Glossary of Rasch Measurement Terminology | |
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Glosario Español www.rasch.org/rmt/glosario.htm | |
Ability | the level of successful performance of the objects of measurement on the variable. |
Agent of Measurement | the tool (items, questions, etc.) used to define a variable and position objects or persons along that variable. |
Anchor | the process of using anchor values to insure that different analyses produce directly comparable results. |
Anchor Value | a 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 Table | the 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 Design | Wright, B.D. & Stone, M.H., Best Test Design: Rasch Measurement. Chicago: Mesa Press, 1979 |
Bias | A change in logit values based on the particular agents or objects measured. |
BOTTOM | The 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. |
Calibration | a difficulty measure in logits used to position the agents of measurement along the variable. |
Categories | levels of performance on an observational or response format. |
Cell | Location of data in the spreadsheet, given by a column letter designation and row number designation e.g. B7 |
Common Scale | a scale of measurement on which all agents and objects can be represented. |
Column | Vertical 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. |
Content | the subject area evoked and defined by an agent. |
Convergence | the 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 Response | a response format of two categories such as correct-incorrect, yes-no, agree-disagree. |
Difficulty | the level of resistance to successful performance of the agents of measurement on the variable. |
Discrepancy | one or more unexpected responses. |
Disturbance | one or more unexpected responses. |
Diverging | the estimated calibrations at the end of an iteration are further from convergence than at the end of the previous iteration. |
Expected Response | the predicted response by an object to an agent, according to the Rasch model analysis. |
Fit Statistic | a summary of the discrepancies between what is observed and what we expect to observe. |
Heading | an identifier or title for use on tables, maps and plots. |
Independent | Not 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. |
Infit | an 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 scale | Scale of measurement on which equal intervals represent equal amounts of the vairable being measured. |
Item | agent of measurement, not necessarily a test question, e.g., a product rating. |
Iteration | one run through the data by the Rasch calculation program, done to improve estimates by minimizing residuals. |
Knox Cube Test | a tapping pattern test requiring the application of visual attention and short term memory. |
Link | Relating the measures derived from one test with those from another test, so that the measures can be directly compared. |
Logit | the unit of measure used by Rasch for calibrating items and measuring persons. A log_{e} odds transformation of the probability of a correct response. |
Map | a bar chart showing the frequency and spread of agents and objects along the variable. |
Matrix | a 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. |
Normal | a random distribution, graphically represented as a "bell" curve which has a mean value of 0 and a standard deviation of 1. |
Normalized | the transformation of the actual statistics obtained so that they are theoretically part of a normal distribution. |
Object of Measurement | people, products, sites, to be measured or positioned along the variable. |
Observed Response | the actual response by an object to an agent. |
Outfit | an 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. |
Outliers | unexpected responses usually produced by agents and objects far from one another in location along the variable. |
Person | the object of measurement, not necessarily human, e.g., a product. |
Plot | an x-y graph used by Rasch to show the fit statistics for agents and objects. |
Point Labels | the placing on plots of the identifier for each point next to the point as it is displayed. |
Poisson Counting | a 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. |
Process | the psychological quality, i.e.,the ability, skill, attitude, etc., being measured by an item. |
PROX | the normal approximation estimation formula, used by some Rasch programs for the first part of the iteration process. |
Rasch, Georg | Danish Mathematician (1906-1980), who first propounded the application of the statistical approach used by Rasch. |
Rasch Model | a 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 log_{e} [P/(1-P)] = B - D |
Rating Scale | A 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 Analysis | Wright, B.D. & Masters, G.N., Rating Scale Analysis: Rasch Measurement. Chicago: Mesa Press, 1982. |
Reliability | the ratio of sample or test variance, corrected for estimation error, to the total variance observed. |
Residuals | the difference between data observed and values expected. |
Response | The value indicating degree of success by an object on an agent, and entered into the appropriate cell of an Input Grid. |
Results Table | a report of Rasch calculations. |
Rigidity | when 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. |
Row | a 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. |
Scale | 1. the quantitive representation of a variable (e.g., a weight scale) 2. ordinal representation of a variable (e.g., a rating scale) |
Score points | the numerical values assigned to responses when summed to produce a score for an agent or object. |
Separation | the ratio of sample or test standard deviation, corrected for estimation error, to the average estimation error. |
Standard Deviation | the root mean square of the differences between the calculated logits and their mean. |
Standard Error | an estimated quantity which, when added to and subtracted from a logit measure or calibration, gives the least distance required before a difference becomes meaningful. |
Steps | the transitions between adjacent categories ordered by the definition of the variable. |
Template | a specially formatted input file. |
TOP | The 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 Category | the response category at which maximum performance is manifested. |
UCON | the unconditional (or "joint" JMLE) maximum likelihood estimation formula, used by some Rasch programs for the second part of the iteration process. |
UNSURE | Rasch was unable to calibrate this data and treated it as missing. |
Unweighted | the situation in which all residuals are given equal significance in fit analysis, regardless of the amount of the information contained in them. |
Variable | the 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. |
Weighted | the 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 | |
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www.rasch.org/rmt/glosario.htm | Rasch Anàlisis - Glosario Español - Spanish Glossary - Español-Inglés del diccionario - Spanish-English Dictionary - Dr. Agustín Tristàn Lòpez |
www.rasch.org/diseno.htm | Spanish language introduction to Diseño de Mejores Pruebas (Best Test Design) |
www.rasch.org/spanish.htm | Diseño de Mejores Pruebas - Spanish-language book of Best Test Design (Wright & Stone, 1979) |
Rasch Publications | ||||
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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 |
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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 | |
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July 30 - Nov., 2018 | Online Introduction to Classical and Rasch Measurement Theories (D.Andrich), University of Western Australia, Perth, Australia, http://www.education.uwa.edu.au/ppl/courses |
Oct. 12 - Nov. 9, 2018, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
Jan. 25 - Feb. 22, 2019, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
April 4 - 8, 2019, Thur.-Mon. | NCME annual meeting, Toronto, Canada.https://ncme.connectedcommunity.org/meetings/annual |
April 5 - 9, 2019, Fri.-Tue. | AERA annual meeting, Toronto, Canada.www.aera.net/Events-Meetings/Annual-Meeting |
May 24 - June 21, 2019, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
June 28 - July 26, 2019, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com |
Aug. 9 - Sept. 6, 2019, Fri.-Fri. | On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com |
Oct. 11 - Nov. 8, 2019, Fri.-Fri. | On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com |
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 |
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