Exploratory analysis of contingency tables by loglinear formulation and generalizations of correspondence analysis

Goodman's (1979, 1981, 1985) loglinear formulation for bi-way contingency tables is extended to tables with or without missing cells and is used for exploratory purposes. A similar formulation is done for three-way tables and generalizations of correspondence analysis are deduced. A generalized version of Goodman's algorithm, based on Newton's elementary unidimensional method is used to estimate the scores in all cases.This research was partially supported by National Science and Engineering Research Council of Canada, Grant No. A8724. The author is grateful to the reviewers and the editor for helpful comments.     

An estimate of the covariance between variables which are not jointly observed

Situations sometimes arise in which variables collected in a study are not jointly observed. This typically occurs because of study design. An example is an equating study where distinct groups of subjects are administered different sections of a test. In the normal maximum likelihood function to estimate the covariance matrix among all variables, elements corresponding to those that are not jointly observed are unidentified. If a factor analysis model holds for the variables, however, then all sections of the matrix can be accurately estimated, using the fact that the covariances are a function of the factor loadings. Standard errors of the estimated covariances can be obtained by the delta method. In addition to estimating the covariance matrix in this design, the method can be applied to other problems such as regression factor analysis. Two examples are presented to illustrate the method. This research was partially supported by NIMH grant MH5-4576     

Où en est t’on du Big Data en médecine ?

Opening of medical datas and files is nowadays an important challenge. French Government increments new rules to allow access to these data files.     

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.     

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.     

“Sherry’s Secret”

The case and commentaries below were developed as part of a project, Graduate Research Ethics Education, undertaken by the Association for Practical and Professional Ethics with funding from the National Science Foundation (NSF Grant No. SBR 9421897 and NSF Grant No. 9817880). The project aims at training graduate students in research ethics and building a community of scientists and engineers who are interested in and capable of teaching research ethics. As part of the project, each graduate student participant develops a case for use in teaching and writes a commentary to go with the case, and then a staff member is asked to write additional commentary on the case. The case below was written in the second year of the project and was published in Research Ethics: Cases and Commentaries edited by B. Schrag, Association for Practical and Professional Ethics, Bloomington, Indiana, Vol. II (1998). Publication of these cases and commentaries will be a recurring feature of Science and Engineering Ethics.     

The Short Mood and Feelings Questionnaire (SMFQ): A Unidimensional Item Response Theory and Categorical Data Factor Analysis of Self-Report Ratings from a Community Sample of 7-through 11-Year-Old Children

Item response theory (IRT) and categorical data factor analysis (CDFA) are complementary methods for the analysis of the psychometric properties of psychiatric measures that purport to measure latent constructs. These methods have been applied to relatively few child and adolescent measures. We provide the first combined IRT and CDFA analysis of a clinical measure (the Short Mood and Feelings Questionnaire—SMFQ) in a community sample of 7-through 11-year-old children. Both latent variable models supported the internal construct validity of a single underlying continuum of severity of depressive symptoms. SMFQ items discriminated well at the more severe end of the depressive latent trait. Item performance was not affected by age, although age correlated significantly with latent SMFQ scores suggesting that symptom severity increased within the age period of 7–11. These results extend existing psychometric studies of the SMFQ and confirm its scaling properties as a potential dimensional measure of symptom severity of childhood depression in community samples.