Inferences on the Means of Two Log-Normal Distributions: A Computational Approach Test

In this article, we propose a novel approach for testing the equality of two log-normal populations using a computational approach test (CAT) that does not require explicit knowledge of the sampling distribution of the test statistic. Simulation studies demonstrate that the proposed approach can perform hypothesis testing with satisfying actual size even at small sample sizes. Overall, it is superior to other existing methods. Also, a CAT is proposed for testing about reliability of two log-normal populations when the means are the same. Simulations show that the actual size of this new approach is close to nominal level and better than the score test. At the end, the proposed methods are illustrated using two examples.     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...     

Standing on Their Own Two Feet: How the New Public Charge Rules Could Impact Non-European LPR Applicants

Population Research and Policy Review - In February 2020, the U.S. government began to implement a new Public Charge rule that greatly expands the definition of “public charge” when...     

Myriad potential for mentoring: Understanding the process of transformational change through a gender equality intervention

This article contributes to understanding transformational change towards gender equality by examining the transformational change potential of a mentoring programme for women, a type of gender equality intervention both criticized and praised for its ability to bring about change. Drawing upon an empirical case study of a mentoring programme for women academics in a Dutch university, we explore three dimensions of transformational change: organizational members (i) discussing and reflecting upon gendered organizational norms and work practices; (ii) creating new narratives; and (iii) experimenting with new work practices. Our findings indicate five specific conditions that enable transformational change: cross‐mentoring, questioning what is taken for granted, repeating participation and individual stories, facilitating peer support networks and addressing and equipping all participants as change agents. We suggest that these conditions should be taken into account when (re)designing effective organizational gender equality interventions.     

Journey towards independent living: a grounded theory investigation of leaving the care of Girls & Boys Town,South Africa

The journey out of care and towards independent living is a challenge for many care-leavers. There has been little research into the social processes involved in this care-leaving journey. This paper presents the results of a grounded theory investigation into the care-leaving journeys of nine young men who had, several years previously, been in the care of Girls & Boys Town in South Africa. Working from a resilience perspective, with an ecological emphasis, four central social processes emerged that together explain the care-leaving experiences of the participants. These processes are striving for authentic belonging; networking people for goal attainment; contextualised responsiveness and building hopeful and tenacious self-confidence. These four processes are located within contextual boundaries and at the social environmental interface. The paper presents these processes in detail, drawing on selected narratives of the participants and integrated with additional theory. It is hoped that this paper may contribute to theory building concerning care-leaving processes and enhance youth care practices for youth in care and leaving care.     

The identification of logistic regression models with errors in the variables

The simple logistic regression model with normal measurement error and normal regressor is shown to be identifiable without any extra information about the measurement error. The multiple logistic regression model with more than one regressor variable measured with error is not identifiable. If the covariance matrix of the measurement error is known up to a scalar factor, the model is identified. Further we discuss why in spite of the identifiability the models cannot be estimated in a reasonable way without extra information about the measurement error.