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The MAS Research Model

From research question to research impact

Most research support begins when the analysis is due and ends when it is delivered. That is the narrowest, least valuable slice of the research process. We work across the whole lifecycle—from the intelligence that decides what to study, through design, analysis, and independent validation, to publication and demonstrated impact. Nine connected stages, each one documented, each one producing something you keep.

Nine-stage model Documented & reproducible Any stage, any scope
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MAS Research Model A nine-stage research pipeline from discovery through analysis and validation to publication, shown as nine connected nodes. mas_model · nine stagesDiscoverImpactquestion → design → analysis → validation → publication
The model stages

Why a model, and not a menu

Research is not a set of independent tasks that can be bought à la carte and bolted together. The value of a good analysis depends on a sound design; the value of a sound design depends on knowing what is genuinely worth studying; the credibility of a published finding depends on the diagnostics and the independent check that came before it. When any of these is skipped or done cheaply, the weakness propagates forward and surfaces—expensively—as a referee's rejection or, worse, a result that does not hold.

The MAS Research Model makes those dependencies explicit. You can still engage us at a single stage—many clients come to us for one analysis, an audit, or help answering a referee—and we do that work to the same standard whatever the scope. But the model shows how the stages connect, and it explains a pattern we see repeatedly: the earlier we are involved, the more we can do to prevent the problems that are impossible to fix later.

The nine stages

The full arc, stage by stage

Each stage is a genuine capability in its own right. Read them in sequence—left to right, top to bottom—to see how a project moves from an open question to a published, defensible result.

01 Research Intelligence

Discover

Before you can add to a field, you have to see it clearly. Most research projects begin with a question the researcher already had in mind—which means the hardest and most consequential decision, what is actually worth studying?, is made on instinct rather than evidence. We start earlier than that. using bibliometric and scientometric analysis, we map what a field is currently publishing, which themes are rising and which are fading, where the genuine gaps sit, and which questions are novel rather than already crowded. The output is not a vague sense of the literature but an evidence-based picture of it: the influential works and authors, the thematic clusters and how they have shifted, the methods in current use, and the specific openings where a new contribution would land.

What you receive A documented research-intelligence brief—field map, gap analysis, and candidate research questions—so the study you commit to is one the field is ready for.

02 Research Design

Frame

A study's fate is largely sealed at the design stage, long before any data is collected. A vague research question, a conceptual framework that doesn't quite fit the theory, variables that don't cleanly operationalize the constructs—these are the flaws that no amount of sophisticated analysis can repair later. So we treat design as real work, not a formality. We help sharpen the research question into something answerable, build a conceptual and theoretical framework the analysis can actually test, select and define variables so each one measures what it's meant to, and specify the model before the data arrives. Every choice is made deliberately and documented, so that when a reviewer asks why the study was set up this way, there is a clear, defensible answer.

What you receive A complete research design: refined question and hypotheses, conceptual and theoretical framework, variable definitions, and a documented model specification.

03 Statistical Planning

Plan

Between design and data collection sits a set of decisions that determine whether a study can answer its question at all: how the sample is drawn, how large it needs to be, and whether it has the statistical power to detect the effect you care about. Underpower a study and it will fail to find a real effect and mislead everyone; overpower it and you waste resources and participants. We compute sample size from a formal power analysis tied to the effect you need to detect and the design you're using—accounting for stratification, clustering, and multiple comparisons where they apply—rather than from a rule of thumb. Where the design is complex, we use simulation-based power analysis. And we settle the analysis plan, and where appropriate pre-register it, before a single observation is gathered.

What you receive A sampling and statistical plan: sampling design, a justified sample size, power analysis, and a pre-analysis plan the study can be held to.

04 Research Data Services

Source

Most empirical projects live or die on data work that never appears in the published paper. Identifying the right source, acquiring it, cleaning it, harmonizing series that don't quite match, constructing a panel, building the variables the analysis needs—this is the unglamorous middle where the majority of a project's time is genuinely spent, and where errors quietly compromise everything downstream. We support the full data lifecycle: sourcing secondary data, extracting from the web or APIs where needed, cleaning and transforming, constructing panels and variables, and documenting every step in a codebook. The result is a dataset that is not only analysis-ready but reproducible from the raw file forward, so that the data underneath your results is as defensible as the results themselves.

What you receive A clean, documented, reproducible dataset with a codebook—every transformation from raw source to analysis file traceable.

05 Analysis

Analyze

This is the stage most people picture when they think of research support, and it is where our fifteen method areas come into play—but the discipline is the same across all of them: the method follows the question and the data, never the other way around. For a short dynamic panel, that may mean System GMM rather than fixed effects; for a causal claim, a difference-in-differences design with the parallel-trends assumption tested; for a latent construct, measurement validated before any structural path is estimated. We choose the estimator or model that fits both the data-generating process and the claim you want to make, justify it against the alternatives, and run it with correct inference. Whatever the method, the analysis is built to be understood, not taken on trust.

What you receive The primary analysis, run correctly with appropriate inference—and the reasoning behind every methodological choice made explicit.

06 Diagnostics

Diagnose

An estimate without diagnostics is a number without a warranty. The tests that decide whether a result is trustworthy—whether the assumptions hold, whether the specification is right, whether there is hidden endogeneity, non-stationarity, or dependence corrupting the standard errors—are exactly the objections a referee will raise, and exactly what separates a publishable result from a rejected one. We run the full diagnostic battery appropriate to the method: stationarity and cointegration for time series, Hausman and serial-correlation tests for panels, endogeneity and instrument-strength checks for causal work, measurement validity for SEM. Where a test fails, we don't paper over it—we return to the specification and fix the underlying problem.

What you receive A complete set of diagnostic, specification, and assumption tests—reported transparently, with any issues resolved rather than hidden.

07 Statistical & Methodological Audit

Validate

Every empirical paper is read twice: once by the people who wrote it, and once by a referee looking for reasons to reject it. The gap between those two readings is where most rejections live. Our validation stage closes that gap by bringing the adversarial read forward—an independent audit of the specification, the assumptions, the robustness, and the reproducibility of the analysis, whether or not we ran it ourselves. We stress-test the result with robustness and sensitivity checks, run placebo and falsification tests where they apply, and—where code is available—replicate the numbers to confirm they reproduce exactly. The deliverable is a structured report: every issue ranked by how likely a reviewer is to raise it, each with a concrete fix.

What you receive An independent audit report—issues ranked by severity, robustness and sensitivity results, and a replication check—the cheapest insurance against an avoidable rejection.

08 Research Reporting

Report

Sound analysis still gets undervalued when it is reported badly—a methods section a referee can't follow, tables that bury the contribution, figures that don't communicate. Reporting is where the rigor of the previous stages either becomes visible or gets lost. We produce publication-quality tables and figures, write the methodology and results in the register your target journal expects, and assemble the supplementary and reproducibility materials—data, code, and documentation—that journals increasingly require. Crucially, this is a description of your work written for you to review, adapt, and make your own; it is substantive research reporting, not ghost-writing, and the authorship remains entirely yours.

What you receive Publication-ready tables, figures, methods and results text, and a reproducibility package—the analysis made legible to editors and referees.

09 Publication & Institutional Impact

Publish & Impact

The final stage turns finished research into published, cited, and—for institutions—demonstrable impact. For an individual paper, that means journal selection and fit analysis informed by real journal intelligence, submission support, and help interpreting referee reports and drafting the technical responses that carry a revision through to acceptance. For a department or a business school, it means aggregating a body of work into research-performance analytics, benchmarking, and the evidence that supports AACSB or EQUIS accreditation. Either way, the arc that began with deciding what to study closes with the research reaching the people it was meant for.

What you receive Journal targeting, submission and reviewer-response support through to acceptance—and, for institutions, the analytics and evidence that demonstrate impact.

Principles

What holds across every stage

The stages change; the standards do not. Six principles govern how we work regardless of where in the model an engagement sits.

Reproducible by default

Every result can be re-run from the raw data. You receive versioned, commented code, documented specifications, and clean data—not just a number in a table, but the full path that produced it.

The method follows the question

We choose the approach the data and the claim actually require, and justify it against the alternatives. We do not have a house method that every project is bent to fit.

Your work stays yours

We support and strengthen your research; we do not write papers on your behalf or manufacture authorship. The contribution, the argument, and the credit remain entirely yours.

Rigor over volume

Fewer things done properly, each tested against the objections a referee will raise before you submit. We would rather deliver one defensible result than three fragile ones.

Confidential throughout

Your unpublished data, ideas, drafts, and analyses are treated in strict confidence at every stage, and are used only for your engagement.

Explained, not black-boxed

Every methodological choice is documented in plain terms, so you can present the work, defend it in a viva or a review, and extend it yourself afterwards.

How engagements actually work

Most engagements begin with a conversation, not a contract. You tell us the research question and the data you have—or the stage you are stuck at—and a methodologist who knows your field scopes it honestly, including telling you when we are not the right fit or when a simpler approach would serve you better. From there we agree the scope, the deliverables, and the timeline in writing before any work begins.

Some clients hand us a single, well-defined task: run this analysis, audit this paper, respond to these referees. Others bring us in at the start of a project and stay with us through the arc. Both are normal. What does not change is that you always know what you are getting, what it will cost, and when—and that everything we hand back is documented well enough for you to own it completely.

Where to start

If you already know what you need—a specific method, an audit, publication support—the individual expertise pages describe exactly how that work is done, and you can write to us referencing it. If you are earlier than that and not sure which method or stage fits, the best starting point is simply to describe your question and your data; matching the right approach to it is part of what we do.

For institutions—universities and business schools building research capacity, or preparing for an accreditation cycle—the engagement is broader and longer, and usually begins with a readiness assessment rather than a single task. Our institutional services page sets out that work in detail.

Common questions

How the model works in practice

The questions researchers and institutions most often ask about how we work.

No. Most engagements involve only part of the model—a single analysis, an audit, or reviewer-response support. The stages exist to show how the work connects and where the leverage is, not to bundle services you don't need. We scope every engagement to what you actually require.
Because the most damaging errors are made before any data is analysed—an unanswerable question, an underpowered sample, a flawed design. These cannot be fixed at the analysis stage; they can only be prevented at the design stage. Involving us early is not about doing more work, it is about avoiding the rework that a late-discovered flaw forces.
Yes—this is common. We regularly join at the analysis, validation, or publication stage. When we do, we start by understanding what has already been done and checking that the foundations are sound, so that we are not building on a problem we haven't seen.
Everything needed to own and defend the work: the cleaned data and codebook, versioned analysis code, the results and figures, a methods write-up, and the documentation to reproduce it all. Nothing is a black box you have to take on trust, and nothing is withheld to keep you dependent.
No, and this is a firm line. We do not write papers on a client's behalf or manufacture authorship. We provide analysis, methodological support, and reporting of your research for you to review, adapt, and make your own. The research and the authorship are yours; we help them reach the standard and the audience they deserve.
Unpublished data, ideas, and analyses are treated in strict confidence, are not shared with third parties, and are used only for your engagement. For sensitive institutional work, we operate under whatever formal confidentiality agreement you require.

Where are you in the process?

Whether you are choosing a question or answering a referee, tell us where you are—we'll meet you there, and scope it honestly.

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