STATISTICAL SOFTWARE
JASP Data Analysis Service
Bayesian and Frequentist Analysis You Can Trace
Our JASP data analysis service carries out Bayesian and frequentist analyses in JASP, working from your data and .jasp files, with the saved project, the exact analysis settings and the prior choices documented where agreed so that each result can be traced and rerun.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Is JASP Used For in Research?
JASP is a free, open-source statistical program developed at the University of Amsterdam, with a point-and-click interface that updates results as the options change. Researchers choose it to run common tests, ANOVA and regression in both frequentist and Bayesian form, and because a saved .jasp file keeps the data, the analyses and their options together. Bayesian output needs its own reading: a Bayes factor or a posterior distribution is not a p-value or a confidence interval under another name. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides a JASP data analysis service in which an in-house PhD-level statistician carries out the agreed analysis in JASP, working from your files and recording the settings so that you can rerun it.
WHEN TO ENGAGE
Four Starting Points for a JASP Project With Our Statisticians
Requests range from a Bayesian analysis that still has to be designed to a finished one whose wording a reviewer has questioned.
Bayesian Analysis to Run You want a Bayesian test, ANOVA or regression, and the prior, the Bayes factor and the posterior summaries have to be chosen, run and written up with their own interpretation.
Output Read as Significance Part of the analysis exists and a Bayes factor is described as a significant result, so the output has to be reread and the manuscript wording corrected before submission.
A .jasp File to Inspect A collaborator's .jasp file needs checking or extending, and JASP statistical analysis here starts from the stored analyses and options, not from a screenshot of the results.
Null Result to Interpret A reviewer asks whether a null result means absence of an effect or too little data, which a Bayesian companion analysis can address beside the frequentist test.
ANALYSES IN JASP
Analyses Carried Out in JASP for Your Research Question
JASP ANOVA, regression and t-test projects often run both frameworks on the same data, and each card below links to the method page where the choice between them is explained.
Bayesian Analysis
Bayes factors are reported with a prior-sensitivity check, so it is clear whether the conclusion depends on the width of the prior or holds across reasonable choices.
Bayesian Regression
Posterior estimates with credible intervals and the inclusion evidence for each predictor are reported, with the model prior stated and the variables entered justified.
Independent Samples t-Test
Frequentist and Bayesian versions run on the same data, with the mean difference, its interval, an effect size and a prior-sensitivity plot shown side by side.
One-Way ANOVA
Group differences are tested with post hoc comparisons that carry a multiplicity correction, and the Bayesian version compares models when evidence for equality matters.
Repeated Measures ANOVA
Within-subject designs are fitted with the sphericity correction applied where needed, and the Bayesian version includes the participant effect when models are compared.
Multiple Linear Regression
Frequentist coefficients are reported with confidence intervals and the diagnostic plots are examined before any Bayesian companion model is run for comparison.
COMMON JASP ISSUES
Where JASP Analyses Go Wrong Before the Conclusion
Bayesian and frequentist output look alike on the screen but answer different questions, so each check below is made before a result is written into a manuscript.
Direction of the Bayes Factor BF10 and BF01 are easy to swap. The subscript is read first, and evidence is described on the scale of the factor, not as significant or not significant.
Prior Settings Stated A default prior is still a choice. Its family and width are reported and varied in a sensitivity analysis, and a conclusion that changes with the prior is described as such.
Credible Versus Confidence Intervals A credible interval is a statement about the parameter given the prior and the data. It is worded accordingly, and never judged against a significance threshold.
Measurement Levels and Factor Coding A column of text labels is read by JASP as nominal, so a Likert item stays out of numeric analyses until recoded from the codebook, which also sets the level order and the reference category.
Model Comparison Output In a Bayesian ANOVA, models are ranked against the null or the best model, so the comparison shown is stated and inclusion factors are read across all models, not from one row.
Sampling Settings for Posteriors Where an analysis samples the posterior, the number of samples and the seed are fixed and recorded, so rerunning the file returns the same summaries.
DELIVERABLES
What You Receive From a JASP Analysis
The files follow a documented JASP project, agreed in the scope and provided where applicable, so that you can trace each Bayes factor and test back to its settings.
Saved .jasp Project A saved .jasp project that keeps the data, the analyses and the options together, so each result can be reopened where it was produced.
Analysis Settings Record A table of the exact options behind every reported result, including priors, tests, corrections and sampling settings, in place of screenshots of the interface.
Cleaned Data and Codebook A cleaned data file with measurement levels and labels set as agreed, plus a codebook of recodes, computed variables and excluded cases.
Tables and Figures Tables and figures ready for the manuscript, including posterior and prior-sensitivity plots, each labeled with the prior, the sample size and the analysis that produced it.
Bayesian Interpretation Bayes factors and posteriors are interpreted on their own terms, beside the frequentist results where both are run, with the limits of the evidence stated plainly.
JASP Version and Modules The JASP version and any modules used are recorded, so that a coauthor or reviewer can reopen the project in a matching setup.
OTHER PLATFORMS
When Another Platform May Be More Appropriate
JASP covers many common designs in both frameworks, and a project that outgrows its menus or follows another team's standard may sit better elsewhere.
ILLUSTRATIVE EXAMPLE
How a JASP Analysis May Look in Practice
An illustration of how a replication study with a modest sample moves from a misread Bayes factor to evidence that is stated correctly.
ILLUSTRATIVE EXAMPLE · COGNITIVE PSYCHOLOGY REPLICATION RESEARCH · UNITED STATES
A psychology laboratory replicated a published attention effect with a smaller sample than the original study and wanted to say whether its data favored the effect, favored no effect or settled neither.
A paired comparison was planned in advance, with a one-sided direction taken from the original report. The first draft of the results section called the Bayes factor significant and quoted it with the subscripts reversed, so that evidence for the null appeared as evidence for the effect. The direction was restated from the settings stored in the .jasp file, the evidence was described on the scale of the factor, and a prior-sensitivity analysis showed how far the conclusion depended on the prior width. The frequentist paired test was reported beside it, each with its own interval, and every option was written to a settings table.
Outcome: The laboratory received the Bayesian and frequentist results with their 95% intervals, the prior-sensitivity analysis, the .jasp project and a settings record for each reported number.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
RESEARCH DISCIPLINES
Research Fields Where JASP Is Common
In psychology and the behavioral sciences, JASP research statistics are common because Bayesian reporting and shared project files are valued there, and the same questions arise wherever small samples need careful evidence statements.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCHER VOICE
What Researchers Say
Feedback from a behavioral science researcher on Bayesian output read as significance testing and a project that could be traced afterward.
“We had already done part of the analysis in JASP, but I was not confident about Bayesian output being read as though it were ordinary frequentist significance testing. The team checked the underlying setup and delivered the results with the relevant scripts or project files, so we could trace what had been done.”
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, including the JASP files described in the scope. The researcher or research team retains the scientific question, substantive interpretation, conclusions and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About JASP Analysis for Research
Short answers to what researchers ask before sharing a JASP project, from Bayesian reporting and priors to existing .jasp files, confidentiality and the assessment.
Do you run both Bayesian and frequentist analyses in JASP?
Yes. JASP Bayesian analysis at OnlineSPSS covers Bayesian tests, ANOVA and regression, and the frequentist versions of the same analyses where your study calls for both. Which framework carries the main conclusion is a design decision, and it is confirmed during the assessment, ideally before data are analyzed.
How should a Bayes factor be reported?
With its direction stated, its value described on the scale of evidence it represents, the prior it depends on and a check of how much the conclusion changes under other reasonable priors. It is not described as significant, and it is reported beside an effect estimate with a credible interval, so the size of the effect is not lost.
Are JASP's default priors acceptable for publication?
Often, provided they are reported and tested. JASP’s defaults, such as the Cauchy prior on the standardized effect size in its t-tests, are designed as reasonable starting points, so the prior width is stated, a robustness check across narrower and wider priors is shown, and an informed prior from earlier studies is used where one can be justified. The final choice stays with the researcher.
Can you continue from a .jasp file I have already started?
Yes. The .jasp file is opened as it is, the stored analyses and options are read, and the setup is checked before anything is added. The results are delivered with the settings that produced them, and a finished analysis can instead be checked through Independent Statistical Review.
How do you protect my research data and confidentiality?
Your datasets and JASP projects are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the dataset and any existing project file, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
JASP Analysis With Us
Tell us the research question, the data and whether any part is already analyzed in JASP. We check the project and the Bayesian settings before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014