454217808059 .507454217808059 999 999 .507454217808059 .507454217808059 999

57 1 .0521649622721339 .238215282499099 .238215282499099 .238215282499099 .238215282499099 .238215282499099 .238215282499099 999

58 1 1.43465274304311 2.1426558324697 999 999 999 2.1426558324697 2.1426558324697 2.1426558324697

59 1 2.72640613683008 2.3463057079243 2.3463057079243 2.3463057079243 999 2.3463057079243 2.3463057079243 2.3463057079243



60 1 -1.20849872180246 -.748491890025146 -.748491890025146 -.748491890025146 -.748491890025146 -.748491890025146 -.748491890025146 999


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