Revisiting das gupta: Refinement and extension of standardization and decomposition

Standardization and decomposition are established and widely used demographic techniques for comparing rates and means between groups with differences in composition. The difference in rates and means has heretofore been resolved in terms of the contribution of variables to compositional effects for each variable and an overall rate effect. This study demonstrates that the resolution of differences is attainable at the categorical level for both compositional effects and rate effects. Refinements to Das Gupta’s equations yield a complete decomposition because of the additivity of categorical compositional and rate effects. Other refinements allow the decomposition of polytomous variables. Extensions to the method provide for the decomposition of the standard deviation and the multivariate index of dissimilarity.     

Some Cautions on the Measurement of User Information Satisfaction*

User information satisfaction (UIS) is important because of its potential effects on MIS department goals, quality of user work life, and extent of voluntary usage of systems. Reliable measurement of UIS is important for providing evaluative information for both researchers and practitioners. This study used 92 managers and executives as subjects to compare the test/retest reliability of a widely used, 13-scale UIS instrument together with four summary questions under experimental and control conditions. The summary questions behaved more reliably than the detailed questions for all groups, perhaps because of problems with scale units and origins and with item heterogeneity. This suggests that researchers need more reliable measures of UIS and that practitioners need to exercise caution when collecting and interpreting UIS scores.     

A Literature Review of Organizational Behavior Management Interventions in Human Service Settings from 1990 to 2016

We reviewed the Journal of Applied Behavior Analysis (JABA), Journal of Organizational Behavior Management (JOBM), and Behavior Analysis in Practice (BAP) from 1990 to 2016, to identify articles that evaluated organizational behavior management interventions in a human service setting. Of those articles, 75 articles met the inclusion criteria for the review, 44 from JABA (1990 to 2016), 22 from JOBM (1990 to 2016), and 7 from BAP (2008 to 2016). We categorized each selected article by setting, employee population, client population, assessment, dependent variable, independent variable, and outcome measures. Results from the review are discussed for all three journals. Recommendations are made to broaden the scope of population and dependent variable targets, include more assessments, and include outcome data when applicable.     

An empirical assessment of the operational performance through internal benchmarking: a case of a global logistics firm

This article solves an operational performance measurement problem of a global logistics firm through an internal benchmarking tool. The intended impact is to enable logistics firms to form a deeper understanding of their own internal processes and metrics. The methodology of this in-depth action research involves a sequential approach with a series of interviews, questionnaire-based surveys, operations data collated through observations and process mapping yielding real-world data. A series of statistical tests are conducted to analyse the collated data. Strategic priorities of the firm are integrated with the firm’s operational performance to ascertain the effective performance by considering both the tangible and intangible measures. The outcomes inform both practitioners and academics how the firm could improve its freight forwarding business’s profitability by ensuring that its operations meet the prioritised criteria. The ‘best practice’ derived from internal benchmarking forms an intermediate step towards external benchmarking. The outcomes facilitate investigating the current business strategy, the standard operating procedures and the scope of improving those.     

Institutional voids,international learning effort and internationalization of emerging market new ventures

Much of the existing scholarly works portray institutional voids (IVs) in emerging economies as impeding forces against the development of new ventures. However, little attention has been paid to how such voids generate positive outcomes in emerging market new ventures. Drawing on the institutional theory, we propose IVs as crucial enablers of new venture internationalization. In addition, we investigate both how and when IVs enhance the degree to which new ventures internationalize by examining international learning effort (ILE) as a mediator and two domestic market environmental factors (i.e., environmental dynamism and competitive intensity) as important contingencies. We test our moderated mediation model using primary data gathered from 211 new ventures from Ghana. We found that ILE mediates the relationship between IVs and new venture internationalization and that both environmental dynamism and competitive intensity moderate the indirect relationship between home-country IVs and new venture internationalization. We discuss the theoretical and practical implications of this study.     

Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose‐Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose‐Response Analysis

Quantitative risk assessments for physical, chemical, biological, occupational, or environmental agents rely on scientific studies to support their conclusions. These studies often include relatively few observations, and, as a result, models used to characterize the risk may include large amounts of uncertainty. The motivation, development, and assessment of new methods for risk assessment is facilitated by the availability of a set of experimental studies that span a range of dose‐response patterns that are observed in practice. We describe construction of such a historical database focusing on quantal data in chemical risk assessment, and we employ this database to develop priors in Bayesian analyses. The database is assembled from a variety of existing toxicological data sources and contains 733 separate quantal dose‐response data sets. As an illustration of the database's use, prior distributions for individual model parameters in Bayesian dose‐response analysis are constructed. Results indicate that including prior information based on curated historical data in quantitative risk assessments may help stabilize eventual point estimates, producing dose‐response functions that are more stable and precisely estimated. These in turn produce potency estimates that share the same benefit. We are confident that quantitative risk analysts will find many other applications and issues to explore using this database.     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

A two-sample empirical likelihood ratio test based on samples entropy

Powerful entropy-based tests for normality, uniformity and exponentiality have been well addressed in the statistical literature. The density-based empirical likelihood approach improves the performance of these tests for goodness-of-fit, forming them into approximate likelihood ratios. This method is extended to develop two-sample empirical likelihood approximations to optimal parametric likelihood ratios, resulting in an efficient test based on samples entropy. The proposed and examined distribution-free two-sample test is shown to be very competitive with well-known nonparametric tests. For example, the new test has high and stable power detecting a nonconstant shift in the two-sample problem, when Wilcoxon’s test may break down completely. This is partly due to the inherent structure developed within Neyman-Pearson type lemmas. The outputs of an extensive Monte Carlo analysis and real data example support our theoretical results. The Monte Carlo simulation study indicates that the proposed test compares favorably with the standard procedures, for a wide range of null and alternative distributions.