# Study Preregistration: [Study title]

> Preregistration template for a confirmatory study: hypotheses, design,
> sampling, variables and analysis plan, written and time-stamped before
> data are collected or examined. The topics follow the concepts used in
> common open-science preregistration forms (such as those offered on the
> Open Science Framework), written in original words for this collection.
> Replace every `[bracketed]` value and every line marked *Example*.

<!--
Guidance: How to use this template
- Register before you see the outcome data. If some data already exist,
  say exactly what you have seen in section 1.3.
- Be specific enough that someone else could run your analysis from this
  document alone. "We will use appropriate tests" is not a plan.
- Keep confirmatory and exploratory analyses apart. Only the confirmatory
  plan supports hypothesis-testing claims.
- Deviations are allowed; report them openly in the final paper with the
  reason. Hiding them defeats the purpose of preregistering.
-->

| Field | Value |
|---|---|
| Registration ID | [PREREG-2026-001] |
| Registry | [OSF Registries / AsPredicted / institutional repository] |
| Version | [1.0] |
| Status | [Draft / Submitted / Registered / Embargoed until YYYY-MM-DD] |
| Authors | [@name], [@name] |
| Date of registration | YYYY-MM-DD |
| Linked protocol or ethics | [Reference] |
| Last updated | YYYY-MM-DD |

## 1. Study information

<!-- Guidance: State the research question and the predictions plainly. Each hypothesis should be falsifiable and linked to one analysis in section 5. -->

This section states what the study asks and what the authors predict.

### 1.1 Research questions

[State the research question or questions in one or two sentences each.]

### 1.2 Hypotheses

| ID | Hypothesis | Direction | Linked analysis |
|---|---|---|---|
| H1 | *Example:* Participants who receive spaced practice will score higher on the delayed test than those who receive massed practice | One-sided, spaced > massed | AN-01 |
| H2 | [Hypothesis] | [Direction or two-sided] | AN-02 |
| H3 | [Interaction hypothesis] | [Direction] | AN-03 |

### 1.3 Existing data

<!-- Guidance: Be honest. Readers judge the value of the preregistration by what you knew when you wrote it. -->

Select one and explain:

- [ ] Registration prior to creation of data
- [ ] Registration prior to any human observation of the data
- [ ] Registration prior to accessing the data
- [ ] Registration prior to analysis of the data
- [ ] Registration following analysis of the data

Explanation: [What data exist, who has seen them and what, and why this
does not bias the confirmatory tests.]

## 2. Design plan

<!-- Guidance: Describe the design so a reader can tell what was manipulated, what was measured, and how participants were assigned. -->

The design plan defines the study type, conditions and assignment.

| Item | Detail |
|---|---|
| Study type | [Experiment / Observational / Meta-analysis / Other] |
| Blinding | *Example:* Participants unaware of hypotheses; experimenter blind to condition during scoring |
| Design | [Between-subjects 2 x 2 factorial / within-subjects / mixed] |
| Factors and levels | [Practice schedule: spaced, massed; Test delay: 1 day, 7 days] |
| Randomisation | [Simple randomisation via survey platform, equal allocation] |
| Counterbalancing | [Order of materials counterbalanced with a Latin square] |

## 3. Sampling plan

<!-- Guidance: Justify the sample size with a power analysis or precision target and state when data collection stops. Without a stopping rule, readers cannot exclude optional stopping. -->

This section explains who will be sampled, how many and when collection
stops.

### 3.1 Data collection procedures

[Population, recruitment source, inclusion and exclusion criteria,
payment, and the timeline of data collection.]

### 3.2 Sample size

[Target number of participants or observations after exclusions.]

### 3.3 Sample size rationale

*Example:* To detect $d = 0.40$ with 90% power at one-sided
$\alpha = 0.025$ in a two-group comparison, the required sample is:

$$
n = \frac{2\left(z_{1-\alpha} + z_{1-\beta}\right)^{2}}{d^{2}} = \frac{2\,(1.96 + 1.28)^{2}}{0.16} \approx 132 \text{ per group}
$$

We will recruit 145 per group to allow for about 9% exclusions.

### 3.4 Stopping rule

[State when data collection ends. *Example:* Data collection stops when
290 complete responses meeting inclusion criteria are recorded or on
YYYY-MM-DD, whichever comes first. No interim analyses will be run.]

## 4. Variables

<!-- Guidance: Define every manipulated and measured variable and every index you will compute. Say exactly how composite scores are built. -->

The variables section defines what is manipulated, measured and
computed.

| ID | Variable | Role | Measurement | Scale |
|---|---|---|---|---|
| VAR-01 | Practice schedule | Manipulated | Random assignment | Binary |
| VAR-02 | *Example:* Delayed test score | Outcome | 40-item multiple-choice test, number correct | 0-40 |
| VAR-03 | [Prior knowledge] | Covariate | [10-item pre-test] | [0-10] |
| VAR-04 | [Attention check] | Exclusion | [2 embedded items] | [Pass / fail] |

**Indices:** [How composite measures are computed, for example the mean
of items 1-8 after reverse-scoring items 3 and 6.]

## 5. Analysis plan

<!-- Guidance: One analysis per hypothesis. Name the model, the test, the covariates and the decision rule. -->

This section describes the confirmatory analyses and how results will
be judged.

### 5.1 Statistical models

| ID | Hypothesis | Model or test | Covariates |
|---|---|---|---|
| AN-01 | H1 | *Example:* Welch's t-test on delayed test score | None |
| AN-02 | H2 | [Linear regression] | [Prior knowledge] |
| AN-03 | H3 | [Two-way ANOVA, interaction term] | [None] |

### 5.2 Transformations

[Planned transformations, centring or recoding, and the coding of
categorical predictors.]

### 5.3 Inference criteria

<!-- Guidance: State the threshold and how you will correct for multiple tests. Say how you will interpret a non-significant result. -->

[Decision rule, for example one-sided $p < 0.025$ for H1; Holm
correction across H2 and H3; equivalence test with bounds of
$d = \pm 0.20$ to interpret null results; Bayes factor thresholds if
used.]

### 5.4 Data exclusion

[Rules for excluding participants or observations, decided now.
*Example:* Exclude participants who fail either attention check, complete
the study in under 5 minutes, or report having taken the test before.
Exclude reaction times more than 3 SD from the participant mean.]

### 5.5 Missing data

[How missing data will be handled, for example listwise deletion if
under 5% missing, otherwise multiple imputation with 20 imputations
using all study variables.]

### 5.6 Exploratory analyses

[Analyses you may run that are not confirmatory. List them so readers can
see they were anticipated, and state that they will be reported as
exploratory.]

## 6. Other

<!-- Guidance: Anything that helps reviewers judge the plan: materials, code, pilot data, ethics. -->

Additional information and links:

| Item | Link or note |
|---|---|
| Materials | [Repository link] |
| Analysis script | [Link to script run on simulated data] |
| Pilot data | [Whether used, and only for feasibility] |
| Ethics approval | [Reference] |

## 7. Timestamps and deviations

<!-- Guidance: The registry timestamp is the evidence. Keep a log of every later deviation so the final paper can report them. -->

This log records when the plan was frozen and every later change.

| Event | Date | Detail |
|---|---|---|
| Plan frozen and registered | YYYY-MM-DD | [Registry DOI or URL] |
| Data collection started | YYYY-MM-DD | |
| Data collection ended | YYYY-MM-DD | [Stopping rule met] |
| Deviation DEV-01 | YYYY-MM-DD | *Example:* Recruitment platform changed after outage; same eligibility screen used |
| Analysis completed | YYYY-MM-DD | |

## Standard and licence note

Based on common open-science practice for study preregistration, with
topics similar to widely used preregistration forms such as those on the
Open Science Framework (https://osf.io/registries). This template is
original text written for this collection and reproduces no form text.
Template licence: MIT.