Quantitative Analysis
Frequency tables, Likert-scale statistics (means & SD), bar charts, AI-written interpretations, discussion, and numbered recommendations — ready for your thesis or manuscript.
₦5,000 / $5 per survey
Resources · Real report preview
Sample Reports
See exactly what Avenza generates after you close a survey and run analysis. Below is a real Quantitative Analysis report — tables, charts, narrative interpretation, discussion, and recommendations — exported from the platform.
Thematic Analysis
Inductive coding of open-ended responses — themes, frequencies, and narrative synthesis for qualitative items. Run from the Analytics dashboard after collecting responses.
₦10,000 / $10 per survey
18
Completed responses
100%
Completion rate
11
Survey questions
5
Report sections
Executive Summary
Note: Narrative sections are AI-assisted; Avenza recommends verifying all statistics against the tables in your exported report.
The study achieved a 100% completion rate among 18 respondents, providing a comprehensive snapshot of AI integration in academic workflows. Respondents strongly agreed that AI improves writing quality (M = 4.56, SD = 0.51) and research productivity (M = 4.47, SD = 0.62).
Despite this optimism, a gap exists between perceived value and actual proficiency: 66.7% describe their AI knowledge as only moderate, and the same proportion identified lack of training as the primary barrier to wider adoption in Nigerian universities.
Distribution of Respondents
Every categorical question in the exported report includes a frequency table, percentage breakdown, and an AI-written interpretation paragraph tied to the study topic.
Table 2 — Gender (n = 18)
| Category | Frequency | Percent |
|---|---|---|
| Male | 16 | 88.9% |
| Female | 2 | 11.1% |
| Total | 18 | 100% |
Table 3 — Academic Rank
| Rank | Frequency | Percent |
|---|---|---|
| Assistant Lecturer | 5 | 27.8% |
| Lecturer I | 5 | 27.8% |
| Senior Lecturer | 3 | 16.7% |
| Lecturer II | 2 | 11.1% |
| Graduate Assistant | 1 | 5.6% |
| Professor | 1 | 5.6% |
| Associate Professor | 0 | 0.0% |
The rank profile shows more than half the sample in early-to-mid lecturer positions — important context when interpreting AI adoption and training needs.
AI Awareness & Tool Usage
2.94
SD = 0.73 · 5-point scale
How would you rate your current level of AI knowledge?
3.44
SD = 0.92 · frequency scale
How often do you use AI tools (ChatGPT, Gemini, Claude, etc.) for academic work?
Table 7 — Which AI tools have you used?
Multiple-response item — percentages based on n = 18; do not sum to 100%.
Perception of AI in Research
Likert clusters include item means, standard deviations, and a grand mean — each with a dedicated interpretation paragraph in the full export.
4.56
SD = 0.51
AI can significantly improve academic writing quality. 100% agreed.
4.47
SD = 0.62
AI can improve research productivity and efficiency.
3.94
SD = 0.87 · Grand mean 4.32
I am concerned about ethical issues associated with AI use in research.
Barriers to AI Adoption
Biggest challenges preventing wider AI adoption in Nigerian universities
The full report also includes a Discussion section (patterns, relationships, implications), a Methodology block, and open-ended response previews — with Thematic Analysis available for full qualitative coding.
Key Findings
1
High perceived utility: 100% of respondents agreed AI improves writing quality (M = 4.56); 94% agreed it improves productivity (M = 4.47).
2
Moderate self-efficacy: 66.7% rated their AI knowledge as moderate (M = 2.94) — perceived value outpaces technical confidence.
3
ChatGPT dominance: 88.9% had used ChatGPT; Gemini (50%) and Claude (33.3%) were secondary choices.
4
Training is the top barrier: 66.7% cited lack of training; 50% cited lack of awareness; 44.4% cited poor internet access.
5
Ethical awareness remains: 72.2% agreed or strongly agreed they are concerned about ethical issues (M = 3.94).
Recommendations (from report)
Avenza generates numbered, evidence-linked recommendations tied directly to your data:
- Establish AI literacy programs — supported by 66.7% citing lack of training as the primary barrier.
- Develop ethical guidelines — 72.2% expressed concern about ethical implications of AI in research.
- Diversify toolkits beyond ChatGPT — Perplexity (27.8%) and DeepSeek (22.2%) remain underused despite research value.
- Improve digital infrastructure — 44.4% cited poor internet access as a structural challenge.
- Target early-career academics — Assistant Lecturers and Lecturer I combined represent 55.6% of the sample.
Every Quantitative Analysis export includes
- Study introduction, objectives, methodology, and analysis plan
- Survey overview with completion statistics
- Numbered tables with frequency, valid %, and cumulative %
- Bar chart references for each categorical item
- AI-written interpretation under every table
- Discussion (patterns, relationships, implications)
- Key findings, recommendations, and conclusion
- Downloadable Word (.docx) format
Open-ended items show sample responses in the quantitative report; run Thematic Analysis from your dashboard for full coded themes.
Generate your own report
Upload a questionnaire, collect responses, then run Quantitative Analysis from your dashboard.