RESEARCH SUPPORT
Data Analysis Help
When Your Data Keep Changing
For the stage where a new wave, a corrected extract or a late record arrives and every table is suddenly out of date. A reproducible data analysis workflow turns rerunning analysis on updated data into one script and a log of what changed.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does It Take to Rerun an Analysis Cleanly?
Data analysis help for a dataset that keeps changing means building the analysis so that it can be run again, in full, whenever the file is revised: a new wave, a corrected export, late records or a reviewer’s request to drop a group. The raw file stays untouched, each revised version is kept and described, and one script runs everything from import to final table, so the analysis can be rerun and every number can be traced to the version that produced it. A decision log records each exclusion, recode and model choice with its reason. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, has this carried out in-house by a PhD-level statistician. The research question, the interpretation and the conclusions are yours.
WHEN TO ENGAGE
Projects Where the File Keeps Moving
A rerunnable analysis is worth building when the data will change again, and the agreement then covers the route from each new file to the updated tables.
Tables That No Longer Match A draft table was made from an earlier version of the file, and nobody can say which version it used or whether its numbers still hold after the latest correction.
Stages Split Across Teams Collection, cleaning and analysis were handled by different people, and the handovers left gaps in what was recoded, excluded or assumed.
Data Still Arriving New records or corrections keep coming in during the project, so the analysis has to be rebuilt from the start whenever the file changes.
A Reviewer Asks for a Change A reviewer or supervisor asks to exclude a group, redefine an outcome or add a covariate, and the whole analysis must be redone without losing track of what moved.
THE ANALYSIS CHAIN
Stages From File to Findings
The stages are connected, so a decision at one of them is carried to the others, and the agreement lists which of them your project needs.
Intake and Screening
The file is checked against the codebook for ranges, duplicates and impossible combinations, and anything unexplained is traced to its source before it is altered.
Cleaning Decisions
Recodes, exclusions and the handling of gaps, with the approaches in Missing Data Analysis, are recorded with their reasons.
Comparing Each Delivery
Every new version is compared with the last, in row counts, changed values and altered variables, so that you know exactly what the data owner revised before any result is updated.
One Script, Start to End
The steps run in a fixed order from a single entry point, with file paths and random seeds set once, so a colleague can reproduce the tables on another machine.
Reading What Moved
After each rerun the estimates are set beside those of the previous run, and every change is traced to the records that were added or corrected before it is carried into Results Reporting & Interpretation.
Rerun and Handover
The chain is built so that the whole analysis can be rerun on a revised file, and the files and the log are handed over together with a short guide to using them.
WHERE THE CHAIN BREAKS
Gaps That Open Between Stages
Errors in a long analysis seldom come from a single step, and they usually come from what was lost between one stage and the next.
Corrections Not Carried Forward A coding error is fixed in the file, but tables made earlier stay in the draft, so the manuscript mixes numbers from two versions of the data.
Exclusions Without a Record Cases are dropped during cleaning with no note of how many or why, and the sample in the results no longer matches the sample that was recruited.
Handover Gaps The person who cleaned the file is not the person who analyzed it, and a recode that changed what a variable means is never passed on.
Numbers Copied by Hand Estimates are moved from output into the manuscript by hand, and a late change to the data leaves some of them out of date with no way to tell which.
Formats Lost Between Programs The file is cleaned in a spreadsheet, analyzed in a statistics program and tabulated in a word processor, and missing-value codes or labels are lost or misread at each conversion.
A Late Update Breaks Everything New data arrive near the end, and because the steps were run by hand and not scripted, every table has to be remade from memory of what was done.
DELIVERABLES
The Record of the Whole Analysis
Together the files trace any reported figure back to the source file, and the steps between them can be repeated on a revised extract.
Chain Decision Log A dated list of every exclusion, recode, handling of missing values and model choice, with its reason and the stage at which it was made.
Versioned Data Files The raw file, kept unchanged, and each cleaned version with a note on how it differs from the one before, so that every table names the version it used.
Run-All Script One script, or a short sequence of scripts, that runs the chain from the raw file to the final tables and figures in your program.
Reporting-Ready Tables Tables and figures in your required style, each carrying the number analyzed, the estimates with their intervals and the version of the data it came from.
Statistical Methods Description Statistical wording describing the methods actually used, in the order of the analysis and matched to the output, for you to check and incorporate.
Handover Note A short guide to the files, the order in which to run them and the points at which a future change to the data would alter the results.
ILLUSTRATIVE EXAMPLE
A Monthly Data Extract That Kept Growing
An illustration of how a chain built to be rerun absorbs new records and corrections without remaking each table by hand.
ILLUSTRATIVE EXAMPLE · COMMUNITY BIKE-SHARE RESEARCH · UNITED STATES
A researcher at a transportation research institute wanted to understand how weather and station location were related to daily ridership, using trip records extracted from a bike-share system over two years.
The raw extract contained trips with missing end stations and days when a sensor outage left a station silent, so screening came first and every decision was logged. The complication was that the operator delivered corrected records twice during the project. Because cleaning, models and tables were scripted as one chain, each delivery was processed by rerunning it, and the log showed which tables changed and why. Outage days were handled as missing data in a stated way, not as zero trips. Results were worded as associations between conditions and ridership.
Outcome: The researcher received the decision log, the versioned data files, the run-all script, the reporting-ready tables with 95% confidence intervals, a methods description and a handover note.
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.
SOFTWARE
One Environment for Every Stage
A chain is only rerunnable if its stages live in one environment, so the choice rests on which program the project and its readers will use.
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.
RESEARCH RESPONSIBILITY
Authors Own What the Chain Produces
Each table in the chain comes from recorded steps, and what those tables mean for the study is decided by its authors.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your research. Authorship of the report, thesis or article remains yours.
Your Research Decisions The study design, the substantive interpretation, the discussion and the conclusions remain yours, with overall responsibility, as does the final version of the report.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Running One Analysis Chain in Practice
Changing files, an untouched original and a clean handover are the practical tests of a single analysis chain.
What does data analysis help look like when my dataset keeps changing?
It means building the analysis as a chain of recorded steps that can be run again on each new version of the file, with a log that shows which results changed and why. The raw file stays unchanged, each version is named, and one script produces the tables. A first analysis from a raw file, without changes expected, is described under Quantitative Data Analysis.
Can the results I submitted earlier still be reproduced?
Yes. Each version of the data is kept under its own name and the script is not overwritten, so any earlier table can be regenerated from the version it cites. When a reviewer asks why a figure moved between submission and revision, both runs and the records that changed between them can be shown. A substantial change to the sample, such as a new group, may alter the scope, and that is discussed with you before it is carried out.
What happens to my original data file during the work?
It is kept unchanged. Every cleaned version is created from it by recorded steps, and each table identifies the version it used, so that the original and the analysis file can be compared at any point and any step can be reversed or questioned.
Can I take over the analysis once it is delivered?
Yes, and the files are organized for that. The handover note explains the order of the scripts and the points at which a change to the data would alter results, and the decision log explains why each choice was made, so that you, a supervisor or a collaborator can continue the work.
How do you protect my research data and confidentiality?
Your data and project details 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.
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 raw file, the codebook and any earlier output, 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, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Update the Tables
Without Starting Over
When the next version of the data arrives, a scripted analysis and a decision log show what changed before any table is touched, and a supervisor or collaborator can follow every step.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014