Modified binary randomized response technique models

Social Desirability Bias (SDB) is the tendency in respondents to answer questions untruthfully in the hope of giving good impression to others. SDB occurs when the survey question is highly sensitive or personal, and responses cause sample statistics to systematically overestimate or underestimate corresponding population parameters. The Randomized Response Technique (RRT) is one of several methods to get around SDB in surveys involving sensitive questions in a face-to-face interview. We first review some of the well-established binary response RRT models including the two-parameter models such as the two-stage RRT model and the optional RRT model. Then, we examine an optional RRT model based on the unrelated question RRT as presented by Gupta, Tuck, Spears Gill, and Crowe (2013). Also, we show another optional RRT model based on the two-stage RRT. Next, we carry out efficiency comparisons between these models and show simulation results. While these two models are all based on the split-sample approach to estimate two unknown parameters of interest ( $\pi$ and $\omega$—the prevalence of sensitive characteristic and the sensitivity level of the underlying question respectively), the next two models utilize the two-question approach instead. One of them relies on the unrelated question RRT model. And the other relies on the two-stage optional RRT model. Again, efficiencies of estimators are compared and simulation results are provided. In the end, simulation results and figures are presented and some conclusions are made regarding which estimator performs better. It turns out that the two-stage optional indirect RRT model with two-question approach performs better than other binary optional RRT models

Randomized response estimation in multiple frame surveys

Large scale surveys are increasingly delving into sensitive topics such as gambling, alcoholism, drug use, sexual behavior, domestic violence. Sensitive, stigmatizing or even incriminating themes are difficult to investigate by using standard datacollection techniques since respondents are generally reluctant to release information which concern their personal sphere. Further, such topics usually pertain elusive population (e.g., irregular immigrants and homeless, alcoholics, drug users, rape and sexual assault victims) which are difficult to sample since not adequately covered in a single sampling frame. On the other hand, researchers often utilize more than one data-collection mode (i.e., mixed-mode surveys) in order to increase response rates and/or improve coverage of the population of interest. Surveying sensitive and elusive populations and mixed-mode researches are strictly connected with multiple frame surveys which are becoming widely used to decrease bias due to undercoverage of the target population. In this work, we combine sensitive research and multiple frame surveys. In particular, we consider statistical techniques for handling sensitive data coming from multiple frame surveys using complex sampling designs. Our aim is to estimate the mean of a sensitive variable connected to undesirable behaviors when data are collected by using the randomized response theory. Some estimators are constructed and their properties theoretically investigated. Variance estimation is also discussed by means of the jackknife technique. Finally, a Monte Carlo simulation study is conducted to evaluate the performance of the proposed estimators and the accuracy of variance estimation..Ministerio de Economía y CompetitividadFPU grant programConsejería de Empleo, Empresa y Comercio, Junta de Andalucí

Accounting for lack of trust in randomized response models

This study addresses a key assumption made while using traditional Randomized Response models in survey sampling when the question being asked pertains to a sensitive topic. It is traditionally assumed that under a randomized response framework, survey participants have no further reason to lie due to privacy concerns. We demonstrate that if this assumption is not true and even if a small proportion of respondents do not trust the RRT model being used in a survey, we get considerably biased estimates. We also propose alternative binary and quantitative models that account for respondents’ lack of trust in traditional RRT models. These proposed models are mixtures of traditional RRT models and in one particular case mixture of an RRT model with an encryption technique, commonly used in the computer science domain. We also incorporate optionality into these models which helps improve the model efficiency. We evaluate the overall model performance using a combined measure of privacy and efficiency. Both theoretical and empirical results confirm that accounting for lack of trust helps us obtain more reliable results when survey respondents may not trust the RRT model used. Simulation studies have also been conducted to verify theoretical results. For sensitive mean estimation, we also propose estimators that utilize the auxiliary information and are more efficient compared to the ordinary mean estimator that does not utilize the auxiliary information

Mean estimation of sensitive variables under measurement errors and non-response

This study mainly consists of three important issues we face in survey sampling: social desirability bias, measurement errors, and non-response. In this dissertation, we study the mean estimation of a sensitive variable under measurement errors and non-response. We propose a generalized mean estimator, then discuss the bias and the mean square error (MSE) of this estimator and present the comparisons with other estimators under the measurement errors and non-response using optional RRT model (ORRT). We also study the performance of the proposed estimator under the same situations using stratified random sampling. Simulation studies are also conducted to verify the theoretical results. Both the theoretical and empirical results show that the generalized mean estimator is more efficient than the ordinary RRT estimator that does not utilize the auxiliary variable, and the ratio estimator which is one of the commonly used mean estimator

Ratio estimation of the mean under RRT models

Ratio estimation is a parameter estimation technique that uses a known auxiliary variable that is correlated with the study variable. In many situations, the primary variable of interest may be sensitive and it cannot be observed directly. However, we can observe directly a non-sensitive variable that is highly correlated with the study variable. In these cases, we have to rely on some Randomized Response Technique (RRT) models to obtain information on the study variable. In this thesis, we first review some RRT models, some general ratio and product estimation techniques, and two Kalucha et al. (2015) ratio estimators that are based on Gupta et al. (2010) additive optional RRT model. One of the Kalucha et al. (2015) estimators, the multiplicative ratio estimator, did not work efficiently and was abandoned. The main focus of this thesis is on fixing the Kalucha et al. (2015) abandoned multiplicative ratio estimator and reevaluating its performance. We discuss the Bias and the Mean Square Error (MSE) of our proposed multiplicative ratio estimator correct up to first order approximation, and present the comparisons with other estimators under the additive optional RRT model. A simulation study is also conducted to verify the theoretical result. Both the theoretical and the empirical results show that the proposed multiplicative ratio estimator is more efficient than the ordinary RRT estimator that does not utilize the auxiliary variable. It also compares well with the additive ratio estimator of Kalucha et al. (2015)

An improved class of estimators in RR surveys

This work proposes a general class of estimators for the population total of a sensitive variable using auxiliary information. Under a general randomized response model, the optimal estimator in this class is derived. Design‐based properties of proposed estimators are obtained. A simulation study reflects the potential gains from the use of the proposed estimators instead of the customary estimators.Ministerio de Educación y CienciaConsejería de Economía, Innovación, Ciencia y Emple

Regression Models in Complex Survey Sampling for Sensitive Quantitative Variables

Randomized response (RR) techniques are widely used in research involving sensitive variables, such as drugs, violence or crime, especially when a population mean or prevalence must be estimated. However, they are not generally applied to examine relationships between a sensitive variable and other characteristics. This type of technique was initially applied to qualitative variables, and studies later showed that a logistic regression may be performed with RR data. Since many of the variables considered in this context are quantitative, RR techniques were extended to these cases to estimate the values required. Regression analysis is a valuable statistical tool for exploring relationships among variables and for establishing associations between responses and covariates. In this article, we propose a design-based regression analysis for complex sample designs based on the unified RR approach. We present estimators of the regression coefficients, study their theoretical properties and consider different ways to estimate their variance. The properties of these estimation techniques were simulated using various quantitative randomized models. The method proposed was also used to analyse the findings from a real-world survey.Ministerio de Ciencia e Innovación of Spai

Financial Costs Incurred by Living Kidney Donors: Findings from a Canadian Multi-centre Prospective Cohort Study

This prospective cohort study across 12 Canadian transplant centres evaluated the costs incurred by 912 living kidney donors. Expenses and resources were captured to 3-months post-donation, and micro-costing was used to appraise the costs incurred by donors. Living kidney donors incurred average total costs of $4790, and direct and indirect costs of$2110 and $2679, respectively. 13.3$10,000, and 8.6% of donors incurred costs \u3e25% of their annual household income. Costs incurred by spousal donors were not significantly different from either unrelated or closely related donors. Similarly, costs incurred by kidney paired donors were not significantly different from other donors. In multivariable analyses, living \u3e100 km from the transplant evaluation centre and being employed were associated with higher total costs. In conclusion, many living kidney donors incur substantial costs associated with donation, and our findings can be used to improve the donation experience

Improving statistical practice in psychological research: Sequential tests of composite hypotheses

Statistical hypothesis testing is an integral part of the scientific process. When employed to make decisions about hypotheses, it is important that statistical tests control the probabilities of decision errors. Conventional procedures that allow for error-probability control have limitations, however: They often require extremely large sample sizes, are bound to tests of point hypotheses, and typically require explicit assumptions about unknown nuisance parameters. As a consequence, the issue of proper error-probability control has frequently been neglected in statistical practice, resulting in a widespread reliance on questionable statistical rituals. In this thesis, I promote an alternative statistical procedure: the sequential probability ratio test (SPRT). In three articles, I implement, further develop, and examine three extensions of the SPRT to common hypothesis-testing situations in psychological research. In the first project, I show that the SPRT substantially reduces required sample sizes while reliably controlling error probabilities in the context of the common t-test situation. In a subsequent project, I seize on the SPRT to develop a simple procedure that allows for statistical decisions with controlled error probabilities in the context of Bayesian t tests. Thus, it allows for tests of distributional hypotheses and combines the advantages of frequentist and Bayesian hypothesis tests. Finally, I apply a procedure for sequential hypothesis tests without explicit assumptions about unknown nuisance parameters to a popular class of stochastic measurement models, namely, multinomial processing tree models. With that, I demonstrate how sequential analysis can improve the applicability of these models in substantive research. The procedures promoted herein do not only extend the SPRT to common hypothesis-testing situations, they also remedy a number of limitations of conventional hypothesis tests. With my dissertation, I aim to make these procedures available to psychologists, thus bridging the gap between the fields of statistical methods and substantive research. Thereby, I hope to contribute to the improvement of statistical practice in psychology and help restore public trust in the reliability of psychological research

Validity of the overclaiming technique as a method to account for response bias in self-assessment questions : analysis on the basis of the PISA 2012 data

The presented work is devoted to study the validity of overclaiming technique (OCT) as a measure of response (positivity) bias. Three main aims of the analyses performed were: a) assess methods' utility to enhance predictive validity of self-report by accounting for response biases, b) investigate proposed mechanisms of overclaiming, c) expand nomological network of the method by presenting a wide set of both individual-level and cluster-level (school) correlates. The obtained results pointed that OCT can be used in order to account for response biases in self-report data. Important differences regarding use and interpretation of the different OCT scoring systems were found and commented. Two systems, one based of signal detection theory (SDT), other on item response theory model (IRT), were proposed as viable scorings of OCT. Choice between them is not trivial as it influences results' interpretation and model specification. Three possible mechanisms of overclaiming were tested: a) motivated response bias (self-favouring bias, socially desirable responding), b) memory bias (overgeneralised knowledge or faulty memory control) and c) response styles and careless responding. The results pointed that all three mechanisms are probable and that overclaiming is most probably a heterogenous phenomenon of multiple causes. However, the analyses pointed out that one of the memory bias hypotheses, the overgeneralised knowledge account, does not hold and that there is much more evidence for the competitive metacognitive account. It is to said that overclaiming is at least partially attributable to insufficient monitoring of one's knowledge. Evidence for a relation between careless responding and overclaiming was also obtained, indicating that at least some of the overclaimed responses can be attributed due to inattentive responding. Obtained results on the relations between response styles and overclaiming were complicated; they warrant further studies as the results here probably greatly depend on the technical details of analysis, e.g. response style definition and coding adopted. The analysed cluster-level covariates demonstrated that only very limited portion of OCT variance can be ascribed to the school-level of analysis. Gender, socio-economic status and locus of control proved to be significantly related to overclaiming among the individual-level correlates assessed. Boys yielded higher overclaiming bias than girls and students of external locus of control were more biased in their self-reports in comparison to students of internal locus of control. The work comprises also analysis of the PISA's OCT latent structure. The results evidenced bifactor structure of the scale, with the general factor interpreted as math ability while the two specific factors were given a tentative explanation concentrated around item difficulty (one specific factor emerged for easy items, one for hard items). These findings point to a multi-dimensional character of OCT and a large role played by domain ability in OCT responding. Moreover, latent class analysis (LCA) performed identified an "overclaiming" group among the participants which was characterised by high overclaiming and unwarrantedly high self-report profile regarding math-related abilities and social life. However, this group counted only around 9% of the total sample. Implications of these findings are commented in the work, along with theoretical integration and ideas for future studies with the use of OCT