Background Although insomnia is a prevalent complaint with significant morbidity, it often remains unrecognized and untreated. Brief and valid instruments are needed both for screening and outcome assessment. This study examined psychometric indices of the Insomnia Severity Index (ISI) to detect cases of insomnia in a population-based sample and to evaluate treatment response in a clinical sample. Methods Participants were 959 individuals selected from the community for an epidemiological study of insomnia (Community sample) and 183 individuals evaluated for insomnia treatment and 62 controls without insomnia (Clinical sample). They completed the ISI and several measures of sleep quality, fatigue, psychological symptoms, and quality of life; those in the Clinical sample also completed sleep diaries, polysomnography, and interviews to validate their insomnia/good sleep status and assess treatment response. In addition to standard psychometric indices of reliability and validity, item response theory analyses were computed to examine ISI item response patterns. Receiver operating curves were used to derive optimal cutoff scores for case identification and to quantify the minimally important changes in relation to global improvement ratings obtained by an independent assessor. Results ISI internal consistency was excellent for both samples (Cronbach α of 0.90 and 0.91). Item response analyses revealed adequate discriminatory capacity for 5 of the 7 items. Convergent validity was supported by significant correlations between total ISI score and measures of fatigue, quality of life, anxiety, and depression. A cutoff score of 10 was optimal (86.1% sensitivity and 87.7% specificity) for detecting insomnia cases in the community sample. In the clinical sample, a change score of -8.4 points (95% CI: -7.1, -9.4) was associated with moderate improvement as rated by an independent assessor after treatment. Conclusion These findings provide further evidence that the ISI is a reliable and valid instrument to detect cases of insomnia in the population and is sensitive to treatment response in clinical patients.

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The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response.

/pdf/international-association-for-the-evaluation-of-educational-2wz56f9flr.pdf

International Association for the Evaluation of Educational Achievement

School Leadership that Works: From Research to Results

Generalized Linear Models (2nd ed.)

In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.

/pdf/internet-addiction-a-systematic-review-of-epidemiological-qbvmoxa16f.pdf

Internet addiction: a systematic review of epidemiological research for the last decade.

A multidimensional Rasch-type item response model, the multidimensional random coefficients multinomial logit model, is presented as an extension to the Adams & Wilson (1996) random coefficients multinomial logit model. The model is developed in a form that permits generalization to the multidimensional case of a wide class of Rasch models, including the simple logistic model, Masters' partial credit model, Wilson's ordered partition model, and Fischer's linear logistic model. Moreover, the model includes several existing multidimensional models as special cases, including Whitely's multicomponent latent trait model, Andersen's multidimensional Rasch model for repeated testing, and Embretson's multidimensional Rasch model for learning and change. Marginal maximum likelihood estimators for the model are derived and the estimation is examined using a simulation study. Implications and applications of the model are discussed and an example is given.

The Multidimensional Random Coefficients Multinomial Logit Model

The Rasch testlet model for both dichotomous and polytomous items in testlet-based tests is proposed. It can be viewed as a special case of the multidimensional random coefficients multinomial logit model (MRCMLM). Therefore, the estimation procedures for the MRCMLM can be directly applied. Simulations were conducted to examine parameter recovery under the dichotomous Rasch testlet model and the partial-credit testlet model. Results indicated that the item and person parameters as well as the random testlet effects could be recovered very accurately under all the simulated conditions. As sample sizes were increased, the root mean square errors of the estimates decreased to an acceptable level. An empirical example of an English test with 11 testlets was given. Index terms: multidimensional item response model, item bundle, marginal maximum likelihood estimation, parameter recovery.

The Rasch Testlet Model

Through simulations, this study investigates the effects of anchor item methods on Type I error and power of detecting differential item functioning (DIF) using the likelihood ratio test within the framework of item response theory. Four anchor item methods were compared: the all-other, 1-item, 4-item, and 10-item methods. The results showed that it is the average signed area between the reference and focal groups rather than the percentage of DIF items in a test that determines the Type I error of the all-other method. The all-other method yields good control over Type I error and reasonable power only when the average signed area approaches zero. The all-other method is not recommended for practical DIF analysis because it is only adequate under very stringent conditions. The other three methods perform appropriately under all the simulated conditions. The more anchor items are used, the higher the power of DIF detection.

Effects of Anchor Item Methods on Differential Item Functioning Detection with the Likelihood Ratio Test

A conventional way to analyze item responses in multiple tests is to apply unidimensional item response models separately, one test at a time. This unidimensional approach, which ignores the correlations between latent traits, yields imprecise measures when tests are short. To resolve this problem, one can use multidimensional item response models that use correlations between latent traits to improve measurement precision of individual latent traits. The improvements are demonstrated using 2 empirical examples. It appears that the multidimensional approach improves measurement precision substantially, especially when tests are short and the number of tests is large. To achieve the same measurement precision, the multidimensional approach needs less than half of the comparable items required for the unidimensional approach.

Improving measurement precision of test batteries using multidimensional item response models.

In this study we investigated the effects of the average signed area (ASA) between the item characteristic curves of the reference and focal groups and three test purification procedures on the uniform differential item functioning (DIF) detection via the Mantel-Haenszel (M-H) method through Monte Carlo simulations. The results showed that ASA, rather than the percentage of DIF items in the test, determines the performances of the conventional one-stage M-H method. For example, the M-H method performed appropriately even when there were 50% DIF items in the test, as long as ASA approaches 0. Under most of the simulated conditions, the two-stage and iterative M-H methods performed much superior to the one-stage M-H method. When tests were short and the mean abilities of the reference and focal groups were far apart, all the three M-H methods yielded inflated Type I error under the two-parameter or three-parameter logistic model. For most of the other situations, the two-stage and iterative M-H methods can ...

Effects of Average Signed Area Between Two Item Characteristic Curves and Test Purification Procedures on the DIF Detection via the Mantel-Haenszel Method.

Wen Chung Wang

Papers

The Multidimensional Random Coefficients Multinomial Logit Model

The Rasch Testlet Model

Effects of Anchor Item Methods on Differential Item Functioning Detection with the Likelihood Ratio Test

Improving measurement precision of test batteries using multidimensional item response models.

Effects of Average Signed Area Between Two Item Characteristic Curves and Test Purification Procedures on the DIF Detection via the Mantel-Haenszel Method.