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It is widely believed that a joint factor analysis of item responses and response time (RT) may yield more precise ability scores that are conventionally predicted from responses only. For this purpose, a simple-structure factor model is often preferred as it only requires specifying an additional measurement model for item-level RT while leaving the original item response theory (IRT) model for responses intact. The added speed factor indicated by item-level RT correlates with the ability factor in the IRT model, allowing RT data to carry additional information about respondents' ability. However, parametric simple-structure factor models are often restrictive and fit poorly to empirical data, which prompts under-confidence in the suitablity of a simple factor structure. In the present paper, we analyze the 2015 Programme for International Student Assessment mathematics data using a semiparametric simple-structure model. We conclude that a simple factor structure attains a decent fit after further parametric assumptions in the measurement model are sufficiently relaxed. Furthermore, our semiparametric model implies that the association between latent ability and speed/slowness is strong in the population, but the form of association is nonlinear. It follows that scoring based on the fitted model can substantially improve the precision of ability scores. [ABSTRACT FROM AUTHOR]

Latent class analysis (LCA) assigns individuals to mutually exclusive classes based on response patterns to a set of indicators. A primary assumption made is local independence, which suggests class indicators are uncorrelated within each class. When the indicators are correlated and unmodeled, parameter estimates can be severely biased. We provide a comprehensive resource for applied researchers to statistically detect local independence violations and model identified correlated residuals. We explain the local independence assumption and illustrate how to detect and model conditional dependence using maximum likelihood (ML) and Bayesian estimation. For ML, we discuss two detection methods (bivariate residual associations, and the modification index) and one modeling technique (LCA residual associations model). We also demonstrate how to use the restrictive prior strategy to detect and model conditional dependence when using Bayesian estimation. These techniques are illustrated with simulated datasets; code is provided in the online supplemental materials. [ABSTRACT FROM AUTHOR]

In educational measurement, various methods have been proposed to infer student proficiency from the ratings of multiple items (e.g., essays) by multiple raters. However, suitable models quickly become numerically demanding or even unfeasible as separate latent variables are needed to account for local dependencies between the ratings of the same response. Therefore, in the present paper we derive a flexible approach based on Thurstone’s law of categorical judgment. The advantage of this approach is that it can be fit using weighted least squares estimation which is computationally less demanding as compared to most of the previous approaches in the case of an increasing number of latent variables. In addition, the new approach can be applied using existing latent variable modeling software. We illustrate the model on a real dataset from the Trends in International Mathematics and Science Study (TIMMSS) comprising ratings of 10 items by 4 raters for 150 subjects. In addition, we compare the new model to existing models including the facet model, the hierarchical rater model, and the hierarchical rater latent class model.

Local item dependence (LID) is one of the most critical assumption in the Rasch model when it comes to the validity of a test. As the field of vocabulary assessment is calling for more clarity and validity for vocabulary tests, such assumption becomes more important than ever. The article offers a Rasch-based investigation into the issue of LID with the focus on the two popular formats of Vocabulary Levels Tests (VLT): multiple-choice and matching. A Listening Vocabulary Levels Test (LVLT) and an Updated Vocabulary Levels Test (UVLT) were given to a single cohort of 311 university students in an English as a Foreign Language (EFL) context. The analyses of raw score and standardized residuals correlations were conducted. The findings found no relationship between the 4-option, multiple-choice format of the LVLT and item local dependence. However, results from score and standardized residuals correlations analyses showed a strong link between the 3-item-per-cluster, matching format and item local dependence. The study calls for greater attention to the format of future vocabulary tests and support the use of meaning-recall formats in vocabulary testing.

Local item dependence (LID) is one of the most critical assumption in the Rasch model when it comes to the validity of a test. As the field of vocabulary assessment is calling for more clarity and validity for vocabulary tests, such assumption becomes more important than ever. The article offers a Rasch-based investigation into the issue of LID with the focus on the two popular formats of Vocabulary Levels Tests (VLT): multiple-choice and matching. A Listening Vocabulary Levels Test (LVLT) and an Updated Vocabulary Levels Test (UVLT) were given to a single cohort of 311 university students in an English as a Foreign Language (EFL) context. The analyses of raw score and standardized residuals correlations were conducted. The findings found no relationship between the 4-option, multiple-choice format of the LVLT and item local dependence. However, results from score and standardized residuals correlations analyses showed a strong link between the 3-item-per-cluster, matching format and item local dependence. The study calls for greater attention to the format of future vocabulary tests and support the use of meaning-recall formats in vocabulary testing. [ABSTRACT FROM AUTHOR]

Estimation of parameters for the many-facets Rasch model requires that conditional on the values of the facets, such as person ability, item difficulty, and rater severity, the observed responses within each facet are independent. This requirement has often been discussed for the Rasch models and 2PL and 3PL models, but it becomes more complex with more facets. To show how violation of conditional independence may be exhibited, three scenarios with different types of dependency are developed: (a) raters rating the same work, (b) a residual ability shared by two tasks, and (c) score on one task dependent on observed score on a previous task. [ABSTRACT FROM AUTHOR]

New decentralization regim implemented since 1 January 2001. Decentralization try to increase the improvement of public services. Based on the statictic data, Java recorded a major contributor to national GDP growth with many challenge on poverty and inequality. Using the share and growth analysis as well as quadrant method, this research tries to provide mapping economy potency and regional independency in Java. From the share analysis, still dominated by provincial government, which the biggest is DKI Jakarta Province. From the smallest share, Gunung Kidul Regency is the weakest independency. Based on growth analysis, Jepara Regency, Tangerang, Ngawi, Mojokerto and Tangsel City became the largest. From the quadrant method, 15 regions are in quadrant I, about 19 other areas are in quadrant II and 37 areas are in quadrant III. And largest part of the Java could classify in quadrant IV. According on these findings, central government must producing several policy to speed up the development regions particularly on all regional in quadrant IV.

Over the past decades, the application of Rasch measurement in language assessment has gradually increased. In the present study, we coded 215 papers using Rasch measurement published in 21 applied linguistics journals for multiple features. We found that seven Rasch models and 23 software packages were adopted in these papers, with many-facet Rasch measurement (n = 100) and Facets (n = 113) being the most frequently used Rasch model and software, respectively. Significant differences were detected between the number of papers that applied Rasch measurement to different language skills and components, with writing (n = 63) and grammar (n = 12) being the most and least frequently investigated, respectively. In addition, significant differences were found between the number of papers reporting person separation (n = 73, not reported: n = 142) and item separation (n = 59, not reported: n = 156) and those that did not. An alarming finding was how few papers reported unidimensionality check (n = 57 vs 158) and local independence (n = 19 vs 196). Finally, a multilayer network analysis revealed that research involving Rasch measurement has created two major discrete communities of practice (clusters), which can be characterized by features such as language skills, the Rasch models used, and the reporting of item reliability/separation vs person reliability/separation. Cluster 1 was accordingly labelled the production and performance cluster, whereas cluster 2 was labelled the perception and language elements cluster. Guidelines and recommendations for analyzing unidimensionality, local independence, data-to-model fit, and reliability in Rasch model analysis are proposed. [ABSTRACT FROM AUTHOR]

Local independence is a central assumption of commonly used item response theory models. Violations of this assumption are usually tested using test statistics based on item pairs. This study presents two quasi-exact tests based on the Q 3 statistic for testing the hypothesis of local independence in the Rasch model. The proposed tests do not require the estimation of item parameters and can also be applied to small data sets. The authors evaluate the tests with three simulation studies. Their results indicate that the quasi-exact tests hold their alpha level under the Rasch model and have higher power against different forms of local dependence than several alternative parametric and nonparametric model tests for local independence. [ABSTRACT FROM AUTHOR]

Abstract Background As the worldwide prevalence of chronic illness increases so too does the demand for novel treatments to improve chronic illness care. Quantifying improvement in chronic illness care from the patient perspective relies on the use of validated patient-reported outcome measures. In this analysis we examine the psychometric and scaling properties of the Patient Assessment of Chronic Illness Care (PACIC) questionnaire for use in the United Kingdom by applying scale data to the non-parametric Mokken double monotonicity model. Methods Data from 1849 patients with long-term conditions in the UK who completed the 20-item PACIC were analysed using Mokken analysis. A three-stage analysis examined the questionnaire’s scalability, monotonicity and item ordering. An automated item selection procedure was used to assess the factor structure of the scale. Analysis was conducted in an ‘evaluation’ dataset (n = 956) and results were confirmed using an independent ‘validation’ (n = 890) dataset. Results Automated item selection procedures suggested that the 20 items represented a single underlying trait representing “patient assessment of chronic illness care”: this contrasts with the multiple domains originally proposed. Six items violated invariant item ordering and were removed. The final 13-item scale had no further issues in either the evaluation or validation samples, including excellent scalability (Ho = .50) and reliability (Rho = .88). Conclusions Following some modification, the 13-items of the PACIC were successfully fitted to the non-parametric Mokken model. These items have psychometrically robust and produce a single ordinal summary score. This score will be useful for clinicians or researchers to assess the quality of chronic illness care from the patient's perspective.

Item response theory(IRT) has so many advantages than its precedent Classical Test Theory(CTT) such as non-changing item parameters, ability parameter estimations free from the items. However, in order to get these advantages, some assumptions should be met and they are; unidimensionality, normality and local independence. However, it is not always so easy to be met these assumptions by datasets. Especially when the normality of data is not provided, another approach for IRT can be applied, which is Non-Parametric Item Response Theory (NIRT). NIRT provides more flexible methods to scale datasets and it is used when the assumptions of Parametric Item Response Theory (PIRT) are not met at a satisfactory level. The assumption of local independence, is one of the situations in which NIRT can be used more effectively than PIRT. In this study, by using a real dataset, taken from TIMSS 2011, the effect of local dependence on the item parameters was investigated. With this goal, a dataset composed of 1,000 students was formed randomly from the TIMSS 2011 eight grade Mathematic test. Firstly, the item parameters were calculated from data set according to the two approaches without any manipulation. After that, two items were arranged as local dependent by changing the response patterns completely the same and the item parameters have been estimated from each sample by using R program, ltm and mokken packages. Two sets of item parameters estimated from data set were compared and the differences of the parameters were analyzed with statistical test. By this way, the effect of local independence has been analyzed on the item parameters have been decided. [ABSTRACT FROM AUTHOR]

Nonparametric cognitive diagnosis methods are useful in cognitive diagnosis modeling for calibration efficiency, especially when sample size is small or large, or the latent attributes are more complex. This article proposes the Mantel-Haenszel chi-squared statistic as an index for detecting the misspecification of latent attributes as well as testlet effects in nonparametric cognitive diagnosis methods. The proposed theoretical considerations are augmented by simulation studies conducted to assess the performance of the Mantel-Haenszel statistic under various conditions within the nonparametric diagnosis framework, with a special focus on situations were the set of latent abilities assumed to underlie the data was underspecified. [ABSTRACT FROM AUTHOR]

Knowledge space theory (KST) structures are introduced within item response theory (IRT) as a possible way to model local dependence between items. The aim of this paper is threefold: firstly, to generalize the usual characterization of local independence without introducing new parameters; secondly, to merge the information provided by the IRT and KST perspectives; and thirdly, to contribute to the literature that bridges continuous and discrete theories of assessment. In detail, connections are established between the KST simple learning model (SLM) and the IRT General Graded Response Model, and between the KST Basic Local Independence Model and IRT models in general. As a consequence, local independence is generalized to account for the existence of prerequisite relations between the items, IRT models become a subset of KST models, IRT likelihood functions can be generalized to broader families, and the issues of local dependence and dimensionality are partially disentangled. Models are discussed for both dichotomous and polytomous items and conclusions are drawn on their interpretation. Considerations on possible consequences in terms of model identifiability and estimation procedures are also provided. [ABSTRACT FROM AUTHOR]

Indonesia has been running a decentralized reform era since 1 January 2001. The goal of decentralization is to accelerate the realization of prosperity through the improvement of public services. Fiscal decentralization then became the main benchmark of the successful creation of indicators of regional autonomy in addressing various problems in the regions, especially inequality and poverty. According to government data, Sumatra recorded a major contributor to national GDP growth while facing poverty, inequality constraints and also the largest mining sector. By using the shared and growth analysis and quadrant method, this research tries to provide policy-making recommendations based on the characteristics of the regions in Sumatra. Based on shared analysis, the biggest region is Bengkalis Regency, Riau Province, Siak Regency, Rokan Hilir Regency, and Musi Banyuasin Regency. For the smallest region, it consists of West Nias Regency, North Nias, Toba Samosir, Gunung Sitoli and South Nias. From the growth analysis, it was found that Mesuji Regency, Southeast Aceh Padang Lawas Utara, Kota Padang Panjang and West Nias Regency have the highest growth in Indonesia. From the quadrant method, 20 regions are in quadrant I, about 54 other areas are in quadrant II, and 35 areas are in quadrant III. The largest part of the regional classification in the region of Sumatra is in quadrant IV. Based on these findings, fiscal policy in the future should be prioritized to develop on regional eradication in quadrant IV in terms of Transfer to Region.

The advantages of using person location estimates from the Rasch model over raw scores for the measurement of change using a common test include the linearization of scores and the automatic handling of statistical properties of repeated measurements. However, the application of the model requires that the responses to the items are statistically independent in the sense that the specific responses to the items on the first time of testing do not affect the responses at a second time. This requirement implies that the responses to the items at both times of assessment are governed only by the invariant location parameters of the items at the two times of testing and the location parameters of each person each time. A specific form of dependence that is pertinent when the same items are used is when the observed response to an item at the second time of testing is affected by the response to the same item at the first time, a form of dependence which has been referred to as response dependence. This paper presents the logic of applying the Rasch model to quantify, control and remove the effect of response dependence in the measurement of change when the same items are used on two occasions. The logic is illustrated with four sets of simulation studies with dichotomous items and with a small example of real data. It is shown that the presence of response dependence can reduce the evidence of change, a reduction which may impact interpretations at the individual, research, and policy levels. [ABSTRACT FROM AUTHOR]

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies. [ABSTRACT FROM AUTHOR]

If items have different levels of difficulty (or sensitivity) relative to some psychological attribute, passing (or endorsing) any one cannot mean the same about a person as passing any other, so percent of items passed regardless of which these are cannot indicate a person's level on any attribute. If persons have different levels on a psychological attribute, an item's being passed by one person cannot mean the same about its difficulty level as being passed by any other person, so percent of persons passing it regardless of which persons these are cannot indicate the item's difficulty level. Percent of items passed by a person and percent of persons passing an item are incommensurate quantities not expressible in terms of the same quality or dimension. Both such percents are dependent on what sample of items and of persons are used. A person's attribute level is not demonstrably probabilistic, because truly independent replicate occasions of a person responding to an item are impossible. Passing an item depends on more than a person's single attribute level, the item's difficulty level, and random chance. On all these matters Item Response Theory relies on assumptions that are logically unjustifiable. [ABSTRACT FROM AUTHOR]