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One of the eas iest to use Software : Winsteps

One of the eas iest to use Software : Winsteps. www.winsteps.com. 1. 2. 3. 4. Developer of the program : John M. Linacre. Tel/Fax: (312)264-2352. Provides processing of data within Rasch analysis. www.winsteps.com. Introduction. Key options of Winsteps. Test and item analysis

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One of the eas iest to use Software : Winsteps

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  1. One of the easiestto use Software: Winsteps www.winsteps.com

  2. 1. 2. 3. 4. Developer of the program: John M. Linacre Tel/Fax: (312)264-2352 Provides processing of data within Rasch analysis www.winsteps.com Introduction

  3. Key optionsof Winsteps • Test and item analysis • Calibration of item difficulty • Investigation of category functioning • Discovering of dimensionality • Construction of scale ability (item map) • Construction of item characteristic curves and item information curves • Differential Item Functioning analysis • Analysis of polytomous response structures (rating scales and partial credit items) • Fit statistics analysis • etc. • Examinee analysis • Analysis of responses of a examinees • etc.

  4. Work-flow with Winsteps Control file Data file Winsteps Report Output File Output tables Graphs

  5. How to make a control File?

  6. The control file tells what analysis you want to do. The template file, TEMPLATE.TXT. gives you an outline to start from &INST ; optional TITLE = "Put your page heading here" ;Input Data Format NAME1 = 1 ; column of start of person information NAMLEN = 30 ; maximum length of person information ITEM1 = ? ; column of first item-level response NI = ?? ; number of items = test length XWIDE = 1 ; number of columns per response PERSON = Person ; Persons are called ... ITEM = Item ; Items are called ... ; DATA = ; data after control specifications

  7. An example of a control file for dichotomous data &INST TITLE = "Biology-1.1" PERSON = Person ; persons are ... ITEM = Item ; items are ... ITEM1 = 2 ; column of response to first item in data record NI = 37 ; number of items NAME1 = 1 ; column of first character of person identifying label NAMELEN = 21 ; length of person label XWIDE = 1 ; number of columns per item response CODES = 01 ; valid codes in data file UIMEAN = 0 ; item mean for local origin USCALE = 1 ; user scaling for logits UDECIM = 2 ; reported decimal places for user scaling GROUPS=0 ; specify that each item has its own rating scale (partial credit) &END ;Put item labels here for NI= lines A1 A2 A3 A4 A5 … END LABELS 11101111111111011011111110111111101111 21100000011110001111110001101111011110 …

  8. An example of a control file for polytomous data (PCM) &INST TITLE="PCM" NAME1=1 XWIDE=1 ITEM1=11 NI=45 CODES=012345 GROUPS=0 PERSON=PERSON ITEM=TASKS &END … END LABELS 1 011000000100000000100110010001010100000010001 2 000000210021002100300211010120012200001010101 3 011001010101211410112210011222310100010122100 4 245401543342303353544534444344252232445255525 121001010221233201010210010000311300000022110 …..

  9. An example of a control file for polytomous data (RSM) &INST TITLE="RSM" NAME1=1 XWIDE=1 ITEM1=11 NI=20 CODES=01234 NEWSCORE=12345 MODELS=R PERSON=PERSON ITEM=TASKS &END … END LABELS 1 01111201211011122101 2 24244323323342231123 3 21010401131301111200 4 00031200102100130212 11322222211201220 ……………………

  10. The process is running…

  11. Getting of outputs

  12. An example of an output table (table 3.1 Summary statistics) TABLE 3.1 Русский язык ZOU287WS.TXT Mar 21 10:46 2012 INPUT: 1464 Person 34 Item REPORTED: 1464 Person 34 Item 90 CATS WINSTEPS 3.72.3 ------------------------------------------------------------------------------------ SUMMARY OF 1464 MEASURED Person ------------------------------------------------------------------------------- | TOTAL MODEL INFIT OUTFIT | | SCORE COUNT MEASURE ERROR MNSQ ZSTD MNSQ ZSTD | |-----------------------------------------------------------------------------| | MEAN 25.0 29.1 .12 .33 1.02 .1 1.01 .0 | | S.D. 10.8 5.2 .92 .07 .27 1.0 .33 1.0 | | MAX. 55.0 34.0 4.05 1.06 2.28 3.7 4.34 4.7 | | MIN. 1.0 7.0 -2.88 .28 .44 -3.0 .34 -2.6 | |-----------------------------------------------------------------------------| | REAL RMSE .36 TRUE SD .84 SEPARATION 2.33 Person RELIABILITY .84 | |MODEL RMSE .34 TRUE SD .85 SEPARATION 2.49 Person RELIABILITY .86 | | S.E. OF Person MEAN = .02 | ------------------------------------------------------------------------------- VALID RESPONSES: 85.7% (APPROXIMATE) Person RAW SCORE-TO-MEASURE CORRELATION = .94 (approximate due to missing data) CRONBACH ALPHA (KR-20) Person RAW SCORE "TEST" RELIABILITY = .90 (approximate due to missing data) SUMMARY OF 34 MEASURED Item ------------------------------------------------------------------------------- | TOTAL MODEL INFIT OUTFIT | | SCORE COUNT MEASURE ERROR MNSQ ZSTD MNSQ ZSTD | |-----------------------------------------------------------------------------| | MEAN 1078.3 1255.1 .00 .05 1.00 -.2 1.01 -.1 | | S.D. 498.2 167.6 .77 .01 .10 2.7 .15 2.8 | | MAX. 2173.0 1442.0 1.56 .07 1.39 9.9 1.67 9.9 | | MIN. 416.0 811.0 -1.20 .04 .88 -5.8 .82 -5.5 | |-----------------------------------------------------------------------------| The number of responses made Fit statistics Estimated person ability Number of correct responses including extreme scores Error of measurement Information-weighted fit statistic Outlier-sensitive fit statistic Item calibration (difficulty) The average value of the statistic Sample standard deviation

  13. An example of output table (table 14.1 Item: entry) the sum of the correct responses to an item by the persons TABLE 14.1 Русский язык ZOU287WS.TXT Mar 21 10:46 2012 INPUT: 1464 Person 34 Item REPORTED: 1464 Person 34 Item 90 CATS WINSTEPS 3.72.3 ------------------------------------------------------------------------------------ Person: REAL SEP.: 2.33 REL.: .84 ... Item: REAL SEP.: 14.45 REL.: 1.00 Item STATISTICS: ENTRY ORDER ------------------------------------------------------------------------------------------------- |ENTRY TOTAL TOTAL MODEL| INFIT | OUTFIT |PT-MEASURE |EXACT MATCH| | |NUMBER SCORE COUNT MEASURE S.E. |MNSQ ZSTD|MNSQ ZSTD|CORR. EXP.| OBS% EXP%| Item G | |------------------------------------+----------+----------+-----------+-----------+------------| | 1 693 1431 .20 .06|1.00 .1|1.03 1.1| .38 .38| 66.0 66.0| R04Q2_1 0 | | 2 1075 1431 -1.16 .07| .93 -2.2| .82 -3.6| .44 .34| 77.1 76.9| R04Q2_2 0 | | 3 1673 1442 -.25 .04| .99 -.3| .98 -.6| .52 .50| 54.7 51.9| R04Q2_3 0 | | 4 850 1425 -.33 .06| .97 -1.4| .94 -1.8| .41 .38| 68.2 67.7| R04Q2_4 0 | | 5 822 1371 -.30 .06| .88 -5.8| .83 -5.5| .51 .38| 73.0 67.7| R04Q2_5 0 | | 6 1022 1293 .61 .04| .98 -.7| .96 -.9| .53 .51| 51.7 48.8| R04Q2_6 0 | | 7 686 1396 .19 .06| .98 -1.2| .97 -1.2| .41 .38| 66.7 65.8| R04Q2_7 0 | | 8 2128 1419 -.93 .04|1.01 .2|1.09 1.5| .47 .48| 59.8 60.5| R04Q2_8 0 | Standardized outlier-sensitive mean square statistic Point-biserial correlation the number of data points used to construct measures Standardized information-weighted mean square statistic The item difficulty in logits The standard error for the estimate … | 30 1433 1177 -.60 .05|1.25 6.0|1.26 6.3| .19 .43| 58.2 64.4| R04Q7_1 0 | | 31 750 1170 -.49 .07|1.02 .7| .98 -.4| .36 .37| 67.5 69.5| R04Q7_2 0 | | 32 624 1092 -.11 .07|1.01 .4|1.00 .0| .37 .38| 66.4 66.8| R04Q7_3 0 | | 33 635 1003 1.12 .05|1.20 4.7|1.27 5.1| .35 .48| 49.0 55.2| R04Q7_4 0 | | 34 884 811 .85 .04|1.01 .3|1.01 .2| .58 .59| 40.8 40.9| R04Q7_5 0 | |------------------------------------+----------+----------+-----------+-----------+------------| | MEAN 1078.3 1255.1 .00 .05|1.00 -.2|1.01 -.1| | 60.9 60.5| | | S.D. 498.2 167.6 .77 .01| .10 2.7| .15 2.8| | 10.6 9.5| | -------------------------------------------------------------------------------------------------

  14. An example of examinee responses (table 7.1 person: responses) NUMBER - NAME -- POSITION ------ MEASURE - INFIT (MNSQ) OUTFIT 887 11121122211120112112121 1.68 1.4 1.2 RESPONSE: 1: 1 1 1 2 1 1 2 2 2 1 Z-RESIDUAL: RESPONSE: 11: 1 1 2 0 1 1 2 1 1 2 Z-RESIDUAL: -3 RESPONSE: 21: 1 2 1 2 1 2 2 2 1 1 Z-RESIDUAL: -3 RESPONSE: 31: 1 2 1 1………………………………………… Z-RESIDUAL: Fit statistics Individual number Test score Significantly negative response

  15. Item map (table 12) TABLE 12.2 Русский язык ZOU000WS.TXT Mar 19 1:19 2012 INPUT: 1464 Person 34 Item REPORTED: 1464 Person 34 Item 90 CATS WINSTEPS 3.72.3 ------------------------------------------------------------------------------------ Person - MAP - Item <more>|<rare> 4 . + | | | | | | 3 . + . | . | . | . | . | . | 2 . T+ .## | .# | .## |T R04Q1_8 R04Q1_9 .### | .#### | .#### | R04Q7_4 1 .####### S+ R04Q1_3 .####### | R04Q7_5 .####### |S R04Q1_5 R04Q8_8 .####### | R04Q1_1 R04Q2_12 R04Q2_6 .########### | R04Q1_2 R04Q1_4 R04Q1_7 R04Q8_4 ########## | .########### M| R04Q2_1 R04Q2_7 0 .########## +M R04Q8_7 .########### | R04Q7_3 .######## | R04Q2_3 R04Q2_4 R04Q2_5 .######## | R04Q7_2 R04Q8_2 ######## | R04Q7_1 R04Q8_3 R04Q8_6 .##### |S R04Q8_5 .##### S| R04Q2_10 R04Q2_8 R04Q8_1 -1 .### + R04Q1_6 R04Q2_9 .### | R04Q2_11 R04Q2_2 .### | .## | .# |T .## T| . | -2 .# + . | . | . | . | | . | -3 + <less>|<frequ> EACH "#" IS 9. EACH "." IS 1 TO 8 Mean (examinees) Logits Mean (items)

  16. Graphs

  17. Item characteristic curve Confidence interval Empirical data Model curve

  18. Category Probability Curve Category “1” Category “0” Category “2”

  19. Item Information Function Curve The lowest error of measurement The biggest error of measurement The biggest error of measurement

  20. Test Information Function The lowest error of measurement The biggest error of measurement The biggest error of measurement

  21. Advantages and limitations of the program + - Provides only Rasch analysis, can not be used for 2Pl or 3Pl analysis • Clear graphs and plots + confidence intervals for model and empirical item characteristic curves (the boundary lines which indicate upper and lower 95% two-sided confidence intervals) • A detailed and easy to use manual • Possibility of examinee responses analysis

  22. Thank you! Questions? www.winsteps.com

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