Logistic regression analysis of randomized response data with missing covariates

Hsieh, Shu-Hui; Lee, S.M.; Shen, Pao‐Sheng

doi:10.1016/j.jspi.2009.09.020

Cited by 28 publications

(17 citation statements)

References 18 publications

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“…About this regard, there are several scholars utilized the Newton-Raphson method to find parameters in the regression models with missing covariates data. Readers may refer to Hsieh et al (2009Hsieh et al ( , 2010, Lee et al (2012Lee et al ( , 2016, Lukusa et al (2016), Wang et al (2002) for more information. In addition, the Newton-Raphson method can work well in the data set having missing values, where some available function in the statistical software is unworkable (see e.g.…”

Section: Iterationmentioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

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Methods to find the optimization solution are fundamental and extremely crucial for scientists to program computational software to solve optimization problems efficiently and for practitioners to use it efficiently. Thus, it is very essential to know about the idea, origin, and usage of these methods. Although the methods have been used for very long time and the theory has been developed too long, most, if not all, of the authors who develop the theory are unknown and the theory has not been stated clearly and systematically. To bridge the gap in the literature in this area and provide academics and practitioners with an overview of the methods, this paper reviews and discusses the four most commonly used methods to find the optimization solution including the bisection, gradient, Newton-Raphson, and secant methods. We first introduce the origin and idea of the methods and develop all the necessary theorems to prove the existence and convergence of the estimate for each method.We then give two examples to illustrate the approaches. Thereafter, we review the literature of the applications of getting the optimization solutions in some important issues and discuss the advantages and disadvantages of each method. We note that all the theorems developed in our paper could be well-known but, so far, we have not seen any book or paper that discusses all the theorems stated in our paper in detail. Thus, we believe the theorems developed in our paper could still have some contributions to the literature. Our review is useful for academics and practitioners in finding the optimization solutions in their studies.

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Section: Iterationmentioning

confidence: 99%

Optimal Solution Techniques in Decision Sciences: A Review

Pho

Tran

et al. 2019

SSRN Journal

View full text Add to dashboard Cite

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“…Contextually, many studies have assessed the validity of RR methods, showing that they can produce more reliable answers than conventional data collection methods (eg, direct questioning (DQ) in face‐to face interviews, SAQs with paper and pencil and CASIs). The RRT has been applied in surveys covering a variety of sensitive topics, as drug use, racism, abortion, sexual victimization, cheating, and so on, as indicated in the articles of Perri et al and Rueda et al We found some more recent studies related with people's sexual practice in the United States, health care study of the dependence structure, inappropriate sexual behavior among university students, contaminants in the air and water, genital cutting in Southern Ethiopia, female sex workers in Taiyuan, China,, drug use in Republic of Georgia, illegal cable TV subscribers, and the right to receive welfare …”

Section: Introductionmentioning

confidence: 99%

Variance jackknife estimation for randomized response surveys: A simulation study and an application to explore cheating in exams and bullying

Arnab

Cobo

2020

Comp and Math Methods

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The aim of the randomized response (RR) technique is to decrease social desirability bias, thus guaranteeing confidentiality, improving respondent cooperation, and obtaining reliable estimates. This technique obtains stronger estimates of sensitive characteristics, compared to direct questioning, by reducing respondents motivation to report falsely their attitudes. In this paper, we have considered the problem of estimation of variance of an estimator based on an RR survey data obtained from a complex survey design by using resampling techniques. The performance of the proposed jackknife modified estimator is tested by simulation studies. The simulation studies reveal that there is a decrease in the relative bias and the relative mean square error for all RR schemes. An RR survey was conducted to investigate sensitive behaviors such as cheating in exams and bullying. The application of the new jackknife confidence intervals revealed substantial increase in high‐tech cheating and bullying in the university campuses.

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“…Ostapczuk et al (2009) studied attitudes toward foreigners, whereas Perri et al (2016) dealt with induced abortion and migrants’ irregular presence. Hsieh et al (2010) considered the proportion of illegal cable TV in Taiwan, and Tu and Hsieh (2017) studied extradyadic sexual behavior. De Jong et al (2012) introduced a polytomous item RRT that increases reporting sexual proclivities related to DQ.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Estimators for Multi-level Randomized Response Data: Evidence from a Case of Sexual Identity

Hsieh

Lukusa

2020

Field Methods

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In surveys, proportions of individuals self-reporting as bisexual and homosexual are likely to suffer from misreports or refusal to answer when directly questioned. For more reliable information, the randomized response technique is often used. Therefore, to reduce social desirability bias, a multi-level randomized response technique has been used in Taiwan to collect self-reported sexual identity. Using the Taiwan Social Change Survey as a case study, we estimate proportions of individuals aged 18–54 years self-reporting as bisexual and homosexual. Empirical results show individual determinants of truthful response differ across data collection modes and estimation methods. Moreover, estimated proportions of non-heterosexuals in small samples obtained by the maximum likelihood method are less meaningful and accurate than Bayes ones.

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Logistic regression analysis of randomized response data with missing covariates

Cited by 28 publications

References 18 publications

Optimal Solution Techniques in Decision Sciences: A Review

Optimal Solution Techniques in Decision Sciences: A Review

Variance jackknife estimation for randomized response surveys: A simulation study and an application to explore cheating in exams and bullying

Comparison of Estimators for Multi-level Randomized Response Data: Evidence from a Case of Sexual Identity

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