Master’s Thesis SPSS Corrections
Scenario: A Master’s student had already revised and resubmitted their thesis after supervisor feedback. However, he was still asked to redo the analysis due to the use of incorrect statistical tests.
What went wrong: After reviewing the submitted work, we found several flaws. The student not only applied the wrong statistical tests but also made errors in data coding, assumption checks, and APA-compliant reporting.
Next step: This example illustrates a common challenge we handle in thesis data analysis.
Want to see how this project was corrected? Read the full Master’s Thesis SPSS Corrections Case Study.
PhD Dissertation Regression Model Revision
Scenario: A PhD candidate realized that their regression model wasn’t producing reliable results. In particular, the candidate explained that while the analysis was correct, none of the regression model was statistically significant. Some independent variables were highly correlated and told us that none of the model was addressing the research questions. causing multicollinearity issues, and the initial interpretation didn’t match the research questions.
What went wrong: After reviewing the dataset and the submitted analysis, it became clear that assumption checks were incomplete. We also noted that there was a multicollinearity issue and wrong variable computation. Additionally, some variables needed transformation.
Next step: This is a typical scenario we encounter with PhD-level regression projects. We’ve received quite a number of such issues from PhD scholars looking for PhD data analysis services.
Want to see how we helped this candidate fix the model and interpret the results correctly? Read the full PhD Regression Model Revision Case Study.
Employee Turnover Logistic Analysis
Scenario: A researcher studying employee turnover in healthcare organizations wanted to identify key factors influencing staff retention. They collected survey data on job satisfaction, work-life balance, and organizational support. However, the researcher struggled to identify which variables actually predicted turnover.
What went wrong: Initial attempts at logistic regression were incomplete. Some variables were coded inconsistently, assumptions weren’t checked, and the interpretation of odds ratios wasn’t clear.
Next step: This example highlights the common challenges in workforce analytics and predictive modeling.
Curious how we corrected the analysis and produced actionable results? See the full Healthcare Employee Turnover Analysis Case Study.
Academic Performance and Social Media Usage
Scenario: A study explored how social media usage impacts university students’ academic performance. The student researcher collected data on study habits, sleep hours, and social media hours. However, she wasn’t sure how to account for multiple influencing factors.
What went wrong: The initial analysis didn’t control for confounding variables. We also noted that the interpretation of the regression results was incorrect. Further investigation revealed that some variables actually needed to be recoded and the assumption test rechecked.
Next step: This is a common type of student project where multiple variables interact, and careful analysis is needed.
See how we helped this researcher correctly analyze the data and interpret results in the Student Academic Performance Analysis Case Study.
Online Learning Engagement Study
Scenario: A student wanted to measure the impact of online learning tools on engagement and academic performance. They collected data from quizzes, discussion participation, and assignment completion, but struggled to connect the variables statistically.
What went wrong: Our dissertation statisticians investigated the issue and realized that the student had missed assumption checks and some of the data points were miscoded. The initial write-up didn’t clearly explain how the results addressed the research questions.
Next step: This example reflects common challenges in analyzing education technology and engagement metrics.
Interested in how we helped the student make sense of the data and improve the results section? Check the full Online Learning Engagement Analysis Case Study.
