Crosswise Model Research Articles

Plagiarism in Student Papers: Prevalence Estimates Using Special Techniques for Sensitive Questions

Summary This article evaluates three different questioning techniques for measuring the prevalence of plagiarism in student papers: the randomized response technique (RRT), the item count technique (ICT), and the crosswise model (CM). In three independent experimental surveys with Swiss and German university students as subjects (two web surveys and a survey using paper and- pencil questionnaires in a classroom setting), each of the three techniques is compared to direct questioning and evaluated based on the “more-is-better” assumption. According to our results the RRT and the ICT failed to reduce social desirability bias in self-reports of plagiarism. In contrast, the CM was more successful in eliciting a significantly higher rate of reported sensitive behavior than direct questioning. One reason for the success of the CM, we believe, is that it overcomes the “self-protective no” bias known from the RRT (and which may also be a potential problem in the ICT).We find rates of up to 22 percent of students who declared that they ever intentionally adopted a passage from someone else’s work without citing it. Severe plagiarism such as handing in someone else’s paper as one’s own, however, seems to be less frequent with rates of about 1 to 2 percent.

Two new models for survey sampling with sensitive characteristic: design and analysis

Sensitive topics or highly personal questions are often being asked in medical, psychological and sociological surveys. This paper proposes two new models (namely, the triangular and crosswise models) for survey sampling with the sensitive characteristics. We derive the maximum likelihood estimates (MLEs) and large-sample confidence intervals for the proportion of persons with sensitive characteristic. The modified MLEs and their asymptotic properties are developed. Under certain optimality criteria, the designs for the cooperative parameter are provided and the sample size formulas are given. We compare the efficiency of the two models based on the variance criterion. The proposed models have four advantages: neither model requires randomizing device, the models are easy to be implemented for both interviewer and interviewee, the interviewee does not face any sensitive questions, and both models can be applied to both face-to-face personal interviews and mail questionnaires.

Bayesian non-randomized response models for surveys with sensitive questions

Three non-randomized response (NRR) models (namely, the non-randomized triangular, crosswise and hidden sensitivity models) have recently been developed for analyzing dichotomous sensitive questions. Unlike existing randomized response (RR) models, no randomizing device is required for NRR models. This helps to reduce the cost, increase the efficiency, ensure the reproducibility, widen the applicability and encourage the cooperation. However, in applications (e.g., estimating the proportion of a rare sensitive attribute in a population) with highly skewed likelihood functions, classical asymptotic methods based on maximum likelihood estimates and their asymptotic standard errors may not be adequate. The purposes of this article are two folds. First, we develop Bayesian approaches for analyzing dichotomous sensitive questions based on the aforementioned NRR models. For both the non-randomized triangular and crosswise models, we obtain the exact posterior distribution and its explicit posterior moments, derive posterior mode via the EM algorithm and provide procedure for generating i.i.d. posterior samples. For the hidden sensitivity model, we consider Bayesian analysis under the commonly used conjugate Dirichlet prior. Second, noting that the covariance structure associated with the Dirichlet distribution is completely nonpositive, we propose three new joint priors for modeling independence structure with restrictions, negative correlation structure and positive correlation structure, respectively. A new hierarchical modeling strategy is provided. Importance sampling and data augmentation algorithm are employed to compute posterior moments and generate posterior samples. Three data sets from a sensitive sexual behavior study, an induced abortion study and a HIV study are used to illustrate the proposed methodologies.