Plagiarism: An Experimental Survey Measuring Plagiarism Using the...

In GLMMRR: Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data

An Experimental Survey Measuring Plagiarism Using the Crosswise Model

Description

A dataset containing the responses to sensitive questions about plagiarism and other attributes of 812 students. The crosswise model (CM) and direct questioning (DQ) were utilized to gather the data. Each row holds the response to one question for one student. The variables are as follows:

Usage

data(Plagiarism)

Format

A data frame in long format with 812 rows and 24 variables

Details

• id. Identification code of the student

• question. Which question was asked (1 and 3: Partial Plagiarism, 2 and 4: Severe Plagiarism)

• response. Binary randomized response

• gender. Gender of the student (0: male, 1: female)

• age. Age in years

• nationality. Nationality of the student (0: German or Swiss, 1: other)

• no_papers. Number of papers

• uni. Location of data collection (1: ETH Zurich, 2: LMU Munich, 3: University Leipzig)

• course. Course in which the data was collected

• Aspired_Degree. Aspired degree of the student

• Semester. semesters enrolled

• ur_none. Used resources: none

• ur_books. Used resources: books

• ur_art. Used resources: articles

• ur_int. Used resources: internet

• ur_fsp. Used resources: fellow students' papers

• ur_other. Used resources: other

• preading. Proofreading

• gradesf. Satisfaction with grades

• pp. Plagiarism indicator (0: Severe Plagiarism, 1: Partial Plagiarism)

• RR. Randomized Response indicator (0: DQ, 1: Crosswise)

• RRp1. Randomized Response parameter p1

• RRp2. Randomized Response parameter p2

• RRmodel. Randomized Response Model

Author(s)

Ben Jann and Laurcence Brandenberger

References

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.7892/boris.51190")}

GLMMRR documentation built on Nov. 5, 2025, 6:20 p.m.