The sensitivity of estimates of the change in population behaviour to realistic changes in bias in repeated surveys

Summary.  The first British National Survey of Sexual Attitudes and Lifestyles (NATSAL) was conducted in 1990–1991 and the second in 1999–2001. When surveys are repeated, the changes in population parameters are of interest and are generally estimated from a comparison of the data between surveys. However, since all surveys may be subject to bias, such comparisons may partly reflect a change in bias. Typically limited external data are available to estimate the change in bias directly. However, one approach, which is often possible, is to define in each survey a sample of participants who are eligible for both surveys, and then to compare the reporting of selected events that occurred before the earlier survey time point. A difference in reporting suggests a change in overall survey bias between time points, although other explanations are possible. In NATSAL, changes in bias are likely to be similar for groups of sexual experiences. The grouping of experiences allows the information that is derived from the selected events to be incorporated into inference concerning population changes in other sexual experiences. We use generalized estimating equations, which incorporate weighting for differential probabilities of sampling and non-response in a relatively straightforward manner. The results, combined with estimates of the change in reporting, are used to derive minimum established population changes, based on NATSAL data. For some key population parameters, the change in reporting is seen to be consistent with a change in bias alone. Recommendations are made for the design of future surveys.     

Improved Exponential Type Estimators of the Mean of a Sensitive Variable in the Presence of Nonsensitive Auxiliary Information

Recently, Koyuncu et al. (2013 Koyuncu, N., Gupta, S., Sousa, R. (2014). Exponential type estimators of the mean of a sensitive variable in the presence of non-sensitive auxiliary information. Communications in Statistics- Simulation and Computation[PubMed], [Web of Science ®] , [Google Scholar]) proposed an exponential type estimator to improve the efficiency of mean estimator based on randomized response technique. In this article, we propose an improved exponential type estimator which is more efficient than the Koyuncu et al. (2013 Koyuncu, N., Gupta, S., Sousa, R. (2014). Exponential type estimators of the mean of a sensitive variable in the presence of non-sensitive auxiliary information. Communications in Statistics- Simulation and Computation[PubMed], [Web of Science ®] , [Google Scholar]) estimator, which in turn was shown to be more efficient than the usual mean estimator, ratio estimator, regression estimator, and the Gupta et al. (2012 Gupta, S., Shabbir, J., Sousa, R., Corte-Real, P. (2012). Regression estimation of the mean of a sensitive variable in the presence of auxiliary information. Communications in Statistics – Theory and Methods 41:2394–2404.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) estimator. Under simple random sampling without replacement (SRSWOR) scheme, bias and mean square error expressions for the proposed estimator are obtained up to first order of approximation and comparisons are made with the Koyuncu et al. (2013 Koyuncu, N., Gupta, S., Sousa, R. (2014). Exponential type estimators of the mean of a sensitive variable in the presence of non-sensitive auxiliary information. Communications in Statistics- Simulation and Computation[PubMed], [Web of Science ®] , [Google Scholar]) estimator. A simulation study is used to observe the performances of these two estimators. Theoretical findings are also supported by a numerical example with real data. We also show how to, extend the proposed estimator to the case when more than one auxiliary variable is available.     

New Reduced-bias Estimators of a Positive Extreme Value Index

Noting that the classical Hill estimator of a positive extreme value index (EVI) is the logarithm of the mean of order-0 of a set of certain statistics, a more general class of EVI-estimators based on the mean of order-p (MOP), p ? 0, of such statistics was recently introduced. The asymptotic behavior of the class of MOP EVI-estimators is reviewed, and compared to their reduced-bias MOP (RBMOP) and optimal RBMOP versions, which are suggested here and studied both asymptotically and for finite samples, through a large-scale simulation study. Applications to simulated datasets are also put forward.     

Effects of Varying Response Formats on Self-ratings of Life-Satisfaction

A sample of 1,737 volunteering students, randomly assigned to 12 conditions, rated their current overall (dis)satisfaction with life. Each condition used 1 of 12 response formats, differing in (1) polarity (bipolar versus unipolar), (2) orientation (horizontal versus vertical), and (3) anchoring (−5 to +5, Not Numbered, and 0 to 10). Results For satisfaction ratings, a negative skew was found for all response formats, but, a higher percentage of respondents scored in the upper part (Midpoint to top) of the scale with anchor points (−5 to +5) than on the scales with other anchor points (Not Numbered and 0 to 10). Our results indicate that, satisfaction ratings obtained with either a unipolar or a bipolar response scale were similar, but, participants experienced some difficulty in rating life dissatisfaction using the bipolar dissatisfaction–satisfaction response scale. Moreover, this study has found that life satisfaction and dissatisfaction show a reciprocal relation, especially when assessed with two independent unipolar rating scales. These results support the inclusion of one-way unipolar response scales in the assessment of satisfaction and dissatisfaction. Conclusion Our results suggest that the type of response format may influence both satisfaction and dissatisfaction ratings as well as the relationships between these two constructs.

Models for potentially biased evidence in meta-analysis using empirically based priors

Summary.  We present models for the combined analysis of evidence from randomized controlled trials categorized as being at either low or high risk of bias due to a flaw in their conduct. We formulate a bias model that incorporates between-study and between-meta-analysis heterogeneity in bias, and uncertainty in overall mean bias. We obtain algebraic expressions for the posterior distribution of the bias-adjusted treatment effect, which provide limiting values for the information that can be obtained from studies at high risk of bias. The parameters of the bias model can be estimated from collections of previously published meta-analyses. We explore alternative models for such data, and alternative methods for introducing prior information on the bias parameters into a new meta-analysis. Results from an illustrative example show that the bias-adjusted treatment effect estimates are sensitive to the way in which the meta-epidemiological data are modelled, but that using point estimates for bias parameters provides an adequate approximation to using a full joint prior distribution. A sensitivity analysis shows that the gain in precision from including studies at high risk of bias is likely to be low, however numerous or large their size, and that little is gained by incorporating such studies, unless the information from studies at low risk of bias is limited. We discuss approaches that might increase the value of including studies at high risk of bias, and the acceptability of the methods in the evaluation of health care interventions.     

Misogyny in New York custody decisions with parental alienation and child sexual abuse allegations

This article analyzes 24 New York (NY) published child custody cases decided between 2001 and 2017 that contained parental alienation and child sexual allegations. It addresses whether there was a tendency toward gendered decisions and the evidence on which the decisions were based. It reveals that most decisions favored alienation allegations over child sexual abuse allegations and transferring custody from mothers communicating sexual abuse allegations in court to fathers defending against them by alleging parental alienation. On appeal, these family court decisions were overwhelmingly upheld. The analysis also shows that the decisions were based as often on implicit misogynistic cultural assumptions in the absence of allegation-specific evidence as they were on allegation-specific evidence. The article adds to the growing understanding of sources of bias by proposing four errors that support biased reasoning. It concludes with suggestions about how experts, attorneys, and judges can question themselves and others to reduce bias.     

Efficient use of multi-auxiliary information in search of good rotation patterns in successive sampling

This article considers the problem of estimating the population mean on the current (second) occasion using multi-auxiliary information in successive sampling over two occasions. A general class of estimators is proposed for estimating population mean on the current occasion and expressions for bias and mean square error for these estimators are obtained up to first degree of approximation. The minimum variance bound estimator in the proposed class is discussed. Many popular estimators have been shown to belong to this class. Optimum replacement policy is also discussed. Finally, the superiority of the proposed class of estimators over multivariate version of chain type ratio estimator envisaged by Singh (2005 Singh, G.N. (2005). On the use of chain type ratio estimator in successive sampling. Stat Transition 7:21–26. [Google Scholar]) is established empirically.     

Some efficient classes of estimators of population mean in two-phase successive sampling under random non response

In this paper, an attempt has been made to present some efficient classes of estimators of population mean on current occasion in two-occasion successive sampling under random non response in two-phase setup. Effectiveness of the proposed classes of estimators has been studied under the assumptions that sampling units follow a distribution under the random non response. To check the performances, the proposed classes of estimators are compared with an estimator under the similar situation under the complete response. Results are demonstrated through empirical studies which show the reliable nature of the proposed classes of estimators. Suitable recommendations have been made to the survey practitioners.     

On generalized exponential chain ratio estimators under stratified two-phase random sampling

Sanaullah et al. (2014 Sanaullah, A., Ali, H.M., Noor ul Amin, M., Hanif, M. (2014). Generalized exponential chain ratio estimators under stratified two-phase random sampling. Appl. Math. Comput. 226:541–547.[Crossref], [Web of Science ®] , [Google Scholar]) have suggested generalized exponential chain ratio estimators under stratified two-phase sampling scheme for estimating the finite population mean. However, the bias and mean square error (MSE) expressions presented in that work need some corrections, and consequently the study based on efficiency comparison also requires corrections. In this article, we revisit Sanaullah et al. (2014 Sanaullah, A., Ali, H.M., Noor ul Amin, M., Hanif, M. (2014). Generalized exponential chain ratio estimators under stratified two-phase random sampling. Appl. Math. Comput. 226:541–547.[Crossref], [Web of Science ®] , [Google Scholar]) estimator and provide the correct bias and MSE expressions of their estimator. We also propose an estimator which is more efficient than several competing estimators including the classes of estimators in Sanaullah et al. (2014 Sanaullah, A., Ali, H.M., Noor ul Amin, M., Hanif, M. (2014). Generalized exponential chain ratio estimators under stratified two-phase random sampling. Appl. Math. Comput. 226:541–547.[Crossref], [Web of Science ®] , [Google Scholar]). Three real datasets are used for efficiency comparisons.     

Efficient separate class of estimators of population mean in stratified random sampling

In this paper, efficient class of estimators for population mean using two auxiliary variates is suggested. It has been shown that the suggested estimator is more efficient than usual unbiased estimator in stratified random sampling, usual ratio and product-type estimators, Tailor and Lone (2012 Tailor, R. and Lone, H. A. (2012). Separate ratio-cum- product estimators of finite population mean using auxiliary information. J. Rajasthan Stat. Assoc. 1(2):94–102. [Google Scholar], 2014) estimators, and other considered estimators. The bias and mean-squared error of the suggested estimator are obtained up to the first degree of approximation. Conditions under which the suggested estimator is more efficient than other considered estimators are obtained. An empirical study has been carried out to demonstrate the performances of the suggested estimator.