Effective Data Collection in Research: Critical Considerations for Success
In scientific research, data collection is a crucial step toward achieving valid and reliable conclusions. While common methodologies, tools, and […]
PhDStats Advisor India
Research Design : strategy for Ph.D. scholars
Research design is a creative process rather than a research process and thus requires both theoretical knowledge and its application.
Research Types : strategy for Ph.D. scholars
As depicted in the figure above, I have described various types of research based on different categories, classes, groups, semantics
Research Methodology for PhD Scholars
As a PhD scholar in India, mastering research methodology is crucial for advancing your academic career. Research methodology encompasses a
Research Gap strategy for Ph.D. scholars
Target knowledge and population research gaps for your PhD By this time a PhD scholar would already have done the
Literature Search strategy for Ph.D. scholars
Conduct a rapid umbrella literature search for scoping and topic selection Objective of conducting a literature search at the stage
Topic Selection strategy for Ph.D. scholars
Data availability, research methodology and research gap are the three basic building blocks of any PhD research topic selection If
Critiques, Guides, PhDStats Advisor India
Critique: Shodhganga Shri Guru Ram Rai University (SGRRU) Patel Nagar Dehradun PhD Thesis
Shodhganga SGRRU Dehradun PhD thesis critique by PhDStats Advisor. Call +91 95575 61661 | Statistical analysis of research data for faculty.
Critiques, Guides, PhDStats Advisor India
Critique: Shodhganga PhD Thesis from Swami Rama Himalayan University (SRHU) Jolly Grant Dehradun
The overall data analysis health score for this PhD thesis evaluates to be -36%,. This PhD thesis does not attempt predictive or prescriptive analysis. Chapter-wise discussion is presented
Case Studies, Guides, Lessons Learned & Best Practices, PhDStats Advisor India
Case Study: Lessons Learned & Best Practices from a Quasi-Experimental Educational Intervention
This case study shares key lessons from automating and refining a quasi‑experimental educational intervention analysis—covering data governance, pipeline automation, diagnostics, and stakeholder communication.