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Functional Requirements for Bibliographic Records (FRBR / ˈ f ɜːr b ər /) is a conceptual entity–relationship model developed by the International Federation of Library Associations and Institutions (IFLA) that relates user tasks of retrieval and access in online library catalogues and bibliographic databases from a user’s perspective.
It uses the same Work, Expression, Manifestation, Item (WEMI) model as FRBR Group 1 entities. The FRBR Group 2 corporate body and FRAD family are combined into a single collective agent type. FRBR Group 3 entities are deprecated. A redefined nomen type encompasses name from FRAD plus nomen, identifier, and controlled access point from FRSAD. [4]
The one-parameter model (1PL) assumes that guessing is a part of the ability and that all items that fit the model have equivalent discriminations, so that items are only described by a single parameter (). This results in one-parameter models having the property of specific objectivity, meaning that the rank of the item difficulty is the same ...
[26] [27] A group-specific method further decomposes each latent factor into two additive parts: One part corresponds to each item (and/or each user), while the other part is shared among items within each item group (e.g., a group of movies could be movies of the same genre). Then once a new item arrives, we can assign a group label to it, and ...
The group of items is then answered by a large number of participants and analyzed using various statistical methods, such as exploratory factor analysis or principal component analysis. These methods allow researchers to analyze natural relationships among the questions and then label components of the scale based on how the questions group ...
Differential item functioning (DIF) is a statistical property of a test item that indicates how likely it is for individuals from distinct groups, possessing similar abilities, to respond differently to the item. It manifests when individuals from different groups, with comparable skill levels, do not have an equal likelihood of answering a ...
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Item-item models use rating distributions per item, not per user. With more users than items, each item tends to have more ratings than each user, so an item's average rating usually doesn't change quickly. This leads to more stable rating distributions in the model, so the model doesn't have to be rebuilt as often.