RESEARCH SUPPORT
Stata Analysis Support for Researchers
Estimation Output Read and Reported
Stata output interpretation that starts before the output exists: the command family chosen to match your outcome and design, the model fitted, and every result read line by line, so that every number you report is tied to the estimation that produced it and to a decision you can explain.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does Stata Analysis Support Involve?
Stata analysis support covers the choice of an estimation command, the fitting of the model and the reading of its output. The command family follows the outcome and the data structure: regress for a continuous outcome, logit for a binary one, poisson or nbreg for counts, xtreg or mixed for repeated or clustered observations. Stata output interpretation then reads the header, the coefficient table and the stored results together, including the number of observations actually used, which can be smaller than the dataset. Each model is fitted, checked and explained by an in-house PhD-level statistician; OnlineSPSS is a specialist statistical consultancy for academic, clinical and professional research. Conclusions about the field rest on your reading of those estimates.
WHEN TO ENGAGE
Output, Data or a Reviewer's Query
Some researchers hold output they cannot read, and others hold data but have yet to choose a command, so the work begins in different places.
Output With Unfamiliar Lines The results window shows a log likelihood, a pseudo R-squared or an ancillary parameter, and it is unclear which numbers belong in a paper and which do not.
Command Not Yet Chosen The outcome and design are known, but several estimation commands are plausible, and the choice depends on how the outcome is measured and how observations are grouped.
Results That Differ From a Coauthor's A collaborator's estimates from SPSS or R do not match the Stata results, and the cause, perhaps different estimation samples or standard errors, has to be found.
Table Needs Backing for Review A reviewer asks which sample a table rests on and which standard errors were used, and the answer must be recoverable from the saved commands and the log.
COMMANDS BY OUTCOME
Matching an Estimation Command to the Question
Stata estimation commands differ in what they assume about the outcome and the observations, and reading the output starts with knowing what those assumptions are.
Linear Outcomes
The regress command fits a linear model, and its header gives the observations used, the F test and R-squared, while the table gives coefficients with standard errors and 95% confidence intervals.
Logit and Logistic
The logit command reports coefficients on the log-odds scale and logistic reports odds ratios, and the pseudo R-squared in the header is not a share of variance explained.
Counts
The poisson command assumes the variance equals the mean, and nbreg adds a dispersion parameter, whose likelihood-ratio test is not printed when standard errors are robust or clustered.
Panel and Clustered Data
The xtreg command, with fixed or random effects, and mixed for multilevel data, handle repeated observations, as set out under Panel Data Analysis (Fixed & Random Effects).
Choice of Standard Errors
Robust and clustered standard errors are chosen from the design within Quantitative Data Analysis, because the vce option changes every standard error and interval in the table.
Stored Results
Stored estimates are compared across specifications on the same estimation sample, and a model already fitted can be re-examined through Independent Statistical Review.
NUMBERS TAKEN AT FACE VALUE
Output Lines That Are Easy to Misread
A results window offers more numbers than any paper reports, and a few of them are routinely taken for something they are not.
Zero Printed as a P-Value A p-value displayed as 0.000 is reported as zero, when it means only that the value is below 0.0005 and should be reported as p < 0.001.
Observations Fewer Than Rows The header shows fewer observations than the dataset holds because cases missing any model variable are dropped, so the analyzed sample differs from the sample described.
Variable Dropped for Collinearity A note in the output says that a predictor was omitted because of collinearity, and the table is then reported without mentioning that the variable never entered the model.
Perfect Prediction Overlooked In a logit model, a predictor that perfectly predicts the outcome is dropped along with the observations it predicts, and the smaller sample in the header goes unreported.
Pseudo R-Squared as Variance Explained McFadden's pseudo R-squared from a logit model is quoted as variance explained, although it is a likelihood-based measure that cannot be read that way.
Standard Errors Left at the Default Clustered observations are analyzed with default standard errors, so the intervals look more precise than the design justifies.
DELIVERABLES
Output, Commands and Notes That Travel With You
Every table can be traced to a command and a log, and the interpretation sits beside the output it explains.
Annotated Do-File The do-file with each estimation command commented with its purpose, in the order run, so that the results can be regenerated from the data.
Estimation Log A log of the final run holding the full output of every model, which is the evidence behind each number in the paper.
Output Reading Notes Interpreting Stata estimation tables line by line, from each model's header to its coefficient table, naming the numbers to report and the ones to leave out.
Stored Results Table Models side by side on the same estimation sample, with coefficients or odds ratios, confidence intervals and the observations used.
Command Choice Memo A short memo on why each command and standard-error option was used and what an alternative would have given.
Reporting Wording for Review Statistical wording for each estimate with its 95% confidence interval, matched line by line to the output it came from, for you to check and incorporate.
ILLUSTRATIVE EXAMPLE
A Count Outcome and an Overdispersion Line
An illustration of how reading one line of output changed the command used for a count outcome.
ILLUSTRATIVE EXAMPLE · SMALL FARM CREDIT ACCESS RESEARCH · UNITED STATES
A development economics researcher analyzed the number of formal loans obtained by farm households in a survey year and asked whether membership in a savings group was associated with more loans.
The first model was a Poisson fit, chosen because the outcome was a count. The complication appeared in the output: after poisson, the Pearson goodness-of-fit statistic showed far more variation than the model allowed, so its standard errors were too small. The researcher moved to nbreg, and the likelihood-ratio test of alpha printed beneath its table supported the change. Because households in one village shared lenders and local conditions, the standard errors were then clustered by village. The header was checked to confirm that no households had been lost to missing covariates, and the estimate was described as an association, since savings group membership had not been allocated.
Outcome: The researcher received the rate ratio for membership with its 95% confidence interval, the comparison of the two models, the do-file and log, and notes reading each block of output.
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
Cross-Checking Stata Results in Other Programs
A second program is occasionally used to confirm an estimate, and a mismatch is itself informative about samples, options or defaults.
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
Reading the Output Versus Drawing the Conclusions
What an estimate means for the field is a scholarly judgment, and the statistical reading of the output exists to inform that judgment, not to replace it.
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 paper, protocol or report. Authorship remains the researcher's.
Your Research Decisions The research question, the claims the study makes, the interpretation of the estimates and the conclusions remain with you, along with overall responsibility.
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
Logit, Estimation Samples and Clustering
Reading the header of a results window, choosing between near-identical commands and knowing which numbers to cite are the recurring sticking points.
What does Stata output interpretation include?
Stata output interpretation at OnlineSPSS covers choosing the estimation command, fitting and checking the model, and reading its output so that the right numbers are reported with confidence intervals. Projects range from a single model to a full set of analyses for a paper, and from a new analysis to output already on screen.
What is the difference between logit and logistic in Stata?
Both fit the same model by maximum likelihood. Logit displays the coefficients on the log-odds scale, whereas logistic displays odds ratios by default, so the fitted model, the log likelihood and the predictions are identical. Which display suits a table depends on the convention of the field.
How can I tell which observations a Stata model actually used?
The function e(sample), set by estimation commands, marks the observations used. Only those with nonmissing values on every variable in the model, and meeting any if condition, enter the estimation, so a gap in one covariate removes the whole case. Tabulating missing values per variable then shows which covariate costs the most cases.
When should standard errors be clustered?
When observations are grouped in a way that makes them alike, such as patients in clinics or repeated measurements on the same people, and the groups, not the individual observations, are the independent units. The design decides, not whether clustering changes the result. With few clusters, cluster-robust standard errors become unreliable and another approach is considered.
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 dataset, any do-files and the output you already have, 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
Make Sense of the
Results Window
Misread Stata tables often trace to three lines of output: the observation count, the scale of the coefficients and the standard-error option behind each interval.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014