ANALYSIS SERVICE
Independent Statistical Review
Service for Research
Our independent statistical review service examines an analysis you have already completed: whether the methods fit the design, whether the assumptions and diagnostics support the conclusions, and whether the results are reported accurately, with the findings set out in a written report.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Independent statistical review is a structured examination of an existing analysis, model, output, or reporting approach, carried out to judge whether the chosen methods suit the study design and data, whether the evidence supports the claims made, and whether the numbers agree across code, tables, and text. The result is a written assessment that ranks findings by their consequence and recommends a course of action. It reports what it finds. It does not rewrite your results or change your conclusions.
WHEN TO ENGAGE
When Do You Need an Independent Statistical Review?
Researchers usually seek a review once an analysis is complete or nearly complete and someone other than its author should check whether it will withstand scrutiny. The situations below are the most common.
01 · When You Need a Second Statistical Opinion An analysis is finished and you want an independent view of whether the approach, the model choice, and the interpretation are defensible for your design.
02 · When You Have Received Reviewer or Committee Comments Journal reviewers, examiners, or a committee have questioned the methods or results, and you need to know which concerns are valid and how they bear on the analysis.
03 · When You Are Unsure About Assumptions or Model Choice The outcome type, clustering, missing data, or diagnostics leave doubt about whether the model you used, and the assumptions behind it, are appropriate.
04 · Before Submission, Defense, or Sign-Off A statistical review before journal submission, a thesis defense, or sign-off of a report or grant application checks accuracy, internal consistency, and reporting standards while changes are still easy to make.
05 · When Results Look Unexpected or Inconsistent Estimates, signs, degrees of freedom, or sample sizes that do not reconcile across tables, or with the design, may point to an analytical or reporting error worth tracing.
06 · When You Have Inherited an Analysis When work comes from a collaborator, a former team member, or an earlier analyst, you may need to verify the statistical analysis before you build on it, extend it, or put your name to it.
DELIVERABLES
What You Receive From the Review
Each statistical analysis review is delivered as a written assessment, with the materials examined, the checks performed, and the form of the findings agreed before work begins.
Methods Appropriateness Assessment A judgment on whether the tests or models fit the design, outcome type, and data structure, including independence, clustering, and repeated measures.
Assumption & Diagnostic Review A record of whether the relevant assumptions were examined, and whether diagnostics, robustness checks, and missing-data handling support the analytic choices.
Output & Reproducibility Check Reported figures are recalculated from the data and code where these are provided, confirming that software output, tables, and manuscript text agree.
Reporting & Interpretation Review Effect sizes, confidence intervals, p-values, notation, and multiplicity are checked, with attention to claims the design cannot support, such as causal wording.
Written Review Report Findings are ordered by importance, separating errors, methodological concerns, and reporting suggestions, each with its rationale and a recommended action.
Mapping to Reviewer Comments Where reviewer or committee comments exist, each is mapped to the findings so that your response is accurate, with revised wording and decisions remaining yours.
REVIEW & CHECKS
What an Independent Statistical Review Examines
A review follows the analytical chain from the research question to the reported conclusion. The depth is agreed in advance, from one model to a full results chapter, and a qualitative or mixed methods strand can be reviewed for coding consistency and evidence trail.
01
Group Comparisons & Hypothesis Tests
We check test selection, the paired or independent structure of the data, distributional assumptions, multiplicity, and the reporting of effect sizes.
Test Selection
Paired vs Independent Data
Multiple Comparisons
Effect Sizes
02
Regression & Predictive Models
We review model specification, functional form, collinearity, influential observations, and overfitting, along with how coefficients were interpreted.
Model Specification
Diagnostics
Multicollinearity
Overfitting
Interactions
03
Scales, Factors & Reliability
We examine factor retention, rotation, loadings, reliability coefficients, and whether scoring decisions suit the instrument and the sample.
Factor Analysis
Cronbach's Alpha
Scale Scoring
Sample Adequacy
04
Structural & Latent Variable Models
We assess identification, fit indices, estimator choice, invariance claims, and whether model modifications are justified and transparently reported.
SEM
Model Fit
Measurement Invariance
Estimator Choice
05
Repeated, Clustered & Multilevel Data
We check whether correlated observations were handled in the analysis, including the random effects structure, covariance choices, and missing data.
Mixed Models
GEE
Random Effects
Missing Data
06
Clinical, Survival & Epidemiological Models
We review time-to-event models, the proportional hazards assumption, competing risks, confounding control, and the effect measures reported.
Cox Regression
Proportional Hazards
Confounding
Propensity Scores
07
Bayesian & Computational Analyses
We evaluate prior specification, convergence diagnostics, sensitivity to priors, and the validity of resampling, simulation, and imputation procedures.
Prior Specification
Convergence
Bootstrapping
Multiple Imputation
08
Meta-Analyses & Evidence Syntheses
We examine effect size extraction, the pooling model, handling of heterogeneity, and publication bias assessment in systematic reviews and meta-analyses.
Pooling Model
Heterogeneity
Publication Bias
Effect Size Extraction
SOFTWARE
Statistical Software We Review
We review analyses produced in leading platforms, working from output files, syntax, scripts, and project files, and we can recalculate results from the dataset where it is provided.
CASE EXAMPLE
How We Work in Practice
A quantitative research example showing how an existing dataset is prepared, analyzed, checked, and translated into clear analytical findings.
ILLUSTRATIVE EXAMPLE · BREAST CANCER RESEARCH · UNITED STATES
A clinical research team had assembled a retrospective cohort of 648 patients with breast cancer across four clinical sites and wanted to identify patient and tumor characteristics associated with a binary treatment outcome.
The source dataset combined demographic, tumor, pathology, and treatment variables extracted from clinical records. Before analysis, we reviewed coding consistency, missing values, duplicate records, category frequencies, and continuous-variable distributions. We then recoded selected variables, derived the required analytical measures, and produced a documented analysis-ready dataset.
We summarized the cohort using descriptive statistics and unadjusted comparisons before fitting a multivariable logistic regression model based on the predefined clinical and research variables. Model development included assessment of multicollinearity, sparse categories, influential observations, model fit, and sensitivity to alternative specifications.
The final analysis reported adjusted odds ratios with 95% confidence intervals, p-values, model diagnostics, and publication-ready tables and figures, with reporting aligned to STROBE for observational research. The complete workflow was produced in R, allowing the analysis to be reproduced and rerun from the prepared dataset if records or analytical decisions were subsequently updated.
Outcome: The research team received the cleaned analytical dataset with variable documentation, reproducible R scripts, model diagnostics, publication-ready tables and figures, and a structured analytical summary interpreting the findings in relation to the research questions.
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.
A Defined Process for Your Research
We review the analysis you have completed and what you need checked before any work begins, so the scope, deliverables, timeline, and fixed project fee are clear from the outset.
01
Share Your Project
Send the manuscript or results section, output files, syntax or scripts, the dataset if available, and any reviewer or committee comments. We will let you know if anything else is needed.
02
Receive Your Assessment
Your statistically trained Research Coordinator reviews the materials and confirms what will be examined, the depth of checking, the form of the report, timeline, and fixed project price.
03
Work Completed
Once you confirm the scope, an in-house statistician carries out the agreed review and delivers a written report of findings and recommendations. Any changes to the analysis remain your decision.
RESEARCHER VOICE
What Researchers Say
“I had a fairly complex dataset with several scales and multiple predictors, and I was not confident the original analysis plan was the best fit. The analysis was clearly structured, the assumptions and model results were explained in a way I could follow, and the final tables and interpretation made the findings much easier to present in my research.”
FREQUENTLY ASKED QUESTIONS
Questions About Independent Statistical Review
Answers to the questions researchers most often ask before an existing analysis is reviewed.
What happens when I request an assessment, and how long does it take?
We respond to assessment requests the same day. You can share the analysis, outputs, and any reviewer or committee comments through the assessment form. We then confirm what will be examined, the deliverables, the timeline, and one fixed project fee by email, with no commitment to proceed.
Will you rewrite or correct my results?
No. The review assesses your analysis as it stands and reports what it finds, so that the findings are not shaped by a stake in the original choices. Corrections, re-analysis, or revised text are decisions for you and your team, and where you want that work carried out, it is scoped separately.
What materials do I need to provide?
Ideally the manuscript or results section, the software output, the syntax or scripts, and the dataset with a codebook. Without the data we can still judge the appropriateness of the methods and the consistency of what is reported, but recalculating results requires the data.
Can you support my response to journal reviewer or committee comments?
Yes. A statistical review of reviewer comments assesses each comment against your analysis and tells you whether it is valid, partly valid, or answerable without new analysis. The response letter and any revised text remain yours, and further analyses requested by reviewers are confirmed with you as additional scope.
What happens if the review finds a serious problem?
It is reported plainly, with its likely consequence for the results and the options available, which may range from a clarification in the text to a different model. We do not change the analysis ourselves. If you decide to correct it, we confirm the scope and fee for that work before proceeding.
Does the review confirm that my conclusions are correct?
It assesses whether the statistical evidence supports the conclusions drawn, and whether the methods and reporting are defensible. It cannot establish that a substantive finding is true, and it does not predict whether a journal, examiner, or committee will accept the work.
How do you protect my research data and confidentiality?
Manuscripts, output files, scripts, datasets, and reviewer correspondence are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
How does statistical consulting fit with academic authorship?
Our role is to provide the agreed methodological, analytical, and statistical reporting support. The research question, substantive interpretation, conclusions, and overall responsibility for the study remain with you or your research team. OnlineSPSS does not claim any intellectual contribution, and authorship remains yours.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Your Analysis. Independently Reviewed.
Findings You Can Act On.
Share your analysis, outputs, and any reviewer comments, and we will assess what needs to be examined, the expected report, the timeline, and the fixed project fee before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014