Curb cheating in Master's project

One of the areas that require emphasis is
One of the areas that require emphasis is quantitative analysis. FILE PHOTO | NMG 

In conclusion of the Business Talk mini series on Kenyan higher education, graduate student research supervisors should incorporate the following techniques into their assessment of projects and theses to significantly reduce the high prevalence of master’s project cheating.

First, nearly all students do not know how to do the quantitative analysis portion of the project. Business statistics courses often show pre-computer formulas and do not provide step-by-step instruction on how to conduct and interpret robust quantitative analysis. Instead of supervisors making the false assumption that students know how to write the analysis section of their thesis by the time they get assigned a research supervisor, the supervisor should rather know that the master’s student likely does not comprehend how to already do analysis and, unless shown by the supervisor how to do it, will likely cheat to get it done by using third party paid writers.

Inasmuch, supervisors should require students to show them all the physical hard copies of the filled-out surveys along with the signing sheet where questionnaire respondents provide their names, signatures, and sometimes identification numbers. Supervisors should check against false completion whereby the same handwriting, pen, and unnatural patterns appear in the surveys, indicating that the sample population did not really fill them out.

Second, the supervisor should do a spot check of the student’s data entry accuracy. Compare several original surveys and the data entered into Excel, Nvivo, Stata, or SPSS. Then the supervisor should check the data entry parameters to make sure the student entered the right type of data, such as ordinal, nominal, or scale and the correct width and decimals. In quantitative projects, the supervisor can then quickly and easily on a one by one basis show the student how to do descriptive statistics, Cronbach’s alpha reliability tests, create summated scales, run correlations matrix, variance components, t-statics, and multiple regressions. More complicated exploratory factor analysis and confirmatory factor analysis in structured equation modelling requiring AMOS or Mplus software is usually reserved for doctoral students.

Third, the student then goes forth and applies the direct one-on-one learning based on their own data to follow through and complete the analysis. Supervisors should always check the direct SPSS output to see if it matches what appears in the student’s analysis section.

Sometimes also spot check the electronic author of the file and see if the Word document matches the student’s regular machine rather than a paid external third party.

Fourth, provide students with sources that instruct them how to write up the analysis results. Professor Andy Fields’ YouTube clips and textbooks found online prove useful for Kenyan master’s students writing their Chapter Four results. If a supervisor fails to provide resources or recommendations, the student will likely turn to cheating by again paying a third party to write their analysis section.

Fifth, every student bases their project off several seminal journal articles that underpin the relationships between the variables in their research questions and hypotheses.

Make the student provide the supervisor with electronic or paper copies of these important articles. Also, every article that contributes towards the survey questions should be shown to the supervisor to make sure that the student is not quoting indirect sources and that their literature review is not misstated.

Sixth, some universities do not require master’s or doctoral students to defend their dissertations. Others hold a defence that involves only hearing comments but not allowing the student to respond verbally. Unfortunately, in such scenarios, little accountability exists to prove original authorship and comprehension. Supervisors should not be afraid to question and challenge students about the meaning behind what they write.

Supervisors should avoid tedious debates on margin size, page numbers, table numbering, and instead focus the most attention on the content and making sure that it makes logical sense and that the student actually wrote and understands what appears in his or her project.

Seventh, in quantitative projects the supervisor should challenge data results whereby the intended scale does not fall within the appropriate reliability range in the Cronbach’s alpha test between 0.70 and 0.95. If reverse scale items are reversed in SPSS, for example, then a supervisor could let the reliability test fall to 0.65.

If the test results fall below 0.65 then either the scale is wrong by combining unlike constructs or respondents filled the survey in randomly.

If the results go over 0.95, then the student either cheated, the scale was poor by asking the same question over and over, or the respondents did not take the questionnaire seriously. The supervisor should visually see the original surveys for patterns indicating unserious completion and get to the bottom of the reliability test results.

While the above steps may seem like overwork for what some supervisors may be used to doing, they prove absolutely necessary. As technology advances and low labour rates for ghost writers prevail, cheating on master’s projects and theses will flourish if we in academia do not take more proactive steps to assert ethical standards and foster real student learning and growth.