Adjusting for information inflation due to local dependency in moderately large item clusters

When multiple items are clustered around a reading passage, the local independence assumption in item response theory is often violated. The amount of information contained in an item cluster is usually overestimated if violation of local independence is ignored and items are treated as locally independent when in fact they are not. In this article we provide a general method that adjusts for the inflation of information associated with a test containing item clusters. A computational scheme was presented for the evaluation of the factor of adjustment for clusters in the restrictive case of two items per cluster, and the general case of more than two items per cluster. The methodology was motivated by a study of the NAEP Reading Assessment. We present a simulated study along with an analysis of a NAEP data set.The research was supported under the National Assessment of Educational Progress (Grant No. R999G30002) as administered by the Office of Educational Research and Improvement, U.S. Department of Education. This work was started when the author was at the Division of Statistics and Psychometrics at the Educational Testing Service. The author thanks Juliet Shaffer, Bob Mislevy, Eric Bradlow, three reviewers and an associate editor for their helpful comments on the paper.     

Models for ordinal hierarchical classes analysis

This paper proposes an ordinal generalization of the hierarchical classes model originally proposed by De Boeck and Rosenberg (1998). Any hierarchical classes model implies a decomposition of a two-way two-mode binary arrayM into two component matrices, called bundle matrices, which represent the association relation and the set-theoretical relations among the elements of both modes inM. Whereas the original model restricts the bundle matrices to be binary, the ordinal hierarchical classes model assumes that the bundles are ordinal variables with a prespecified number of values. This generalization results in a classification model with classes ordered along ordinal dimensions. The ordinal hierarchical classes model is shown to subsume Coombs and Kao's (1955) model for nonmetric factor analysis. An algorithm is described to fit the model to a given data set and is subsequently evaluated in an extensive simulation study. An application of the model to student housing data is discussed.     

Social interest,extreme response style,and implications for adjustment

As part of a series of studies examining Adler's proposition that social interest, or interest and concern for others, is a major factor facilitating personal adjustment, the research investigated the relation between social interest and Extreme Response Style (ERS). ERS was chosen as an indirect measure of maladjustment since research has confirmed Adler's contention that it is especially prevalent among neurotic individuals. In three studies involving a total of 221 subjects, inverse relations were found between a measure of social interest and ERS scores derived from four questionnaires concerning attitudes toward self, others, and a wide variety of attitudinal issues. The data also indicated that the results were not due to either apathy or repression being associated with social interest. The results provided further evidence that positive relations between social interest and adjustment are not limited to self-reports of the latter.     

Job crafting and its relationships with person–job fit and meaningfulness: A three-wave study

Although scholars imply that job crafting contributes to person–job fit and meaningful work, to date, no study examined the relationships between these variables. The present three-wave weekbook study was designed to gain more knowledge about the influence of job crafting on person–job fit and meaningfulness. We collected data among a heterogeneous group of employees (N = 114) during three consecutive weeks (N = 430 occasions). At the end of their working week, employees reported their job crafting behaviors, their person–job fit (demands–abilities fit and needs–supplies fit), and the meaningfulness of their work that week. Results indicated that individuals who crafted their job by increasing their job resources (e.g., support, autonomy) and challenging job demands (e.g., participate in new projects), and by decreasing their hindering job demands (e.g., less emotional job demands) reported higher levels of person–job fit the next week. In turn, demands–abilities fit related to more meaningfulness in the final week. No support was found for alternative causal models. These findings suggest that by crafting their job demands and job resources, individuals can proactively optimize their person–job fit and as a consequence experience their work as meaningful.     

How to handle missing data: A comparison of different approaches

Many researchers face the problem of missing data in longitudinal research. Especially, high risk samples are characterized by missing data which can complicate analyses and the interpretation of results. In the current study, our aim was to find the most optimal and best method to deal with the missing data in a specific study with many missing data on the outcome variable. Therefore, different techniques to handle missing data were evaluated, and a solution to efficiently handle substantial amounts of missing data was provided. A simulation study was conducted to determine the most optimal method to deal with the missing data. Results revealed that multiple imputation (MI) using predictive mean matching was the most optimal method with respect to lowest bias and the smallest confidence interval (CI) while maintaining power. Listwise deletion and last observation carried backward also scored acceptable with respect to bias; however, CIs were much larger and sample size almost halved using these methods. Longitudinal research in high risk samples could benefit from using MI in future research to handle missing data. The paper ends with a checklist for handling missing data.     

Disclosure of Tests and Raw Test Data to the Courts: A Need for Reform

Psychologists' current practices with respect to the disclosure of tests and raw test data to courts are damaging their reputations as scientists, undermining their credibility as honest professionals acting in good faith, and contrary to the best interests of consumers. The profession is conducting itself in a contradictory fashion that deserves reform. There is need for an interdisciplinary panel drawn from the American Psychological Association and American Bar Association to develop reasonable procedures for disclosure of tests and test data in legal proceedings. These procedures should be developed with input from experienced psychological expert witnesses, attorneys, and judges from a variety of state and federal jurisdictions, diverse geographical areas, and different types of legal proceedings. This expert panel should also address the issue of attorney coaching of clients in preparation for assessment by psychologists.     

Evaluating interobserver reliability of interval data

Previous recommendations to employ occurrence, nonoccurrence, and overall estimates of interobserver reliability for interval data are reviewed. A rationale for comparing obtained reliability to reliability that would result from a random-chance model is explained. Formulae and graphic functions are presented to allow for the determination of chance agreement for each of the three indices, given any obtained per cent of intervals in which a response is recorded to occur. All indices are interpretable throughout the range of possible obtained values for the per cent of intervals in which a response is recorded. The level of chance agreement simply changes with changing values. Statistical procedures that could be used to determine whether obtained reliability is significantly superior to chance reliability are reviewed. These procedures are rejected because they yield significance levels that are partly a function of sample sizes and because there are no general rules to govern acceptable significance levels depending on the sizes of samples employed.     

La protection des données personnelles des patients face à la modernisation de notre système de santé

With the Internet and the artificial intelligence, the collection of personal data is becoming a strategic element for the digital economy. The collection of information touching the health or the body must respect the patients’ rights. The GDPR came into effect on May 25th, 2018. The healthcare professionals who are established in a Member state, must be careful because they can base their exercise on clinical or biological elements of their patients.