Methoda is a visual tool for building research designs. Instead of writing paragraphs about your methodology, you build it visually by defining variables, connecting relationships, and validating your logic.
💡 Key Concept: Methoda teaches you to think about research structure BEFORE writing. Your research question will emerge FROM your variable structure—not the other way around.
What You'll Learn
How to identify independent vs dependent variables
The difference between correlation and causation
Why control variables matter
How to operationalize abstract concepts
What design types allow you to claim (and what they don't)
Choose one of the example studies (Social Anxiety or Sleep)
Click through all 5 steps to see the complete flow
Export the design summary to see what the final output looks like
✓ Recommended: Try an example first before building your own. It's easier to learn by seeing a complete study.
Step-by-Step Walkthrough
1Research Idea
Start by describing what you want to study in plain language.
What to do:
Choose an example study OR click "Start From Scratch"
Answer: "What do you want to study?"
Answer: "What do you think changes?"
Good Example
What to study: "I want to study how social anxiety affects college students during classroom discussions"
What changes: "Students with higher anxiety levels tend to participate less frequently in class"
⚠️ Common Mistake: Being too vague. "I want to study anxiety" isn't specific enough. Say what aspect of anxiety, in what context, affecting what outcome.
2Define Structure
Identify the pieces of your research: independent variables (IV), dependent variables (DV), and control variables (CV).
Variable Types Explained:
Independent Variable (IV): What you think causes change or what you're observing/manipulating
Dependent Variable (DV): What you think is affected—the outcome you're measuring
Control Variable (CV): Factors you need to account for that might also affect the DV
Example Structure
IV: Social Anxiety Level
DV: Class Participation Frequency
CV: Class Size (larger classes might reduce participation opportunities)
How to add variables:
Click the variable type from the left panel
A new variable appears on the canvas
Click the variable name to rename it
Repeat for all your variables
💡 Tip: You need at least 1 IV and 1 DV to continue. Control variables are optional but strengthen your design.
3Build Relationships
Show how your variables relate to each other. This is where you visualize your hypothesis.
How to connect variables:
Click a variable to select it
Click "Connect Selected" in the toolbar
Click the target variable
Choose the relationship type:
"Is associated with": Variables are related (correlational)
"Affects": One variable causes changes in another (causal)
⚠️ IMPORTANT: Causation Requires Commitment
If you choose "affects" (causal claim), the system will ask you to commit to ONE of:
"This is an experimental design (I will manipulate the IV)"
"This is a theoretical claim only"
"I'll revise to 'associated with' instead"
This teaches you that causation has requirements—you can't claim it lightly.
Correlational vs Causal
Correlational: "Social Anxiety Level" ↔ is associated with ↔ "Class Participation" You're observing a relationship but not claiming one causes the other.
Define HOW you'll actually measure each variable. This is operationalization.
For each variable, specify:
Measurement Type: Quantitative (numerical) or Qualitative (categorical)
Scale Type: Nominal, Ordinal, Interval, or Ratio
Operational Definition: Exact method of measurement
Good Operationalization
Variable: Social Anxiety Level
Measurement Type: Quantitative
Scale Type: Interval
Operational Definition: "Score on Beck Anxiety Inventory (0-63 scale, administered at study start)"
⚠️ Vague = Bad:
❌ "Measure anxiety with a survey"
✅ "Score on Beck Anxiety Inventory (0-63 scale)"
Scale Types Quick Reference:
Nominal: Categories with no order (gender, major, yes/no)
Ordinal: Categories with order but unequal intervals (class rank, Likert scales)
Interval: Equal intervals, no true zero (temperature in °C, IQ scores)
Ratio: Equal intervals + true zero (height, age, number of errors)
5Validate Design
Review your complete design and address any methodological issues.
What you'll see:
Research Question: Generated FROM your variable structure
Hypotheses: H1 (alternative) and H0 (null)
Validation Feedback: Warnings about design issues
Design Summary: Complete overview of your methodology
Common validation warnings:
"Consider additional variables" — If you only have 1 IV and 1 DV with no controls
"Experimental design requirement" — If you claimed causation
"Unconnected control variable" — If you added a CV but didn't connect it to the DV
✓ Ready to Export: Click "Export Design for Submission" to get a professional summary you can submit to your professor or use as the foundation for your methods section.
Example Projects
Example 1: Social Anxiety & Participation (Correlational)
Research Question: How does social anxiety level relate to class participation frequency?
Design Type: Correlational (observational)
Variables:
IV: Social Anxiety Level (Beck Anxiety Inventory score)
DV: Class Participation Frequency (number of times raised hand per 50-min class)
CV: Class Size (total enrolled students)
Relationship: Social Anxiety ↔ is associated with ↔ Participation
Can Claim: Correlation/association only (not causation)
Example 2: Sleep & Academic Performance (Correlational)
Research Question: How does sleep duration relate to exam performance?
Design Type: Correlational
Variables:
IV: Hours of Sleep per Night (self-reported average)
DV: Exam Score (percentage on final exam)
CV: Study Hours per Week
Relationship: Sleep Hours ↔ is associated with ↔ Exam Score
Can Claim: Relationship between sleep and performance (not causation)
Example 3: Study Intervention (Experimental - Causal)
Research Question: Does active learning intervention improve exam scores?
Design Type: Experimental
Variables:
IV: Study Intervention (active learning vs control group)
DV: Exam Score (percentage on standardized test)
CV: Prior GPA
Relationship: Study Intervention → affects → Exam Score
Requirements: Random assignment, manipulation of IV, control group
Can Claim: Causation (if executed properly)
Best Practices
1. Start With Examples
If you're new to research design, choose one of the built-in examples first. Walk through the entire flow to understand how the pieces fit together.
2. Be Specific About Variables
Don't say "stress" — say "cortisol level" or "score on Perceived Stress Scale"
Don't say "memory" — say "number of words recalled" or "digit span test score"
3. Think Before You Connect
Before connecting two variables, ask yourself:
Am I claiming one CAUSES the other? (requires experimental design)
Or am I just saying they're RELATED? (correlational is fine)
4. Always Add Control Variables
Even simple studies benefit from controls. Ask: "What else might affect my DV?"
5. Operationalize Everything
Every variable needs a concrete measurement method. If you can't measure it, you can't study it.
6. Read the Validation Feedback
The warnings aren't just errors—they're teaching moments. Click on them to understand why they matter.
7. Use the Export Function
The export isn't just a summary—it's a learning document that shows your methodological decisions.
Avoiding Common Mistakes
Mistake #1: Confusing Correlation with Causation
Wrong: "Hours studied CAUSES higher grades" (based on survey data)
Right: "Hours studied is ASSOCIATED WITH higher grades"
Why: Unless you randomly assigned study hours and controlled the environment, you're observing a correlation, not proving causation.
Why: Specificity allows replication and shows you've thought through measurement.
Mistake #3: Forgetting Control Variables
Wrong: Only studying "Tutoring → Grades"
Right: Studying "Tutoring → Grades" while controlling for prior achievement
Why: High achievers might seek tutoring more. Without controls, you can't rule out this alternative explanation.
Mistake #4: Backwards Causation
Wrong: Connecting DV → IV (e.g., "Test Score → Study Hours")
Right: IV → DV (e.g., "Study Hours → Test Score")
Why: Methoda will warn you if direction seems backwards, but pay attention to logic.
Frequently Asked Questions
Can I save my project?
Currently, projects save in your browser's local storage. They'll be there when you return, but clearing browser data will delete them. We're working on cloud saving for paid accounts.
Can I edit a project after exporting?
Yes! You can go back through the steps and make changes anytime. Just re-export when done.
How do I know if my design is good?
The validation step will flag critical errors. If you have zero critical errors and you've thought through measurement, you're in good shape. But remember: Methoda teaches structure, not domain expertise. Your professor knows your field better.
What if I don't know what design type to use?
Click "Learn About Design Types" in Step 3. It explains experimental, correlational, quasi-experimental, and longitudinal designs with examples.
Can I use this for qualitative research?
Partially. You can map variables and relationships, but Methoda is optimized for quantitative designs. Qualitative methods (ethnography, grounded theory) have different logic.
Will this write my methods section for me?
No. It gives you the structure and key decisions documented, but YOU still write the prose. That's intentional—writing is part of learning.
Is this considered cheating?
No. Methoda is a thinking tool, not a writing tool. It forces you to make methodological decisions and teaches you why they matter. Most professors see this as legitimate scaffolding.
Troubleshooting
I can't move to the next step
Cause: You haven't completed the minimum requirements for the current step.
Solution: Check what's needed:
Step 1: Fill in both text boxes (20+ characters each)
Step 2: Add at least 1 IV and 1 DV
Step 3: Create at least 1 connection between variables
Step 4: Define measurement type for at least 1 variable
My variables won't connect
Cause: You haven't selected a source variable first.
Solution: Click a variable to select it (it will highlight), THEN click "Connect Selected", THEN click the target variable.
The validation says I'm missing something but I'm not
Cause: Validation is looking at structure, not names.
Solution: Make sure you actually connected your IV to your DV (there should be an arrow between them on the canvas).
Export isn't working
Cause: Browser pop-up blocker.
Solution: Allow pop-ups for this site, then try again. The export opens a print dialog.
I lost my project
Cause: Browser data was cleared or you're on a different browser/device.
Solution: Projects currently only save locally. For important work, export frequently and save the PDF/text.
👨🏫 For Professors: Using Methoda in Your Course
Methoda is pedagogy-first. It scaffolds thinking, forces commitments, and makes student reasoning visible—without doing the work for them.
Classroom Use Cases
Use Case 1: In-Class Activity (30 minutes)
Setup: Students work individually or in pairs while you circulate
Timing: One class session
Deliverable: Export design summary by end of class
Benefit: You see student thinking in real-time—common misconceptions become visible immediately
Assessment: Participation credit (complete all 4 steps) or check for specific elements (e.g., "Must include ≥1 control variable")
Use Case 2: Homework Assignment (1 week)
Setup: Students build research proposal design outside class
Timing: Week-long assignment
Deliverable: Methoda export + 2-page written methods section
Benefit: Structure is forced before prose, reducing "word salad" submissions
Assessment: Graded on design quality (see rubric below) + written explanation clarity
Suggested Assignment Structure
Assignment: Research Design Proposal
Part 1: Methoda Design (50 points)
Complete all 4 steps in Methoda
Export design summary (PDF or text)
Submit via LMS
Due: [Date]
Part 2: Written Methods Section (50 points)
2-3 pages explaining your design
Must include: research question, hypothesis, operationalization, expected results
Design must match your Methoda export
Due: [Date]
Sample Grading Rubric
Criterion
Points
Clear IV and DV identified
15
Appropriate relationship type (causal vs correlational)
20
Control variables included and justified
15
Operational definitions provided
20
Design addresses validation issues
15
Written explanation clarity
15
Total
100
⚖️ Academic Integrity & Methoda
Methoda is explicitly designed to support learning, not circumvent it.
What Methoda Does:
Scaffolds research design thinking
Forces structural commitments
Makes student reasoning visible
Teaches methodology through building
What Methoda Does NOT Do:
Write prose or methods sections
Generate hypotheses or research questions
Fabricate data or results
Replace critical thinking
Why This Matters:
Like concept mapping software or statistical tools, Methoda requires students to make all conceptual decisions themselves. The export shows their reasoning process—making plagiarism harder, not easier. Students still must understand and justify their choices in writing.