# Experimental Design Checklist

## Research Question Formulation

### Is the Question Well-Formed?
- [ ] **Specific:** Clearly defined variables and relationships
- [ ] **Answerable:** Can be addressed with available methods
- [ ] **Relevant:** Addresses a gap in knowledge or practical need
- [ ] **Feasible:** Resources, time, and ethical considerations allow it
- [ ] **Falsifiable:** Can be proven wrong if incorrect

### Have You Reviewed the Literature?
- [ ] Identified what's already known
- [ ] Found gaps or contradictions to address
- [ ] Learned from methodological successes and failures
- [ ] Identified appropriate outcome measures
- [ ] Determined typical effect sizes in the field

## Hypothesis Development

### Is Your Hypothesis Testable?
- [ ] Makes specific, quantifiable predictions
- [ ] Variables are operationally defined
- [ ] Specifies direction/nature of expected relationships
- [ ] Can be falsified by potential observations

### Types of Hypotheses
- [ ] **Null hypothesis (H₀):** No effect/relationship exists
- [ ] **Alternative hypothesis (H₁):** Effect/relationship exists
- [ ] **Directional vs. non-directional:** One-tailed vs. two-tailed tests

## Study Design Selection

### What Type of Study is Appropriate?

**Experimental (Intervention) Studies:**
- [ ] **Randomized Controlled Trial (RCT):** Gold standard for causation
- [ ] **Quasi-experimental:** Non-random assignment but manipulation
- [ ] **Within-subjects:** Same participants in all conditions
- [ ] **Between-subjects:** Different participants per condition
- [ ] **Factorial:** Multiple independent variables
- [ ] **Crossover:** Participants receive multiple interventions sequentially

**Observational Studies:**
- [ ] **Cohort:** Follow groups over time
- [ ] **Case-control:** Compare those with/without outcome
- [ ] **Cross-sectional:** Snapshot at one time point
- [ ] **Ecological:** Population-level data

**Consider:**
- [ ] Can you randomly assign participants?
- [ ] Can you manipulate the independent variable?
- [ ] Is the outcome rare (favor case-control) or common?
- [ ] Do you need to establish temporal sequence?
- [ ] What's feasible given ethical, practical constraints?

## Variables

### Independent Variables (Manipulated/Predictor)
- [ ] Clearly defined and operationalized
- [ ] Appropriate levels/categories chosen
- [ ] Manipulation is sufficient to test hypothesis
- [ ] Manipulation check planned (if applicable)

### Dependent Variables (Outcome/Response)
- [ ] Directly measures the construct of interest
- [ ] Validated and reliable measurement
- [ ] Sensitive enough to detect expected effects
- [ ] Appropriate for statistical analysis planned
- [ ] Primary outcome clearly designated

### Control Variables
- [ ] **Confounding variables identified:**
  - Variables that affect both IV and DV
  - Alternative explanations for findings
- [ ] **Strategy for control:**
  - Randomization
  - Matching
  - Stratification
  - Statistical adjustment
  - Restriction (inclusion/exclusion criteria)
  - Blinding

### Extraneous Variables
- [ ] Potential sources of noise identified
- [ ] Standardized procedures to minimize
- [ ] Environmental factors controlled
- [ ] Time of day, setting, equipment standardized

## Sampling

### Population Definition
- [ ] **Target population:** Who you want to generalize to
- [ ] **Accessible population:** Who you can actually sample from
- [ ] **Sample:** Who actually participates
- [ ] Difference between these documented

### Sampling Method
- [ ] **Probability sampling (preferred for generalizability):**
  - Simple random sampling
  - Stratified sampling
  - Cluster sampling
  - Systematic sampling
- [ ] **Non-probability sampling (common but limits generalizability):**
  - Convenience sampling
  - Purposive sampling
  - Snowball sampling
  - Quota sampling

### Sample Size
- [ ] **A priori power analysis conducted**
  - Expected effect size (from literature or pilot)
  - Desired power (typically .80 or .90)
  - Significance level (typically .05)
  - Statistical test to be used
- [ ] Accounts for expected attrition/dropout
- [ ] Sufficient for planned subgroup analyses
- [ ] Practical constraints acknowledged

### Inclusion/Exclusion Criteria
- [ ] Clearly defined and justified
- [ ] Not overly restrictive (limits generalizability)
- [ ] Based on theoretical or practical considerations
- [ ] Ethical considerations addressed
- [ ] Documented and applied consistently

## Blinding and Randomization

### Randomization
- [ ] **What is randomized:**
  - Participant assignment to conditions
  - Order of conditions (within-subjects)
  - Stimuli/items presented
- [ ] **Method of randomization:**
  - Computer-generated random numbers
  - Random number tables
  - Coin flips (for very small studies)
- [ ] **Allocation concealment:**
  - Sequence generated before recruitment
  - Allocation hidden until after enrollment
  - Sequentially numbered, sealed envelopes (if needed)
- [ ] **Stratified randomization:**
  - Balance important variables across groups
  - Block randomization to ensure equal group sizes
- [ ] **Check randomization:**
  - Compare groups at baseline
  - Report any significant differences

### Blinding
- [ ] **Single-blind:** Participants don't know group assignment
- [ ] **Double-blind:** Participants and researchers don't know
- [ ] **Triple-blind:** Participants, researchers, and data analysts don't know
- [ ] **Blinding feasibility:**
  - Is true blinding possible?
  - Placebo/sham controls needed?
  - Identical appearance of interventions?
- [ ] **Blinding check:**
  - Assess whether blinding maintained
  - Ask participants/researchers to guess assignments

## Control Groups and Conditions

### What Type of Control?
- [ ] **No treatment control:** Natural course of condition
- [ ] **Placebo control:** Inert treatment for comparison
- [ ] **Active control:** Standard treatment comparison
- [ ] **Wait-list control:** Delayed treatment
- [ ] **Attention control:** Matches contact time without active ingredient

### Multiple Conditions
- [ ] Factorial designs for multiple factors
- [ ] Dose-response relationship assessment
- [ ] Mechanism testing with component analyses

## Procedures

### Protocol Development
- [ ] **Detailed, written protocol:**
  - Step-by-step procedures
  - Scripts for standardized instructions
  - Decision rules for handling issues
  - Data collection forms
- [ ] Pilot tested before main study
- [ ] Staff trained to criterion
- [ ] Compliance monitoring planned

### Standardization
- [ ] Same instructions for all participants
- [ ] Same equipment and materials
- [ ] Same environment/setting when possible
- [ ] Same assessment timing
- [ ] Deviations from protocol documented

### Data Collection
- [ ] **When collected:**
  - Baseline measurements
  - Post-intervention
  - Follow-up timepoints
- [ ] **Who collects:**
  - Trained researchers
  - Blinded when possible
  - Inter-rater reliability established
- [ ] **How collected:**
  - Valid, reliable instruments
  - Standardized administration
  - Multiple methods if possible (triangulation)

## Measurement

### Validity
- [ ] **Face validity:** Appears to measure construct
- [ ] **Content validity:** Covers all aspects of construct
- [ ] **Criterion validity:** Correlates with gold standard
  - Concurrent validity
  - Predictive validity
- [ ] **Construct validity:** Measures theoretical construct
  - Convergent validity (correlates with related measures)
  - Discriminant validity (doesn't correlate with unrelated measures)

### Reliability
- [ ] **Test-retest:** Consistent over time
- [ ] **Internal consistency:** Items measure same construct (Cronbach's α)
- [ ] **Inter-rater reliability:** Agreement between raters (Cohen's κ, ICC)
- [ ] **Parallel forms:** Alternative versions consistent

### Measurement Considerations
- [ ] Objective measures preferred when possible
- [ ] Validated instruments used when available
- [ ] Multiple measures of key constructs
- [ ] Sensitivity to change considered
- [ ] Floor/ceiling effects avoided
- [ ] Response formats appropriate
- [ ] Recall periods appropriate
- [ ] Cultural appropriateness considered

## Bias Minimization

### Selection Bias
- [ ] Random sampling when possible
- [ ] Clearly defined eligibility criteria
- [ ] Document who declines and why
- [ ] Minimize self-selection

### Performance Bias
- [ ] Standardized protocols
- [ ] Blinding of providers
- [ ] Monitor protocol adherence
- [ ] Document deviations

### Detection Bias
- [ ] Blinding of outcome assessors
- [ ] Objective measures when possible
- [ ] Standardized assessment procedures
- [ ] Multiple raters with reliability checks

### Attrition Bias
- [ ] Strategies to minimize dropout
- [ ] Track reasons for dropout
- [ ] Compare dropouts to completers
- [ ] Intention-to-treat analysis planned

### Reporting Bias
- [ ] Preregister study and analysis plan
- [ ] Designate primary vs. secondary outcomes
- [ ] Commit to reporting all outcomes
- [ ] Distinguish planned from exploratory analyses

## Data Management

### Data Collection
- [ ] Data collection forms designed and tested
- [ ] REDCap, Qualtrics, or similar platforms
- [ ] Range checks and validation rules
- [ ] Regular backups
- [ ] Secure storage (HIPAA/GDPR compliant if needed)

### Data Quality
- [ ] Real-time data validation
- [ ] Regular quality checks
- [ ] Missing data patterns monitored
- [ ] Outliers identified and investigated
- [ ] Protocol deviations documented

### Data Security
- [ ] De-identification procedures
- [ ] Access controls
- [ ] Audit trails
- [ ] Compliance with regulations (IRB, HIPAA, GDPR)

## Statistical Analysis Planning

### Analysis Plan (Prespecify Before Data Collection)
- [ ] **Primary analysis:**
  - Statistical test(s) specified
  - Hypothesis clearly stated
  - Significance level set (usually α = .05)
  - One-tailed or two-tailed
- [ ] **Secondary analyses:**
  - Clearly designated as secondary
  - Exploratory analyses labeled as such
- [ ] **Multiple comparisons:**
  - Adjustment method specified (if needed)
  - Primary outcome protects from inflation

### Assumptions
- [ ] Assumptions of statistical tests identified
- [ ] Plan to check assumptions
- [ ] Backup non-parametric alternatives
- [ ] Transformation options considered

### Missing Data
- [ ] Anticipated amount of missingness
- [ ] Missing data mechanism (MCAR, MAR, MNAR)
- [ ] Handling strategy:
  - Complete case analysis
  - Multiple imputation
  - Maximum likelihood
- [ ] Sensitivity analyses planned

### Effect Sizes
- [ ] Appropriate effect size measures identified
- [ ] Will be reported alongside p-values
- [ ] Confidence intervals planned

### Statistical Software
- [ ] Software selected (R, SPSS, Stata, Python, etc.)
- [ ] Version documented
- [ ] Analysis scripts prepared in advance
- [ ] Will be made available (Open Science)

## Ethical Considerations

### Ethical Approval
- [ ] IRB/Ethics committee approval obtained
- [ ] Study registered (ClinicalTrials.gov, etc.) if applicable
- [ ] Protocol follows Declaration of Helsinki or equivalent

### Informed Consent
- [ ] Voluntary participation
- [ ] Comprehensible explanation
- [ ] Risks and benefits disclosed
- [ ] Right to withdraw without penalty
- [ ] Privacy protections explained
- [ ] Compensation disclosed

### Risk-Benefit Analysis
- [ ] Potential benefits outweigh risks
- [ ] Risks minimized
- [ ] Vulnerable populations protected
- [ ] Data safety monitoring (if high risk)

### Confidentiality
- [ ] Data de-identified
- [ ] Secure storage
- [ ] Limited access
- [ ] Reporting doesn't allow re-identification

## Validity Threats

### Internal Validity (Causation)
- [ ] **History:** External events between measurements
- [ ] **Maturation:** Changes in participants over time
- [ ] **Testing:** Effects of repeated measurement
- [ ] **Instrumentation:** Changes in measurement over time
- [ ] **Regression to mean:** Extreme scores becoming less extreme
- [ ] **Selection:** Groups differ at baseline
- [ ] **Attrition:** Differential dropout
- [ ] **Diffusion:** Control group receives treatment elements

### External Validity (Generalizability)
- [ ] Sample representative of population
- [ ] Setting realistic/natural
- [ ] Treatment typical of real-world implementation
- [ ] Outcome measures ecologically valid
- [ ] Time frame appropriate

### Construct Validity (Measurement)
- [ ] Measures actually tap intended constructs
- [ ] Operations match theoretical definitions
- [ ] No confounding of constructs
- [ ] Adequate coverage of construct

### Statistical Conclusion Validity
- [ ] Adequate statistical power
- [ ] Assumptions met
- [ ] Appropriate tests used
- [ ] Alpha level appropriate
- [ ] Multiple comparisons addressed

## Reporting and Transparency

### Preregistration
- [ ] Study preregistered (OSF, ClinicalTrials.gov, AsPredicted)
- [ ] Hypotheses stated a priori
- [ ] Analysis plan documented
- [ ] Distinguishes confirmatory from exploratory

### Reporting Guidelines
- [ ] **RCTs:** CONSORT checklist
- [ ] **Observational studies:** STROBE checklist
- [ ] **Systematic reviews:** PRISMA checklist
- [ ] **Diagnostic studies:** STARD checklist
- [ ] **Qualitative research:** COREQ checklist
- [ ] **Case reports:** CARE guidelines

### Transparency
- [ ] All measures reported
- [ ] All manipulations disclosed
- [ ] Sample size determination explained
- [ ] Exclusion criteria and numbers reported
- [ ] Attrition documented
- [ ] Deviations from protocol noted
- [ ] Conflicts of interest disclosed

### Open Science
- [ ] Data sharing planned (when ethical)
- [ ] Analysis code shared
- [ ] Materials available
- [ ] Preprint posted
- [ ] Open access publication when possible

## Post-Study Considerations

### Data Analysis
- [ ] Follow preregistered plan
- [ ] Clearly label deviations and exploratory analyses
- [ ] Check assumptions
- [ ] Report all outcomes
- [ ] Report effect sizes and CIs, not just p-values

### Interpretation
- [ ] Conclusions supported by data
- [ ] Limitations acknowledged
- [ ] Alternative explanations considered
- [ ] Generalizability discussed
- [ ] Clinical/practical significance addressed

### Dissemination
- [ ] Publish regardless of results (reduce publication bias)
- [ ] Present at conferences
- [ ] Share findings with participants (when appropriate)
- [ ] Communicate to relevant stakeholders
- [ ] Plain language summaries

### Next Steps
- [ ] Replication needed?
- [ ] Follow-up studies identified
- [ ] Mechanism studies planned
- [ ] Clinical applications considered

## Common Pitfalls to Avoid

- [ ] No power analysis → underpowered study
- [ ] Hypothesis formed after seeing data (HARKing)
- [ ] No blinding when feasible → bias
- [ ] P-hacking (data fishing, optional stopping)
- [ ] Multiple testing without correction → false positives
- [ ] Inadequate control group
- [ ] Confounding not addressed
- [ ] Instruments not validated
- [ ] High attrition not addressed
- [ ] Cherry-picking results to report
- [ ] Causal language from correlational data
- [ ] Ignoring assumptions of statistical tests
- [ ] Not preregistering changes literature bias
- [ ] Conflicts of interest not disclosed

## Final Checklist Before Starting

- [ ] Research question is clear and important
- [ ] Hypothesis is testable and specific
- [ ] Study design is appropriate
- [ ] Sample size is adequate (power analysis)
- [ ] Measures are valid and reliable
- [ ] Confounds are controlled
- [ ] Randomization and blinding implemented
- [ ] Data collection is standardized
- [ ] Analysis plan is prespecified
- [ ] Ethical approval obtained
- [ ] Study is preregistered
- [ ] Resources are sufficient
- [ ] Team is trained
- [ ] Protocol is documented
- [ ] Backup plans exist for problems

## Remember

**Good experimental design is about:**
- Asking clear questions
- Minimizing bias
- Maximizing validity
- Appropriate inference
- Transparency
- Reproducibility

**The best time to think about these issues is before collecting data, not after.**