Glossary of Rasch Measurement Terminology
Glosario Español www.rasch.org/rmt/glosario.htm
Abilitythe level of successful performance of the objects of measurement on the variable.
Agent of Measurementthe tool (items, questions, etc.) used to define a variable and position objects or persons along that variable.
Anchorthe process of using anchor values to insure that different analyses produce directly comparable results.
Anchor Valuea pre-set logit value assigned to a particular object, agent or step to be used as a reference value for determining the measurements or calibrations of other objects, agents or steps.
Anchor Tablethe table of Anchor Values used during Rasch analysis of an Input Grid and so included in the Results Table produced. The Anchor Table has the same format as the Results Table.
Best Test DesignWright, B.D. & Stone, M.H., Best Test Design: Rasch Measurement. Chicago: Mesa Press, 1979
BiasA change in logit values based on the particular agents or objects measured.
BOTTOMThe value shown in the Results Table for an agent on which all objects were successful, (so it was of bottom difficulty), or for an object which had no success on any agent (so it was of bottom ability)
Bottom Category the response category at which no level of successful performance has been manifested.
Calibrationa difficulty measure in logits used to position the agents of measurement along the variable.
Categorieslevels of performance on an observational or response format.
CellLocation of data in the spreadsheet, given by a column letter designation and row number designation e.g. B7
Common Scalea scale of measurement on which all agents and objects can be represented.
ColumnVertical line of data in the Spreadsheet data, usually representing in an Input Grid all responses to a particular item, or in a Results Table, all statisitics measuring the same attribute of agents or objects.
Construct
Construct Validity
What we want to measure. It is usually defined by a theory, the construct theory, and oeprationalized by the items in the test/instrument. The construct theory predicts the difficulty ordering (hierarchy) of the items. If the theoretical and empirical orderings of the items agree, then Construct Validity is confirmed.
Contentthe subject area evoked and defined by an agent.
Convergencethe point at which further improvement of the item and person estimates makes no useful difference in the results. Rasch calculation ends at this point.
Dichotomous Responsea response format of two categories such as correct-incorrect, yes-no, agree-disagree.
Difficultythe level of resistance to successful performance of the agents of measurement on the variable.
Discrepancyone or more unexpected responses.
Disturbanceone or more unexpected responses.
Divergingthe estimated calibrations at the end of an iteration are further from convergence than at the end of the previous iteration.
Expected Responsethe predicted response by an object to an agent, according to the Rasch model analysis.
Fit Statistica summary of the discrepancies between what is observed and what we expect to observe.
Headingan identifier or title for use on tables, maps and plots.
IndependentNot dependent on which particular agents and objects are included in the analysis. Rasch analysis is independent of agent or object population as long as the measures are used to compare objects or agents which are of a reasonably similar nature.
Infitan information weighted fit statistic that focuses on the overall performance of an item or person, i.e, the information-weighted average of the squared standardized deviation of observed performance from expected performance. The statistic plotted and tabled by Rasch is this mean square normalized.
Interval scaleScale of measurement on which equal intervals represent equal amounts of the vairable being measured.
Itemagent of measurement, not necessarily a test question, e.g., a product rating.
Iterationone run through the data by the Rasch calculation program, done to improve estimates by minimizing residuals.
Knox Cube Testa tapping pattern test requiring the application of visual attention and short term memory.
LinkRelating the measures derived from one test with those from another test, so that the measures can be directly compared.
Logitthe unit of measure used by Rasch for calibrating items and measuring persons. A loge odds transformation of the probability of a correct response.
Mapa bar chart showing the frequency and spread of agents and objects along the variable.
Matrixa rectangle of responses with rows (or columns) defined by objects and columns (or rows) defined by agents.
Measure
Measurement
the process of quantifying the amount of a variable, or the result of the process. Rasch measurements are locations (usually in logits) on the latent variable. The Rasch measure for persons is the person ability. The Rasch measure for items is the item difficulty.
Normala random distribution, graphically represented as a "bell" curve which has a mean value of 0 and a standard deviation of 1.
Normalizedthe transformation of the actual statistics obtained so that they are theoretically part of a normal distribution.
Object of Measurementpeople, products, sites, to be measured or positioned along the variable.
Observed Responsethe actual response by an object to an agent.
Outfitan outlier sensitive fit statistic that picks up rare events that have occurred in an unexpected way. It is the average of the squared standardized deviations of the observed performance from the expected performance. Rasch plots and tables use the normalized unweighted mean squares so that the graphs are symmetrically centered on zero.
Outliersunexpected responses usually produced by agents and objects far from one another in location along the variable.
Personthe object of measurement, not necessarily human, e.g., a product.
Plotan x-y graph used by Rasch to show the fit statistics for agents and objects.
Point Labelsthe placing on plots of the identifier for each point next to the point as it is displayed.
Poisson Countinga method of scoring tests based on the number of occurences or non-occurences of an event, e.g. spelling mistakes in a pice of dictation.
Processthe psychological quality, i.e.,the ability, skill, attitude, etc., being measured by an item.
PROXthe normal approximation estimation formula, used by some Rasch programs for the first part of the iteration process.
Rasch, GeorgDanish Mathematician (1906-1980), who first propounded the application of the statistical approach used by Rasch.
Rasch Modela mathematical formula for the relationship between the probability of success (P) and the difference between an individual's ability (B) and an item's difficulty (D). P=exp(B-D)/(1+exp(B-D)) or loge [P/(1-P)] = B - D
Rating ScaleA format for observing responses wherein the categories increase in the level of the variable they define, and this increase is uniform for all agents of measurement.
Rating Scale AnalysisWright, B.D. & Masters, G.N., Rating Scale Analysis: Rasch Measurement. Chicago: Mesa Press, 1982.
Reliabilitythe ratio of sample or test variance, corrected for estimation error, to the total variance observed.
Residualsthe difference between data observed and values expected.
ResponseThe value indicating degree of success by an object on an agent, and entered into the appropriate cell of an Input Grid.
Results Tablea report of Rasch calculations.
Rigiditywhen agents, objects and steps are all anchored, this is the logit inconsistency between the anchoring values, and is reported on the Iteration Screen and Results Table. 0 represents no inconsistency.
Rowa horizontal line of data on a Spreadsheet, usually used, in the Input Grid, to represent all responses by a particular object. The top row of each spreadsheet is reserved for Rasch control information.
Scale1. the quantitive representation of a variable (e.g., a weight scale)
2. ordinal representation of a variable (e.g., a rating scale)
Score pointsthe numerical values assigned to responses when summed to produce a score for an agent or object.
Separationthe ratio of sample or test standard deviation, corrected for estimation error, to the average estimation error.
Standard Deviationthe root mean square of the differences between the calculated logits and their mean.
Standard Erroran estimated quantity which, when added to and subtracted from a logit measure or calibration, gives the least distance required before a difference becomes meaningful.
Stepsthe transitions between adjacent categories ordered by the definition of the variable.
Templatea specially formatted input file.
TOPThe value shown in the Results Table for an agent on which no objects were successful, (so it was of top difficulty), or for an object which succeeded on every agent (so it was of top ability)
Top Categorythe response category at which maximum performance is manifested.
UCONthe unconditional (or "joint" JMLE) maximum likelihood estimation formula, used by some Rasch programs for the second part of the iteration process.
UNSURERasch was unable to calibrate this data and treated it as missing.
Unweightedthe situation in which all residuals are given equal significance in fit analysis, regardless of the amount of the information contained in them.
Variablethe idea of what we want to measure. A Rasch variable is defined by the items or agents of measurement used to elicit its manifestations or responses.
Weightedthe adjustment of a residual for fit analysis, according to the amount of information contained in it.

Based on: Wright, B.D. & Linacre J.M. (1985) Microscale Manual. Westport, Conn.: Mediax Interactive Technologies, Inc.

Your additions welcome!

Rasch publications in Spanish - Español
www.rasch.org/rmt/glosario.htmRasch Anàlisis - Glosario Español - Spanish Glossary - Español-Inglés del diccionario - Spanish-English Dictionary - Dr. Agustín Tristàn Lòpez
www.rasch.org/diseno.htmSpanish language introduction to Diseño de Mejores Pruebas (Best Test Design)
www.rasch.org/spanish.htmDiseño de Mejores Pruebas - Spanish-language book of Best Test Design (Wright & Stone, 1979)


Please help with Standard Dataset 4: Andrich Rating Scale Model



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

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Jan. 25-26, 2017, Wed.-Thurs. In-person workshop: Measurement with the Rasch Model (M. Pampaka, J. Williams, Winsteps), Manchester, UK, website
Feb. 27 - June 24, 2017, Mon.-Sat. On-line: Advanced course in Rasch Measurement Theory (EDUC5606), Website
March 31, 2017, Fri. Conference: 11th UK Rasch Day, Warwick, UK, www.rasch.org.uk
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June 30 - July 29, 2017, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
July 31 - Aug. 3, 2017, Mon.-Thurs. Joint IMEKO TC1-TC7-TC13 Symposium 2017: Measurement Science challenges in Natural and Social Sciences, Rio de Janeiro, Brazil, imeko-tc7-rio.org.br
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Aug. 11 - Sept. 8, 2017, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Aug. 18-21, 2017, Fri.-Mon. IACAT 2017: International Association for Computerized Adaptive Testing, Niigata, Japan, iacat.org
Sept. 15-16, 2017, Fri.-Sat. IOMC 2017: International Outcome Measurement Conference, Chicago, jampress.org/iomc2017.htm
Oct. 13 - Nov. 10, 2017, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 5 - Feb. 2, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
Jan. 10-16, 2018, Wed.-Tues. In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement
Jan. 17-19, 2018, Wed.-Fri. Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website
May 25 - June 22, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
June 29 - July 27, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com
Aug. 10 - Sept. 7, 2018, Fri.-Fri. On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com
Oct. 12 - Nov. 9, 2018, Fri.-Fri. On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com
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