A controversial census topic: race and ethnicity in the British census

"This paper examines attempts to collect data on a politically controversial topic, race and ethnicity, in the British Census of Population in the post-war period. It discusses an indirect, proxy method of inferring race or ethnicity by asking for the country of birth of the respondent and of his parents, and a direct question where the respondent is asked to identify his racial or ethnic group. Different versions of the direct question are examined, as is the 1979 Census test, which resulted in considerable public resistance to the question. Following the exclusion of the direct question from the 1981 Census, the subject was reviewed by the Parliamentary Home Affairs Committee, the results of whose report--including practical suggestions as to question wording--are discussed."     

Ethnic group and the British census: the search for a question

"The 1991 census contained, for the first time, a question on the ethnic group of each member of the population of Great Britain. This paper reports on how a question was developed which has a sufficiently high degree of acceptance from all the main ethnic groups, and which is answered sufficiently accurately, to justify inclusion in the census."     

Respondent hazards in the unrelated question randomized response model

The randomized response technique is a method of interviewing which allows estimation of population proportion or mean of a sensitive characteristic while maintaining anonymity of response. This is accomplished by use of two questions in the interview, one sensitive and one not so, with the respondent applying a random device to determine which question to answer. The statistician knows the parameters of the random device but is kept from knowledge of individual outcomes. When the nonsensitive question bears on a matter quite foreign to the sensitive characteristic, it is termed an unrelated question, and the associated technique carries that designation. Critical in the construction of such a model are the assigned probability p that the respondent will select the sensitive question and the proportion Π_y of the population having the unrelated nonsensitive characteristic. The choice of these parameters is discussed herein with reference to the risks perceived by respondents.     

典型相关分析的延拓研究

在典型相关分析中,求得典型相关变量的表达式并没有全部完成任务,例如需要确定典型相关变量的个数和变量选择。针对典型相关变量的个数问题,发现了常用的卡方检验和冗余分析方法的不足,进而提出了一种新的算法。针对原始变量的选择问题,提出了三种可能的路径。最后利用人体尺寸数据对相关结论进行了验证。     

A sensitivity analysis of probabilistic sensitivity analysis in terms of the density function for the input variables

Probabilistic sensitivity analysis (SA) allows to incorporate background knowledge on the considered input variables more easily than many other existing SA techniques. Incorporation of such knowledge is performed by constructing a joint density function over the input domain. However, it rarely happens that available knowledge directly and uniquely translates into such a density function. A naturally arising question is then to what extent the choice of density function determines the values of the considered sensitivity measures. In this paper we perform simulation studies to address this question. Our empirical analysis suggests some guidelines, but also cautions to practitioners in the field of probabilistic SA.     

The Teacher's Corner: A Simplified Method of Solving Simple and Multiple Regression Equations

An introductory statistics course that emphasized consistency of student requirements and diversity of instructors was taught at the University of New Hampshire. Over a four-year period, 24 instructors from 12 departments taught this course. Student requirements remained essentially the same from term to term or instructor to instructor because exams were prepared by sampling question frames that were largely independent of instructors. Student performance appeared to be buffered against instructor variation through a combination of visible question frames, abundant tutorial help, and exams that could be retaken.     

The effects of correlation among observations on the consistency property of sample variance

It is well known that even when the sample observations are correlated and not normal the sample variance, S² converges in probability to E(S²). But the required sample size for S² to be a consistent estimator of E(S²) is an open question. Some light is shed on this question in this paper. In particular the relation between the rate of convergence and the correlation property of the observations is explored. It is shown that the retardation to the rate of convergence is not appreciable if the correlation is moderate but it can be severe for extreme correlations.     

Measurement of Hispanic ethnicity in the U.S. census: an evaluation based on latent-class analysis

"Two models, the U.S. census model and the latent-class model, are compared in their application to evaluating measurements of ethnicity. Although the census approach assumes that the response categories of a questionnaire item correspond to groups in the population, the latent-class approach seeks to assess whether any set of response categories can represent observed ethnic heterogeneity. Data collected using the 1990 census Hispanic-origin question and other instruments for measuring ethnicity suggest that the latent-class approach is superior whenever the response categories are not known to be valid. In particular, using the latent-class model, this article rejects the census model's assumption of a single dimension of meaning underlying responses to the Hispanic-origin question."     

On the gain in power when using additional observations

In the present paper the question in which cases additional observations are useless for testing two simple hypotheses is considered. It turns out that in all but trivial cases two additional observations strictly decrease the risk, while for one additional observation a characterization of this problem is given.     

Analysis of incomplete durations with application to contraceptive use

Models for analysing incomplete durations obtained from cross-sectional surveys are presented. The aim of the paper is to develop a framework for analysing the incomplete duration of episodes in progress at the time of the survey by formulating generalized linear models and fitting and assessing them by using standard statistical packages. The maximum quasi-likelihood method is used for model fitting. The choice of the distribution and the diagnostic procedures are discussed. Simulated data from two distributions (the Weibull and log-logistic distributions) are used to evaluate the methodology developed and to assess model misspecifications. A data set on the current use of the contraceptive pill from a cross-sectional survey in Egypt is analysed.     

Households and families according to the 1984 census

The results of the 1984 population and household census indicate that the trend toward smaller households in Poland has been reversed. The causes of this change, which goes against the general trend in developed countries, are considered. In addition to the impact of changed economic conditions, the author notes changes in the interpretation of the term household that affect the results in question.     

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Keynote and panel discussion on communicating heterogeneous treatment effects across populations

How do we communicate nuanced regulatory information to different audiences, recognizing that the consumer audience is very different from the physician audience? In particular, how do we communicate the heterogeneity of treatment effects - the potential differences in treatment effects based on sex, race, and age? That is a fundamental question at the heart of this panel discussion. Each panelist addressed a specific “challenge question” during their 5-minute presentation, and the list of questions is provided. The presentations were followed by a question and answer session with members of the audience and the panelists.     

A note on the Hodges–Lehmann estimator

The Hodges‐Lehmann estimator was originally developed as a non‐parametric estimator of a shift parameter. As it is widely used in statistical applications, the question is investigated what it is estimating if the shift model does not hold. It is shown that for data whose distributions are symmetric about their median the Hodges–Lehmann estimator based on the Wilcoxon Rank Sum test estimates the difference between the medians of the distributions. This result does generally not hold if the symmetry assumption is violated. Copyright © 2009 John Wiley & Sons, Ltd.     

设立原假设中的辩证分析

 该文从统计决策角度提出了“如何选取单侧假设检验中原假设”的问题,综合分析了相关文献中对该问题的各种解释及解决方法,同时运用唯物辩证法原理来理解并解决该问题中的各种矛盾,最后简要阐述了假设检验、可信区间和统计决策之间的辨证关系。     

Sampling issues in the administration of justice

Colin Aitken first became interested in sampling in the context of the administration of justice when the case of US versus Shonubi was brought to his attention. Shonubi was a Nigerian working in New York. He was arrested at Kennedy Airport when seen behaving in a suspicious manner in the baggage hall, having returned on a flight from Nigeria. He was found to have 103 bags of a white substance inside him, four of which were examined. The white substance was identified as heroin. Shonubi was arrested, tried and found guilty of drug smuggling. The question then arose as to the length of sentence he should be given.     

Topic selection for the Australian census of population and housing

"We describe how topics were selected for the Australian Census of Population and Housing. Australia has adopted an open assessment of topics submitted by users and the general public and then performed field tests of difficult or sensitive questions. For the 1986 Census, special attention was given to the development of a census question on ethnic origin."     

A Monte Carlo comparison of three consistent bootstrap procedures

Since bootstrap samples are simple random samples with replacement from the original sample, the information content of some bootstrap samples can be very low. To avoid this fact, several variants of the classical bootstrap have been proposed. In this paper, we consider two of them: the sequential or Poisson bootstrap and the reduced bootstrap. Both of these, like the ordinary bootstrap, can yield second-order accurate distribution estimators, that is, the three bootstrap procedures are asymptotically equivalent. The question that naturally arises is which of them should be used in a practical situation, in other words, which of them should be used for finite sample sizes. To try to answer this question, we have carried out a simulation study. Although no method was found to exhibit best performance in all the considered situations, some recommendations are given.     

A randomized response procedure for multiple-sensitive questions

The Randomized Response (RR) technique is a well-established interview procedure which guarantees privacy protection in social surveys dealing with sensitive items. The RR method assumes a stochastic mechanism to create uncertainty about the true status of the respondents in order to ensure privacy protection and to avoid tendencies to dissimulate or respond in a socially desirable direction. A very general model for the RR method was introduced by Franklin (Commun Stat Theory Methods 18:489?C505, 1989) when a single-sensitive question is under study. However, since social surveys are often based on questionnaires containing more than a single-sensitive question, the analysis of multivariate RR data is of considerable interest. This paper focuses on the generalization of the Franklin model in a multiple-sensitive question setting and on related inferential issues.     

A combined test for differences in scale based on the interquantile range

A class of tests due to Shoemaker (Commun Stat Simul Comput 28: 189–205, 1999) for differences in scale which is valid for a variety of both skewed and symmetric distributions when location is known or unknown is considered. The class is based on the interquantile range and requires that the population variances are finite. In this paper, we firstly propose a permutation version of it that does not require the condition of finite variances and is remarkably more powerful than the original one. Secondly we solve the question of what quantile choose by proposing a combined interquantile test based on our permutation version of Shoemaker tests. Shoemaker showed that the more extreme interquantile range tests are more powerful than the less extreme ones, unless the underlying distributions are very highly skewed. Since in practice you may not know if the underlying distributions are very highly skewed or not, the question arises. The combined interquantile test solves this question, is robust and more powerful than the stand alone tests. Thirdly we conducted a much more detailed simulation study than that of Shoemaker (1999) that compared his tests to the F and the squared rank tests showing that his tests are better. Since the F and the squared rank test are not good for differences in scale, his results suffer of such a drawback, and for this reason instead of considering the squared rank test we consider, following the suggestions of several authors, tests due to Brown–Forsythe (J Am Stat Assoc 69:364–367, 1974), Pan (J Stat Comput Simul 63:59–71, 1999), O’Brien (J Am Stat Assoc 74:877–880, 1979) and Conover et al. (Technometrics 23:351–361, 1981).     

Mean and sensitivity estimation in optional randomized response models

Optional randomized response models were introduced by Gupta et al. (2002). These models are based on the basic premise that a question may be sensitive for one respondent but may not be sensitive for another. In an optional RRT (randomized response technique) model, a respondent is asked to provide a scrambled response only if the respondent considers the question sensitive. Otherwise, the respondent provides a truthful response. The researcher does not know which type of response is provided. The proportion of respondents who provide a scrambled response is known as the sensitivity level of the question. In this paper, we estimate simultaneously the mean and the sensitivity level of a quantitative-response sensitive question using a two stage optional RRT model. The estimators are unbiased and asymptotically normally distributed. We discuss the advantages and disadvantages of using additive and multiplicative scrambling.