ability scores, 10, 77·88 ability scores, validity of, 78 achievement tests, 102
adaptive testing. See computerized adap- tive testing
Aitkin, M., H, 46
American Educational Research AsS()cia- tion, I
American l'sydlOlogical Association, I anchor test design, 128-129. 136-141 ANCILLES, 47,49
Andersen, E. B. 46.58 Antlridl. 1> .• 26 Anl\off, W. II., 123, 125 Ansley, T_ N .• 58 area statistic, 113 115 Arter. J. A .. 147 ASCAL. 47.4')
AssessfIlt'nt ｓｹｳｬｲｭｾ＠ Corpnrlltion. 4749
ｉｉｳｳｵｭｰｴｩＨＩｮＮｾＮ＠ 55· 59
assumptions. (;hc('king of. 55-59.61-6.1 authentic measurement, 15.1
Averill. M, 120 Haker. F. H .. 15.45,57 nfll'lnn, M. A., 4(;
Heyesian estinllllion procruures, J8-:\<), 4.1-44
Hayesiall item selection. 148 Hejar, I. I.. 56
Dell. S. R .. 42 AlC'AL, 42. 46.48 OIGSCALE. 42, 48 RlLOG, 41. 47, 50
hinomial trials model. 27 Birnbaum, A., 14,91-92,94
Rock, R. n., 26.37,43,47-47,49,56, 146,I5J
Boekkooi-Timminga, E., 10 I, 103 Bunurrson, C. V., 145
Burket, O. R .• 37.95 Camilli, G .• 57. 120 Carlson, J. E .• 47.49
characteristic curve method. 134·135 classical item tli friculty. 2, 5 dassicHI item discrimination. 5 clussical measurement, 2-5 dassicaltrue scort' model. 4, 7 co!(nilive <':ollll'ollenis. 154 ('"hrll. A. S ..
n
,'Oflllllnll !,t'''OIlS (ksi!!lI. 1211
(:(llllllllll.'ri/rd Hllal'live ｴｴＧｾｴｩｮｬＯＮＬ＠ 14S-1 S2 cOllcurrell1 (lIlihrlllinll. IJS· 136 condilional ｮｬ｡ｾｩｲｮｵｭ＠ likelihood ･ｾｬｩｭＢＭ
tion.46 Cook, L. L.. 5. 58 Cmta, M. G., 27
Cfll ill , R. D., 12()
,'I ilt'rinn . n,I'('re1Il'l."d ｬ･ｾｬｓＮ＠ 78, 102 (,lIshlmi/.,'d 1"'ling. 117
cUI-off score, 1<5 DATAGEN,54
de Gruijler, D. N, M .. 86, 94,102 differemial ilem funclioning. 109-120 Divgi, I). R., 5.1
domain ｾＨｮｲ･Ｌ＠ ｒＮｾ＠
Jot)
170 FUNDAMENTALS OF ITEM RESPONSE TIII'ORY Ullr;IIIS. N. 1. .'111·.'\9
Drasgow. r .. ｾＶＮ＠ ｾｉｉＬ＠ 62. 113. I ｾＴ＠
EAP estimates. Su expC'cted 1\ posteriori estimales
eigenvalue plot, 56 Eignor, D. R., .'I, 58 Embretson, S. E., 154 equating, classical. 123·126 equating, definition, 123 equaling. IRT, 126·142
equipercentlle equaling, 123 124 equivalent group design, 1211
expected a posteriori estimates, 39,44 Forsyth, R. A .• 58
Fraser, C, 41,49 Gelson, P. R .• 109 Gibhons, R., ｾＶ＠
Gifford, J. A., 42-44. 46 (JIM, C. 41·41'1 Goldstein. H.. 154 goodness of fil. 24, 53·14 goodness·of·fit statistics, 61 graded response model. 26·27 Green. 8. E, 146. 141'1 Green, D. R., 95
group dependent ilem statistics. 3 growlh. measurement of, 102 Gulliksen, H.. 57
Guslafsson, J. E., 41-48.58 Haebara, T., 134
Hambleton. R. K., 4-5, 36-37.43.54·59, 10,18,82.86-87.102-103,112.
114,133.136,148 IInmisch, D. L., 120 Harris. D .• 15 lIastings. eN., 112 lIallie, J. A .• 53. 56, 58 heuristic estimation, 46 lIolhmd. P. W .• 115. 119 110m, J. L .. 56
lIumphreys. L. G .• 56, 146
inapproprialeness measuremenl, 154·155 indeterminacy problem, 41·42
tllrorlllnli"n hllll'l iUII . .'il'l' Il'S! 01 ;relit information function
informotion nlalrix, 44 Inouye, D. K., 145
il1yuriance, R<sessmcnt of. <;7, ｾＧｩＧＩＮ＠ 63-66 invariance. property of, 18·25
invariant model parameters, '8 • /ronson, G. II .. 120
item billS. 109
item characteristic curve. 7 item charncteristic function, 7 item difficulty parameter, 13 item discrimination parameler, 15 item inforrnalinn runction, 91-94,99-
106,147
item misfil slatistics, 61, 72·14 item selection, 99·106 Jarjollra, D., 128
joint Bayesian estimation, 46
joint muimlllll likelihood estimation, 41·
44. 46 Jones, R.
w.,
10.\Kendall, M. G .• 39 Kingsbury, G. G .. 148, 152 Kingston, N. M., 58-59. 102 Klein, L. W., 12K
Knight, D. L., 109 Kolen, M. J., 123 Lam. R., 61 IRtenl ｾｰ｡｣･Ｌ＠ 10
Levine, M. V .• SR. ItO, 154·155 Lieberman, M., 43
likelihood function, 34 Linacre. J. M .• 42 linear eqUAting, 124-12.5 linear programming, 103 linking designs, 128·129
Linn, R. L., 1R, 87. 110, 120, 132. 146 Lissak. R. I.. 56, 62
Incal independence, 9-12 LOGIST. 42. 46-47, 49. 135
ｉｏｬＡｩｾｴｩ｣＠ models;
one·parameter. 12·14, R I-Kl two·parnmeler. 14-17 three-parameter. 17 ·18
/1/(/1'.1
l<lj!islic ｉＨＧｾｲｲｳｳｪｯｬｬ＠ pron.llure, 11'1 log!!s, 11.1
Lord. F M., ＴｾＬ＠ II, 14,43,45-46,57- 51(,95,100-101,110,112,125, I.HIVI, 147·147,154 lower asymplote. 13, 16-17 Ludlow, L. H., 58 Mack. J. S., 120
Mantel·Haenszel method, 115 marginal Bayesian estinlation, 46 marginAl maximum likelihood ･ｾｬｩｭ｡ﾷ＠
lion, 43, 48 Masters, G. N., 26. 153
maximum information item selection, 148 maximum likelihood criterion, 33 maximum likelihood estimation, 33-38 McDonald. R. P., 10.26.46-41,49,56,
154
McLaughlin, M. E .• 58, 113. 155 Mead, R. J .• 42
mean and sigma method. 131-132.139- 141
Mediltll Interactive Technologies. 47·48 Mehrens. W. A., 58
Millenbergh. G., 120 MICROSCALE.41-48 MIRTE,50
Millman, 1 .• 141
Mislevy, R. }" 37, 43·41, 49 model fit. Set goodness of fit
ｬｬｬｵＱｴｩ､ｩｭ･ｮｾｩｯｭｬｬ＠ ｭｯ､･ｬｾＬ＠ 10. 154·155
MlJLTILOG,50 Muraki. E .• 56 NAEP, 102
National Council on Measurement in Education, I
Newton·Raphson procedure, 36,40 NOHARM, 47,50
nominal response model. 26 non-linear factor analYRis, 46, 56 normal ogive model, 14·15 Novick, M. R., II, 154 odds for ｾｕｃｃ･ｴｬｓＬ＠ 81-83 Olsen, J. B .• 145
optimal hem selection. Set Item selection
Owen. It I., 1411 Ilarallel Irsb, 4 Parsons, C. K .. 511 Phillip", S E., 511
!'ierers, J M.P., 141(
PML,47-48
Poisson counts model. 28
171
polytomous response models, 26-28, 153-
ＱＮｾＴ＠
predictions, W-61
pseudo-chllnee level parameter. 17 Q, statistic, 54·61
Raju, N. S., 113·114, 121 RASCAL,48
Rasch. G., 14,46 Rasch model. 14 Reckase, M. D .• 56, 146 regression method. 130 regression models, 19. 32 relative efficiency, ＹＮｾＭＹＶ＠
reliahility. ､｡ｾｾｫ｡ｬＮ＠ 4.94 Reshetar. R., 149 residuals, '\9·61, 66·61 RIl)A.47-4I1, 135
robusl melln and sigma melhod, 132·13:1 ROg"fS. B. 1..5.1.55.103,114-115.119 Rovinelli. R. J .• 54,56,70
Roznowski. M. A., 56 Rudner, L. M .• 109-110. 113 Sofril, M. 1., 27
Samejima. F .. 26, 95,153·154 scaling. 125·126
seal ing constants, 129-135 Schulz, M .• 42
score transformations, 78-87 Shepard. L. A., 57,120 single-group design. 128 speededness, ｡ｳｳ･ｾｳｭ･ｮｴ＠ of. 57 Spray, 1., 26·27
standard error. Su Mandard error of esti·
mation
MandMd error of e,timation, 44·45. 94- 95. 106
standard error of measurement, 4, 94
172 FUNDAMENTALS OF ITEM RESPONSE TIIEORY slllndllnlized residuals. 5<)-61, 611-72
Sleinherg, L.. 153
Stocking, M. L.. 63. 102, 133-135 Stone, M. If .. 5, 14.57-58 Sluart, A .• 19
Subkoviak. M. J .• 120
Swnminnlhftn. H., 5, 36·:'1.42·46. Y'i, 57-59,711.112. 112, 119, 13.1. U6 Sykes, R. C. 37
Taft. H. L.. 58
lailored tesling. Su compulerized adap- live lesling
largel informalion funclion. 10 1-102 Tatsuoka. K. K .. 511
lesl Chllr8clerialic curve. 115 lesl dependelll abililY scores. <;
lesl developmenl. 92·96.99-106 lesl fairness, 109
lesl informalion function. 38,44, 94-95.
100-106 Te .• , Sltlndard.f, I Thayer. D. T .• 115. 119 Thissen, D. M., 47. 49. 58, 153 Traub, R. E .• 58. 61
Irue proponion-correct score. 115 Irue score. 2. 84-87
Tucker, L. R .• 56
IInhlilllell,iunalily.9 10. ｾＶ＠ "'7 Urry. V.
w..
46·49Vale. C. D .. 1211. 142
vall lkr Lindell. W. 1.. 4.101.10.1 Wainer. II. 511.145·146 .•
Wnrtlrnl). J. t .. 112
Weiss, n. J. 5. 145-146. 1411. 152 Williams. D. M., 57, 120 Wingersky, M. S., 42, 46, 411 WITS scale. 110
Wolfe. R. G., 511. 61 Wood. R .• 154 Woodcock. R. W .• 110
Woodcod· Johnson P5ydlO" Educnl ional Rallery. 110
Wright. R. D., 5. 14,26.42.46-411,57·
59, 7R, 153
Yen. W. M., 37, 54. 5R, 61, 95, IOJ ZlIal, J. N .. 1411
Zara, A. R .• 1411
About the Authors
Ronald K. Hambleton is Professor of Education and Psychology and Chairman of the Laboratory of Psychometric and Evaluative Rellearch at the Univerllity of Massachusetts at Amherst. He received his Ph.D.
in psychometric methods from the University of Toronto in 1969. His principal research interests are in the areas of criterion-referenced measurement and item response theory. His most recent books are Item Response Theory: Prindples and Applications (co-authored with H. Swaminathllll), A Practical Guide to Criterion-Referenced Testing, and, forthcoming. Advances in Educational and Psychological Te.ftinK (co-edited with Jac Zaa/). He has served as an Allsociate Editor to the Journal of Educational Statistics (1981-1989) and currently Ilerves on Ihe editorial boards of Applied MNUlireml'nt ill Education, Multivariate Behovioral Rl'.fl'tlrch, ApI,lied ｐＬｦｹ｣ｬｷｬｯｾｩ｣ｬｬｩ＠ Mellsurement, JOllrnal of Ecillcat;oflal Mrosurement, Edllf(JtiOlw/ and PsycholoKical Measure- ment, Evaluation and the lIealtil I'mfe,u;OIIS, and Psit'ot"emo. He hall served also as President of the International Test Commission (1990-
1994) and as President of the National Council on Measurement in Education (1989-1990).
H. Swami nathan is Professor of Education and Psychology at the University of Massachusetts at Amherst. He received his Ph.D. in psychometric methods and statistics from the University of Toronto in 1971. He has held the positions of Associate Dean of Academic Affairs and Acting Dean of the School of Education. He has served as an Associate Editor to the Journal of Edw'otiollal Statistics and currently is an Associate Editor of Psicothema and Rev;.sta Edllcao Potuguese.
He has served also 8s the President of Educational Statisticians, a special interest group of the American Educational Research Associa- tion; co-program chair of Division D of AERA; and as a member of the
173
174 FUNDAMENTALS OF ITEM RESPONSE TlIEORY
Gmduale Records Exarnilllliiolls BtHtrtl. lIis principal research interests are in the areas of item response theory. mullivariale statislics, and Bayesian analysis.
1-1. Jane Rogers is Assistant Professor :II Teachers College. Columbia University. She received her Ph.D. in psychometric'methods frolll the University of Massachusetts in 1989. lIer research interests include item response theory, large-scale assessmenl, methods for the detection of differential item funcl ioning, Bayesian methods. and multivariate statistics.