Abstracts
This study aims to provide statistical evidence of the complementarity between classical test theory and item response models for certain educational assessment purposes. Such complementarity might support, at a reduced cost, future development of innovative procedures for item calibration in adaptive testing. Classical test theory and the generalized partial credit model are applied to tests comprising multiple choice, short answer, completion, and open response items scored partially. Datasets are derived from the tests administered to the Portuguese population of students enrolled in the 4^{th} and 6^{th} grades. The results show a very strong association between the estimates of difficulty obtained from classical test theory and item response models, corroborating the statistical theory of mental testing.
Generalized partial credit; Item response model; Classical test theory; Educational assessment
O presente artigo tem por objetivo fornecer evidência estatística sobre a complementaridade entre a teoria clássica dos testes e os modelos de resposta ao item para determinados fins de avaliação educacional. Essa complementaridade pode contribuir para o desenvolvimento futuro de processos inovadores de calibração dos items no contexto de testes adaptativos, a custo reduzido. A teoria clássica dos testes e o modelo de resposta ao item de crédito parcial generalizado são aplicados a testes compostos por items de múltipla escolha, de resposta curta, de completamento e de resposta aberta, parcialmente classificados. Os conjuntos de dados advêm dos testes realizados junto da população portuguesa de estudantes inscritos no 4º e no 6º ano. Os intervalos de confiança de 95% baseados em 1.000 amostras bootstrap revelam uma forte associação entre as estimativas da dificuldade do item, corroborando a teoria estatística de testes psicológicos.
Crédito parcial generalizado; Modelo de resposta ao item; Teoria classica dos testes; Avaliação educacional
El presente estudio tiene como finalidad presentar evidencia estadística de la correlación entre la Teoría Clásica de los Tests (TCT) y los modelos de la Teoría de Respuesta al Ítem (TRI) para determinados fines de evaluación educativa. Dicha correlación podría contribuir al desarrollo de futuros procedimientos innovadores, a bajo costo, para la calibración de los ítems en el contexto de los sistemas de evaluación adaptables. La Teoría Clásica de los Tests y el Modelo del Crédito Parcial Generalizado de Respuesta al Ítem, se aplican a pruebas que están formadas por ítems de opción múltiple, de respuestas breves, de completar espacios o de respuesta abierta que se califican de manera parcial. Los conjuntos de datos se extrajeron de las pruebas administradas a población portuguesa compuesta por estudiantes procedentes de 4° y 6° grado. Los intervalos de confianza del percentil 95º obtenidos mediante muestras bootstrap ponen de relieve una fuerte relación entre las estimaciones de la dificultad del ítem y por ende, corroboran la teoría estadística de los tests mentales.
Crédito parcial generalizado; Modelos de respuesta al ítem; Teoría clásica de los tests; Evaluación educativa
1 Introduction
The increasing usability of computers and Webbased assessments requires innovative
approaches to the development, delivery, and scoring of tests. Statistical methods
play a central role in such frameworks. The item response model (IRM) (LORD; NOVICK, 1968LORD, F. M.; NOVICK, M. R. Statistical theories of mental
test scores. Oxford: AddisonWesley, 1968.) has been the most common
statistical method used. In computerbased adaptive testing (CAT), IRM allows
adaptive item selection from an item bank, according to examinee proficiency during
test administration. The efficiency of CAT is realized through the targeting of item
difficulty to the examinee proficiency (WISE;
KINGSBURY, 2000WISE, S. L.; KINGSBURY, G. G. Practical issues in developing and
maintaining a computerized adaptive testing program developing and maintaining
an item pool for use in an adaptive test. Psicológica, v. 21, n. 1, p. 13555,
2000.). It implies an item bank or multiple item banks properly
developed. A good item bank should cover all aspects of the construct to be measured
(content validity) and contain a sufficient number of items to ensure measurement
accuracy in the domain, i.e., for all scale values. Items should fulfill
requirements set in the American Educational Research Association (AERA), the
American Psychological Association (APA), and the National Council on Measurement in
Education (NCME) (1999). Stocking (1994)STOCKING, M. L. Three practical issues form modern adaptive
testing item pools. Princeton: Educational Testing Research, 1994.
(ETS Research Report, 932).
found that doubling the number of item banks reduced test overlap to a much greater
extent than doubling the number of items in each bank (apud NYDICK; WEISS, 2009NYDICK, S. W.; WEISS, D. J. A hybrid simulation procedure for the
development of CATs. In: 2009 GMAC® CONFERENCE ON COMPUTERIZED ADAPTIVE TESTING,
2009, Minneapolis. Proceedings. Disponível em:
<http://publicdocs.iacat.org/cat2010/cat09nydick.pdf>. Acesso em: 20 fev
2014.
http://publicdocs.iacat.org/cat2010/cat0...
). The development of an item bank for CAT
is a complex and multidisciplinary process that follows seven major steps (e.g.,
BJORNER et al., 2007BJORNER, J. et al. Developing tailored instruments: item banking and
computerized adaptive assessment. Quality of Life Research, v.
16, n. 1 Suppl., p. 95108, Aug. 2007.
DOI:10.1007/s1113600791686) represented in
Diagram 1, thus, requiring experts from the subject–scientific areas of Construct
Framework (steps 1, 2, 5, and 7), Statistics (steps 3, 4, 5, 6, and 7), and Computer
Science and Informatics (steps 3, 6, and 7). Once an item bank is available for CAT
use, its management requires decisions on several issues such as item bank size and
control, security protocols (including item exposure control), statistical modeling,
item removal and revision, item addition, maintenance of scale consistency, and use
of multiple banks (WISE; KINGSBURY, 2000WISE, S. L.; KINGSBURY, G. G. Practical issues in developing and
maintaining a computerized adaptive testing program developing and maintaining
an item pool for use in an adaptive test. Psicológica, v. 21, n. 1, p. 13555,
2000.).
Thereafter, CAT administration is basically the repetition of a twophase process.
As Wise and Kingsbury (2000)WISE, S. L.; KINGSBURY, G. G. Practical issues in developing and
maintaining a computerized adaptive testing program developing and maintaining
an item pool for use in an adaptive test. Psicológica, v. 21, n. 1, p. 13555,
2000. explain, first,
an item with difficulty matched to the examinee’s current proficiency estimate is
administered. Second, the examinee’s response to the item is scored, and the
proficiency estimate is updated. This sequence is repeated until some stopping
criterion is met, usually a predetermined maximum number of items or measurement
precision. Thus, despite obvious advantages of adaptive testing, there are still
some limitations, such as the high cost related to item bank development. However,
the cost could be reduced by decreasing expenses on item writing, pretesting, and
calibrating new items (VELDKAMP; MATTEUCCI,
2013VELDKAMP, B. P.; MATTEUCCI, M. Bayesian computerized adaptive
testing. Ensaio: Avaliação e Políticas Públicas em Educação, v.
21, n. 78, p. 5782, jan./mar. 2013.
DOI:10.1590/S010440362013005000001), involving steps 2, 3, and 4 of Figure 1.
Since Classical Test Theory (CTT) methods are less demanding of sample size, the complementarity between CTT and IRMs jointly with the existence of multiple item banks, offer exceptional research opportunities for reducing such costs. As a previous step, this study examines the empirical relationship between indexes and parameters resulting from both approaches in order to justify and support the use of CTT in item pretesting and precalibration, thus reducing the cost of item bank development. Further work remains for demonstration of how any arbitrary scale derived from the precalibration step can be transformed into the scale adopted by the assessment system. Throughout the paper, we will address two research questions: (1) What is the level of association between CTT indexes and IRM parameter estimates? (2) Can CTT provide initial item difficulty estimates for posterior IRM use in CAT?
The CTT model and the generalized partial credit model (GPCM) are applied to data collected from the Portuguese student population enrolled in the 4^{th} and 6^{th} grades and to those who were administered with mathematics and motherlanguage tests. The number of students involved is approximately 108,000 in each grade. Estimates of item discrimination and difficulty are obtained and compared. Percentile confidence intervals based on 1000 bootstrap samples are presented for correlation between item difficulty estimates. The study is organized as follows: the next section describes the data and statistical methods used. The results are presented in section three, and conclusions are considered in the last section.
2 Methodology
This section comprises three parts. The first part presents details and characteristics of the data. The second addresses the statistical specification of models in use and explains how to quantify the level of association between estimates obtained from the CTT and the IRM. The third presents a brief description of various steps in the CAT framework.
2.1 Data
In Portugal, Primary School Assessment Tests (Provas de Aferição do Ensino Básico) are the responsibility of GAVE (Gabinete de Avaliação Educacional), the office of educational assessment, which aims to evaluate how objectives established for each education cycle are achieved. These instruments are yearly administered to all students enrolled in the fourth and sixth years of schooling, in mathematics, and in the mothertongue language (Portuguese), according to provisions of law no. 2351/2007, of February 14, II Series. GAVE tests are always administered to the population nationwide and are based on specific competences of the mathematics and Portuguese subjects presented in the document National Curriculum of Primary School: Key competences and the current syllabus. The mathematics test assesses understanding of concepts and procedures, reasoning and communication abilities, and competence for using mathematics in analysis and problem solving. In the academic year 2006–2007, the mathematics test was administered to 108,441 students attending the 4^{th} grade and also to 108,296 students attending the 6^{th} grade. These tests were composed of two identical parts, including 27 items and containing multiple choice, short answer, completion, and openended questions, covering the following content: numbers and calculation; geometry and measurement; statistics and probabilities; and algebra and functions. From now onward, these tests are called Math4 and Math6 for the 4^{th} and 6^{th} grades, respectively.
Portuguese tests involved 108,447 students in the 4^{th} grade and 108,548 students in the 6^{th} grade. Three competences were assessed: reading comprehension, explicit knowledge of language, and written expression. These tests were composed of two parts. The first part mainly contained short answer items, completion, right or wrong association, and multiple choice questions. The second included extensive composition items in which a text of 20–25 lines is produced. Portuguese tests were composed of 27 and 33 items for the 4^{th} and 6^{th} grades, respectively. From now onward, Portuguese tests of the 4^{th} and 6^{th} grades are called Port4 and Port6, respectively. Before statistical modeling, partial scoring of openended answers and extensive composition was performed by experts. The tests’ reliability, as demonstrated by the coefficient of internal consistency, i.e., the coefficient of Kuder–Richardson, is p ≥ 0.85.
2.2 Statistical methods
Fundamentals of statistical methods for educational measurement are presented in Statistical Theories of Mental Test Scores by Lord and Novick (1968)LORD, F. M.; NOVICK, M. R. Statistical theories of mental test scores. Oxford: AddisonWesley, 1968.. According to them, the definition of measurement is “a procedure for the assignment of numbers (scores, measurements) to specified properties of experimental units in such a way as to characterize and preserve specified relationships in the behavioral domain” (p. 17). Two main statistical approaches are used in educational measurement: Classical Test Theory (CTT) and Item Response Models (IRM). Some examples of introductory readings and reviews may be found in Hambleton, Swaminathan, and Rogers (1991), Hambleton (2004)HAMBLETON, R. K. Theory, methods, and practices in testing for the 21st century. Psicothema, v. 16, n. 4, p. 696701, 2004. and Klein (2013)KLEIN, R. Alguns aspectos da teoria de resposta ao item relativos à estimação das proficiências. Ensaio: Avaliação e Políticas Públicas Em Educação, v. 21, n. 78, p. 3556, jan./mar. 2013. DOI:10.1590/S010440362013005000003. The rest of this section presents the model and assumptions underlying classical test theory, explanation and functional specification of the generalized partial credit model, and a brief review of the complementarity of these statistical methods.
2.2.1 Classical test theory
It is assumed that variable X represents competencies/skills gained by the student during the learning process. The observable variable X^{0} is generally obtained by test administration. If tests were instruments with absolute precision, the observed value X^{0 }, regardless of the test used, would be equal to true value X. In a hypothetical situation where the student is tested t times, equation (1) represents the relationship between the true and the observed value,
where ε represents the measurement error. Measurement error is assumed to be nonsystematic, homoscedastic, and noncorrelated with the true value X.
Characteristics of items are quantified through the discrimination index (c_{i}) and the difficulty index (p_{i}). The discrimination index measures capacity of the item to distinguish the high performance group of students from the low performance group of students, and its values vary from −1 to 1. The difficulty index (p_{i}) is provided by the proportion of correct answers to the item i (e.g., Guilford; Fruchter, 1978GUILFORD, J. P.; FRUCHTER, B. Fundamental statistics in psychology and education. 6. ed. New York: McGrawHill, 1978.). Therefore, high values indicate easy questions.
2.2.2 Item response models
Item response models (IRM) rest on two basic postulates (HAMBLETON; SWAMINATHAN; ROGERS, 1991; HAMBLETON, 2004HAMBLETON, R. K. Theory, methods, and practices in testing for the 21st century. Psicothema, v. 16, n. 4, p. 696701, 2004.). According to the first postulate, the examinees’ performance on an item can be explained by their ability; according to the second, the relationship between the probability of a correct answer to the item and the examinee’s ability is described by a function called the item characteristic curve. In this class of models, item response may be dichotomous or polytomous. Additionally, the various IRMs classification depends on the number of latent traits the item represents, giving rise to unidimensional and multidimensional models. The Generalized Partial Credit Model (GPCM) (MURAKI, 1993MURAKI, E. Information functions of the generalized partial credit model. Applied Psychological Measurement, v. 17, n. 4, p. 35163, Dec.1993., 1997MURAKI, E. A generalized partial credit model. In: VAN DER LINDEN, W. J.; HAMBLETON, R. K. (Eds.). Handbook of modern item response theory. New York: Springer, 1997. p. 15364.; MURAKI; BOCK, 2002MURAKI, E.; BOCK, R. IRT based test scoring and item analysis for graded openended exercises and performance tasks (Version 3). Chicago: Scientific Software, 2002.) is a unidimensional model for analyzing responses scored in two or more ordered categories. The aim is to extract from an item more information about the examinee’s level than simply whether the examinee correctly answers the item. Items are ranked in which examinees receive partial credit for successfully completing the various levels of performance needed to complete an item. This model relaxes the assumption of items’ uniform discriminating power and includes parameters to represent item difficulty and discrimination. The model is applied to several types of items, such as multiple choice, short answer, completion, and open response items (with the previous items that were gradually scored). Thus, the GPCM suitable for such data is specified by equation (2),
where
i is the item number (i = 1,.,I; I is the total number of items in the test);
P_{ik }(θ) is the probability that an examinee with latent factor θ selecting the k^{th} category from m_{i} possible categories for the polytomous item i; a_{i} is the discrimination parameter for item i, using a logistic metric. In addition, β_{ij} = b_{i}  d_{j}, where b_{i } is the difficulty/location parameter of item I, and d_{j }is the parameter of the intercept category, with d_{1} = 0.
According to equation (2), the probability of the student to answer (or to be ranked) in the k category is a conditional probability on the answer to the k1 category. That is to say, the answer to category k has underlying response criteria satisfaction that is associated with the previous category. Estimates are obtained by maximum likelihood procedure, using the EM algorithm. This model, estimation procedures, and maths data were utilized by Ferrão, Costa, and Oliveira (2015) for linking scales and by Ferrão and Prata (2014)FERRÃO, M. E.; PRATA, P. Item response models in computerized adaptive testing: a simulation study. Lecture Notes in Computer Science, v. 8581, p. 55265, 2014. Apresentado no 14th International Science and Its Applications – ICCSA 2014, Guimarães, Portugal, 2014. DOI:10.1007/9783319091501_40 for a simulation CAT study.
2.2.3 Complementarity
In the paper “The taxonomy of item response models,” Thissen and Steinberg (1988)THISSEN, D.; STEINBERG, L. The taxonomy of item response models. Psychometrika, v. 51, n. 4, p. 56777, Dec. 1988. DOI:10.1007/BF02295596 propose three distinct classes of models with which models are distinguished by their assumptions and constraints on their parameters. Additionally, Goldstein and Wood (1989)GOLDSTEIN, H.; WOOD, R. Five decades of item response modelling. British Journal of Mathematical and Statistical Psychology, v. 42, n. 2, p. 13967, Nov. 1989. DOI:10.1111/j.20448317.1989.tb00905.x present arguments in favor of
the unity of item response models by sitting them within an explicit linear modelling framework. The logistic models […] can be seen merely to be one class out of many possible classes of models. […] In practice, the simple identity models used over the effective response range, typically give near equivalent results (p. 163).
The paper published by Hambleton and Jones (1993)HAMBLETON, R. K.; JONES, R. W. Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues and Practice, v. 12, n.3, p. 3847, Sep. 1993. DOI:10.1111/j.17453992.1993.tb00543 describes and compares (similarities and differences of) the methodological approaches mentioned above. Two of these approaches are relevant for this paper’s purpose. They concern the relationship between the IRM item difficulty parameter, the CTT index of difficulty, and the relationship between the IRM discrimination parameter and the CTT biserial correlation. Lord (1980)LORD, F. M. Applications of item response theory to practical testing problems. Hillsdale: Lawrence Erlbaum, 1980. describes a monotonic relationship between the CTT index of item difficulty (p_{i}) and the IRM item difficulty parameter (b_{i}) so that as p_{i} increases, b_{i} decreases when all items discriminate equally. If items have unequal discrimination values, then the relationship between them depends on the item biserial correlation. Lord also demonstrates that, under certain conditions, the item biserial correlation r_{i} and the IRM item discrimination parameter approximately monotonically increase functions of one another, i.e.,
where
a_{i} is the item i discrimination parameter estimate, and
r_{i} is the item i biserial correlation.
2.3 Computerbased adaptive testing
As aforementioned, in CAT, item response models are applied to establish a relationship between observed responses and ability of the examinee, enabling the item selection adaptively, from an item bank, according to examinee ability during test administration. Thus, the test is tailored to each examinee, and it begins by selecting an initial item. If the examinee answers incorrectly, then an easier item is selected for administration; however, if not, a complex one is administered. Each item is scored, and an estimate of the examinees’ ability is obtained. This process of selection and evaluation is iteratively conducted until a termination criterion is met. Thus, despite being a realtime computing platform, the process implies the existence of a calibrated item bank. Several areas of knowledge are involved in the use of CAT. Figure 2 presents the knowledge areas and their relationships that support the platform.
The CAT platform concerns operations from modular structures of Statistical Methods (S), Content (C), and Informatics (I), which provide elements to be integrated throughout the Adaptive Test Developer (ATD). The modular structure S comprises statistical methods for item calibration, scoring, scale fitting, and linking, examinees’ ability modeling, test measurement error, and reliability; structure I contains a computer or Web application with interfaces to examinees via desktop or mobile devices. The server connects the database that contains the item bank (module C) and the statistical methods (module S) using the ATD to adapt tests to examinees; structure C includes the item bank (in general, each item record is defined by question, by type of question and field specification, correct answer, its statistical propertiesdiscrimination, difficulty, information, level of exposure to date, and whether it is an anchor item), and the item bank manager, which is software for operations with items.
3 Results
CTT and GPCM were applied to Math4, Port4, Math6, and Port6 data. Tables 1 to 4 contain discrimination and difficulty indexes, biserial correlations, and estimates of GCPM discrimination and difficulty parameters. Since intersection parameters are not used for any research questions addressed in this study, their estimates are not presented. The chisquare hypotheses test for goodness of fit suggests this IRM as an adequate model at the 5% level of significance.
Regarding Math4 test, joint analysis of item properties based on CTT and IRM, presented in Table 1, indicates that most items discriminate; items 4, 9, and 14 slightly discriminate; and item 19 is very discriminatory in both approaches. Concerning the difficulty parameter, we verify that approximately 44% of items are easy, whereas items 4 and 14 are very easy. In general, results demonstrate that the tests are mainly composed of discriminative and very discriminative items and, additionally, items of all difficulty levels.
A joint analysis of Port4 reveals that, in general, the test items do discriminate, with the exception of item 6, which slightly discriminates, and item 26, which is very discriminative and has a medium difficulty level. Additionally, items 2, 5, 8, 12, 16, 17, and 20 are very easy.
Concerning Math6 items, analysis based on CTT and IRM shows that the most discriminative items are 13, 15, 16, 19, and 21; the least discriminative items are 10 and 20. The difficulty index and parameter indicate that the easiest items are items 1, 3, 5, 7, and 18; the most difficult items are 10, 12, and 20. In particular, item 12 is slightly discriminative according to the CTT approach and discriminative according to the GPCM approach.
For Port6 items, analysis based on the two approaches reveals that items 3, 4, 5, 7, 9, 13, 14, 15, 17, 18, 19, and 26 discriminate slightly and that there is one set of six very easy items (4, 5, 7, 9, 15, and 26) and a set of three very difficult items (14, 17, and 18). The relationship between the biserial correlation (r) and the discrimination parameter estimate (a), given by formula (3), indicates a moderate correlation varying from 0.4 to 0.5.
Concerning difficulty, the correlation between p and b is very strong since it ranges from −0.8 to −0.9, i.e., the correlation is −0.83 in Mathematics 4^{th }grade, −0.88 in Portuguese 4^{th} grade, −0.88 in Mathematics 6^{th} grade, and −0.80 in Portuguese 6^{th} grade. Percentile confidence intervals of 95% based on 1000 bootstrap samples are presented in Table 5. The intervals confirm that in the population, the correlation is strong since its absolute value is always greater than 0.71. In this sense, results support this study’s purpose of providing empirical evidence on the complementarity between the two statistical approaches regarding the estimate of item difficulty.
4 Conclusion
The results obtained in this study show a very strong correlation between the CTT index of difficulty and the IRM item difficulty parameter estimate. The correlation is −0.83 in Mathematics 4^{th }grade, −0.88 in Portuguese 4^{th} grade, −0.88 in Mathematics 6^{th} grade, and −0.80 in Portuguese 6th grade. The results also suggest that the level of association does not depend on subject or on grade. A moderate relationship between the IRM estimate of discrimination and the approximation given by the biserial function was verified. In addition, it was shown that even when items do not discriminate equally, a monotonic relationship exists between the CTT index of item difficulty and the IRM item difficulty parameter. Therefore, CTT may be utilized as initial estimates for item pretesting and precalibration in item bank development, particularly supporting implementation of Webbased adaptive tests. Since the sample size required for item pretesting and calibration is a crucial aspect for development of item banks, these are promising results for the future of computer or Webbased testing. Further work is needed to determine whether changes in pretesting and in algorithms related to adaptive test design and administration affect score precision and reliability.
Acknowledgments
This study is based on work sponsored in part by the Portuguese Ministry of Education under the protocol signed with Universidade da Beira Interior in November 2007, with the purpose of developing the project “Improving the quality of educational assessment tests and scales of measurement.”
This work was partially funded by Fundação para a Ciência e Tecnologia throught the project PEstOE/EGE/UI0491/2014.
References
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 FERRÃO, M. E.; COSTA, P.; OLIVEIRA, P. N. Generalized partial credit item response model: linking scales in the assessment of learning. Journal of Interdisciplinary Mathematics, v. 18, n. 4, p. 33954, Jun. 2015. DOI: 10.180/09720502.2014.932119
 FERRÃO, M. E.; PRATA, P. Item response models in computerized adaptive testing: a simulation study. Lecture Notes in Computer Science, v. 8581, p. 55265, 2014. Apresentado no 14^{th} International Science and Its Applications – ICCSA 2014, Guimarães, Portugal, 2014. DOI:10.1007/9783319091501_40
 GOLDSTEIN, H.; WOOD, R. Five decades of item response modelling. British Journal of Mathematical and Statistical Psychology, v. 42, n. 2, p. 13967, Nov. 1989. DOI:10.1111/j.20448317.1989.tb00905.x
 GUILFORD, J. P.; FRUCHTER, B. Fundamental statistics in psychology and education. 6. ed. New York: McGrawHill, 1978.
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 HAMBLETON, R. K.; JONES, R. W. Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues and Practice, v. 12, n.3, p. 3847, Sep. 1993. DOI:10.1111/j.17453992.1993.tb00543
 HAMBLETON, R.; SWAMINATHAN, H.; ROGERS, H. J. Fundamentals of item response theory. California: Sage, 1991.
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 LORD, F. M. Applications of item response theory to practical testing problems Hillsdale: Lawrence Erlbaum, 1980.
 LORD, F. M.; NOVICK, M. R. Statistical theories of mental test scores Oxford: AddisonWesley, 1968.
 MURAKI, E. Information functions of the generalized partial credit model. Applied Psychological Measurement, v. 17, n. 4, p. 35163, Dec.1993.
 MURAKI, E. A generalized partial credit model. In: VAN DER LINDEN, W. J.; HAMBLETON, R. K. (Eds.). Handbook of modern item response theory New York: Springer, 1997. p. 15364.
 MURAKI, E.; BOCK, R. IRT based test scoring and item analysis for graded openended exercises and performance tasks (Version 3) Chicago: Scientific Software, 2002.
 NYDICK, S. W.; WEISS, D. J. A hybrid simulation procedure for the development of CATs. In: 2009 GMAC® CONFERENCE ON COMPUTERIZED ADAPTIVE TESTING, 2009, Minneapolis. Proceedings Disponível em: <http://publicdocs.iacat.org/cat2010/cat09nydick.pdf>. Acesso em: 20 fev 2014.
» http://publicdocs.iacat.org/cat2010/cat09nydick.pdf>  STOCKING, M. L. Three practical issues form modern adaptive testing item pools Princeton: Educational Testing Research, 1994. (ETS Research Report, 932).
 THISSEN, D.; STEINBERG, L. The taxonomy of item response models. Psychometrika, v. 51, n. 4, p. 56777, Dec. 1988. DOI:10.1007/BF02295596
 VELDKAMP, B. P.; MATTEUCCI, M. Bayesian computerized adaptive testing. Ensaio: Avaliação e Políticas Públicas em Educação, v. 21, n. 78, p. 5782, jan./mar. 2013. DOI:10.1590/S010440362013005000001
 WISE, S. L.; KINGSBURY, G. G. Practical issues in developing and maintaining a computerized adaptive testing program developing and maintaining an item pool for use in an adaptive test. Psicológica, v. 21, n. 1, p. 13555, 2000.
Publication Dates

Publication in this collection
JulySept 2015
History

Received
09 June 2014 
Accepted
30 Apr 2015