Content
# Mixed Methods Research MCP Server
### *Your AI-Powered Research Assistant for Mixed Methods Analysis*
> **For Academic Researchers**: A comprehensive Model Context Protocol (MCP) server designed to streamline your mixed methods research workflow - from raw data collection to publication-ready joint displays and integrated findings.
---
## 🎓 What is This Tool?
As a researcher conducting mixed methods studies, you face the challenge of integrating quantitative statistics with qualitative themes, maintaining research rigor while managing complex datasets, and documenting your analytical decisions. This MCP server acts as your intelligent research assistant, available 24/7 through Claude Code or other MCP-compatible platforms.
Think of it as having a research methods expert, a statistician, and a qualitative coding specialist all working together in one integrated system.
### 🌟 Key Capabilities
- **📊 Quantitative Analysis**: Descriptive statistics, correlations, t-tests, ANOVA, regression, outlier detection
- **📝 Qualitative Coding**: Document management, thematic analysis, code co-occurrence, memo writing
- **🔄 Methods Integration**: Joint displays, purposeful sampling, quantitizing/qualitizing transformations
- **📈 Publication-Ready Visuals**: 300 DPI charts, correlation heatmaps, word clouds, theme networks
- **🧠 Research Memory**: Persistent context tracking, decision logging, analysis audit trail
- **🤖 AI-Assisted Workflows**: Automated coding suggestions, statistical test recommendations
---
## 📖 Understanding Mixed Methods Research with This Tool
### The Classic Mixed Methods Designs
#### **1. Convergent Design** (Parallel Analysis)
```
Quantitative Data Collection → QUAN Analysis
↓
INTEGRATION & COMPARISON
↓
Qualitative Data Collection → QUAL Analysis
```
**Example Research Question**: *"How do student anxiety levels (quantitative) relate to their lived experiences of online learning (qualitative)?"*
**Workflow with this tool**:
1. Load survey data (`load_quantitative_data`)
2. Analyze anxiety scores (`describe_statistics`, `correlation_analysis`)
3. Load interview transcripts (`load_documents`)
4. Code themes about online learning experiences (`apply_code`)
5. Create joint display linking anxiety levels to themes (`create_joint_display`)
6. Compare patterns (`compare_patterns`)
---
#### **2. Explanatory Sequential Design** (QUAN → qual)
```
Phase 1: QUANTITATIVE Phase 2: QUALITATIVE
Survey Analysis → Follow-up Interviews
Identify Patterns → Explain Why Patterns Exist
↓
INTEGRATION
```
**Example Research Question**: *"What factors predict teacher burnout (quantitative), and why do these factors matter to teachers (qualitative)?"*
**Workflow with this tool**:
1. Analyze teacher survey data (`load_quantitative_data`, `compare_groups`)
2. Identify high/low burnout cases (`identify_outliers`, `filter_dataset`)
3. Use purposeful sampling to select interview participants (`purposeful_sample`)
4. Conduct and code interviews with selected teachers (`load_documents`, `apply_code`)
5. Merge quantitative scores with qualitative themes (`merge_by_id`)
6. Generate integrated report (`export_integrated_report`)
---
#### **3. Exploratory Sequential Design** (QUAL → quan)
```
Phase 1: QUALITATIVE Phase 2: QUANTITATIVE
Interviews/Focus Groups → Survey Development
Generate Themes → Test Themes at Scale
↓
INTEGRATION
```
**Workflow with this tool**:
1. Code exploratory interviews (`load_documents`, `apply_code`)
2. Identify emerging themes (`theme_frequency`, `code_cooccurrence`)
3. Quantitize themes into variables (`quantitize_codes`)
4. Load large-scale survey based on themes (`load_quantitative_data`)
5. Test relationships statistically (`correlation_analysis`, `compare_groups`)
---
## 🚀 Getting Started: Installation & Setup
### Step 1: Install the Server
#### **Option A: Automatic Setup** (Recommended for most researchers)
```bash
cd /home/tayyabcheema777/MMR_MCP/mixed-methods-mcp
./scripts/setup-env.sh
source .venv/bin/activate
```
This creates a virtual environment and installs all dependencies automatically.
#### **Option B: Manual Setup** (For advanced users)
```bash
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
pip install -r requirements.txt
```
### Step 2: Connect to Claude Code (or other MCP client)
1. **Start the MCP server**:
```bash
mixed-methods-mcp
```
2. **Configure Claude Code**:
- Open Claude Code settings
- Add a new MCP server
- Set command to: `mixed-methods-mcp`
- The server will automatically register all 40+ research tools
3. **Verify connection**:
- Ask Claude: *"Can you show me my research context status?"*
- Claude will use the `context_status` tool to confirm connection
---
## 📂 File Organization: Where Everything Goes
### **Your Research Files**
You have complete flexibility in where you store your files. Here are three common approaches:
#### **Approach 1: Use the Examples Directory** (Quick Start)
```
/home/tayyabcheema777/MMR_MCP/mixed-methods-mcp/examples/
├── sample_survey.csv ← Quantitative data
├── sample_interview_1.txt ← Qualitative transcripts
├── sample_interview_2.txt
└── sample_interview_3.txt
```
**To use**: `load_quantitative_data(path="examples/sample_survey.csv")`
---
#### **Approach 2: Create a Dedicated Research Project Folder** (Recommended for Real Research)
```bash
# Create your research project structure
mkdir -p ~/my_dissertation_study/{data,interviews,outputs}
```
```
~/my_dissertation_study/
├── data/
│ ├── pilot_survey.xlsx ← Your Excel files
│ ├── main_survey.csv ← Your CSV files
│ └── demographics.xlsx
├── interviews/
│ ├── participant_001.txt ← Interview transcripts
│ ├── participant_002.txt
│ ├── focus_group_1.txt
│ └── field_notes.txt
└── outputs/
├── reports/ ← Generated reports
├── visualizations/ ← Charts and graphs
└── codebooks/ ← Coding schemes
```
**To use**:
```python
# Through Claude Code, simply say:
"Load the quantitative data from ~/my_dissertation_study/data/main_survey.csv"
"Load all interview transcripts from ~/my_dissertation_study/interviews/"
"Export visualizations to ~/my_dissertation_study/outputs/visualizations/"
```
---
#### **Approach 3: Automated Processing Folders** (For Batch Analysis)
```bash
# For automatic quantitative processing
mkdir -p ~/auto_analysis/quant/{input,processed,reports}
# For automatic qualitative processing
mkdir -p ~/auto_analysis/qual/{input,processed,stage1,stage2,stage3}
```
Drop files into `input/` folders and the system processes them automatically.
---
### **System-Managed Files** (Automatic - No Action Needed)
| What | Where | Purpose |
|------|-------|---------|
| **Research Context** | `~/.mixed_methods_mcp/project_context.json` | Remembers your loaded data, codes, decisions |
| **Analysis History** | Same file | Logs every analysis step you take |
| **Coded Segments** | Same file | Stores all your qualitative codes |
| **Research Decisions** | Same file | Documents your analytical choices |
**Note**: This file is automatically created and updated. You never need to manually edit it.
---
## 🔧 Complete Tool Reference
### 📊 **Quantitative Analysis Tools** (8 tools)
| Tool | What It Does | Example Research Use |
|------|--------------|---------------------|
| `load_quantitative_data` | Import Excel/CSV files | Load your survey responses |
| `describe_statistics` | Mean, SD, skewness, kurtosis | Describe demographic variables |
| `correlation_analysis` | Pearson, Spearman, Kendall | Test relationships between variables |
| `compare_groups` | t-test, ANOVA | Compare treatment vs. control groups |
| `identify_outliers` | Z-score, IQR methods | Find unusual response patterns |
| `filter_dataset` | Subset data by conditions | Select only completed surveys |
| `visualize_distribution` | Histograms with KDE | Check if scores are normally distributed |
| `visualize_relationships` | Scatter plots with regression | Show relationship between two variables |
---
### 📝 **Qualitative Analysis Tools** (8 tools)
| Tool | What It Does | Example Research Use |
|------|--------------|---------------------|
| `load_documents` | Import text files | Load interview transcripts |
| `search_documents` | Find keywords/patterns | Search for mentions of "anxiety" |
| `apply_code` | Tag text segments with themes | Code segment as "coping strategies" |
| `retrieve_coded_segments` | Get all codes/segments | Review all "teacher burnout" codes |
| `generate_wordcloud` | Visual word frequency | Show most common words in interviews |
| `theme_frequency` | Count code occurrences | See which themes appear most often |
| `code_cooccurrence` | Find co-occurring themes | Find themes that appear together |
| `export_codebook` | Document coding scheme | Export your codes to Word/PDF |
---
### 🔄 **Integration Tools** (9 tools)
| Tool | What It Does | Example Research Use |
|------|--------------|---------------------|
| `merge_by_id` | Link qual + quant by participant ID | Match survey scores to interview data |
| `create_joint_display` | Side-by-side qual/quant comparison | Show anxiety scores next to coping themes |
| `purposeful_sample` | Select cases based on criteria | Choose extreme cases for interviews |
| `quantitize_codes` | Convert themes to numbers | Count how many times each theme appears |
| `compare_patterns` | Test qual/quant convergence | Check if themes align with statistics |
| `integration_matrix_viz` | Visual integration display | Matrix showing codes × scores |
| `create_integration_memo` | Document integration insights | Write about how data sources relate |
| `export_integrated_report` | Comprehensive mixed methods report | Generate final integrated findings |
| `context_status` | Check current research state | See what data is loaded and next steps |
---
### 📈 **Visualization Tools** (6 tools)
| Tool | What It Does | Publication Quality |
|------|--------------|---------------------|
| `correlation_heatmap` | Matrix of all correlations | 300 DPI, colorblind-friendly |
| `comparison_chart` | Group comparison bars/boxes | APA-style formatting |
| `wordcloud_by_group` | Separate clouds per group | High resolution PNG |
| `theme_network` | Network diagram of themes | Shows theme relationships |
| `create_dashboard` | Multi-panel overview | Comprehensive visual summary |
| `export_visualizations` | Save all charts to files | Ready for journal submission |
---
### 🧠 **Research Memory Tools** (5 tools)
| Tool | What It Does | Why It Matters |
|------|--------------|----------------|
| `save_context` | Store current research state | Resume work in next session |
| `load_context` | Retrieve saved state | Pick up where you left off |
| `log_decision` | Document analytical choice | Create audit trail for dissertation |
| `get_analysis_history` | Review past analyses | See what you've already done |
| `context_status` | Current project summary | Quick overview of loaded data |
---
## 💡 Real Research Scenarios
### **Scenario 1: Dissertation Study on Student Engagement**
**Your Research Question**: *"What is the relationship between self-efficacy and engagement, and how do students with different engagement levels describe their learning experiences?"*
**Step-by-Step Workflow**:
```python
# Step 1: Load and analyze your survey data (n=150 students)
"Load the survey data from ~/dissertation/student_survey.xlsx"
# System responds with variable summary and next steps
# Step 2: Explore relationships
"Show me descriptive statistics for self_efficacy and engagement scores"
"Create a correlation heatmap for all variables"
"Test if high vs low self-efficacy groups differ in engagement"
# Step 3: Select interview participants (Explanatory Sequential)
"Use purposeful sampling to select 6 students with extreme engagement scores"
# System identifies 3 high and 3 low engagement students
# Step 4: Load and code interview transcripts
"Load all interview files from ~/dissertation/interviews/"
"Search for text segments mentioning motivation or barriers"
# Review results, then code them
"Apply code 'intrinsic motivation' to document participant_high_1,
characters 450-680, with memo 'student describes genuine interest in subject'"
# Step 5: Create integrated findings
"Merge the survey data with interview codes using participant_id"
"Create a joint display showing engagement scores alongside motivation themes"
"Compare patterns between quantitative engagement and qualitative themes"
# Step 6: Generate outputs
"Export correlation heatmap to ~/dissertation/outputs/figures/"
"Export integrated report to ~/dissertation/outputs/chapter4_findings.docx"
"Save my current research context"
# Step 7: Document your process
"Log decision: 'Used median split for high/low engagement groups'
with rationale 'balanced sample sizes needed for qualitative follow-up'"
```
---
### **Scenario 2: Program Evaluation Study**
**Your Research Question**: *"How effective was the professional development program, and what specific aspects did teachers find most valuable?"*
```python
# Convergent Design - analyze both data sources in parallel
# Quantitative strand
"Load pre and post program survey data from ~/program_eval/surveys.xlsx"
"Compare pre_test vs post_test scores for teacher confidence"
"Identify outliers in the improvement scores"
"Create comparison chart for pre vs post across all domains"
# Qualitative strand (at the same time)
"Load focus group transcripts from ~/program_eval/focus_groups/"
"Apply codes for themes like 'practical_strategies', 'peer_collaboration',
'time_constraints', 'curriculum_alignment'"
"Generate word cloud to see most frequent terms"
"Show theme frequency to identify most common themes"
# Integration
"Create joint display with teacher_id, improvement_score, and primary themes"
"Compare patterns - do teachers with high improvement mention different themes?"
"Export integrated report documenting both strands' findings"
```
---
### **Scenario 3: Grounded Theory Exploration**
**Your Research Question**: *"What factors influence faculty adoption of educational technology?"* (Exploratory Sequential)
```python
# Phase 1: Qualitative exploration
"Load initial interview transcripts from ~/tech_adoption/phase1_interviews/"
"Apply open codes as themes emerge from the data"
"Check code co-occurrence to find related themes"
"Generate theme network to visualize relationships"
"Quantitize codes to see frequency distribution"
# Phase 2: Survey development (based on Phase 1 themes)
"Load Phase 2 survey data testing identified themes at scale"
"Test correlations between the factors identified in Phase 1"
"Use regression to identify strongest predictors"
# Integration
"Compare patterns between qualitative themes and quantitative factors"
"Create integration memo documenting how qual themes were confirmed/refined"
```
---
## 🎯 Visual Workflow Guide
```
┌─────────────────────────────────────────────────────────────┐
│ YOUR RESEARCH WORKFLOW │
└─────────────────────────────────────────────────────────────┘
QUANTITATIVE STRAND QUALITATIVE STRAND
↓ ↓
┌──────────────────────┐ ┌──────────────────────┐
│ Load Survey Data │ │ Load Interview Files │
│ (.xlsx, .csv) │ │ (.txt, .docx) │
└──────────┬───────────┘ └──────────┬───────────┘
│ │
↓ ↓
┌──────────────────────┐ ┌──────────────────────┐
│ Descriptive Stats │ │ Read & Code Text │
│ • Mean, SD │ │ • Apply themes │
│ • Distributions │ │ • Write memos │
└──────────┬───────────┘ └──────────┬───────────┘
│ │
↓ ↓
┌──────────────────────┐ ┌──────────────────────┐
│ Inferential Tests │ │ Thematic Analysis │
│ • Correlations │ │ • Theme frequency │
│ • Group comparisons │ │ • Co-occurrence │
│ • Regression │ │ • Word clouds │
└──────────┬───────────┘ └──────────┬───────────┘
│ │
└──────────────┬───────────────┘
↓
┌──────────────────────────┐
│ INTEGRATION PHASE │
│ • Merge by ID │
│ • Joint displays │
│ • Pattern comparison │
│ • Integration memos │
└─────────────┬────────────┘
↓
┌──────────────────────────┐
│ FINAL OUTPUTS │
│ • Integrated report │
│ • Publication figures │
│ • Codebook │
│ • Research context │
└──────────────────────────┘
```
---
## 🤖 AI-Assisted Automation Features
### **Automated Qualitative Coding Pipeline**
Perfect for researchers with large volumes of interview data who need consistent, systematic coding.
**What it does**: Automatically processes interview transcripts through 3 stages of coding using AI assistance.
#### **Stage 1: Initial Open Coding**
- Segments your transcripts (1-3 sentences per unit)
- Assigns preliminary codes grounded in the text
- No interpretation - only codes directly supported by quotes
- Output: `Stage1_Initial_Coding.xlsx`
#### **Stage 2: Theme Development**
- Groups related codes from Stage 1
- Identifies emerging themes and sub-themes
- All themes must be grounded in actual segments (no speculation)
- Output: `Stage2_Code_Grouping.xlsx`
#### **Stage 3: Theoretical Framework**
- Links themes to your research questions
- Creates final thematic framework
- Every theme supported by direct quotes and segment IDs
- Output: `Stage3_Thematic_Framework.xlsx`
**How to use**:
```bash
# Set up your folders
mkdir -p ~/auto_coding/{input,processed,outputs}
# Start the automated watcher
mixed-methods-mcp-cli watch ~/auto_coding/input \
--processed-dir ~/auto_coding/processed \
--stage1-cmd "your-ai-command-stage1" \
--stage2-cmd "your-ai-command-stage2" \
--stage3-cmd "your-ai-command-stage3"
# Drop your transcript files (.csv or .xlsx) into ~/auto_coding/input/
# The system processes them automatically every 30 seconds
```
**Research Integrity Notes**:
- All codes are grounded in actual text (no external knowledge)
- Every theme linked to specific segment IDs (full audit trail)
- Verbatim quotes preserved throughout all stages
- You maintain full control - review and refine all AI suggestions
---
### **Automated Statistical Analysis Pipeline**
Perfect for researchers who want statistical guidance while learning or managing multiple datasets.
**What it does**: Analyzes your dataset structure, recommends appropriate tests, and generates statistical reports.
#### **The Process**:
1. **Dataset Detection**: Monitors folder for new Excel/CSV files
2. **Statistical Consultation**: AI analyzes your variables and suggests tests:
- "Based on your continuous DV and binary IV, an independent t-test is appropriate"
- "With 3+ groups, consider one-way ANOVA"
- "Check normality assumptions with Shapiro-Wilk first"
3. **Your Approval**: Reviews recommendations and asks permission
4. **Automated Execution**: Runs approved tests (t-tests, ANOVA, correlations, chi-square, etc.)
5. **Report Generation**: Creates Word and PDF reports with:
- Tables of results
- Effect sizes
- Assumption checks
- Embedded visualizations
- Statistical interpretations
**How to use**:
```bash
# Set up folders
mkdir -p ~/auto_stats/{input,processed,reports}
# Start the watcher
mixed-methods-mcp-cli quant-watch ~/auto_stats/input \
--processed-dir ~/auto_stats/processed \
--reports-dir ~/auto_stats/reports \
--llm-cmd "your-statistical-advisor-command"
# Place your dataset in ~/auto_stats/input/
# Review recommended tests
# Approve or decline
# Receive comprehensive statistical report
```
**Supported Tests**:
- Independent & Paired t-tests
- One-way & Repeated Measures ANOVA
- Pearson, Spearman, Kendall correlations
- Chi-square (independence & goodness-of-fit)
- Linear & Logistic Regression
- Kruskal-Wallis & Mann-Whitney (nonparametric)
- Shapiro-Wilk (normality)
- Cronbach's Alpha (reliability)
---
## 📊 Understanding Your Research Context
At any time during your research, you can check your current status:
```python
"What's my current research context status?"
```
**The system tells you**:
- ✅ Which quantitative dataset is loaded (path + variable names)
- ✅ How many qualitative documents are loaded (names + word counts)
- ✅ Number of coded segments and unique codes
- ✅ Number of decisions logged in your audit trail
- ✅ Suggested next steps based on what you've done so far
**Example Response**:
```json
{
"quantitative_data": "~/dissertation/survey_n150.xlsx",
"loaded_variables": ["participant_id", "self_efficacy", "engagement", "gender", "year"],
"qualitative_documents": 6,
"document_names": ["participant_001.txt", "participant_002.txt", ...],
"coded_segments": 45,
"unique_codes": 8,
"codes_used": ["intrinsic_motivation", "time_management", "peer_support", ...],
"decisions_logged": 3,
"suggestions": [
"Consider running correlation analysis on self_efficacy and engagement",
"You have both data types - ready to create joint display",
"Export your codebook before proceeding to integration"
]
}
```
---
## 🎓 Methodological Rigor & Quality Criteria
This tool helps you maintain research quality standards:
### **Quantitative Rigor**
- ✅ **Assumption Checking**: Normality tests, homogeneity of variance
- ✅ **Effect Sizes**: Not just p-values - includes Cohen's d, eta squared, etc.
- ✅ **Outlier Detection**: Multiple methods (z-score, IQR)
- ✅ **Missing Data**: Automatically reports missing values
- ✅ **Audit Trail**: All analyses logged with timestamps
### **Qualitative Trustworthiness**
- ✅ **Grounding**: All codes linked to specific text segments
- ✅ **Transparency**: Full codebook export with definitions
- ✅ **Audit Trail**: Every coding decision documented
- ✅ **Member Checking**: Export segments for participant verification
- ✅ **Thick Description**: Memos attached to coded segments
### **Integration Quality**
- ✅ **Convergence Testing**: Compare patterns statistically
- ✅ **Joint Displays**: Side-by-side comparison tables
- ✅ **Integration Memos**: Document how strands relate
- ✅ **Purposeful Sampling**: Systematic case selection
- ✅ **Meta-Inferences**: Supported by both data types
---
## 📚 Sample Data for Learning
We provide realistic sample data so you can practice before using your own:
### **Quantitative Sample**: `examples/sample_survey.csv`
- **What**: Student survey responses (n=20)
- **Variables**:
- `competence_score` (1-10 scale)
- `confidence_score` (1-10 scale)
- `engagement_score` (1-10 scale)
- `anxiety_score` (1-10 scale)
- `years_experience` (continuous)
- **Use for**: Practicing correlations, group comparisons, visualizations
### **Qualitative Samples**: `examples/sample_interview_*.txt`
- **What**: Three student interview transcripts (200-300 words each)
- **Topics**: Online learning experiences, challenges, coping strategies
- **Use for**: Practicing coding, theme development, word clouds
### **Practice Workflow**:
```python
# Start with the samples to learn the system
"Load examples/sample_survey.csv for practice"
"Show me correlations between competence, confidence, and engagement"
"Create a correlation heatmap"
"Load all sample interviews from examples/ folder"
"Search for mentions of 'challenge' or 'difficult'"
"Apply code 'learning_barriers' to the relevant segments"
"Generate word cloud from all interviews"
"Show theme frequency"
```
---
## 🔍 Troubleshooting Common Issues
### **"ModuleNotFoundError: No module named 'mcp'"**
**Solution**: Activate your virtual environment first
```bash
cd /home/tayyabcheema777/MMR_MCP/mixed-methods-mcp
source .venv/bin/activate
```
### **"FileNotFoundError: Quantitative data file not found"**
**Solution**: Use absolute paths or confirm you're in the right directory
```bash
# Instead of: "examples/data.csv"
# Use: "/home/tayyabcheema777/MMR_MCP/mixed-methods-mcp/examples/data.csv"
# Or: "~/MMR_MCP/mixed-methods-mcp/examples/data.csv"
```
### **"Permission denied writing to ~/.mixed_methods_mcp"**
**Solution**: Check folder permissions or delete and recreate
```bash
rm -rf ~/.mixed_methods_mcp
# The system will recreate it automatically
```
### **Visualizations not appearing**
**Solution**: Kaleido must be installed for Plotly exports
```bash
pip install kaleido
# Or re-run setup: ./scripts/setup-env.sh
```
### **Context seems incorrect or corrupted**
**Solution**: Reset your research context
```bash
rm ~/.mixed_methods_mcp/project_context.json
# The system creates a fresh one
```
---
## 🎯 Best Practices for Researchers
### **1. Document Your Decisions**
```python
"Log decision: Used median split for creating high/low groups
with rationale: Balanced sample sizes needed for qualitative follow-up
with alternatives: Could have used tertile split or quartiles"
```
### **2. Save Context Regularly**
```python
# After major analysis steps:
"Save my research context"
# This protects your work and creates timestamps
```
### **3. Export Codebook Early and Often**
```python
"Export codebook to ~/my_study/codebook_v1.docx"
# Update version numbers as your coding evolves
```
### **4. Use Integration Memos**
```python
"Create integration memo with these highlights:
- High anxiety students mentioned time management issues (convergence)
- Low anxiety students described peer support (qualitative expansion)
- Gender differences in coping strategies (divergence - needs explanation)"
```
### **5. Maintain Audit Trail**
```python
# Check your history before major decisions
"Show me my analysis history for the last 20 steps"
```
---
## 📖 Methodological References
This tool supports research designs described in:
- Creswell, J. W., & Plano Clark, V. L. (2018). *Designing and Conducting Mixed Methods Research* (3rd ed.). SAGE.
- Fetters, M. D. (2020). *The Mixed Methods Research Workbook*. SAGE.
- Morse, J. M., & Niehaus, L. (2009). *Mixed Method Design: Principles and Procedures*. Routledge.
- Tashakkori, A., & Teddlie, C. (2010). *SAGE Handbook of Mixed Methods in Social & Behavioral Research*. SAGE.
For statistical tests, see:
- Field, A. (2018). *Discovering Statistics Using IBM SPSS Statistics* (5th ed.). SAGE.
- Cohen, J. (1988). *Statistical Power Analysis for the Behavioral Sciences* (2nd ed.). Routledge.
For qualitative coding:
- Saldaña, J. (2021). *The Coding Manual for Qualitative Researchers* (4th ed.). SAGE.
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. *Qualitative Research in Psychology, 3*(2), 77-101.
---
## 🛠️ Advanced Features
### **Command-Line Workflows** (Without MCP Client)
Run pre-defined analysis pipelines:
```bash
# Create a workflow YAML file: my_workflow.yaml
# Then execute it:
mixed-methods-mcp-cli run my_workflow.yaml --save-output results.json
```
Example `my_workflow.yaml`:
```yaml
steps:
- tool: load_quantitative_data
args:
path: data/survey.xlsx
- tool: describe_statistics
- tool: correlation_analysis
args:
method: pearson
- tool: load_documents
args:
path: interviews/
pattern: "*.txt"
- tool: create_joint_display
args:
id_column: participant_id
metrics: ["anxiety_score", "engagement_score"]
```
### **Custom Python Integration**
```python
from mixed_methods_mcp import QuantitativeToolkit, QualitativeToolkit
# Use toolkits directly in your Python scripts
quant = QuantitativeToolkit()
quant.load_quantitative_data("data.xlsx")
stats = quant.describe_statistics()
qual = QualitativeToolkit()
qual.load_documents("interviews/")
qual.apply_code("interview1.txt", "coping_strategy", 100, 250)
```
---
## 🤝 Contributing & Support
### **Found a Bug or Have a Suggestion?**
This is research software - feedback from the research community is invaluable!
- **Report Issues**: Document what happened, what you expected, and steps to reproduce
- **Request Features**: Describe your research use case and how a feature would help
- **Share Workflows**: Contribute example workflows for different research designs
### **Roadmap** (Community-Driven)
- 📊 APA-formatted tables (direct export to Word)
- 📝 Enhanced memo templates (research journal, reflexivity notes)
- 🔄 MAXQDA/NVivo export compatibility
- 📈 SPSS/R syntax generation from analyses
- 🌐 Multi-language support for international research
---
## 📜 License & Citation
**License**: MIT License - Free for academic and commercial use
**How to Cite** (APA 7th):
```
Ilyas, M. T. (2025). Mixed Methods MCP Server (Version 0.1.0) [Computer software].
https://github.com/your-repo/mixed-methods-mcp
```
**For Dissertation/Thesis**:
```
All data analysis was conducted using the Mixed Methods MCP Server
(Ilyas, 2025), an AI-assisted research analysis platform implementing
procedures described by Creswell and Plano Clark (2018).
```
---
## 👨🔬 About the Author
**Muhammad Tayyab Ilyas**
Research Methods Specialist & Software Developer
- 📧 Email: ceo@pakedx.com
- 💼 LinkedIn: [linkedin.com/in/tayyabcheema777](https://www.linkedin.com/in/tayyabcheema777/)
- 🎓 Passionate about making rigorous mixed methods research accessible to all researchers
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## 🎓 Final Words for Researchers
Mixed methods research is challenging - you're essentially conducting two studies and integrating them. This tool doesn't replace your expertise, theoretical knowledge, or critical thinking. It serves as your research assistant, handling the mechanical aspects of analysis while you focus on interpretation, theory development, and meaningful insights.
Remember:
- **The tool codes, but YOU decide what the codes mean**
- **The tool calculates statistics, but YOU interpret their significance**
- **The tool creates displays, but YOU construct the narrative**
- **The tool tracks decisions, but YOU make them**
Use this tool to enhance rigor, transparency, and efficiency - but never as a substitute for deep engagement with your data and thoughtful methodological decision-making.
Good luck with your research! 🚀
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**Version**: 0.1.0
**Last Updated**: November 2025
**Documentation**: For detailed API reference, see `docs/` folder
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mcp.json
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