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Adaptive testing, depending on the item selection algorithm , may reduce exposure of some items because examinees typically receive different sets of items rather than the whole population being administered a single set. The first issue encountered in CAT is the calibration of the item pool. In order to model the characteristics of the items e. To achieve this, new items must be mixed into the operational items of an exam the responses are recorded but do not contribute to the test-takers' scores , called "pilot testing," "pre-testing," or "seeding.

For example, it is impossible to field an operational adaptive test with brand-new, unseen items; [4] all items must be pretested with a large enough sample to obtain stable item statistics. This sample may be required to be as large as 1, examinees. Although adaptive tests have exposure control algorithms to prevent overuse of a few items, [2] the exposure conditioned upon ability is often not controlled and can easily become close to 1.

That is, it is common for some items to become very common on tests for people of the same ability. This is a serious security concern because groups sharing items may well have a similar functional ability level. In fact, a completely randomized exam is the most secure but also least efficient. Review of past items is generally disallowed. Adaptive tests tend to administer easier items after a person answers incorrectly. Supposedly, an astute test-taker could use such clues to detect incorrect answers and correct them.

Or, test-takers could be coached to deliberately pick wrong answers, leading to an increasingly easier test. After tricking the adaptive test into building a maximally easy exam, they could then review the items and answer them correctly--possibly achieving a very high score.

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Test-takers frequently complain about the inability to review. This list does not include practical issues, such as item pretesting or live field release. A pool of items must be available for the CAT to choose from. Typically, item response theory is employed as the psychometric model. In CAT, items are selected based on the examinee's performance up to a given point in the test.

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However, the CAT is obviously not able to make any specific estimate of examinee ability when no items have been administered. So some other initial estimate of examinee ability is necessary. If some previous information regarding the examinee is known, it can be used, [1] but often the CAT just assumes that the examinee is of average ability - hence the first item often being of medium difficulty.

As mentioned previously, item response theory places examinees and items on the same metric. Therefore, if the CAT has an estimate of examinee ability, it is able to select an item that is most appropriate for that estimate. After an item is administered, the CAT updates its estimate of the examinee's ability level. If the examinee answered the item correctly, the CAT will likely estimate their ability to be somewhat higher, and vice versa.

This is done by using the item response function from item response theory to obtain a likelihood function of the examinee's ability. Two methods for this are called maximum likelihood estimation and Bayesian estimation. The latter assumes an a priori distribution of examinee ability, and has two commonly used estimators: expectation a posteriori and maximum a posteriori. The CAT algorithm is designed to repeatedly administer items and update the estimate of examinee ability.

This will continue until the item pool is exhausted unless a termination criterion is incorporated into the CAT. Often, the test is terminated when the examinee's standard error of measurement falls below a certain user-specified value, hence the statement above that an advantage is that examinee scores will be uniformly precise or "equiprecise. In many situations, the purpose of the test is to classify examinees into two or more mutually exclusive and exhaustive categories.

This includes the common "mastery test" where the two classifications are "Pass" and "Fail," but also includes situations where there are three or more classifications, such as "Insufficient," "Basic," and "Advanced" levels of knowledge or competency. For example, a new termination criterion and scoring algorithm must be applied that classifies the examinee into a category rather than providing a point estimate of ability. There are two primary methodologies available for this. The more prominent of the two is the sequential probability ratio test SPRT.

Note that this is a point hypthesis formulation rather than a composite hypothesis formulation [8] that is more conceptually appropriate. Only an average of 14 items was needed to establish a sufficiently accurate estimate of the trait studied. Below this stopping value, the CAT exhausts all items before reaching the stopping criterion. The main goal of this research is to develop a CAT for assessing the organizational climate in a representative sample of workers in the public health service of the Principality of Asturias. The most common way to evaluate the organizational climate is through self-reports that measure the various dimensions of the construct Ekvah, The development of a CAT for organizational climate, in addition to the psychometric and measurement advantages involved, examines the construct of organizational climate in detail, confirming the hypothesis of a general factor of organizational climate that reflects an essentially unidimensional underlying structure, which has facilitated the construction of a CAT.

The bank of items that we developed showed a high internal consistency of the items as well as a moderate unidimensionality and it behaved optimally when used in the adaptive computerized version. In this bank, the correlation between the theta estimated latent trait by the CAT and the total score in the items that comprise the bank was 0. A second phase of item screening, to items, enabled us to increase the unidimensionality up to This advantage in the unidimensionality of the item test, compared with the data from the other item banks, does not seem to compensate for the loss of information which occurs when these 20 items are removed from the initial bank.

Neither does the efficiency of the CAT with this bank appear to improve significantly from the item bank, as can be examined with the information function.

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The bank of 50 items, with 37 common items to the original CLIOR scale, is clearly unidimensional and it substantially improves the internal structure of the initial bank going from As for the validity of the CAT, this item bank does not exceed the correlations found between the estimated theta score of the trait with the bank of items and the total score with the CLIOR scale. However, when considering the correlations between the theta score obtained with the CAT and the total score in the bank, these exceed the scores of the item bank by 0. With regards to the efficiency of the CAT, this adaptive test in conjunction with the bank of items works reasonably well in requiring an average of From the point of view of the information provided by the different item banks studied, it is important to highlight the gradual loss of information which occurs in the banks as the number of items decreases.

This can be seen more clearly in the case of the bank of 50 items, which loses information especially at the extreme values. The high Pearson correlation found between the CLIOR scale adaptively administered until the bank is exhausted compared with the same scale conventionally administered using pen and paper, enables the two instruments to be compared isometrically. Although in this case the adaptive test did not provide any advantages regarding a reduction in the number of items presented, as the same 50 items were administered to each of the participants in the study sample, this high correlation found allows us to justify empirically the validity of the subsequent contrasts that were performed in this study.

More specifically, the adaptive version achieved the screening of the original CLIOR scale, increasing its unidimensionality and thus its reliability and evidence of validity. Moreover, the results seem to support the suitability of this adaptive procedure in significantly reducing the number of items needed for accurate estimation of the latent trait. Although the bank of items presents indicators of unidimensionality that are inferior to those of the 50 item bank, it does however enable us to more fully explore the construct of organizational climate with minimum guarantees of reliability, especially in the extreme values of the trait.

Finally, the large variety of items calibrated in this bank will enable further estimates of the trait to be made in the future, avoiding the repetition and overexposure of the same items. Although to date there are few studies on the administration of CAT in the organizational context, previous studies have found results that are similar to those presented in this paper.

For example, Chien et al. According to the results obtained, we have a CAT with good psychometric functioning, which is simple and fast and allows the assessment of organizational climate in contexts of both public and private organizations. The fact that the tests are short and are administered in a computerized context makes the CAT very appealing and can overcome the lack of interest shown by many participants when faced with excessively long and repetitive traditional tests. It would even be possible to have different tests that measure the same trait, which will assess the efficacy of interventions for promoting health at work and employee satisfaction.

Finally, another line of work that remains open, going forward, is a detailed analysis of the facets that comprise the construct of organizational climate for each CAT, thus aiming to explore the possibilities that these adaptive tests offer us for extracting substantive information about the organizational factors that are involved in a diagnostic assessment of the organizational climate of work environments.

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Elements of Adaptive Testing / Edition 1

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