MISSCORE= scoring of missing data codes = -1, ignore

This is NOT the missing-value code in your data. All codes NOT in CODES= are missing value codes. Use this control specification when you want missing data to be treated as valid responses. Winsteps and Missing Data: No Problem!

 

Winsteps processes one observation at a time. For each observation, Xni by person n on item i, it computes an expectation Eni, based on the current person measure estimate Bn and the current item measure Di and, if relevant, the current rating (or partial credit) scale structure (calibrations) {Fk}. Pnik is the probability of observing category k for person n on item i.

 

In this computation it skips over, omits, ignores "missing" data.

 

It then compares sum(Xni) with sum(Eni) for each person n, and adjusts Bn.

It then compares sum(Xni) with sum(Eni) for each item i, and adjusts Di

It then compares the count of (Xni=k) with the sum (Pnik) for each k, and adjusts Fk

 

These sums and counts are only over the observed data. There is no need to impute missing data.

 

There are no pairwise, listwise or casewise deletions associated with missing data.

 


MISSCORE= says what to do with characters that are not valid response codes, e.g. blanks and data entry errors. Usually any characters not in CODES= are treated as missing data, and assigned a value of -1 which mean "ignore this response." This is usually what you want when such responses mean "not administered". If they mean "I don't know the answer", you may wish to assign missing data a value of 0 meaning "wrong", or, on a typical attitude survey, 3, meaning "neutral" or "don't know".

 

MISSCORE=255 is the same as MISSCORE=-1

MISSING=0 is the same as MISSCORE=0 meaning that all codes in the data not listed in CODES= are to be scored 0.

 

Non-numeric codes included in CODES= (without rescoring/recoding) or in NEWSCORE= or IVALUE= are always assigned a value of "not administered", -1.

 

Much more at www.rasch.org/rmt/rmt142h.htm

 

Example 0a: In my data file, missing data are entered as 9. I want to score them 0, wrong answers. Valid codes are 0 and 1.

 CODES = 01 ; do not specify a 9 as valid

 MISSCORE = 0 ; specifies that all codes not listed in CODES=, e.g., 9's. are to be scored 0.

 

Example 0b: In my data file, missing data are entered as 9. I want to ignore them in may analysis. Valid codes are 0 and 1.

   CODES = 01 do not specify a 9 as valid

; the following line is the standard, it can be omitted.

 MISSCORE = -1 specifies that all codes not listed in CODES=, e.g., 9's. 

     are to be treated as "not administered"

 

Example 1: Assign a code of "0" to any responses not in CODES=

   MISSCORE=0 missing responses are scored 0.

 

Example 2: In an attitude rating scale with three categories (0, 1, 2), you want to assign a middle code of "1" to missing values

   MISSCORE=1 missing responses scored 1

 

Example 3: You want blanks to be treated as "wrong" answers, but other unwanted codes to be ignored items, on a questionnaire with responses "Y" and "N".

   CODES="YN " blank included as valid response

   NEWSCORE=100 new response values

   RESCORE=2  rescore all items

   MISSCORE=-1 ignore missing responses (standard)

 

Example 4: Your optical scanner outputs an "@" if two bubbles are marked for the same response. You want to ignore these for the analysis, but you also want to treat blanks as wrong answers:

   CODES ="1234 " blank is the fifth valid code

   KEY1  =31432432143142314324    correct answers

   MISSCORE=-1  applies to @ (standard)

 

Example 5: Unexpected codes are scored "wrong", but 2's to mean "not administered".

   CODES = 012

   NEWSCORE= 01X ; X is non-numeric, matching 2's ignored

   MISSCORE= 0  ; all non-CODES= responses scored 0

 

Example 6: You have a long 4-option MCQ test with data codes ABCD. Most students do not have the time to complete all the items. This requires a two-stage item analysis:

 

1. Estimate the item difficulties without allowing missing data to bias them

Missing data = -1 ; not administered. Perform the analysis estimate the item difficulties. Save them with IFILE=if.txt

 

2. Estimate the person measures lowering the estimates if there is missing data

Missing data = 0 ; incorrect. Anchor the item difficulties with  IAFILE=if.txt to estimate the person measures and report them.

 

So, in this Example:

Stage 1. Item calibration:

Deliberately skipped responses are coded "S" and scored incorrect. The student could not answer the question.

Not-reached items are coded "R" and scored "not administered". This prevents easy items at the end of the test being calibrated as "very difficult".

   CODES="ABCDS"

   KEY1="CDBAD....."

   MISSCORE=-1 

   IFILE=ITEMCAL.TXT ; write out the item calibrations

 

Stage 2. Person measurement:

The convention with MCQ tests is that all missing responses are scored incorrect when measuring the persons.

   IAFILE=ITEMCAL.TXT ; anchor on the Stage 1 item calibrations

   CODES="ABCDS"

   KEY1="CDBAD....."

   MISSCORE=0  ; all missing data are scored incorrect


Help for Winsteps Rasch Measurement and Rasch Analysis Software: www.winsteps.com. Author: John Michael Linacre

Facets Rasch measurement software. Buy for $149. & site licenses. Freeware student/evaluation Minifac download
Winsteps Rasch measurement software. Buy for $149. & site licenses. Freeware student/evaluation Ministep download

Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn, 2024 George Engelhard, Jr. & Jue Wang Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
Facets Tutorials - free
Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan
Other Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
As an Amazon Associate I earn from qualifying purchases. This does not change what you pay.

facebook Forum: Rasch Measurement Forum to discuss any Rasch-related topic

To receive News Emails about Winsteps and Facets by subscribing to the Winsteps.com email list,
enter your email address here:

I want to Subscribe: & click below
I want to Unsubscribe: & click below

Please set your SPAM filter to accept emails from Winsteps.com
The Winsteps.com email list is only used to email information about Winsteps, Facets and associated Rasch Measurement activities. Your email address is not shared with third-parties. Every email sent from the list includes the option to unsubscribe.

Questions, Suggestions? Want to update Winsteps or Facets? Please email Mike Linacre, author of Winsteps mike@winsteps.com


State-of-the-art : single-user and site licenses : free student/evaluation versions : download immediately : instructional PDFs : user forum : assistance by email : bugs fixed fast : free update eligibility : backwards compatible : money back if not satisfied
 
Rasch, Winsteps, Facets online Tutorials


 

 

 

Our current URL is www.winsteps.com

Winsteps® is a registered trademark