RESEARCH DISCIPLINE
Statistical Analysis for Accounting Research
Consulting for Financial Reporting & Audit Studies
Accounting research statistical analysis for firm-year financial statement data, earnings and disclosure studies, audit research and experiments with accountants, with skewed ratios and outlier rules handled openly and the results prepared for journal review.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Statistical Analysis for Accounting Research
Accounting research statistical analysis is the statistical design, estimation and reporting of studies of financial reporting, auditing, disclosure and managerial accounting, mostly from firm-year financial statement data and from experiments or surveys of accountants and auditors. Firm-years repeat within companies, ratios and accruals are skewed and depend on how they are scaled, and key constructs such as earnings quality or audit quality have no direct measure, so a regression that treats every firm-year as independent and trims extreme values without a stated rule produces fragile estimates. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides accounting data analysis through in-house PhD-level statisticians, who settle the firm-year structure, the variable definitions and the outlier rule with you before any model is estimated.
WHEN TO ENGAGE
Statistical Support at Different Stages of Accounting Research
Accounting studies usually reach us while proxies and outlier rules are still open, or later, when editors ask for alternative specifications.
Defining Variables Before Estimation You are starting an earnings, disclosure or audit study and need the sample, the proxies, the scaling of ratios and the outlier rule fixed before any estimate is seen.
Estimating on Firm-Year Data Your firm-year dataset is ready, and the fixed effects, the clustering by firm and the treatment of skewed ratios now decide which estimates hold.
Running a Behavioral Study You are testing the judgments of auditors or managers in an experiment or survey and need the design, the manipulation checks and the analysis of repeated judgments settled.
Revising for a Journal Editors or referees have asked for other proxies, different fixed effects or another outlier treatment, and the new specifications need to be estimated and reported consistently.
RESEARCH CONTEXT
Research Designs and Data We Work With in Accounting Research
Accounting data come from financial statements, audit records and filings, and each source has a structure that determines how its variables can be used.
01
Firm-Year Financial Statement Panels
Accounting variables for listed firms across years, unbalanced as firms enter and exit, with industry and year effects and ratios scaled by firm size.
02
Earnings and Accrual Studies
Measures of earnings quality, accruals and restatements built from reported statements, where the proxy is itself an estimate with its own error.
03
Audit Fee and Opinion Records
Auditor, fee and opinion data for firms, where the choice of auditor is not random and firms switch from one auditor to another.
04
Disclosure and Filing Text
Tone or readability scores computed from filings and linked to firm-years, where each document yields one observation and the score is a noisy measure.
05
Regulation and Standard-Change Studies
A new reporting standard or rule that applies to some firms or jurisdictions and not others, observed before and after its adoption.
06
Experiments and Surveys of Practitioners
Accountants and auditors judging case materials under randomized conditions, with several judgments from each participant.
METHODOLOGICAL CONSIDERATIONS
Statistical Issues We Consider in Accounting Research
These checks come first in an archival accounting study, because the way variables are built and trimmed can matter more than the model that is estimated.
Firm-Year Clustering A firm's observations are correlated across years, and a shock can hit every firm in the same year. Standard errors are clustered by firm, and by year as well when enough years exist.
Skewed Ratios and Winsorizing Ratios with small denominators produce extreme values. A winsorizing or trimming rule is set in advance and reported in full, and the main results are shown with and without it.
Proxies With Error Earnings quality and audit quality have no direct measure, so the proxies are modeled estimates. Results are checked across alternative definitions of the key variable, not only the first one tried.
Choice of Fixed Effects Firm, industry and year effects answer different questions. The specification follows the comparison of interest, and the estimate is shown under alternatives to see which results survive.
Firms Choose Their Auditors Firms select their auditor, accounting method or disclosure level, so a coefficient can reflect who chooses. Selection is addressed by design or the estimate is worded as an association.
Sample Restrictions Dropping financial firms, small firms or firms with missing items changes the population studied. Each restriction is listed and the attrition from the full sample is reported.
STATISTICAL METHODS
Methods Selected for the Research Question and Study Design
Each method family is chosen for the outcome and the data structure of your study, and the line under each title explains why it is used in financial reporting research statistics.
Longitudinal & Multilevel Analysis
Firm-year panel data analysis uses firm and year effects with clustered errors, and a change in a reporting rule supports a difference-in-differences comparison with firms it did not affect.
Regression & Predictive Modeling
Continuous outcomes such as accruals, binary events such as restatements or going-concern opinions, and ordered ratings are each modeled on the scale that suits them.
Clinical, Survival & Epidemiological
Where firms chose their auditor or policy, matching adjusts for observed differences only, and an instrument can address the rest when a credible one exists, with strength reported.
Bayesian & Computational Methods
Missing financial statement items are examined before observations are dropped, and a bootstrap that resamples whole firms gives intervals for the difference in median ratios between groups, which has no simple standard error formula.
Multivariate, Scale & Reliability
Questionnaires given to managers or auditors, and composite disclosure indices, need reliability and dimensionality checks before they serve as variables in a model.
Comparative & Hypothesis Testing
Experiments with randomized conditions are analyzed by treatment contrasts and covariate adjustment, and designs that add repeated judgments per participant use repeated-measures or mixed designs.
DELIVERABLES
Statistical Outputs Prepared for Your Research
The outputs follow the structure of an accounting study, from the review of how variables were built to the scripts that regenerate every reported table.
Data & Study Review The sample selection, variable definitions and scaling, the source of each financial statement item and the pattern of missing items are reviewed before any model is estimated.
Statistical Analysis The agreed panel regressions, matching or difference-in-differences analyses, with diagnostics for heteroskedasticity, influence and multicollinearity and the planned alternative specifications.
Effect Estimates & Uncertainty Coefficients and marginal effects with confidence intervals from firm-clustered standard errors, shown under the prespecified outlier treatment and again with alternative proxies.
Tables & Figures Descriptive statistics and correlation tables for the sample, regression tables in journal layout with specifications side by side, and a sample-selection table that lists each restriction.
Statistical Interpretation Each estimate is explained in reporting terms within what the design supports, associations are worded as such, and limitations such as proxy error are stated for the discussion.
Analysis Files Stata do-files, SAS programs or R scripts, with the sample construction and the outlier rule documented so that every table can be regenerated.
REPORTING
Reporting Accounting Research
Accounting journals set their own requirements, so the archival or experimental conventions of your target journal come first.
Archival studies are reported with each sample-selection step, the definition of every variable and the outlier rule, and many journals expect the code, and the data where licensing permits, to accompany the paper. Where restricted or licensed data are used, the journal’s policy on sharing is checked at the start. Experiments with accountants or auditors are reported with their manipulation checks and the analysis that was planned in advance, and may be preregistered, which some journals now encourage. Estimates appear with confidence intervals and with the specifications placed side by side, and the standard errors are described so that referees can see how the dependence within firms was handled.
ILLUSTRATIVE EXAMPLE
How the Analysis May Look in Practice
An illustration of how a firm-year question moves from a financial statement extract to a result whose outlier rule is stated.
ILLUSTRATIVE EXAMPLE · AUDIT QUALITY AND RESTATEMENT RESEARCH · UNITED STATES
An accounting researcher wanted to know whether higher audit fees relative to firm size were associated with fewer financial restatements, using firm-year data for listed firms.
Fees scaled by total assets were highly skewed, firms appeared in many years and a firm’s choice of auditor was not random. Before any estimate was examined, we fixed the winsorizing percentiles, the scaling and the sample restrictions in a written plan. Restatement was modeled with logistic regression including industry and year effects, with standard errors clustered by firm. The main result was then shown without winsorizing, with alternative fee measures and under alternative fixed effects, and the estimate was worded as an association because auditor choice reflects firm characteristics that were only partly observed.
Outcome: The researcher received the odds ratio for restatement with its 95% confidence interval, the alternative-specification table, a sample-selection table, a methods paragraph and the scripts.
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.
RESEARCHER VOICE
What Researchers Say
Feedback from an accounting researcher on a field-specific problem, on clustered data not treated as independent rows and on tables written for the research context.
“Our project had a very field-specific problem around the clustered structure of the data rather than treating every row as independent. The team understood why it mattered in Accounting and chose the analysis accordingly. The final tables and interpretation felt written for our research context rather than copied from a generic statistics template.”
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.
SOFTWARE
Statistical Software for Accounting Research
Accounting analyses are run in the package your group or journal workflow already uses, with syntax or scripts provided where applicable.
Statistical Support Within Your Research OnlineSPSS provides the agreed methodological, analytical, and statistical reporting support. The researcher or research team retains the scientific question, substantive interpretation, conclusions, and overall responsibility for the research.
FREQUENTLY ASKED QUESTIONS
Questions About Statistical Analysis for Accounting Research
Short answers to what accounting researchers ask before sharing a study, from method choice and winsorizing to causal wording, behavioral experiments and confidentiality.
What statistical methods are used in accounting research?
An accounting research statistician is most often asked for regression on firm-year panels with firm, industry and year effects, logistic models for events such as restatements, matching or instrumental-variable analysis where firms select into a choice, and experimental methods for studies of auditors and managers. The choice follows the unit of observation and how the key variables are defined. Our Statistical Methods pages describe each family.
When should I winsorize my accounting variables?
Winsorizing is an analytical choice, so the rule is set before estimation and reported in full. Extreme values in scaled ratios can be data errors or real firms, and the percentiles chosen change the estimates, so there is no universal standard. We apply the rule you specify or recommend one, show the results with and without it and record how many observations it affects.
Can firm-year data show that a reporting choice causes an outcome?
Not on their own. A firm’s auditor, accounting method or disclosure level reflects its characteristics, and a regression with controls adjusts only for what is observed. A causal claim needs a design such as a regulatory change affecting some firms and not others, with its assumptions examined. Otherwise the result is reported as an association, with the limitations stated.
Can you support an experiment on auditors or accountants?
Yes. We can plan the design and the number of participants, set out the primary comparison and the manipulation checks, and analyze judgments that are repeated within participants. Studies with practitioners are often small, so intervals are reported in full and exploratory analyses are labeled as such.
How is confidential firm-level or audit data handled?
Firm-level files, audit data and project details are handled confidentially, and project information is not published. 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 send your research question, the dataset or its description and any draft specification. A statistically trained Research Coordinator reviews them and responds the same day, and the analysis, deliverables, timeline and one fixed project fee are confirmed before work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Discuss Your
Accounting Research With Us
Share your research question, the firm-year data you have and your variable definitions, even if only a plan exists. We review how variables are built and treated and confirm the approach before work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014