This article will give you tips on how to analyze responses from a college doctoral student survey about authorship practices using AI-powered methods and practical prompts.
Choosing the right tools to analyze college doctoral student survey data
How you approach analyzing survey responses depends entirely on the type and structure of the data you’ve collected. There are some fundamental distinctions:
Quantitative data: If your survey has questions like “How many publications have you co-authored?” or “Did you receive clear authorship guidelines?” you can easily tally the responses in a spreadsheet program like Excel or Google Sheets. These tools let you count, graph, and pivot data for quick wins.
Qualitative data: Open-ended survey responses—such as why someone chose a particular authorship order, or reflections on fairness—are a goldmine for insights. But reading and manually coding all that text takes forever, and there’s only so much coffee you can drink. For this, you’ll need to lean on AI tools.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy, paste, and chat: One straightforward approach is exporting your open-ended responses and pasting them into ChatGPT, Claude, or similar. Then you prompt the AI for insights or summaries.
Not as smooth as it sounds: Handling lots of survey data this way isn’t fun. It requires a lot of copying and pasting, tracking prompts, managing AI context limits, and keeping things organized. Still, it’s a budget-friendly entry point—especially since ChatGPT is already used by 66% of students who tap into AI for academic work [1].
All-in-one tool like Specific
Purpose-built for survey analysis: Tools like Specific combine survey creation, follow-up probing, and AI-driven analysis under one roof. They’re built to help you collect richer qualitative data—in real time, Specific’s AI can ask follow-up questions to dig deeper on thorny topics like academic credit, transparency, or conflicts.
No more manual wrangling: Once the responses are in, Specific summarizes, clusters, and tags key themes for you. You can chat directly with the data (just like you would with ChatGPT), ask about trends, compare cohorts, and even filter which answers the AI sees. The workflow is clean, collaborative, and designed for scale.
Curious how it feels to run such a workflow end-to-end? Check out the survey generator for doctoral student authorship practices or learn more about Specific’s AI survey response analysis features.
Useful prompts that you can use to analyze college doctoral student survey data on authorship practices
The right AI prompt turns a heap of raw text into actionable insights, fast. Here are some practical examples I recommend to get the most out of your survey analysis with ChatGPT, Claude, or Specific.
Prompt for core ideas: If you just want the main themes, this prompt (used by Specific) is gold for surfacing what’s truly on the minds of doctoral students and their experiences with authorship practices:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
Give AI more context: AI always works better with details about your survey and your research goals. For instance:
You’re analyzing open feedback from a doctoral student survey on authorship practices at U.S. universities. The goal is to uncover pain points around authorship policies, clarity, and fairness across STEM and humanities disciplines. Prioritize issues that affect collaborations and publication submissions.
Dive deeper on big findings: Ask for more detail about a pattern or core idea:
Tell me more about “lack of clear authorship guidelines”
Validate specific concerns: To see if anyone raised a red flag or mentioned something you’re tracking, prompt:
Did anyone talk about “conflicts in author order”? Include quotes.
Prompt for personas: To discover the distinct “types” of doctoral students in your dataset and how they talk about authorship:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: To spot common struggles faced by doctoral students:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Motivations and drivers: Uncover what makes students tick or what influences their approach to authorship:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Sentiment analysis: Gauge whether the overall vibes are positive, negative, or mixed:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
For more question ideas specific to this audience and topic, visit our guide on the best questions for a college doctoral student survey about authorship practices.
How Specific analyzes qualitative data from different question types
One of the hardest parts of survey analysis is handling all the nuanced question types you might use. Specific is purpose-built to break this down automatically:
Open-ended questions (with or without followups): Specific groups and summarizes every response to these questions and, if there are follow-ups, connects the responses to dig deeper into why students feel a certain way about things like authorship order, fairness, or transparency.
Choices with followups: For questions like “Was your contribution recognized in the final manuscript? (Yes/No)+Why?” Specific creates a separate summary for the follow-up responses based on each answer. This approach spotlights differences between, for example, those who feel their work was valued and those who don’t.
NPS: If you measure Net Promoter Score (NPS) for satisfaction with authorship practices, Specific automatically segments summaries of follow-up answers for detractors, passives, and promoters. This makes it easy to spot what delights some and frustrates others—super useful for surfacing gaps or biases in existing authorship policies.
You can replicate this layering in ChatGPT, but it requires more grunt work—manually filtering responses and feeding them to the AI as separate batches.
Tactics to work around AI context size limits
Once you collect enough feedback from doctoral students, you’ll soon hit AI “context” limits—the max amount of data you can feed into a model like ChatGPT before it forgets the start of your text.
Filtering: Cut down on noise by having the AI review only those conversations where students actually replied to a selected question about, say, authorship conflicts or policy clarity. Less is more when it comes to insight density.
Cropping by question: Select only the most critical questions for analysis when sending to the AI. This lets you stay under the context limit, while still getting deep on the topics that matter most to your research goals.
Specific bakes both of these approaches into the workflow, so you focus on insights, not troubleshooting context errors. For an NPS-driven analysis path, test-drive the College Doctoral Student NPS survey builder.
Collaborative features for analyzing college doctoral student survey responses
Coordinating analysis across a research team—or bringing faculty advisors into the loop—can be a headache, especially with big qualitative datasets.
Analyze by chatting with AI: On Specific, just start a new AI chat with your survey data and dig into themes on authorship practices—anything from “How do STEM students differ from humanities?” to “What are the main causes of authorship disputes?”
Multiple chat threads and filters: You can run as many parallel chats as you want, each with its own filters (e.g., segment by year of study, mentor support, or country). Each chat clearly shows who started it, making teamwork seamless.
Track who said what: As you and your colleagues collaborate, every message displays the sender’s avatar. This keeps your analysis discussions transparent, and it’s way easier to build on each other’s findings when you know who made which observation.
Trying out collaborative data analysis for this audience? The how-to guide for creating surveys about doctoral authorship practices is a practical place to start.
Create your college doctoral student survey about authorship practices now
Start collecting actionable insights and uncover real opinions on authorship practices—effortlessly, collaboratively, with AI-powered analysis tailored to your college doctoral student audience.