RFID har sedan 80-talet vuxit fram inom industri- och transport-branschen som ett sätt att under-lätta spårning av tillgångar och övervakning av informationsflöden genom logistikkedjan. Tek-nologin har idag många olika applikationsområden och används inom en mängd olika verksam-heter. Item-level Tagging (ILT) är ett sätt att utnyttja tekniken genom att märka enskilda varor för att på så sätt ge dem unika identiteter, vilket erbjuder enorm potential inom detaljhandel. Givet potentialen med tekniken vill vi med vår studie undersöka vilka förutsättningar som kommer att krävas för en ökad användning av ILT och RFID inom detaljhandeln. Detta mål uppnås genom att undersöka de barriärer som tidigare har hindrat en ökad användning samt hur dessa barriärer uppfattas i dagsläget. Efter en grundlig litteraturgenomgång samt intervjuer med respondenter med skilda perspektiv inom området kunde vi dra slutsatsen att det i dagsläget är fyra förutsätt-ningar som behövs för att användningen skall öka; (1) Gemensamma standarder för hur RFID-data skall kodas, (2) Konkurrensmässig press, (3) En ökad kunskap om nyttoeffekter för specifika verksamheter, (4) En ökad kompetens gällande systemlösningar. / Since the eighties RFID has been used in industry and logistics as a way to ease tracking of assets and supervising information flows through the logistical chain. Today, the technology can be applied in a number of different ways and is used in a variety of businesses and branches. Item-level Tagging (ILT) is one way to apply the technology by tagging separate items and thus giving them unique identities, which offers enormous potential within retail. Given the technology’s potential our goal with this study is to discover which prerequisites are necessary for an increased use of ILT and RFID in retail. We achieve this goal by studying earlier barriers to adoption and how these barriers are perceived today. A thorough literary review and interviews with respond-ents from differing perspectives within the field enabled us to draw the conclusion that there are four prerequisites that are necessary today for an increased adoption; (1) Common standards for encoding RFID-data, (2) Competitive pressure, (3) Increased knowledge concerning beneficial effects of adoption, (4) Increased know-how concerning system solutions.

[pt] No Brasil a teoria da Resposta ao Item (TRI) tem sido empregada principalmente na produção de índices de proficiência para alunos que participam de testes de avaliação educacional em larga escala. No entanto, seus diferentes modelos permitem construir indicadores com as mais variadas finalidades, e este é o caso dos indicadores de condição sócio econômica. Existem poucos estudos no Brasil que abordam técnicas empregadas para a produção de indicadores da condição sócio-econômica tendo como base a teoria da resposta ao item. Neste trabalho, propõe-se construir outros tipos de indicadores da classificação sócioeconômica, além do Critério Brasil, utilizando-se modelos específicos da Teoria da Resposta ao Item. Esses indicadores serão comparados, interpretados, e comparados com o indicador do Critério Brasil. / [en] The IRT (Item Response Theory) has been used in Brazil mainly in the production of proficiency indices related to large scale educational assessment. However, the distinct models include in the formulation allow broader applications in the construction of indices, as; for instance, social-economical index (SEI). These are only a few published studies on techniques to formulates SEI specially those using the IRT. In this paper it is proposed a new formulation for the SEI in Brazil based on the IRT the obtained index is compared with the official one, knows as Critério Brasil.

To control various response biases and rater errors in noncognitive assessment, multidimensional forced choice (MFC) measures have been proposed as an alternative to single-statement Likert-type scales. Historically, MFC measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for inter-individual comparisons. However, with the recent advent of classical test theory and item response theory scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components of personnel and educational assessment systems. This dissertation presents developments concerning a GGUM-based MFC model henceforth referred to as the GGUM-RANK. Markov Chain Monte Carlo (MCMC) algorithms were developed to estimate GGUM-RANK statement and person parameters directly from MFC rank responses, and the efficacy of the new estimation algorithm was examined through computer simulations and an empirical construct validity investigation. Recently derived GGUM-RANK item information functions and information indices were also used to evaluate overall item and test quality for the empirical study and to give insights into differences in scoring accuracy between two-alternative (pairwise preference) and three-alternative (triplet) MFC measures for future work. This presentation concludes with a discussion of the research findings and potential applications in workforce and educational setting.

Every single day, lots of users actively participate in social media sites (e.g., Facebook, YouTube, Last.fm, Flicker, etc.) upload photos, videos, share bookmarks, write blogs and annotate/comment on content provided by others. With the recent proliferation of social media sites, users are overwhelmed by the huge amount of available content. Therefore, organizing and retrieving appropriate multimedia content is becoming an increasingly important and challenging task. This challenging task led a number of research communities to concentrate on social tagging systems (also known as folksonomy) that allow users to freely annotate their media items (e.g., music, images, or video) with any sort of arbitrary words, referred to as tags. Tags assist users to organize their own content, as well as to find relevant content shared by other users. In this thesis, we first analyze how useful a folksonomy is for improving personalized services such as tag recommendation, tag-based search and item annotation. We then propose two new algorithms for social media retrieval and tag recommendation respectively. The first algorithm computes the latent preferences of tags for users from other similar tags, as well as latent annotations of tags for items from other similar items. We then seamlessly map the tags onto items, depending on an individual user’s query, to find the most desirable content relevant to the user’s needs. The second algorithm improves tag-recommendation and item annotation by adapting the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. In this algorithm we model folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide personalized tag recommendation for individual users. We evaluate our algorithms on two real-world folksonomies collected from Last.fm and CiteULike. The experimental results demonstrate that the proposed algorithms improve the search and the recommendation performance, and obtain significant gains in cold start situations where relatively little information is known about a user or an item

Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and variability of the item parameter estimates from the marginal maximum a posteriori estimation via an expectation-maximization algorithm (MMAP/EM) and the Markov chain Monte Carlo Gibbs sampling (MCMC/GS) approach. In the study, the various factors which have an impact on the accuracy and variability of the item parameter estimates are discussed, and then further evaluated through a large scale simulation. The factors of interest include the composition and length of tests, the distribution of underlying latent traits, the size of samples, and the prior distributions of discrimination, difficulty, and pseudo-guessing parameters. The results of the two estimation methods are compared to determine the lower limit--in terms of test length, sample size, test characteristics, and prior distributions of item parameters--at which the methods can satisfactorily recover item parameters and efficiently function in reality. For practitioners, the results help to define limits on the appropriate use of the BILOG-MG (which implements MMAP/EM) and also, to assist in deciding the utility of OpenBUGS (which carries out MCMC/GS) for item parameter estimation in practice.

The common practice for testing measurement invariance is to constrain parameters to be equal over groups, and then evaluate the model-data fit to reject or fail to reject the restrictive model. Posterior predictive checking (PPC) provides an alternative approach to evaluating model-data discrepancy. This paper explores the utility of PPC in estimating measurement invariance. The simulation results show that the posterior predictive p (PP p) values of item parameter estimates respond to various invariance violations, whereas the PP p values of item-fit index may fail to detect such violations. The current paper suggests comparing group estimates and restrictive model estimates with posterior predictive distributions in order to demonstrate the pattern of misfit graphically.

The ceiling and basal rules of the Wechsler Adult Intelligence Scale – Fourth Edition (WAIS-IV; Wechsler, 2008) only function as intended if subtest items proceed in order of difficulty. While many aspects of the WAIS-IV have been researched, there is no literature about subtest item difficulty and precise item difficulty values are not available. The WAIS-IV was developed within the framework of Classical Test Theory (CTT) and item difficulty was most often determined using p-values. One limitation of this method is that item difficulty values are sample dependent. Both standard error of measurement, an important indicator of reliability, and p-values change when the sample changes. A different framework within which psychological tests can be created, analyzed and refined is called Item Response Theory (IRT). IRT places items and person ability onto the same scale using linear transformations and links item difficulty level to person ability. As a result, IRT is said to be produce sample-independent statistics. Rasch modeling, a form of IRT, is one parameter logistic model that is appropriate for items with only two response options and assumes that the only factors affecting test performance are characteristics of items, such as their difficulty level or their relationship to the construct being measured by the test, and characteristics of participants, such as their ability levels. The partial credit model is similar to the standard dichotomous Rasch model, except that it is appropriate for items with more than two response options. Proponents of standard dichotomous Rasch model argue that it has distinct advantages above both CTT-based methods as well as other IRT models (Bond & Fox, 2007; Embretson & Reise, 2000; Furr & Bacharach, 2013; Hambleton & Jones, 1993) because of the principle of monotonicity, also referred to as specific objectivity, the principle of additivity or double cancellation, which “establishes that two parameters are additively related to a third variable” (Embretson & Reise, 2000, p. 148). In other words, because of the principle of monotonicity, in Rasch modeling, probability of correctly answering an item is the additive function of individuals’ ability, or trait level, and the item’s degree of difficulty. As ability increases, so does an individual’s probability of answering that item. Because only item difficulty and person ability affect an individual’s chance of correctly answering an item, inter-individual comparisons can be made even if individuals did not receive identical items or items of the same difficulty level. This is why Rasch modeling is referred to as a test-free measurement. The purpose of this study was to apply a standard dichotomous Rasch model or partial credit model to the individual items of seven core perceptual, verbal and working memory subtests of the WAIS-IV: Block Design, Matrix Reasoning, Visual Puzzles, Similarities, Vocabulary, Information, Arithmetic Digits Forward, Digits Backward and Digit Sequencing. Results revealed that WAIS-IV subtests fall into one of three categories: optimally ordered, near optimally ordered and sub-optimally ordered. Optimally ordered subtests, Digits Forward and Digits Backward, had no disordered items. Near optimally ordered subtests were those with one to three disordered items and included Digit Sequencing, Arithmetic, Similarities and Block Design. Sub-optimally ordered subtests consisted of Matrix Reasoning, Visual Puzzles, Information and Vocabulary, with the number of disordered items ranging from six to 16. Two major implications of the result of this study were considered: the impact on individuals’ scores and the impact on overall test administration time. While the number of disordered items ranged from 0 to 16, the overall impact on raw scores was deemed minimal. Because of where the disordered items occur in the subtest, most individuals are administered all the items that they would be expected to answer correctly. A one-point reduction in any one subtest is unlikely to significantly affect overall index scores, which are the scores most commonly interpreted in the WAIS-IV. However, if an individual received a one-point reduction across all subtests, this may have a more noticeable impact on index scores. In cases where individuals discontinue before having a chance to answer items that were easier, clinicians may consider testing the limits. While this would have no impact on raw scores, it may provide clinicians with a better understanding of individuals’ true abilities. Based on the findings of this study, clinicians may consider administering only certain items in order to test the limits, based on the items’ difficulty value. This study found that the start point for most subtests is too easy for most individuals. For some subtests, most individuals may be administered more than 10 items that are too easy for them. Other than increasing overall administration time, it is not clear what impact, of any, this has. However, it does suggest the need to reevaluate current start items so that they are the true basal for most people. Future studies should break standard test administration by ignoring basal and ceiling rules to collect data on more items. In order to help clarify why some items are more or less difficult than would be expected given their ordinal rank, future studies should include a qualitative aspect, where, after each subtest, individuals are asked describe what they found easy and difficult about each item. Finally, future research should examine the effects of item ordering on participant performance. While this study revealed that only minimal reductions in index scores likely result from the prematurely stopping test administration, it is not known if disordering has other impacts on performance, perhaps by increasing or decreasing an individual’s confidence.

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