A generalized class of estimators for sensitive variable in the presence of measurement error and non-response under stratified random sampling

Zahid, Erum; Shabbir, Javid; Alamri, Osama Abdulaziz

doi:10.1016/j.jksus.2021.101741

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…Tis superiority reveals the utility of the proposed class in practice and will perform very well in practical surveys. In future studies, the work can be extended in light of [34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Shahzad

Ahmad

Al‐Noor

et al. 2022

Journal of Mathematics

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Surprising perceptions may happen in survey sampling. The arithmetic mean estimator is touchy to extremely enormous or potentially small observations, whenever selected in a sample. It can give one-sided (biased) results and eventually, enticed to erase from the selected sample. These extremely enormous or potentially small observations, whenever known, can be held in the sample and utilized as supplementary information to expand the exactness of estimates. Also, a supplementary variable is consistently a well-spring of progress in the exactness of estimates. A suitable conversion/transformation can be utilized for getting much more precise estimates. In the current study, regarding population mean, we proposed a robust class of separate type quantile regression estimators under stratified random sampling design. The proposed class is based on extremely enormous or potentially small observations and robust regression tools, under the framework of Särndal. The class is at first defined for the situation when the nature of the study variable is nonsensitive, implying that it bargains with subjects that do not create humiliation when respondents are straightforwardly interrogated regarding them. Further, the class is stretched out to the situation when the study variable has a sensitive nature or theme. Sensitive and stigmatizing themes are hard to explore by utilizing standard information assortment procedures since respondents are commonly hesitant to discharge data concerning their own circle. The issues of a population related to these themes (for example homeless and nonregular workers, heavy drinkers, assault and rape unfortunate casualties, and drug users) contain estimation errors ascribable to nonresponses as well as untruthful revealing. These issues might be diminished by upgrading respondent participation by scrambled response devices/techniques that cover the genuine value of the sensitive variable. Thus, three techniques (namely additive, mixed, and Bar-Lev) are incorporated for the purposes of the article. The productivity of the proposed class is also assessed in light of real-life dataset. Lastly, a simulation study is also done to determine the performance of estimators.

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Section: Discussionmentioning

confidence: 99%

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Shahzad

Ahmad

Al‐Noor

et al. 2022

Journal of Mathematics

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“…This study used stratified random sampling (Zahid, Shabbir, & Alamri, 2022) and a structured questionnaire-based cross-sectional survey (Alam et al. , 2022a) to investigate the proposed theoretical framework built with the S-O-B-A behavioral paradigm.…”

Section: Methodsmentioning

confidence: 99%

An integration of S-O-B-A paradigm to explore university students' entrepreneurial attitude, intention and action: do university and family support matter?

Awal

Faisal-E-Alam

Husain

2022

AGJSR

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PurposeThe primary purpose of this study is to integrate the stimulus-organism-behavior-accomplishment (S-O-B-A) paradigm to investigate the chain effect of university students' perceived university and family support on their entrepreneurial action (EA) with a serial mediation of their attitude and intention.Design/methodology/approachThis study introduces stratified random sample to choose respondents and a cross-sectional research design. partial least square-structural equation modeling (PLS-SEM) has applied to thoroughly investigate the behavioral intention concerned with students' entrepreneurship action.FindingsThe findings explored that perceived university support and family supports positively impact students' entrepreneurship attitude, where perceived family support creates statistically more powerful implications than university support. Students' attitude toward entrepreneurship positively affects their entrepreneurial intent, and finally, the entrepreneurial pursuit has an affirmative impact on students' EA.Originality/valueThis study incorporates the S-O-B-A paradigm for the very first time to investigate the effects of students' environmental support on their EA with double mediation by their attitude and intention.

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“…Several researchers proposed various estimators using stratified sampling in the presence of auxiliary information. One may review the studies of Kadilar and Cingi (2006), Singh and Vishwakarma (2008), Khan et al (2010), Tailor et al (2011), Ozel et al (2014), Solanki and Singh (2015), Bhushan and Misra (2017), Khare and Jha (2017), Muneer et al (2017), Shabbir and Gupta (2017), Cekim and Kadilar (2019), Qureshi et al (2020), Singh et al (2021), Zaman and Bulut (2021) and Zahid et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heterogeneous Population Variance using Memory-type Estimators based on EWMA statistic in the presence of Measurement Error for Time-Scaled Surveys

Qureshi

Tariq

Xiong

et al. 2023

Preprint

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In this present article, we have suggested memory-type ratio, exponential ratio, product and exponential product estimators based on exponentially weighted moving average statistic for the estimation of heterogeneous population variance using stratified sampling design in presence of measurement error for time-scaled surveys. Mathematical expressions of approximate mean square error are derived using Taylor and exponential expansions for the proposed memory-type estimators. We have also discussed the situations in which the memory-type estimators would perform efficiently than the conventional estimators. The results of extensive simulation study revealed that the proposed memory-type estimators based exponentially weighted moving average statistic would perform better than the conventional estimators in the presence measurement error for time-scaled surveys under certain condition.

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A generalized class of estimators for sensitive variable in the presence of measurement error and non-response under stratified random sampling

Cited by 15 publications

References 24 publications

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

An integration of S-O-B-A paradigm to explore university students' entrepreneurial attitude, intention and action: do university and family support matter?

Estimation of Heterogeneous Population Variance using Memory-type Estimators based on EWMA statistic in the presence of Measurement Error for Time-Scaled Surveys

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