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Form: Psychological ResearchPreregistration-Quantitative (aka PRP-QUANT) Template (v1)
This vignette shows the Psychological ResearchPreregistration-Quantitative (aka PRP-QUANT) Template form. It can beinitialized as follows:
initialized_prpQuant_v1<- preregr::prereg_initialize("prpQuant_v1" );
After this, content can be specified withpreregr::prereg_specify()orpreregr::prereg_justify.To check the next field(s) for which content still has to be specified,usepreregr::prereg_next_item().
The form’s metadata is:
| field | content |
|---|
| title | Psychological Research Preregistration-Quantitative(aka PRP-QUANT) Template |
| author | Preregistration Task Force members from the AmericanPsychological Association (APA): Fred Oswald - Open Science andMethodology Committee Member; Rose Sokol-Chang - Publisher, APA Journalsand Books; Amanda Clinton - Director, APA International Affairs, fromthe British Psychological Society (BPS): Daryl O’Connor - Chair of theBPS Research Board; Lisa Coulthard - Head of Research and Impact, fromthe German Psychological Society (DGP): Christian Fiebach - Secretaryand Open Science Committee Member, from the Leibniz Institute forPsychology (ZPID): Michael Bosnjak - Director; Stefanie Müller - Head ofstudy planning, data collection, and data analysis services; Camila Azúa- Research Assistant, from the Center for Open Science (COS): DavidMellor - Director of Policy Initiatives |
| date | 2020-02-18 |
| comments | NA |
| source | Aczel, B., Szaszi, B., Sarafoglou, A., … Wagenmakers,E.-J. (2020). A consensus-based transparency checklist. Nature HumanBehaviour, 4(1), 4–6.https://doi.org/10.1038/s41562-019-0772-6 |
American Psychological Association. (2020). Publication manual of theAmerican Psychological Association (7th ed.).https://doi.org/10.1037/0000165-000 Appelbaum, M.,Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M.(2018). Journal article reporting standards for quantitative research inpsychology: The APA Publications and Communications Board task forcereport. American Psychologist, 73(1), 3–25.https://doi.org/10.1037/amp0000191
Bowman, S. D., DeHaven, A. C., Errington, T. M., Hardwicke, T. E.,Mellor, D. T., Nosek, B. A., & Soderberg, C. K. (2016). OSF PreregTemplate. Retrieved from osf.io/preprints/metaarxiv/epgjd Simonsohn, U.,Simmons, J., & Nelson, L. (2017). AsPredicted. Retrieved fromhttps://aspredicted.org/messages/terms.php1/15/2020
Van den Akker, O., Weston, S. J., Campbell, L., Chopik, W. J., Damian,R. I., Davis-Kean, P., Hall, A., Kosie, J., Kruse, E., Olsen, J.,Stuart, R., Valentine, K., van ’t Veer, A., & Bakker, M. (2019,November 20). Preregistration of secondary data analysis: A template andtutorial.https://doi.org/10.31234/osf.io/hvfmr | |version |1.0|
The form is defined as follows (usepreregr::form_show()to show the form in the console, instead):
preregr::form_knit("prpQuant_v1");
Psychological Research Preregistration-Quantitative (aka PRP-QUANT)Template
Instructions
Intended Use
As an international effort toward increasing psychology’s commitmentto creating a stronger culture and practice of preregistration, amulti-society Preregistration Task Force* was formed, following the 2018meeting of the German Psychological Society in Frankfurt, Germany. TheTask Force created a detailed preregistration template that benefitedfrom the APA JARS Quantitative Research guidelines, as well as acomprehensive review of many other preregistration templates.
This entry features the template, PRP_QUANT, in its current (andprevious) version. The template can be downloaded here as .xlsx(Microsoft Excel), .docx (Microsoft Word), .odt (Libre Office) or .ipynb(JupyterLab) file or it can be filled out online viahttps://forms.gle/9YgAoJn4ZYPXtHGk9 (a PDF will beautomatically generated and send via email).
For more information about preregistration and the template inparticular, we recommend watching the following webinar:https://zpid.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=10e5776c-363a-4658-b458-acae007121a2(or browse the slides via the link under “related items”). It shows thelaunch of the template on October 27, 2020, featuring two keynotespeakers: Simine Vazire of University of Melbourne, and E. J.Wagenmakers of University of Amsterdam.
Sections and items
Section: Title and title page
Contributors, Affiliations, and Persistent IDs (recommend ORCID iD)
T2
Provide in separate entries the full name of each contributor, eachcontributor’s professional affiliation, and each contributor’spersistent ID. See ORCID iD for an example of persistent ID (hyperlink:
https://orcid.org/).Optional: include the intended contribution of each person listed(e.g. statistical analysis, data collection; see CRediT, hyperlink:
https://credit.niso.org/).
Date of Preregistration
T3
This is assigned by the system upon preregistration submission.
This is assigned by the system upon submission of original andsubsequent revisions. Should be a persistent identifier, if not a DOI.
This unique identifier is assigned by the system upon submission.
Estimated duration of project
T6
Include best estimate for how long the project will take frompreregistration submission to project completion.
IRB Status (Institutional Review Board/Independent EthicsCommittee/Ethical Review Board/Research Ethics Board)
T7
If the study will include human or animal subjects, provide a briefoverview of plans for the treatment of those subjects in accordance withestablished ethical guidelines. If appropriate institutional approvalhas been obtained for the study, provide the relevant identifier here.If the study will be exempt from ethical board review, provide reasoninghere.
Conflict of Interest Statement
T8
Identify any real or perceived conflicts of interest with this studyexecution. For example, any interests or activities that might be seenas influencing the research (e.g., financial interests in a test orprocedure, funding by pharmaceutical companies for research).
Include terms specific to your topic, methodology, and population. Usenatural language and avoid words used in the title or overly generalterms. If you need help with keywords, try a keyword search using yourproposed keywords in a search engine to check results.
Data accessibility statement and planned repository
T10
We plan to make the data available (yes / no) If “yes”, pleasespecify the planned data availability level by selecting one of theoptions:
- Data access via download; usage of data for all purposes (public usefile)
- Data access via download; usage of data restricted to scientificpurposes (scientific use file)
- Data access via download; usage of data has to be agreed and definedon an individual case basis
- Data access via secure data center (no download, usage/analysis onlyin a secure data center)
- Data available upon email request by member of scientific community-Other (please specify)
Optional: Code availability
T11
We plan to make the code available (yes / no) If “yes”, please specifythe planned code availability (use same descriptors of data in T10)
Optional: Standard lab practices
T12
Standard lab practices refer to a (timestamped) document, softwarepackage, or similar, which specifies standard pipelines, analyticaldecisions, etc. which always apply to certain types of research in alab. Specify here and refer to at the appropriate positions in theremainder of the template: We plan to make the standard lab practicesavailable (yes / no). If “yes”, please specify the planned standard labpractices availability level (use same descriptors of data in T10).
Section: Abstract (150 words)
Objectives and Research questions
A2
(See introduction I2)
Section: Introduction (no word limit)
Provide a brief overview that justifies the research hypotheses.
Objectives and Research question(s)
I2
Outline objectives and research questions that inform the methodologyand analyses (below).
Provide hypothesis for predicted results. If multiple hypotheses,uniquely number them (e.g. H1, H2a, H2b) and refer to them the same wayat other points in the registration document and in the manuscript.
Exploratory research questions (if applicable; E1, E2, …)
I4
If planning exploratory analyses, provide rationale for them here. Ifmultiple exploratory analyses, uniquely number them (E1, E2, …) andrefer to them in the same way in the registration document and in futurepublications.
Section: Method
Time point of registration
M1
Select one of the options:
- 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
- Other (please specify; might include if T1 longitudinal data hasbeen analyzed, but T2 has not yet been analyzed)
Proposal: Use of pre-existing data (re-analysis or secondary dataanalysis)
M2
Will pre-existing data be used in the planned study? If yes, indicate ifthe data were previously published and specify the source of the data(e.g., DOI or APA style reference of original publication). Specify yourlevel of knowledge of the data (e.g., descriptive statistics fromprevious publications), whether or not this is relevant for thehypotheses of the present study, and how it is assured that you areunaware of results or statistical patterns in the data of relevance tothe present hypotheses.
Sample size, power and precision
M3
- Relevant sample sizes: e.g., single groups, multiple groups, andsample sizes (or sample ranges) found at each level of multilevel data.(2) Provide power analysis (e.g. power curves) for fixed-N designs. Forsequential designs, indicate your ‘stopping rule’ such as the points atwhich you intend to be viewing your data and in any way analyzing them(e.g., t-tests and correlations, but even descriptively such as withhistograms).
Participant recruitment, selection, and compensation
M4
Indicate (a) methods of recruitment (e.g., subject pool advertisement,community events, crowdsourcing platforms, snowball sampling); (b)selection and inclusion/exclusion criteria (e.g., age, visual acuity,language facility); (c) details of any stratification sampling used; (d)planned participant characteristics (gender, race/ethnicity, sexualorientation and gender identity, SES, education level, age, disabilityor health status, geographic location); (e) compensation amount andmethod (e.g., same payment to all, pay based on performance, lottery).
How will participant drop-out be handled?
M5
Indicate any special treatment for participants who drop out (e.g.,there is follow-up in a manner different from the main sample, lastvalue carried forward) or whether participants are replaced.
Masking of participants and researchers
M6
Indicate all forms of masking and/or allocation concealment (e.g.,administrators, data collectors, raters, confederates are unaware ofcondition to which participants were assigned).
Data cleaning and screening
M7
Indicate all steps related to data quality control, e.g., outliertreatment, identification of missing data, checks for normality, etc.
How will missing data be handled?
M8
Indicate any procedures that will be applied during the analysis to dealwith missing data, such as (a) case deletions; (b) averaging acrossscale items (to handle missing items for some); (c) test of missingness(MAR, MCAR, MNAR assumptions; (d) imputation procedures (FIML vs. MI);(e) Intention to treat analysis and per protocol analysis (asappropriate).
Other information (optional)
M9
For example, training of raters/participants or anything else not yetspecified.
Type of study and study design
M10
Indicate the type of study (e.g., experimental, observational,crosssectional vs. longitudinal, single case, clinical trial) andplanned study design (e.g., between vs. within subjects, factorial,repeated measures, etc.), number of factors and factor levels, etc..
Randomization of participants and/or experimental materials
M11
If applicable, describe how participants are assigned to conditions ortreatments, how stimuli are assigned to conditions, and how presentationof tests, trials, etc. is randomized. Indicate the randomizationtechnique and whether constraints were applied (pseudo-randomization).Indicate any type of balancing across participants (e.g., assignments ofresponses to hands, etc.).
Measured variables, manipulated variables, covariates
M12
This section shall be used to unambiguously clarify which variables areused to operationalize the hypotheses specified above (item I3). Please(a) list all measured variables, and (b) explicitly state the functionalrole of each variable (i.e., independent variable, dependent variable,covariate, mediator, moderator). It is important to (c) specify for eachhypothesis how it is operationalized, i.e., which variables will be usedto test the respective hypothesis and how the hyothesis will beoperationally defined in terms of these variables. The description hereshall be consistent with the statistical analysis plans specified underAP6 (below).
Please describe any relevant study materials. This could include, forexample, stimulus materials used for experiments, questionnaires usedfor rating studies, training protocols for intervention studies, etc.
Please describe here any relevant information about how the study willbe conducted, e.g., the number and timing of measurement time points forlongitudinal research, the number of blocks or runs per session of anexperiment, laboratory setting, the group size in group testing, thenumber of training sessions in interventional studies, questionnaireadministration for online assessments, etc.
Other information (optional)
M15
NA
Section: Analysis plan
Criteria for post-data collection exclusion of participants, if any
AP1
Describe all criteria that will lead to the exclusion of a participant’sdata (e.g. performance criteria, non-responding in physiologicalmeasures, incomplete data). Be as specific as possible.
Criteria for post-data collection exclusions on trial level (ifapplicable)
AP2
Describe all criteria that will lead to the exclusion of a trial or item(e.g. statistical outliers, response time criteria). Be as specific aspossible.
Describe all data manipulations that are performed in preparation of themain analyses, e.g. calculation of variables or scales, recoding, anydata transformations, preprocessing steps for imaging or physiologicaldata (or refer to publicly accessible standard lab procedure, cf. T12).
Reliability analysis (if applicable)
AP4
Specify the type of scale reliability that will be estimated, whether itis internal consistency (e.g. Cronbach’s alpha, omega), test-retestreliability, or some other form (e.g., a confirmatory factor analysisincorporating multiple factors as sources of variance). In a studyinvolving measure development, researchers should specify criteria forremoving items from measures a priori (e.g., largest factor loadingmagnitude, smallest drop in alpha-if-item removed).
Descriptive statistics
AP5
Specify which descriptive statistics will be calculated for whichvariables. If appropriate, specify which indices of effect size will beused. If descriptive statistics are linked to specific hypotheses,explicitly link the information given here to the respective hypothesis.
Statistical models (provide for each hypothesis if varies)
AP6
Specify the statistical model (e.g. t test, ANOVA, LMM) that will beused to test each of your hypotheses. Give all necessary informationabout model specification (e.g., variables, interactions, plannedcontrasts) and follow-up analyses. Include model selection criteria(e.g., fit indices), corrections for multiple testing, and tests forstatistical violations, if applicable. Wherever unclear, describe howeffect sizes will be calculated (e.g., for d-values, use the control SDor the pooled SD).
Specify the criteria used for inferences (e.g., p values, Bayes factors,effect size measures) and the thresholds for accepting or rejecting yourhypotheses. If possible, define a smallest effect size of interest. Ifinference criteria differ between hypotheses, specify separately foreach hypothesis and respective statistical model by explicitly referringto the numbers of the hypotheses. Describe which effect size measureswill be reported and how they are calculated.
Exploratory analysis (optional)
AP8
Describe any exploratory analyses to be conducted with your data.Include here any planned analyses that are not confirmatory in the senseof being a direct test of one of the specified hypotheses.
Other information (optional)
AP9
NA
Section: Other information, optional
Other information (optional)
O1
NA
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