Unique nontransitive measurement on finite sets

Two themes in the theory of measurement that have been studied extensively in the past few years are numerical representations of nontransitive binary comparison structures and uniqueness in finite measurement systems. This paper brings the two together by exploring the solutions to a nontransitive, additive model that are unique up to multiplication by a positive constant. The model relates to various contexts including decision under risk, evaluation of objectives, comparative probability, and voting theory. The family of unique solutions for the model is shown to be extremely rich and varied.     

学科难度与专业劳动复杂程度模糊谱系的建构--以中国大学本科教育为例

本文综合运用心理学、哲学、经济学、教育学的成果,以中国大学本科教育为例,对劳动复杂程度的模糊度量问题进行了探讨.认为专业劳动的复杂度由专业学习难度决定,专业学习难度由其横断学科和经验知识含量决定,而这些含量由专家系统提供的主要课程设置来体现.并由此建立了专业学科难度模糊谱系和相应的专业劳动复杂度模糊谱系.     

Investigating the Structural Validity of Ryff’s Psychological Well-Being Scales Across Two Samples

Ryff’s (1989b) Psychological Well-Being (PWB) scales measure six related constructs of human functioning. The present paper examined the validity of Ryff’s 6-factor PWB model, using data from a life events study (N = 401) and an organisational climate study (N = 679). Previous validation studies, using Confirmatory Factor Analysis (CFA), have identified alternative PWB models, but limitations include the use of shorter scale versions with items relating to a number of life domains within the same PWB factor, and failure to examine the influence of participants’ socio-demographic characteristics on PWB. In this study, Exploratory Factor Analysis (EFA) mostly found consistency in the PWB items and structure between the two studies whereby a 3-factor model delineated between items relating to Autonomy, Positive Relations and a super-ordinate factor comprising the other PWB factors. Using CFA, Goodness of Fit indices reached acceptable levels for the adjusted PWB model identified by the EFA, whilst differences between adjusted models of PWB previously identified in the literature were hardly evident. Post-hoc analysis by gender demonstrated socio-demographic effects on the structure and items that comprise PWB. Further development of PWB measures is needed to reflect its hierarchical and multi-dimensional nature. In the scales’ current form, the construct validation of the PWB factors will continue to be problematic and will fail to adequately evaluate the nature and impact of PWB.     

Measuring Sexual Prejudice Against Gay Men and Lesbian Women: Development of the Sexual Prejudice Scale (SPS)

The presence of bias against gay men and lesbian women remains an ongoing issue, and accurate measurement is essential to targeted intervention. A validation study of a new instrument, the Sexual Prejudice Scale, is reported. Students (N = 851) from 4 different universities participated in this study. An exploratory factor analysis and confirmatory factor analysis were conducted, and results of these analyses indicated a 3-factor solution (affective – valuation, stereotyping, and social equality beliefs) for each of the sex-specific scales. Evidence of validity and the results of the reliability analysis are reported. Implications for future research are discussed.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Estimation of discrete survival function for error‐prone diagnostic tests

The product limit or Kaplan‐Meier (KM) estimator is commonly used to estimate the survival function in the presence of incomplete time to event. Application of this method assumes inherently that the occurrence of an event is known with certainty. However, the clinical diagnosis of an event is often subject to misclassification due to assay error or adjudication error, by which the event is assessed with some uncertainty. In the presence of such errors, the true distribution of the time to first event would not be estimated accurately using the KM method. We develop a method to estimate the true survival distribution by incorporating negative predictive values and positive predictive values, into a KM‐like method of estimation. This allows us to quantify the bias in the KM survival estimates due to the presence of misclassified events in the observed data. We present an unbiased estimator of the true survival function and its variance. Asymptotic properties of the proposed estimators are provided, and these properties are examined through simulations. We demonstrate our methods using data from the Viral Resistance to Antiviral Therapy of Hepatitis C study.     

A statistical procedure for representing state fragility and transition paths

State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.     

Oracle GMM estimation for misspecified models via thresholding

We propose a thresholding generalized method of moments (GMM) estimator for misspecified time series moment condition models. This estimator has the following oracle property: its asymptotic behavior is the same as of any efficient GMM estimator obtained under the a priori information that the true model were known. We propose data adaptive selection methods for thresholding parameter using multiple testing procedures. We determine the limiting null distributions of classical parameter tests and show the consistency of the corresponding block-bootstrap tests used in conjunction with thresholding GMM inference. We present the results of a simulation study for a misspecified instrumental variable regression model and for a vector autoregressive model with measurement error. We illustrate an application of the proposed methodology to data analysis of a real-world dataset.     

Estimation of finite population mean in stratified sampling using scrambled responses in the presence of measurement errors

This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME). A generalized estimator of population mean is proposed by using additively scrambled responses for the sensitive variable. The expressions for the bias and mean square error (MSE) of the proposed estimator are derived. The performance of the proposed estimator is evaluated both theoretically and empirically. Results are also applied to a real data set.     

A new class of measurement-error models,with applications to dietary data

Measurement-error modelling occurs when one cannot observe a covariate, but instead has possibly replicated surrogate versions of this covariate measured with error. The vast majority of the literature in measurement-error modelling assumes (typically with good reason) that given the value of the true but unobserved (latent) covariate, the replicated surrogates are unbiased for latent covariate and conditionally independent. In the area of nutritional epidemiology, there is some evidence from biomarker studies that this simple conditional independence model may break down due to two causes: (a) systematic biases depending on a person's body mass index, and (b) an additional random component of bias, so that the error structure is the same as a one-way random-effects model. We investigate this problem in the context of (1) estimating distribution of usual nutrient intake, (2) estimating the correlation between a nutrient instrument and usual nutrient intake, and (3) estimating the true relative risk from an estimated relative risk using the error-prone covariate. While systematic bias due to body mass index appears to have little effect, the additional random effect in the variance structure is shown to have a potentially important effect on overall results, both on corrections for relative risk estimates and in estimating the distribution of usual nutrient intake. However, the effect of dietary measurement error on both factors is shown via examples to depend strongly on the data set being used. Indeed, one of our data sets suggests that dietary measurement error may be masking a strong risk of fat on breast cancer, while for a second data set this masking is not so clear. Until further understanding of dietary measurement is available, measurement-error corrections must be done on a study-specific basis, sensitivity analyses should be conducted, and even then results of nutritional epidemiology studies relating diet to disease risk should be interpreted cautiously.