Magnus Hedemark
487f8923dc
PhD-level data science expertise with decision framework, five reference documents (statistical methodology, experimental design, causal inference, regression modeling, Bayesian workflow), five automation scripts (power analysis, assumption diagnostics, model comparison, effect size calculator, experimental design generator), and two report templates. Python default with --engine r flag for R output. Dual language support.
4.8 KiB
Experimental Plan Template (Pre-Registration Style)
1. Study Information
Title: [Descriptive title]
Authors: [Names]
Date: [Pre-registration date]
Version: [1.0]
2. Research Questions & Hypotheses
Primary research question: [What is the main question this study answers?]
Hypotheses:
- H₀ (null): [Statement of no effect]
- H₁ (alternative): [Statement of expected effect]
Secondary questions: [1-3 additional questions, if any]
Exploratory questions: [Questions without directional predictions]
3. Design
Design type: [ ] Randomized experiment [ ] Quasi-experiment [ ] Observational study [ ] Other: ______
Design specification: [ ] CRD [ ] RCBD [ ] Factorial [ ] Crossover [ ] Longitudinal [ ] Case-control [ ] Cohort
Randomization unit: [ ] Individual [ ] Cluster [ ] Block [ ] Other: ______
Analysis unit: [Same as randomization unit, or specify if different]
Blinding: [ ] None [ ] Single-blind [ ] Double-blind
4. Sample Size / Power
Power analysis parameters:
| Parameter | Value | Source |
|---|---|---|
| α (Type I error) | 0.05 | Convention |
| Power (1 − β) | 0.80 | Convention |
| Expected effect size | [value and metric] | [prior study, pilot, or MDE] |
| Minimum detectable effect | [value] | [computed] |
| Required sample size (total) | [N] | [computed] |
| Attrition adjustment | [additional N] | [expected dropout rate] |
| Final target N | [N] |
Power analysis output: [Attach or reference the power analysis script output]
5. Participants / Subjects
Target population: [Who/what are we studying?]
Inclusion criteria:
- [Criterion 1]
- [Criterion 2]
Exclusion criteria:
- [Criterion 1]
- [Criterion 2]
Recruitment: [How will participants be identified and recruited?]
Assignment: [How will participants be assigned to conditions?]
6. Variables
Primary Outcome
| Variable | Definition | Measurement | Type |
|---|---|---|---|
| [name] | [operational definition] | [instrument, units] | [continuous/binary/time-to-event] |
Secondary Outcomes
| Variable | Definition | Measurement | Type |
|---|---|---|---|
| [name] |
Predictors / Treatments
| Variable | Levels | Assignment | Notes |
|---|---|---|---|
| [name] | [level1, level2, ...] | [randomized/observed] |
Covariates (pre-registered)
| Variable | Rationale |
|---|---|
| [name] | [why this variable is included] |
7. Procedure
Timeline:
| Phase | Activity | Duration |
|---|---|---|
| 1 | Recruitment | [time] |
| 2 | Pre-treatment measurement | [time] |
| 3 | Treatment administration | [time] |
| 4 | Post-treatment measurement | [time] |
| 5 | Follow-up | [time] |
Detailed protocol: [Step-by-step description of what happens to each participant]
8. Analysis Plan
Primary Analysis
Method: [e.g., Independent t-test, linear regression, ANCOVA]
Model specification: [e.g., outcome ~ treatment + covariate1]
Assumptions to be checked:
| Assumption | Diagnostic | Remediation If Violated |
|---|---|---|
| Normality | Shapiro-Wilk, Q-Q plot | Transform data or use nonparametric test |
| Equal variance | Levene's test | Welch's correction |
| Independence | Durbin-Watson | GLS / mixed model |
| [other] | [method] | [alternative] |
Secondary Analyses
[Method for each secondary question]
Exploratory Analyses
[Methods for exploratory questions]
Subgroup Analyses (pre-specified)
| Subgroup | Rationale |
|---|---|
| [group] | [why this subgroup might differ] |
Missing Data Handling
Expected missingness: [Amount and pattern expected]
Primary approach: [ ] Complete case analysis [ ] Multiple imputation [ ] Maximum likelihood [ ] Last observation carried forward [ ] Other: ______
Multiple Testing
Correction method: [ ] Bonferroni [ ] Holm [ ] Benjamini-Hochberg [ ] None (confirmatory study with single primary outcome) [ ] Other: ______
Number of comparisons: [ ]
9. Data Collection & Management
Data collection tools: [ ] Survey platform [ ] Lab equipment [ ] Administrative records [ ] Other: ______
Data storage: [How and where will data be stored?]
Quality control: [Checks for data quality during collection]
10. Ethics & Reporting
Ethics approval: [ ] Obtained (IRB #: ______) [ ] Pending [ ] Not required
Consent: [ ] Written [ ] Verbal [ ] Waived
Conflicts of interest: [None / describe]
Data availability: [Where will data and code be posted?]
11. Changes from Original Plan
[If this is an update to a pre-registration, document any changes here with date and rationale]