Validation of immigrants’ prior foreign learning as a framing practice

This paper explores the attempts by public officials and caseworkers to manage an overflow of immigrants in the labour market in Sweden. I draw on notions of framing and overflowing, inspired by Michel Callon's (1998) work on the organizing of market-based exchange relationships. I argue that validation is best understood as a framing practice, aimed at creating an understanding for the vastness of foreign experience – including skills and competence – of recent immigrants to Sweden, to make this experience measurable and manageable. Validation as a framing practice thereby exemplifies the widespread trust in framing as a way to normalise overflows – to turn overflows into normal flows. However, as the ethnography-inspired study reported here shows, repeated framing does not remove overflows; instead, it produces new and different types of overflows. In the conclusions, I emphasise the heavy investments required by validation in contrast with the fragility of the results it produces in the context of migration management.     

Validation of Nonparametric Two-sample Bootstrap in ROC Analysis on Large Datasets

The nonparametric two-sample bootstrap is applied to computing uncertainties of measures in receiver operating characteristic (ROC) analysis on large datasets in areas such as biometrics, speaker recognition, etc. when the analytical method cannot be used. Its validation was studied by computing the standard errors of the area under ROC curve using the well-established analytical Mann–Whitney statistic method and also using the bootstrap. The analytical result is unique. The bootstrap results are expressed as a probability distribution due to its stochastic nature. The comparisons were carried out using relative errors and hypothesis testing. These match very well. This validation provides a sound foundation for such computations.     

Toward an integrated Bayesian network approach to measurement error detection and correction

In this article, the quality of data produced by national statistical institutes and by governmental institutions is considered. In particular, the problem of measurement error is analyzed and an integrated Bayesian network decision support system based on non-parametric Bayesian networks is proposed for its detection and correction. Non-parametric Bayesian networks are graphical models expressing dependence structure via bivariate copulas associated to the edges of the graph. The network structure and the misreport probability are estimated using a validation sample. The Bayesian network model is proposed to decide: (i) which records have to be corrected; (ii) the kind and amount of correction to be adopted. The proposed correction procedure is applied to the Banca d’Italia Survey on Household Income and Wealth and, specifically, the bond amounts are analyzed. Finally, the sensitivity of the conditional distribution of the true value random variable given the observed one to different evidence configurations is studied.     

Validation of the Australian Midwifery Standards Assessment Tool (AMSAT): A tool to assess midwifery competence

Background

There is no current validated clinical assessment tool to measure the attainment of midwifery student competence in the midwifery practice setting. The lack of a valid assessment tool has led to a proliferation of tools and inconsistency in assessment of, and feedback on student learning.

A unified approach to regression analysis under double-sampling designs

We propose a unified approach to the estimation of regression parameters under double-sampling designs, in which a primary sample consisting of data on the rough or proxy measures for the response and/or explanatory variables as well as a validation subsample consisting of data on the exact measurements are available. We assume that the validation sample is a simple random subsample from the primary sample. Our proposal utilizes a specific parametric model to extract the partial information contained in the primary sample. The resulting estimator is consistent even if such a model is misspecified, and it achieves higher asymptotic efficiency than the estimator based only on the validation data. Specific cases are discussed to illustrate the application of the estimator proposed.     

The Development and Initial Validation of a Psychoanalysis Use/Non-Use Scale among a Sample of Hong Kong Helping Professionals.

ABSTRACT

This article reports on the construction and initial validation of the Psychoanalysis Use/Non-Use scale (PUNU) and the complementary Conflicts with Hong Kong Chinese Culture scale (CHKCC), developed for assessing the penetration of psychoanalysis among counseling professionals in Hong Kong. A total of 217 Hong Kong social workers, psychologists, and counselors were surveyed to assess the scales' internal psychometric properties. Factor analysis confirmed a four-factor structure for the PUNU, and a unidimensional structure for the CHKCC, with 17 and 9 items, respectively. Internal reliability of both scales was good and discriminative validity was supported by comparisons with background information. These scales could help to identify factors that affect the understanding of psychoanalysis among professionals and could be useful for educational purposes.     

Critical evaluation of simulation studies in maintenance systems.

The purpose of this paper is to examine how far simulation studies of maintenance systems are neglecting simulation-related statistical issues. This negligence may leave simulation results suspicious and hard to explain. Several simulation factors are used to identify the strength of executed simulation experiments and to evaluate the level of clarity and reliability of the simulation models. The factors includes purpose of simulation, simulation model, model assumptions, distribution and random variables, simulation languages and computers, program verification and model validation, design of experiment, and analysis of the output. For this purpose, the literature is reviewed and subjected to the evaluation. It is observed that most papers define clearly their objectives, simulation languages, and model performance measures. However, verification, validation, experimental design, and output analysis are the most unclear factors.     

Parameterization and inference for nonparametric regression problems

We consider local likelihood or local estimating equations, in which a multivariate function () is estimated but a derived function () of () is of interest. In many applications, when most naturally formulated the derived function is a non-linear function of (). In trying to understand whether the derived non-linear function is constant or linear, a problem arises with this approach: when the function is actually constant or linear, the expectation of the function estimate need not be constant or linear, at least to second order. In such circumstances, the simplest standard methods in nonparametric regression for testing whether a function is constant or linear cannot be applied. We develop a simple general solution which is applicable to nonparametric regression, varying-coefficient models, nonparametric generalized linear models, etc. We show that, in local linear kernel regression, inference about the derived function () is facilitated without a loss of power by reparameterization so that () is itself a component of (). Our approach is in contrast with the standard practice of choosing () for convenience and allowing ()> to be a non-linear function of (). The methods are applied to an important data set in nutritional epidemiology.     

Failure time regression with continuous covariates measured with error

We consider failure time regression analysis with an auxiliary variable in the presence of a validation sample. We extend the nonparametric inference procedure of Zhou and Pepe to handle a continuous auxiliary or proxy covariate. We estimate the induced relative risk function with a kernel smoother and allow the selection probability of the validation set to depend on the observed covariates. We present some asymptotic properties for the kernel estimator and provide some simulation results. The method proposed is illustrated with a data set from an on-going epidemiologic study.     

Development and validation of the Legitimate Monitoring and Control Questionnaire (LMCQ)

In this paper, we present the multidimensional Legitimate Monitoring and Control Questionnaire (LMCQ), which is based on social-exchange and institutional theory. Our aim was to develop and validate a widely applicable leadership inventory that accounts for comparable criterion variance as transformational leadership. While transformational leadership scales emphasize charismatic or visionary behavior, the basis for the LMCQ is the belief that perceptions of control legitimacy are essential and lead to high-quality social-exchange relationships between supervisors and subordinates. To build the dimensions comprising the LMCQ, an exploratory study (study 1, 38 respondents) was conducted to investigate which kinds of socioemotional benefits actually drive subordinates' perceptions of control legitimacy. The interview data were used to compile an initial item pool that was condensed as a subsequent step (study 2, 494 respondents). Lastly, the resulting measurement instrument representing six dimensions of legitimacy-enhancing supervisory behavior was validated (study 3, 936 respondents).