RESEARCH SUPPORT
Regression analysis support
for building your model
Covariate selection for regression, from the aim of the analysis to the form of each predictor, with diagnostics read in context and the final model reported with confidence intervals.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
How Is a Research Regression Model Built?
Regression analysis support means working through one model with you, from the outcome and its scale to the covariates, the shape of each predictor and the checks of fit, until a reviewer can accept it. Covariate selection for regression follows the aim, since adjusting for confounders, estimating one association and predicting new cases call for different choices, and selection by p-value rarely serves any of them. Residuals, influence and the design are read together, not through a single test. A PhD-level statistician on the in-house team of OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, specifies, fits and documents each model. Reading the coefficients for your field remains your task.
WHEN TO ENGAGE
Where Model Building Needs Judgment
Model building can start before anything is fitted, or later, when a reviewer's comment or an unexplained stepwise run means the model has to be rebuilt.
Variables, No Model Yet A dataset holds an outcome and many candidate predictors, and the model has not been specified, so the aim is settled first and the covariates follow from it.
A Model a Reviewer Questioned A reviewer has asked why certain covariates were included, whether the relationship is linear or whether influential cases drive the result, and the model needs to be defended or rebuilt.
Selection Output Inherited A model came from automated selection and the reason for each term was never recorded, so its terms are reconsidered against the research question and the literature.
A Second Look Before Submission A model is already built, and a supervisor, committee or co-author should see its covariates, curves and diagnostics checked before it goes to a journal or a thesis chapter.
MODEL BUILDING SCOPE
Decisions That Make Up the Model
A regression model is the sum of several separate decisions, and the scope names the ones that your question, your outcome and your data make necessary.
Events and Model Size
How many terms a model can carry depends on the sample in Multiple Linear Regression and on the number of events in Logistic Regression, so candidates are counted first.
Prespecified vs Data-Driven Covariates
A covariate set fixed beforehand from the question and the literature is weighed against data-driven options such as penalized regression, and the instability that comes from selecting on the sample is reported.
Functional Form
The shape of each continuous predictor is examined, with splines, polynomial terms or transformations where a straight line misrepresents it, and the choice is explained.
Interactions and Blocks
Interactions are included when the question calls for them, and predictors entered in theory-based steps are modeled through Hierarchical Regression.
Diagnostics and Fit
Residuals, leverage, influence and collinearity are read together with the design, and a conclusion is checked for how far it changes when a doubtful case or assumption is altered.
Reporting and Wider Analysis
The final model is reported with estimates and intervals, and the analysis sits within Quantitative Data Analysis and the family of Regression & Predictive Modeling.
MODEL BUILDING FAULTS
How a Regression Model Loses Credibility
Reviewers do not object to the software output, they object to the decisions behind it, and the same few decisions account for most of the objections.
Covariates Chosen After the Fact The covariate list is revised after seeing which variables move the estimate, so the final model is one of many tried and its interval ignores the search. A set fixed beforehand is the usual alternative to stepwise regression.
Correlated Predictors, Unstable Estimates Two strongly correlated predictors enter the model together, so each coefficient has a wide interval or an unstable sign, and the pair is read as if each effect were precisely estimated.
A Straight Line Assumed A curved relationship is fitted as linear, so residuals show a pattern, and the single slope reported describes the relationship poorly at both ends of the range.
Too Many Terms for the Data A model with many predictors is fitted to a small sample or a binary outcome with few events, which overfits and produces unstable estimates with wide intervals.
Influential Cases Unexamined A few observations with high leverage or large residuals determine a coefficient, and the result is never checked with them set aside.
Main Effects Read Past an Interaction An interaction is in the model, yet each main effect is reported as an overall effect, though it now describes the slope only where the other variable is zero, which may be an impossible value unless it was centered.
DELIVERABLES
The Model and the Reasoning Behind It
Every decision behind the model is written down, so that it can be defended in review and rebuilt if the data or the question change.
Pre-Fit Specification Note A statement of the aim, the outcome, the model family and the planned covariates, fixed before the final model is fitted.
Covariate Rationale Table Each covariate with the reason it was included or left out, and the source of that reason, whether a published paper, prior work or the design.
Functional Form Check Plots and comparisons showing how each continuous predictor was modeled, and why the chosen form was retained over the alternatives.
Diagnostics Summary The residual, influence and collinearity checks in plain terms, with a note on any observation that changes a conclusion when set aside.
Final Model Table Estimates with 95% confidence intervals, the number of observations and the fit measures that suit the model, in the format your field uses.
Fitting Script The commands that build the model from the cleaned data in your program, so a supervisor or reviewer can rerun each version.
ILLUSTRATIVE EXAMPLE
Too Many Candidates and One Curved Predictor
An illustration of how a regression model is built from a question when the candidate predictors are many and one relationship is curved.
ILLUSTRATIVE EXAMPLE · RESIDENTIAL WATER USE RESEARCH · UNITED STATES
A researcher at a municipal research institute wanted to explain differences in household water use across a metropolitan area from dwelling features, household composition and the season of the reading.
Of about thirty candidate predictors, the analysis plan named the ones tied to the question by the literature and the design, and left the rest out. The complication was floor area: its relationship with water use flattened for larger dwellings, and a few very large properties had high leverage and drove the single slope. A restricted cubic spline replaced the straight line, the effect was reported as differences between typical dwelling sizes, and a sensitivity analysis showed the estimates with and without the largest properties.
Outcome: The researcher received the specification note, the covariate rationale table, the diagnostics summary, the final model table with 95% confidence intervals, the sensitivity analysis and the fitting script.
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
Fitting and Checking the Model in Software
Diagnostics differ more between programs than the fitted coefficients do, which is why each card names the options or commands that report residuals and influence.
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
The Model Serves Your Research Question
Building the model is the statistical work, and deciding what the findings mean for the field is the investigator's.
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 research question, the theory behind the covariates, the interpretation of the coefficients and the conclusions remain yours, 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
Covariates, Curves and Skewed Outcomes
Stepwise selection, a curved predictor and a skewed outcome each tempt a shortcut that weakens the model.
What does regression model building involve?
Regression model building involves choosing the outcome scale and model family, selecting covariates for the aim of the analysis, examining the functional form of the predictors and checking the fit with diagnostics. It ends in a model table with confidence intervals and a record of each decision, so the model can be defended and rerun.
What are the alternatives to stepwise regression?
The main alternatives to stepwise regression are a covariate set fixed before fitting, from the question and the literature, and penalized regression such as the lasso, which shrinks coefficients within one fit instead of testing terms one at a time. Where some selection is unavoidable, it is repeated in bootstrap samples to show how stable the chosen set is, and the intervals are read with that search in mind.
How should a predictor with a curved relationship be modeled?
Usually by keeping it continuous and letting its shape vary, with a restricted cubic spline or a polynomial term, rather than cutting it into categories, which discards information and assumes steps that are rarely real. The chosen form is checked against the residuals, and the effect is reported as differences between meaningful values of the predictor.
Should I transform a skewed outcome to meet normality?
Only if the residuals call for it. The assumption concerns the errors and not the raw outcome, so a skewed outcome can give acceptable residuals, and a log scale is often chosen because it stabilizes the spread and makes coefficients proportional. A generalized model is an alternative that avoids transforming at all.
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 variable list, the dataset and any current model, 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
Name the Question the
Model Must Answer
A covariate set fixed before fitting, a checked shape for each continuous predictor and a record of influential cases are what reviewers ask for, and all three can be settled before the final model is run.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014