Small Number Of Items Research Articles

Adaptation of the screen for adolescents anxiety related emotional disorders in Down syndrome

Background/ObjectivePeople with Down syndrome (DS) experience anxiety problems more often than the normal population, but there are no adequate screening tests for diagnosis. This study therefore analyses the psychometric properties of the Screen for Child Anxiety Related Emotional Disorders in Spanish adolescents with DS. MethodDescriptive statistics and item analysis (N = 412), exploratory factorial analysis (n = 200) and confirmatory (n = 212) and scale reliability analyses were performed and evaluated the internal consistency with various indices as well as the stability of the measurement of the resulting scale with better goodness-of-adjustment indices. ResultsA new scale was obtained with three factors and a smaller number of items (RMSEA [95% CI] = 03[0.02; 0.05]; CFI = 0.98; TLI = 0.98; GFI = 0.89; AGFI = 0.89) as well as high internal consistency (α = 0.89; ω = 0.90) and adequate measurement stability (rxx = 0.90). ConclusionsThe importance of validating psychopathological anxiety tests for children and youth with DS in order to build good mental health is discussed, emphasizing the need to provide easy, short-duration tests on both cognitive and emotional aspects in this clinical sub-population. This new version represents a valid and reliable tool to evaluate anxiety in DS.

Adaptation of the Screen for Child Anxiety Related Emotional Disorders in Spanish with Nonspecific Intellectual Disability.

Anxiety continues to be one of the most frequent disorders with typically developing children and youth. However, people with intellectual disability (ID) lack validated diagnostic tests backed by sufficient methodological rigor. Analyze the psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED) in this clinical population, specifically in children and young Spanish with Nonspecific Intellectual Disability. Descriptive statistics and item analysis (N = 542), exploratory factorial analysis (n = 245) and confirmatory (n = 297) and scale reliability analyses were performed and evaluated the internal consistency with various indices (Cronbach's alpha and omega) and the stability of the measurement (test-retest) of the resulting scale with better goodness-of-adjustment indices. A new scale called SCARED-DI was obtained in this clinical sample with three factors and a smaller number of items (22), offering important goodness-of-fit indices (RMSEA [95% CI] 03[0.01; .04]; CFI = 0.99; TLI = 0.98; GFI = 0.88; AGFI = 0.89) and high internal consistency (α = 0.91; ω = 0.93) and adequate measurement stability (rxx = 0.92). The importance of validating psychopathological anxiety tests for children and youth with ID in order to build good mental health is discussed, emphasizing the need to provide easy, short-duration tests on both cognitive and emotional aspects in this clinical sub-population. In addition, the results are assessed in terms of future research and practical implications. This new version of SCARED-ID represents a valid and reliable tool to evaluate the anxiety in people with intellectual disabilities.

Short Index of Job Satisfaction: Validity evidence from Portugal and Brazil.

Job satisfaction is an important construct that is known to be associated with workers' performance and wellbeing. As such, to properly measure it, one must use adapted measures that show adequate validity evidence for the desired context. Such measures should preferably be short to allow the parsimonious use of various measures/constructs in the same data collection. The aim of this paper is to adapt the Portuguese version for Brazil and Portugal of the Short Index of Job Satisfaction (SIJS). The SIJS is a psychometric instrument that measures job satisfaction through five items. A cross-sectional study was conducted with two multi-occupational workers samples, one from Brazil (n = 599) and one other from Portugal (n = 572). The SIJS presented good validity evidence based on its internal structure, namely dimensionality, reliability, and measurement invariance across countries and sexes. It also revealed to be positively correlated with work engagement, and quality of work life (convergent evidence). It also has shown to be negatively associated with burnout (discriminant evidence). The SIJS showed promising validity evidence. The SIJS can be useful to be used together with other instruments, due to its small number of items, producing data with good psychometric properties.

SCOL: similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.

SCOL: similarity and credibility-based approach for opinion leaders detection in collaborative filtering-based recommender systems

Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.

Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering

Nowadays e-commerce has spread all over the world. The e-shops are not similar to the physical shops. The e-shops can have hundreds or thousands of items independent of physical boundaries. The information about all these products is available on the Internet. So now customer is overloaded with information. Recommendation System (RS) finds users interest by utilizing implicit or explicit user’s action on the e-commerce website and recommends items that best matches user’s preferences. In this way RS helps in alleviating information overload problem. The two mainstream recommendation systems are Content Based Filtering (CBF) and Collaborative Filtering (CF). The CBF recommends items that have similar characteristics as items used by the user in the past. The CF creates a group of similar users and recommends items to the target user which is preferred in his group. Another variant of collaborative filtering finds and recommends items similar to items rated by the user, it is known as Item based Collaborative Filtering. The CF usually creates a very big recommendation list but users like only small number of items as recommendations. The proposed approach has merged the content and user-item based collaborative filtering and created a single RS that generates relatively small number of recommendations. The main contribution of the paper is to curtail the size of the recommendation list and helps in placing good items in relatively smaller list of recommended items. The experimental results show that the proposed approach surpasses other traditional benchmark recommendation methods.

Perceptual grouping constrains inhibition in time-based visual selection

In time-based visual selection, task-irrelevant, old stimuli can be inhibited in order to allow the selective processing of new stimuli that appear at a later point in time (the preview benefit; Watson & Humphreys, 1997). The current study investigated if illusory and non-illusory perceptual groups influence the ability to inhibit old and prioritize new stimuli in time-based visual selection. Experiment 1 showed that with Kanizsa-type illusory stimuli, a preview benefit occurred only when displays contained a small number of items. Experiment 2 demonstrated that a set of Kanizsa-type illusory stimuli could be selectively searched amongst a set of non-illusory distractors with no additional preview benefit obtained by separating the two sets of stimuli in time. Experiment 3 showed that, similarly to Experiment 1, non-illusory perceptual groups also produced a preview benefit only for a small number of number of distractors. Experiment 4 demonstrated that local changes to perceptually grouped old items eliminated the preview benefit. The results indicate that the preview benefit is reduced in capacity when applied to complex stimuli that require perceptual grouping, regardless of whether the grouped elements elicit illusory contours. Further, inhibition is applied at the level of grouped objects, rather than to the individual elements making up those groups. The findings are discussed in terms of capacity limits in the inhibition of old distractor stimuli when they consist of perceptual groups, the attentional requirements of forming perceptual groups and the mechanisms and efficiency of time-based visual selection.

Construction and Psychometric Properties of a Short Form of the Personal Experiences Checklist.

Peer victimization is a significant problem concerning young people. However, there are few brief measures that cover the key domains of victimization, including cyber victimization. Therefore, the objective of the present study was to construct and validate a short form version of the Personal Experiences Checklist (PECK) to be able to measure victimization efficiently and effectively. The PECK was originally developed to measure a young person's experience of victimization and provides scores for four subscales (relational-verbal, cyber, physical, and victimization in relation to culture) as well as a total score. A sample of N = 1,040 school students from Grades 4 to 9 (mean age = 12.2 years) derived from two studies was used to construct and cross-validate the structure of a Personal Experiences Checklist Short Form (PECK-SF) using confirmatory factor analysis and item response theory analysis. Based on discriminative ability and consideration of item content, 14 items were selected to form the PECK-SF, which demonstrated good internal consistency (Cronbach's α = .83). Given the small number of items, the use of subscales is not advised. However the PECK-SF total scale represents a potential alternative to the long form of the PECK and provides a brief and general index of victimization.

Open Domain Event Attribute Extraction Method

To improve the extraction of attribute information related to open domain events, a method using Semantic Dependency Parsing (SDP) and a rule model is proposed. First, a small number of items are extracted from the corpus, and the template library corresponding to the attribute information is summarized. SDP is then used to process the entire corpus, identify unrecognized attribute information templates, and add them to the template library. Finally, the error-driven modification method is used to adjust the generated template library. Experimental results demonstrate that this method can enhance the performance of event attribute information extraction.

Simplified Chinese translation of 13 adult item banks from the Quality of Life in Neurological Disorders (Neuro-QoL)

BackgroundThe Quality of Life in Neurological Disorders (Neuro-QoL) item banks evaluate and monitor the physical, mental, and social health of individuals with neurological conditions. Neuro-QoL items can be administered via short form or computerized adaptive testing. This paper describes the English-to-Simplified Chinese translation of 299 items from 13 adult item banks, which are publicly available.MethodsItems were translated according to the Functional Assessment of Chronic Illness Therapy (FACIT) method, including forward and backward translation, reconciliation, expert reviews, and cognitive debriefing with both general and clinical populations in China.ResultsMost of the 299 Simplified Chinese items were well understood by the respondents. Revisions were made on a small number of items after cognitive debriefing. Although some difficulties were encountered in the translation process, all 13 item banks were linguistically validated with acceptable translations.ConclusionAll Chinese adult Neuro-QoL measures are linguistically equivalent to their English sources. Future work includes psychometric validation of these measures in order to create a final version of the item banks. The translation methodology used in this study can serve as a blueprint for researchers in other countries interested in translating the Neuro-QoL.

The Academic Performance Stages of Change Inventory (APSCI): An application of the Transtheoretical Model to academic performance

ABSTRACT The Transtheoretical Model (TTM) conceptualizes change as a process with five identifiable stages. Each stage has psychological characteristics that are manifestations of an underlying continuum of change. Surprisingly few studies have applied the TTM to understand the process involved with academic development. The objective of this study was to assess a new instrument designed to measure the stages of change in the development of academic performance: the Academic Performance Stages of Change Inventory (APSCI). High-school students (N = 564) were sampled from the 10th, 11th, and 12th grades of a secondary school in Portugal. Results showed that a correlated five-factor structure, corresponding to the five stages of change, had good empirical fit. The latent factors of this model (stages) were shown to have the same meaning across school grade and gender. We concluded that the five APSCI subscales had reasonable internal consistency considering the small number of items per factor. Students in the later stages of change tended to have better academic performances and to be more engaged in school than those in the earlier stages, particularly the pre-contemplation stage. This study provides good preliminary evidence that the APSCI is a suitable tool for assessing stages of change of academic performance.

Attribute-level Item Selection Method for DCM-CAT

ABSTRACTDiagnostic classification models (DCMs) can provide multidimensional diagnostic feedback about students’ mastery levels of knowledge components or attributes. One advantage of using DCMs is the ability to accurately and reliably classify students into mastery levels with a relatively small number of items per attribute. Combining DCMs with computerized adaptive testing can further shorten a test by strategically administering different items to different examinees. Current studies about item selection methods select the next item to increase the classification accuracy for the overall attribute profile and have been explored with item pools that have equal attribute information for all attributes on the assessment. In practice, the attribute information for diagnostic assessment is usually not balanced in the item pool. We propose a new attribute-level item selection method based on Cognitive Diagnostic Index at the Attribute Level (CDI_A; Henson et al., 2008) that helps balance classification accuracies among attributes on an assessment when item pools are not balanced across attributes. We conducted simulation studies to compare the performance of the CDI_A to other leading item selection methods; a pair of studies was theoretically based, and the last study was empirically based. Results showed that the new method can increase the classification accuracy and the reliability for attributes with weaker items in the item pool by administering more items to measure the attribute. Although using fewer items, the method retains reasonable accuracies for the attributes with stronger items in the pool. Thus, the CDI_A provides a trade-off to maintain an acceptable level of estimation accuracy for all attributes.

A Comparison of Three Empirical Reliability Estimates for Computerized Adaptive Testing (CAT) Using a Medical Licensing Examination.

Arithmetic mean, Harmonic mean, and Jensen equality were applied to marginalize observed standard errors (OSEs) to estimate CAT reliability. Based on different marginalization method, three empirical CAT reliabilities were compared with true reliabilities. Results showed that three empirical CAT reliabilities were underestimated compared to true reliability in short test length (<40), whereas the magnitude of CAT reliabilities was followed by Jensen equality, Harmonic mean, and Arithmetic mean when mean of ability population distribution is zero. Specifically, Jensen equality overestimated true reliability when the number of items is over 40 and mean ability population distribution is zero. However, Jensen equality was recommended for computing reliability estimates because it was closer to true reliability even if small numbers of items was administered regardless of the mean of ability population distribution, and it can be computed easily by using a single test information value at θ = 0. Although CAT is efficient and accurate compared to a fixed-form test, a small fixed number of items is not recommended as a CAT termination criterion for 2PLM, specifically for 3PLM, to maintain high reliability estimates.

Minimum tiling of a rectangle by squares

We consider a two-dimensional problem in which one is required to split a given rectangular bin into the smallest number of items. The resulting items must be squares to be packed, without overlapping, into the bin so as to cover all the given rectangle. We present a mathematical model and a heuristic algorithm that is proved to find the optimal solution in some special cases. Then, we introduce a relaxation of the problem and present different exact approaches based on this relaxation. Finally, we report computational experiments on the performances of the algorithms on a large set of randomly generated instances.

Data imputation using a trust network for recommendation via matrix factorization

Existing recommendation methods suffer from the data sparsity problem which means that most of users have rated only a very small number of items, often resulting in low recommendation accuracy. In addition, for cold start users evaluating only few items, rating predictions with the methods also produce low accuracy. To address these problems, we propose a novel data imputation method that effectively substitutes missing ratings with probable values (i.e., imputed values). Our method successfully improves accuracy of recommendation methods from the following three aspects: (1) exploiting a trust network, (2) imputing only a part of missing ratings, and (3) applying them to any recommendation methods. Our method employs a bidirectional connection structure within a distance level for finding reliable users in exploiting a trust network as useful information. In addition, our method imputes only some missing ratings, called fillable ratings, whose imputed values are expected to be accurate with a sufficient level of confidence. Moreover, our imputation method is independent of, thus applicable to, any recommendation methods that may include application-specific ones and the most accurate one in each domain. We conduct experiments on three real-life datasets which arise from Epinions and Ciao. Our experimental results demonstrate that our method has recommendation accuracy better than existing recommendation methods equipped with imputation methods or trust networks, especially for cold start users.

Psychometric Properties of Korean version of Nurse Emotional Labor Strategy Scale (K-NELSS)

Purpose: Purposes of this study were to develop a Nurse Emotional Labor Strategy Scale in Korean (K-NELSS) and to evaluate its psychometric properties. Methods: The 14 items Emotional Labor Strategy Scale (ELSS) was translated into Korean and modified to apply to nurses. Two hundred and fifty nurses working in various units completed the questionnaire. Factor validity using confirmatory factor analysis, construct validity using correlation with burn-out, and criterion validity using correlation with Korean-Emotional Labor Scale (K-ELS) were identified. Reliability was tested by Cronbach’s ⍺. Results: K-NELSS has 3 domains: surface acting, deep acting, and expression of naturally felt emotions. Confirmatory factor analysis showed moderate goodness of fit (RMSEA=.80, SRMR=.07, CFI=93, TFI=.92). Correlation between surface acting and burn-out was r=.37 (p＜.001), and between K-NELSS and K-ELS were r=.57 (p＜.001) in surface action and r=.62 (p＜.001) in deep acting. Cronbach’s ⍺s for surface acting, deep acting, and expression of naturally felt emotions were .89, .88, .84, respectively. Conclusion: Psychometric properties of K-NELSS indicate that it is a useful and reliable tool to assess emotional labor of Korean nurses. In addition, with a small number of items, it is relatively easy to use.

A cross-validation of the Violence Risk Appraisal Guide-Revised (VRAG-R) within a correctional sample.

The Violence Risk Appraisal Guide-Revised (VRAG-R) was developed to replace the original VRAG based on an updated and larger sample with an extended follow-up period. Using a sample of 120 adult male correctional offenders, the current study examined the interrater reliability and predictive and comparative validity of the VRAG-R to the VRAG, the Psychopathy Checklist-Revised, the Statistical Information on Recidivism-Revised, and the Two-Tiered Violence Risk Estimate over a follow-up period of up to 22 years postrelease. The VRAG-R achieved moderate levels of predictive validity for both general and violent recidivism that was sustained over time as evidenced by time-dependent area under the curve (AUC) analysis. Further, moderate predictive validity was evident when the Antisociality item was both removed and then subsequently replaced with a substitute measure of antisociality. Results of the individual item analyses for the VRAG and VRAG-R revealed that only a small number of items are significant predictors of violent recidivism. The results of this study have implications for the application of the VRAG-R to the assessment of violent recidivism among correctional offenders. (PsycINFO Database Record

Validation of the Korean version of the 16-Item Prodromal Questionnaire in a Non-Help-Seeking College Population.

Objective To examine the reliability and validity of the Korean version of the 16-item Prodromal Questionnaire (KPQ-16) in non-help-seeking university students. Methods Among 2,246 university students participated in the initial screening, 257 subjects with KPQ-16 scores ≥4 were interviewed. The criteria for ultra-high risk (UHR) of psychosis of the Comprehensive Assessment of At-Risk Mental States (CAARMS) were the gold standard for diagnosis. An exploratory modified version of the questionnaire (mKPQ-16), to which three items from the Eppendorf Schizophrenia Inventory were added, was also used to compensate for items on thought and cognitive problems. Results Seventeen subjects met the CAARMS criteria for UHR of psychosis. The area under the receiver operating characteristic curve (AUROC) was highest for the mKPQ-16 total score (AUROC=0.831, p<0.001). The use of cutoff total scores of 7 for the mKPQ-16 and 6 for the KPQ-16 resulted in the best balance of sensitivity (76.5% and 64.7%, respectively) and specificity (75.4% and 71.2%, respectively). Conclusion The Korean versions of the PQ-16 are good instruments for screening for psychosis risk in university students. This validation of a questionnaire version with a small number of items may make it feasible to screen large numbers of young adults in the community.

German Language Version and Validation of the Risk-Taking Behaviour Scale (RBS-K) for High-Risk Sports

High-risk sports, particularly climbing, kayaking and extreme skiing, have become increasingly popular. The most widely used psychological survey instrument with regard to risk behaviour in sports is the Sensation Seeking Model, mostly assessed by the Sensation Seeking Scale (SSS-V). Until recently, the literature discussed risk behaviour solely through this model. However, this scale does not measure risk-taking behaviours. In contrast, the Risk-Taking Behaviour Scale (RBS-K) is a three-item scale that measures risk behaviour in high-risk sports. This study aimed to validate a German language version of the RBS-K. The RBS-K was translated and back-translated between English and German. High-risk sports participants (n = 2399) completed the German version of the RBS-K. Of those participants, 820 completed the RBS-K in person as part of a field survey and 1579 participated in an online survey. To validate the questionnaire, the SSS-V, accident involvement, age and sex were evaluated. The RBS-K divides the sample into deliberate risk takers (mean + standard deviation) and risk-averse persons (mean - standard deviation). We tested for internal consistency and correlations with SSS-V, age, sex and accident involvement. Group differences were calculated between deliberate risk takers and risk-averse persons. For internal consistency, we obtained a Cronbach's alpha of 0.56 and a McDonald's omega of 0.63. Significant correlations were shown between RBS-K and SSS-V as well as age and sex. Compared to risk-averse persons (n = 643, 26.8 %), deliberate risk takers (n = 319, 13.3 %) scored significantly higher in sensation seeking, were significantly younger and primarily male and had a significantly higher accident involvement. The RBS-K discriminates well for age, sex and accident involvement. Also, correlations between the RBS-K and the well-established SSS-V are acceptable. With regard to the results and its compact design, the scale seems to be well suited for field surveys. We discuss the relatively modest internal consistency in the context of the small number of items and the different dimensions of risk-taking.

Developing and evaluating a computerized adaptive testing version of the Word Part Levels Test

The knowledge about affix plays a vital role in the development of word knowledge and vocabulary acquisition. A test for diagnostic information on the level of affix knowledge would be useful in order to inform the test users of what learners have gained or lacked in this integral component of vocabulary knowledge. This paper reports the development and evaluation of a computerized adaptive testing (CAT) version of the Word Part Levels Test (WPLT), created by Sasao and Webb (2017). The CAT-WPLT was developed to maximize further the effectiveness of the WPLT as a diagnostic test. It was administered to 760 Japanese university EFL (English as a foreign language) learners. The evaluation was based on the comparison of measurement accuracy with the fixed-item version of the WPLT. The results show that the CAT-WPLT can provide test users with diagnostic information on test-taker’s strengths and weaknesses in affix knowledge with smaller number of items and with the same or greater precision than the previous versions of the WPLT. Pedagogical implications for using the CAT-WPLT are discussed along with issues in utilizing computer adaptivity.