A Turbo Pascal routine has been developed to estimate person measures when item and step measures are known (RMT 12:2 p. 638). It was designed for the VF-14 questionnaire. This probes 14 daily life activities (items) affected by cataracts. On each item, patients rate their difficulty of doing the activity (0 'no difficulty' to 4 'unable'). The specific parts of this routine related to the VF-14 are underlined and can be easily modified for other instruments.
Mercè Comas, Gemma Vilagut, Luís Prieto
ESTIMATING MEASURES WITH KNOWN POLYTOMOUS ITEM DIFFICULTIES WITH TURBO PASCAL
Mercè Comas Gemma Vilagut, Luís Prieto
Comas M, Vilagut G, Prieto L.
The following application, programmed in Turbo Pascal, is intended to automate the estimation of measures with known polytomous item difficulties using the method proposed by Linacre (1998):
(Eq. 1)
(Eq. 2)
(Eq. 3)
(Eq. 4)
(Eq. 5)
(Eq. 6)
The Turbo Pascal routine was initially developed to be applied to the responses of the VF-14 questionnaire. The VF14 measures functional capacity related to vision based on 14 daily life activities (items) affected by cataracts (Steinberg, 1994). Patients rate their difficulty doing each activity (0 "no difficulty" to 4 "unable"), final scores ranging from 0 (worst) to 100 (best). The item and step difficulties of the VF-14 were calibrated in a sample of 198 Spanish patients participating in an International study.
The specific parts of the routine related to this VF-14 are written in bold italics and must be replaced in case of using any other instrument.
PROGRAM RASCHVF14 (INPUT,OUTPUT);
CONST MIN=0; {minimum category of responses}
MAX=4; {maximum category of responses}
MAXI=14; {number of items}
MAXCAT=5; {number of response categories}
TYPE ITEMS = ARRAY [1..MAXI] OF REAL; {structure for item information}
CATEG = ARRAY [1..MAXCAT] OF REAL; {structure for category information}
PROB = ARRAY [1..MAXI] OF CATEG; {matrix of probabilities (MAXI x MAXCAT)}
CONST
D:ITEMS=(1.27,0.48,-1.47,-1.58,-0.35,-0.13,0.64,0.26,-0.67,-0.5,-1.32,
-0.38,1.41,2.33); {difficulty measures of items. Insert your own item
difficulties}
STEPS:CATEG=(0,-0.42,-0.49,0.98,-0.07); {steps for categories. Insert your own steps}
VAR R,Dmean,M,M2,SCORE,V:REAL; L:INTEGER; PUNT,DP:ITEMS; P:PROB;{R = Raw Score; Dmean = mean of difficulty measures of the answered items}
{M = M; M2 = M updated; SCORE = Score; V = variance of Rasch measurement}
{L = number of answered items; PUNT = array of responses}
{DP = array of difficulty measures of the answered items}
{P = matrix of probabilities}
{Initialization of variables}
PROCEDURE INITIALIZE(VAR R,M,M2:REAL;VAR L:INTEGER;VAR DP,PUNT:ITEMS;VAR P:PROB);
VAR I,J:INTEGER;
BEGIN
R:=0;M:=0;M2:=0;L:=0;
FOR I:=1 TO MAXI DO
BEGIN
PUNT[I]:=9; {the array of responses is initialized as missing
values}
DP[I]:=0; FOR J:=1 TO MAXCAT DO P[I][J]:=0;
END
END;
{Reads responses from keyboard and computes Dmean}PROCEDURE READ_INPUT(VAR PUNT,DP:ITEMS;VAR L:INTEGER;VAR Dmean:REAL);
VAR I:INTEGER;
P:ITEMS;
SUMADI:REAL;
BEGIN
WRITE('1. Reading small prints: ');READLN(P[1]);
WRITE('2. Reading a newspaper: ');READLN(P[2]);
WRITE('3. Reading large prints: ');READLN(P[3]);
WRITE('4. Recognizing people: ');READLN(P[4]);
WRITE('5. Seeing steps, stairs: ');READLN(P[5]);
WRITE('6. Reading traffic signs: ');READLN(P[6]);
WRITE('7. Doing fine handwork: ');READLN(P[7]);
WRITE('8. Writing checks: ');READLN(P[8]);
WRITE('9. Playing games: ');READLN(P[9]);
WRITE('10. Taking part in activities: ');READLN(P[10]);
WRITE('11. Preparing meals: ');READLN(P[11]);
WRITE('12. Watching television: ');READLN(P[12]);
WRITE('13. Driving during the day: ');READLN(P[13]);
WRITE('14. Driving at night: ');READLN(P[14]);
L:=0;
SUMADI:=0;
FOR I:=1 TO MAXI DO
BEGIN
IF ((P[I]>=MIN) AND (P[I]<=MAX)) THEN
BEGIN
L:=L+1; {number of valid responses}
PUNT[L]:=MAX-P[I]; {Rescoring of initial responses of VF-14}
DP[L]:=D[I]; {array with difficulty measures of the
answered items}
SUMADI:=SUMADI+D[I]; {sum of difficulty measures}
END;
END;
IF L<>0 THEN Dmean:=SUMADI/L; {computation of the average item
difficulty.
Eq. 1}
END;
{Computation of Raw Score R}
PROCEDURE RAW_SCORE_M(VAR R,M:REAL;Dmean:REAL;PUNT:ITEMS;L:INTEGER);
VAR I,Rmin,Rmax:INTEGER;
BEGIN
I:=1;
FOR I:=1 TO L DO R:=R+PUNT[I];
Rmin:=MIN*L;
Rmax:=MAX*L;
IF R=Rmin THEN R:=R+0.5;
IF R=Rmax THEN R:=R-0.5;
M:=Dmean+LN((R-Rmin)/(Rmax-R)); {Eq. 2}
END;
{Computation of the matrix of probabilities and M}
PROCEDURE PROBABILITIES(VAR P:PROB;PUNT,DP:ITEMS;M:REAL;VAR SCORE,V,M2:REAL;L:INTEGER;R:REAL);
VAR I,J:INTEGER;
F,DEN,EXP2,E2XP,E:REAL;
NUM:CATEG;
DIF:ITEMS;
BEGIN
FOR I:=1 TO L DO FOR J:=1 TO MAXCAT DO P[I][J]:=0;
SCORE:=0;
V:=0;
F:=0;
FOR I:=1 TO MAXI DO DIF[I]:=0;
FOR I:=1 TO L DO
BEGIN
F:=0;
DEN:=0;
FOR J:=1 TO MAXCAT DO NUM[J]:=0;
FOR J:=1 TO MAXCAT DO
BEGIN
F:=F+STEPS[J];
E:=((J-1)*(M-DP[I]))-F;
NUM[J]:=EXP(E);
DEN:=DEN+NUM[J];
END;
FOR J:=1 TO MAXCAT DO
BEGIN
P[I][J]:=NUM[J]/DEN; {Eq. 3}
SCORE:=SCORE+(J-1)*P[I][J]; {Eq. 4}
END;
END;
FOR I:=1 TO L DO
BEGIN
E2XP:=0;EXP2:=0;
FOR J:=1 TO MAXCAT DO
BEGIN
E2XP:=P[I][J]*SQR(J-1)+E2XP;
EXP2:=P[I][J]*(J-1)+EXP2;
END;
DIF[I]:=E2XP-SQR(EXP2);
V:=DIF[I]+V; {Eq. 5}
END;
M2:=M+((R-SCORE)/V); {Eq. 6}
END;
{Computation of Rasch measurement and its standard error and CI95%}
PROCEDURE RESULTS(M,V:REAL;VAR Q:CHAR;L:INTEGER);
VAR SE,PT,ICMIN,ICMAX:REAL;
BEGIN
SE:=0;
PT:=0;
ICMIN:=0;
ICMAX:=0;
WRITE ('VF-14 Rasch score: ');
IF L=0 THEN WRITELN('No valid responses')
ELSE
BEGIN
PT:=50.64+(11.75*M); {Rasch measurement on a 0 to 100 scale}
SE:=11.75*(1/SQRT(V)); {standard error}
ICMAX:=PT+1.96*SE; {upper confidence interval limit (CI95%)}
ICMIN:=PT-1.96*SE; {lower confidence interval limit (CI95%)}
IF (ICMAX>100) THEN ICMAX:=100;
IF (ICMIN<0) THEN ICMIN:=0;
{output:} WRITELN(PT:4:0,' (SE: ',SE:4:0,')');
WRITELN('IC: [',ICMIN:4:0,',',ICMAX:4:0,']');
END;
END;
{Main program}BEGIN INITIALIZE(R,M,M2,L,DP,PUNT,P); READ_INPUT(PUNT,DP,L,Dmean); IF L>0 THEN BEGIN
RAW_SCORE_M(R,M,Dmean,PUNT,L);
PROBABILITIES(P,PUNT,DP,M,SCORE,V,M2,L,R);
WHILE ABS(M2-M)>0.01 DO
BEGIN
M:=M2;
PROBABILITIES(P,PUNT,DP,M,SCORE,V,M2,L,R);
END;
END;
RESULTS(M,V,Q,L);
END.
REFERENCES
Alonso J, Espallargues M, Andersen TF, et al. International Applicability of the VF-14. An Index of Visual Function in Patients with Cataracts. Ophtalmology 1997; 104: 799-807.
Linacre JM. Estimating Measures with Known Polytomous Item Difficulties. Rasch Measurement Transactions 1998; 12: 638.
Prieto L, Alonso J, Valderas JM, et al. A Proposal of Content-Based Interpretation of Health-Related
Quality of Life Scores. Quality of Life Research 1998; 7: 652-653.
Steinberg EP, Tielsch JM, Schein OD, et al. The VF-14: an Index of Functional Impairment in Patients with Cataract. Archives of Ophtalmology 1994; 112: 630-638.
Turbo Pascal Measurement Program Prieto L, Comas M, Vilagut G … Rasch Measurement Transactions, 1999, 12:4 p.
Rasch Publications | ||||
---|---|---|---|---|
Rasch Measurement Transactions (free, online) | Rasch Measurement research papers (free, online) | Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch | Applying the Rasch Model 3rd. Ed., Bond & Fox | Best Test Design, Wright & Stone |
Rating Scale Analysis, Wright & Masters | Introduction to Rasch Measurement, E. Smith & R. Smith | Introduction to Many-Facet Rasch Measurement, Thomas Eckes | Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. | Statistical Analyses for Language Testers, Rita Green |
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar | Journal of Applied Measurement | Rasch models for measurement, David Andrich | Constructing Measures, Mark Wilson | Rasch Analysis in the Human Sciences, Boone, Stave, Yale |
in Spanish: | Análisis de Rasch para todos, Agustín Tristán | Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez |
Forum | Rasch Measurement Forum to discuss any Rasch-related topic |
Go to Top of Page
Go to index of all Rasch Measurement Transactions
AERA members: Join the Rasch Measurement SIG and receive the printed version of RMT
Some back issues of RMT are available as bound volumes
Subscribe to Journal of Applied Measurement
Go to Institute for Objective Measurement Home Page. The Rasch Measurement SIG (AERA) thanks the Institute for Objective Measurement for inviting the publication of Rasch Measurement Transactions on the Institute's website, www.rasch.org.
Coming Rasch-related Events | |
---|---|
Aug. 14 - 16, 2019. Wed.-Fri. | An Introduction to Rasch Measurement: Theory and Applications (workshop led by Richard M. Smith) https://www.hkr.se/pmhealth2019rs |
August 25-30, 2019, Sun.-Fri. | Pacific Rim Objective Measurement Society (PROMS) 2019, Surabaya, Indonesia https://proms.promsociety.org/2019/ |
Oct. 11 - Nov. 8, 2019, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
Nov. 3 - Nov. 4, 2019, Sun.-Mon. | International Outcome Measurement Conference, Chicago, IL,http://jampress.org/iomc2019.htm |
Jan. 24 - Feb. 21, 2020, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
May 22 - June 19, 2020, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 26 - July 24, 2020, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com |
Aug. 7 - Sept. 4, 2020, Fri.-Fri. | On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com |
Oct. 9 - Nov. 6, 2020, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 25 - July 23, 2021, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com |
The URL of this page is www.rasch.org/rmt/rmt124i.htm
Website: www.rasch.org/rmt/contents.htm