WHAT IS PSYCHOMETRIC ANALYSIS?JUST AN OVERVIEW

NOW THIS IS WHAT PROMETRIC SAYS ABOUT PSYCHOMETRIC ANALYSIS

PSYCHOMETRIC ANALYSIS

Prometric employs a robust staff of psychometric analysis experts, whose sole responsibility is to ensure that your final test meets design objectives, market requirements and legal standards.

Each test and its items are subjected to various analyses aimed at determining the measurement quality of the assessment. We can help ensure that your exam, and every subsequent version of it, continues to meet this high standard of quality. We use specialized and industry-standard modeling for all exam studies and custom research. We utilize Rasch/Item Responses, standard stetting, criterion validity, simulationTheory, differential item functioning (DIF) and item parameter drift studies, the modified Angoff and Borderline Group methods(????????) and other industry standards.(??????)

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INFORMATION WE GOT ON SEARCHING WORDS THEY TOLD WHICH PROMETRIC USE FOR SCALING

1.WHAT IS RASCH ITEM RESPONSE THEORY

The Rasch model is often considered to be the 1PL IRT model. However, proponents of Rasch modeling prefer to view it as a completely different approach to conceptualizing the relationship between data and the theory.[11] Like other statistical modeling approaches, IRT emphasizes the primacy of the fit of a model to observed data,[12] while the Rasch model emphasizes the primacy of the requirements for fundamental measurement, with adequate data-model fit being an important but secondary requirement to be met before a test or research instrument can be claimed to measure a trait.[13] Operationally, this means that the IRT approaches include additional model parameters to reflect the patterns observed in the data (e.g., allowing items to vary in their correlation with the latent trait), whereas the Rasch approach requires both the data fit the Rasch model and that test items and examinees confirm to the model, before claims regarding the presence of a latent trait can be considered valid. Therefore, under Rasch models, misfitting responses require diagnosis of the reason for the misfit, and may be excluded from the data set if substantive explanations can be made that they do not address the latent trait.[14] Thus, the Rasch approach can be seen to be a confirmatory approach, as opposed to exploratory approaches that attempt to model the observed data. As in any confirmatory analysis, care must be taken to avoid confirmation bias.

The presence or absence of a guessing or pseudo-chance parameter is a major and sometimes controversial distinction. The IRT approach includes a left asymptote parameter to account for guessing in multiple choice examinations, while the Rasch model does not because it is assumed that guessing adds randomly distributed noise to the data. As the noise is randomly distributed, it is assumed that, provided sufficient items are tested, the rank-ordering of persons along the latent trait by raw score will not change, but will simply undergo a linear rescaling. Three-parameter IRT, by contrast, achieves data-model fit by selecting a model that fits the data,[15] at the expense of sacrificing specific objectivity.

In practice, the Rasch model has at least two principal advantages in comparison to the IRT approach. The first advantage is the primacy of Rasch's specific requirements,[16] which (when met) provides fundamental person-free measurement (where persons and items can be mapped onto the same invariant scale).[17] Another advantage of the Rasch approach is that estimation of parameters is more straightforward in Rasch models due to the presence of sufficient statistics, which in this application means a one-to-one mapping of raw number-correct scores to Rasch estimates

2 DIFFERENTIAL ITEM FUNCTIONING

Differential item functioning (DIF), also referred to as measurement bias, occurs when people from different groups (commonly gender or ethnicity) with the same latent trait (ability/skill) have a different probability of giving a certain response on a questionnaire or test.[1] DIF analysis provides an indication of unexpected behavior of items on a test. An item does not display DIF if people from different groups have a different probability to give a certain response; it displays DIF if and only if people from different groups with the same underlying true ability have a different probability of giving a certain response. Common procedures for assessing DIF are Mantel-Haenszel, item response theory (IRT) based methods, and logistic regression

THIS IS WHAT NEET SITE TELLS ABOUT SCALING

RESULTS – EQUATING & SCALING 

The question paper of NEET-PG comprises of 240 multiple choice questions each with four options and only one correct response. Multiple question papers are used for NEET-PG for different sessions and days.

A standard psychometrically-sound approach is employed for the scoring process of NEET-PG. This approach has been applied to score all large scale Computer Based Examination utilizing multiple question papers.

Step 1: Calculation of Raw Marks

Raw marks are calculated based on the number of questions answered correctly, incorrectly or omitted.

Correct Answer +1 point

Incorrect Answer/Omitted 0 point

Step 2: Raw Marks are equated

While all papers (forms) are carefully assembled to ensure that the content is comparable, the difficulty of each form may be perceived by different subjects undertaking the test to slightly vary. Such minor differences in the overall difficulty level are accurately measured after all the different question papers (forms) have been administered and the results analyzed. A post-equating process is necessary to ensure validity and fairness.

Equating is a psychometric process to adjust differences in difficulty so that scores from different test papers (forms) are comparable on a common metric and therefore fair to candidates testing across multiple papers (forms). To facilitate this comparison, each form contains a pre-defined number of questions (items) selected from a large item bank, called an equating block, which is used as an anchor to adjust candidates scores to the metric of the item bank. Taking into account of candidates’ differential performance on these equating blocks, each individual’s raw marks are adjusted for difference in paper (form) difficulties.

During post-equating, test items are concurrently analyzed and the estimated item parameters (item difficulty and discrimination) are put onto a common metric. Item Response Theory (IRT), a psychometrically supported statistical model, is utilized in this process. The result is a statistically equated raw score that takes into account the performance of the candidate along with the difficulty of the form administered.

Step 3: Equated raw score is scaled 

In order to ensure appropriate interpretation of an equated raw score, the scores must be placed on a common scale or metric. A linear transformation is used for this scaling process, which is a standard practice for such test administration.

Post equating takes into account any statistical differences in examination difficulty and ensures all candidates are evaluated on a common scale. The aforesaid steps ensure that all examination scores are valid, equitable and fair. Merit List shall be prepared on the basis of scaled score obtained by the candidates

THIS  IS DATA WE OBTAINED ABOUT PSYCHOMETRIC ANALYSIS..SO CONFUSING..BUT YOU MAY BE ABLE TO GET SOME IDEA

REFERENCE FROM WIKIPEDIA ,PROMETRIC SITE,NEET WEBSITE

LINK TO WIKIPEDIA TO READ MORE ON ITEM RESPONSE THEORY