CorrMinds · Correlational Research — a specialized subdomain of RSMinds
Structure Correlational Research
From Idea to Synopsis
10 correlational subtypes. 11-section workflow. Covariate-control planning that keeps you honest about association vs causation. Built on STROBE observational reporting.
correlational subtypes across 4 families
workflow sections — idea to synopsis
reporting standards integrated platform-wide
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How CorrMinds works
The 11-section workflow
Each section is its own AI-assisted accordion. Outputs flow forward — your variables inform sampling; covariates inform analysis; everything assembles into a STROBE-oriented synopsis.
Idea & Correlational Subtype
Inputs
AI action
Output
Best-fit design across 10 correlational subtypes (4 families) with alternatives for review.
Variables Framework
Inputs
AI action
Output
Variable 1, Variable 2, and key covariates defined with operational precision.
Research Question & FINER
Inputs
AI action
Output
3 directional/non-directional question drafts + FINER scoring (Feasibility, Interest, Novelty, Ethics, Relevance).
Hypothesis & Objectives
Inputs
AI action
Output
H1 / H0 statements with expected relationship direction — flags causal language for associational research.
Theory & Framework
Inputs
AI action
Output
3 theory candidates with citations + conceptual framework linking the predicted relationship.
Population & Eligibility Criteria
Inputs
AI action
Output
Sampling frame, inclusion/exclusion criteria, and recruitment rationale.
Sampling Strategy
Inputs
AI action
Output
Probability or non-probability strategy matched to your design, with covariate-control plan.
Sample Size & Power
Inputs
AI action
Output
Transparent N from expected effect size r, with explained assumptions for correlation/regression.
Measurement Protocol
Inputs
AI action
Output
Operationalization of each variable with validity and reliability considerations.
Analysis Plan
Inputs
AI action
Output
Correlation coefficients, regression models, assumption tests (normality, VIF), and sensitivity checks.
Synopsis Generation
Inputs
AI action
Output
STROBE-oriented synopsis in three detail tiers, exportable as DOCX, PDF, or Markdown.
Subtype coverage
10 correlational subtypes — all covered
From bivariate associations to canonical correlation. Every subtype gets its own design guidance, covariate-control plan, and effect-size-based sample-size logic — with no causal claims.
Basic & Multiple
4 designsBivariate Correlation
Association between two variables.
Multiple Correlation
One outcome predicted from several variables.
Partial Correlation
Association with covariates held constant.
Point-Biserial
Continuous with a dichotomous variable.
Time-Lagged
2 designsAutoregressive Cross-Lagged
Reciprocal effects over repeated waves.
Cross-Lagged Panel
Directional association across two time points.
Ecological
3 designsEcological Correlation
Association measured at group/aggregate level.
Ecological Time Trend
Aggregate association tracked over time.
Multi-Group Ecological
Aggregate comparison across populations.
Canonical
1 designCanonical Correlation
Association between two sets of variables.
Compliance
Built on the observational standards
your reviewers expect
Your protocol is scored against the applicable STROBE checklist in real time.
Why this matters
Correlation isn't causation —
so we plan the covariates
Generic AI output
States an association. Implies a cause.
Unstructured output mixes assumptions, slips into causal wording, and ignores confounders. A reviewer who spots an uncontrolled covariate — or an aggregate finding read as an individual one — sends the protocol back.
- Unstructured with mixed assumptions
- No clear step progression
- Harder for teams to review and revise
- Confidence without boundaries
CorrMinds workflow
Maps relationships, controls confounders.
A dedicated covariate-control step names confounders and moderators up front, keeps hypothesis wording association-safe, and flags the ecological fallacy on aggregate designs.
- Connected sequence from idea to synopsis
- Variables framework ensures operational clarity
- Review-ready starting point for teams
- STROBE-oriented output with transparent logic
Relationship
r = .34
sleep duration ↔ semester GPA
Covariates entered
GAD-7 · SES
hierarchical regression · VIF checked
Claim guard
Association
No causal language — wording verified
Plans
Simple pricing
Access 1m
15,000 Mindful AI Tokens
- All 10 correlational subtypes
- 11-section workflow
- STROBE synopsis exports
Access 2m
15,000 Mindful AI Tokens / month
- Everything in 1m
- Priority AI throughput
- Save ₹99 vs monthly
Access 3m
15,000 Mindful AI Tokens / month
- Everything in 2m
- Quarterly project cadence
- Save ₹198 vs monthly
FAQ
Frequently asked questions
Common questions from researchers, supervisors, and IEC reviewers.
What correlational designs does CorrMinds support?
CorrMinds covers 10 subtypes across 4 families: Basic & Multiple (bivariate, multiple, partial, point-biserial), Time-Lagged (autoregressive cross-lagged, cross-lagged panel), Ecological (ecological correlation, ecological time trend, multi-group ecological), and Canonical correlation.
Does CorrMinds support causal claims?
No. Correlational designs establish association, not causation. CorrMinds flags causal language in hypothesis framing and guides you to appropriate directional or non-directional wording.
How is the Variables Framework different from PICO?
The Variables Framework (Variable 1, Variable 2, Covariates) is tailored for associational research where there is no experimental intervention or controlled exposure — it maps relationships rather than effects.
What is the ecological-fallacy warning?
For the three ecological subtypes, CorrMinds surfaces an explicit warning that aggregate-level findings cannot be inferred to individuals. The note follows the design through to the synopsis so reviewers see it stated up front.
Is the sample-size logic transparent?
Yes. CorrMinds derives N from your expected effect size r, alpha, and power, and documents every assumption for the correlation or regression model so a supervisor or ethics committee can audit the number.
Will my ethics committee accept the synopsis?
Output is STROBE-oriented for observational research, the format Indian and international ethics committees recognise. You retain full editorial control — every line is editable before export.
Can I edit the AI output?
Every section is editable. The AI drafts; you decide what ships. Edits propagate downstream — change your variables and the question, hypothesis, and analysis plan all re-evaluate.
What if I run out of Mindful Tokens mid-month?
Top up any time with Boost (₹149), Lab (₹349), or Project (₹699) packs. Top-up tokens never expire while you remain subscribed.
Do you store my research data?
Your study details stay in your account. We never train models on your data. PII is redacted before any AI call. Exports are yours to keep even after subscription ends.
What export formats are supported?
DOCX, PDF, and Markdown, in APA 7, Academic, or Institutional styles — with title page, table of contents, numbered sections, and an Appendix A study-parameters table.
What's included free?
Design prediction (Step 1) is free with login — identify the best correlational design for your study idea. All design guides and methodology references are also free for members.
Can I cancel anytime?
Yes — cancel from Settings. Access continues to the end of the paid period. No questions asked, no retention calls.
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“The quality of your research can never exceed the clarity of your method.” — Rajesh S.K., Founder