Experimental manipulation of prior experience: Effects on item and associative recognition

Frequency of exposure to very low- and high-frequency words was manipulated in a three-phase (familiarisation, study, and test) design. During familiarisation, words were presented with their definition (once, four times, or not presented). One week (Experiment 1) or one day (Experiment 2) later, participants studied a list of homogeneous pairs (i.e., pair members were matched on background and familiarisation frequency). Item and associative recognition of high- and very low-frequency words presented in intact, rearranged, old-new, or new-new pairs were tested in Experiment 1. Associative recognition of very low-frequency words was tested in Experiment 2. Results showed that prior familiaris ation improved associative recognition of very low-frequency pairs, but had no effect on high-frequency pairs. The role of meaning in the formation of item-to-item and item-to-context associations and the implications for current models of memory are discussed.

The role of context in producing item interactions and false memories

Cued recall with an extralist cue poses a challenge for contemporary memory theory in that there is a need to explain how episodic and semantic information are combined. A parallel activation and intersection approach proposes one such means by assuming that an experimental cue will elicit its preexisting semantic network and a context cue will elicit a list memory. These 2 sources of information are then combined by focusing on information that is common to the 2 sources. Two key predictions of that approach are examined: (a) Combining semantic and episodic information can lead to item interactions and false memories, and (b) these effects are limited to memory tasks that involve an episodic context cue. Five experiments demonstrate such item interactions and false memories in cued recall but not in free association. Links are drawn between the use of context in this setting and in other settings.

A comparison of the Yale-Brown obsessive compulsive scale for "Heavy drinking" with a single item craving measure: Construct validity and clinical utility

The measurement of alcohol craving began with single-item scales. Multifactorial scales developed with the intention to capture more fully the phenomenon of craving. This study examines the construct validity of a multifactorial scale, the Yale-Brown Obsessive Compulsive Scale for heavy drinking (Y-BOCS-hd). The study compares its clinical utility with a single item visual-analogue craving scale. The study includes 212 alcohol dependent subjects (127 males, 75 females) undertaking an outpatient treatment program between 1999-2001. Subjects completed the Y-BOCS-hd and a single item visual-analogue scale, in addition to alcohol consumption and dependence severity measures. The Y-BOCS-hd had strong construct validity. Both the visual-analogue alcohol craving scale and Y-BOCS-hd were weakly associated with pretreatment dependence severity. There was a significant association between pretreatment alcohol consumption and the visual-analogue craving scale. Neither craving measure was able to predict total program abstinence or days abstinent. The relationship between obsessive-compulsive behavior in alcohol dependence and craving remains unclear.

Item validity of the physical demands from the Dictionary of Occupational Titles for the functional capacity evaluation of clients with chronic back pain

This study assessed the item validity of 15 of the physical demands from the Dictionary of Occupational Titles (DOT), as evaluated in a new approach to functional capacity evaluation (FCE) for clients with chronic back pain, the Gibson Approach to FCE (GAPP FCE). Fifty-two occupational therapists were sent the specifications of the items in the GAPP FCE procedures and were asked to rate the items in terms of item-objective congruence, relevance and difficulty. A response rate of 59.2% was obtained. The majority of the therapists agreed that most of the items were congruent with the objectives based on the definition of the physical demands from the DOT. The items evaluating Balancing and Pushing and Pulling had the lowest item-objective congruence. The evaluation of Balancing and the Lifting, Carrying and Pushing and Pulling of loads greater than light-medium weight (10–16 kg) were not considered significantly relevant. Concerns were raised about the difficulty and safety of the evaluation of Lifting, Carrying and Pushing and Pulling with clients with chronic back pain, particularly if the therapist evaluates the manual handling of medium to heavy loads. These results may have implications for other FCEs, particularly those which are based on the DOT, or when assessing clients with chronic back pain.

Validation of the Action Research Arm Test using item response theory in patients after stroke

Objective: To validate the unidimensionality of the Action Research Arm Test (ARAT) using Mokken analysis and to examine whether scores of the ARAT can be transformed into interval scores using Rasch analysis. Subjects and methods: A total of 351 patients with stroke were recruited from 5 rehabilitation departments located in 4 regions of Taiwan. The 19-item ARAT was administered to all the subjects by a physical therapist. The data were analysed using item response theory by non-parametric Mokken analysis followed by Rasch analysis. Results: The results supported a unidimensional scale of the 19-item ARAT by Mokken analysis, with the scalability coefficient H = 0.95. Except for the item pinch ball bearing 3rd finger and thumb'', the remaining 18 items have a consistently hierarchical order along the upper extremity function's continuum. In contrast, the Rasch analysis, with a stepwise deletion of misfit items, showed that only 4 items (grasp ball'', grasp block 5 cm(3)'', grasp block 2.5 cm(3)'', and grip tube 1 cm(3)'') fit the Rasch rating scale model's expectations. Conclusion: Our findings indicated that the 19-item ARAT constituted a unidimensional construct measuring upper extremity function in stroke patients. However, the results did not support the premise that the raw sum scores of the ARAT can be transformed into interval Rasch scores. Thus, the raw sum scores of the ARAT can provide information only about order of patients on their upper extremity functional abilities, but not represent each patient's exact functioning.

Evaluating the properties of a stage-specific self-efficacy scale for physical activity using classical test theory, confirmatory factor analysis and item response modeling

The purpose of this paper was to evaluate the psychometric properties of a stage-specific selfefficacy scale for physical activity with classical test theory (CTT), confirmatory factor analysis (CFA) and item response modeling (IRM). Women who enrolled in the Women On The Move study completed a 20-item stage-specific self-efficacy scale developed for this study [n = 226, 51.1% African-American and 48.9% Hispanic women, mean age = 49.2 (67.0) years, mean body mass index = 29.7 (66.4)]. Three analyses were conducted: (i) a CTT item analysis, (ii) a CFA to validate the factor structure and (iii) an IRM analysis. The CTT item analysis and the CFA results showed that the scale had high internal consistency (ranging from 0.76 to 0.93) and a strong factor structure. Results also showed that the scale could be improved by modifying or eliminating some of the existing items without significantly altering the content of the scale. The IRM results also showed that the scale had few items that targeted high self-efficacy and the stage-specific assumption underlying the scale was rejected. In addition, the IRM analyses found that the five-point response format functioned more like a four-point response format. Overall, employing multiple methods to assess the psychometric properties of the stage-specific self-efficacy scale demonstrated the complimentary nature of these methods and it highlighted the strengths and weaknesses of this scale.

Collaborative filtering on data streams

Collaborate Filtering is one of the most popular recommendation algorithms. Most Collaborative Filtering algorithms work with a static set of data. This paper introduces a novel approach to providing recommendations using Collaborative Filtering when user rating is received over an incoming data stream. In an incoming stream there are massive amounts of data arriving rapidly making it impossible to save all the records for later analysis. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively although an inevitable trade off between accuracy and amount of memory used is introduced. By adding a simple personalization step using a hierarchy of the items, it is possible to improve the predicted ratings made by each decision tree and generate recommendations in real-time. Empirical studies with the dynamically built decision trees show that the personalization step improves the overall predicted accuracy.

Time weight collaborative filtering

Collaborative filtering is regarded as one of the most promising recommendation algorithms. The item-based approaches for collaborative filtering identify the similarity between two items by comparing users' ratings on them. In these approaches, ratings produced at different times are weighted equally. That is to say, changes in user purchase interest are not taken into consideration. For example, an item that was rated recently by a user should have a bigger impact on the prediction of future user behaviour than an item that was rated a long time ago. In this paper, we present a novel algorithm to compute the time weights for different items in a manner that will assign a decreasing weight to old data. More specifically, the users' purchase habits vary. Even the same user has quite different attitudes towards different items. Our proposed algorithm uses clustering to discriminate between different kinds of items. To each item cluster, we trace each user's purchase interest change and introduce a personalized decay factor according to the user own purchase behaviour. Empirical studies have shown that our new algorithm substantially improves the precision of item-based collaborative filtering without introducing higher order computational complexity.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.