ANALYSIS SERVICE
Quantitative Data Analysis Services for Research
We prepare and analyze research data, selecting statistical methods that are appropriate to the study design, variables, and research questions, with assumption testing, model diagnostics, clear interpretation, and publication-ready analytical outputs for academic, clinical, and professional research.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
Quantitative data analysis services involve preparing, examining, and statistically analyzing research data to answer defined research questions and test study hypotheses. The process covers data cleaning and restructuring, descriptive analysis, hypothesis testing, and statistical modeling, with analytical outputs including effect sizes, confidence intervals, tables, and figures interpreted in the context of the study.
WHEN TO ENGAGE
When Do You Need a Statistician for Data Analysis?
Researchers typically involve us once data are available and the analytical decisions need to be made, implemented, or extended. The situations below are where statistical input most often changes the analysis.
01 · When Your Data Are Ready for AnalysisYou have collected or obtained the data and need them prepared, checked, and analyzed according to the study design, variables, hypotheses, and research questions.
02 · When You Are Unsure Which Methods to Use The appropriate analysis follows from the outcome type, study design, distribution, sample structure, covariates, and research question, rather than from a statistical test chosen in isolation.
03 · When Statistical Assumptions Are Not Met Missing data, non-normal distributions, unequal variances, sparse categories, influential observations, or other data characteristics may require the original analytical approach to be reconsidered.
04 · When Your Data Require More Complex Modeling Repeated observations, clustered samples, multiple outcomes, time-to-event data, latent variables, or complex relationships may require methods beyond standard group comparisons or simple regression.
05 · When Additional Analyses Have Been Requested A committee, journal reviewer, collaborator, or research team may request additional models, subgroup analyses, sensitivity analyses, effect estimates, or further investigation of the findings.
06 · When an Existing Analysis Needs to Be Extended You may already have preliminary analyses but need additional variables, models, comparisons, or diagnostics to address further research questions or reporting requirements.
DELIVERABLES
What We Deliver From Your Data
We turn your research data into a defined set of analytical outputs, with the required preparation, statistical analysis, diagnostics, and interpretation agreed before work begins.
Data Preparation & Analytical Dataset Data are checked, cleaned, coded, recoded, restructured, and prepared for analysis, with missing values, variable formats, and derived measures handled as required.
Analysis Syntax & Reproducible CodeSPSS syntax, R scripts, Stata do-files, or equivalent analysis code can be provided where applicable, allowing key results to be traced, reproduced, and rerun if the data change.
Assumption Checks & Model DiagnosticsRelevant statistical assumptions, model fit, residuals, influential observations, multicollinearity, and other diagnostics are assessed and documented where required.
Statistical Tables & FiguresKey findings are presented in clear, publication-ready tables and figures appropriate to the analysis, research field, and intended use.
Effect Estimates & Statistical OutputsResults include the appropriate effect estimates, confidence intervals, test statistics, p-values, model coefficients, and other measures required for the selected analyses.
Interpretation & Analytical SummaryThe statistical findings are explained in relation to the research questions, including the direction, magnitude, uncertainty, and practical meaning of the observed results.
ANALYSIS & METHODS
Statistical Methods for Quantitative Research
The statistical approach is selected according to the research question, study design, outcome structure, and characteristics of the data. Our work ranges from foundational comparisons to complex longitudinal, latent variable, clinical, and computational models.
01
Data Preparation & Descriptive Analysis
We examine, prepare, and summarize the dataset before formal modeling, addressing data quality, distributions, missingness, and outliers.
Descriptive Statistics
Data Cleaning
Outlier Analysis
Multiple Imputation
Likert Scale Analysis
02
Comparative & Hypothesis Testing
We test differences between groups, conditions, or time points using methods appropriate to the design, variable type, and distribution.
Independent Samples t-Test
One-Way ANOVA
Two-Way ANOVA
ANCOVA
Mann-Whitney U
03
Regression & Predictive Modeling
We model associations, adjusted effects, probabilities, and outcomes while accounting for predictors, covariates, and interactions.
Linear Regression
Logistic Regression
Hierarchical Regression
Mediation & Moderation Analysis
04
Multivariate, Scale & Reliability
We evaluate measurement instruments and multivariable patterns, including dimensions, internal consistency, associations, and agreement.
Exploratory Factor Analysis
PCA
Correlation Analysis
Cronbach's Alpha
Likert Scale Analysis
05
Structural & Latent Variable Modeling
We examine complex relationships involving observed and latent constructs through measurement and structural models.
SEM
Confirmatory Factor Analysis
Path Analysis
PLS-SEM
Measurement Invariance
06
Longitudinal & Multilevel Analysis
We analyze repeated, clustered, nested, and time-dependent observations using models that account for correlated data.
Linear Mixed Model
GEE
GLMM
Panel Data Analysis
Time Series Analysis
07
Clinical, Survival & Epidemiological
We examine complex relationships involving observed and latent constructs through measurement and structural models.
Kaplan-Meier Analysis
Cox Regression
Competing Risks
ROC Curve Analysis
PSM
08
Bayesian & Computational Methods
We use Bayesian, simulation, and resampling approaches for estimation, uncertainty, prediction, and research questions requiring more flexible analytical methods.
Bayesian Analysis
Bayesian Regression
Bootstrapping
Monte Carlo Simulation
Software
Statistical Software We Work With
We work across leading quantitative analysis platforms, using the software best suited to the statistical methods, data structure, reproducibility requirements, and intended outputs of each study.
R Programming
Statistical computing & reproducibilityGraphPad Prism
Biostatistics & scientific visualizationSmartPLS
PLS-SEM & path modelingCase Example
How We Work in Practice
A quantitative research example showing how an existing dataset is prepared, analyzed, checked, and translated into clear analytical findings.
ILLUSTRATIVE EXAMPLE · BREAST CANCER RESEARCH · UNITED STATES
A clinical research team had assembled a retrospective cohort of 648 patients with breast cancer across four clinical sites and wanted to identify patient and tumor characteristics associated with a binary treatment outcome.
The source dataset combined demographic, tumor, pathology, and treatment variables extracted from clinical records. Before analysis, we reviewed coding consistency, missing values, duplicate records, category frequencies, and continuous-variable distributions. We then recoded selected variables, derived the required analytical measures, and produced a documented analysis-ready dataset.
We summarized the cohort using descriptive statistics and unadjusted comparisons before fitting a multivariable logistic regression model based on the predefined clinical and research variables. Model development included assessment of multicollinearity, sparse categories, influential observations, model fit, and sensitivity to alternative specifications.
The final analysis reported adjusted odds ratios with 95% confidence intervals, p-values, model diagnostics, and publication-ready tables and figures, with reporting aligned to STROBE for observational research. The complete workflow was produced in R, allowing the analysis to be reproduced and rerun from the prepared dataset if records or analytical decisions were subsequently updated.
Outcome: The research team received the cleaned analytical dataset with variable documentation, reproducible R scripts, model diagnostics, publication-ready tables and figures, and a structured analytical summary interpreting the findings in relation to the research questions.
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.
RELATED SERVICES & FIELDS
Where Quantitative Analysis Fits
Quantitative Data Analysis covers the analytical stage once research data are available. Depending on where your project stands, another service may be more appropriate before or after the analysis.
Research Planning & Study Design
If your study design, outcomes, sample size, or Statistical Analysis Plan still need to be defined before data collection, statistical planning should come first.
Explore Research Planning & Study DesignResults Reporting & Interpretation
If the statistical analysis is complete and the findings need to be structured, interpreted, and written as a Results section or chapter, reporting is the next stage.
Explore Results Reporting & InterpretationIndependent Statistical Review
If an existing analysis needs to be independently checked rather than extended, we can review the methods, assumptions, outputs, and statistical interpretation.
Explore Independent Statistical ReviewResearch Disciplines
Quantitative analysis differs across disciplines because study designs, outcome types, data structures, and reporting expectations vary. We apply statistical methods in the context of the research field, not as isolated techniques.
A Defined Process for Your Research
We review your dataset, research questions, and analytical requirements before any work begins, so the scope, deliverables, timeline, and fixed project fee are clear from the outset.
01
Share Your Project
Send your dataset, research questions, methodology, and any existing analysis plan or supporting documents. We will let you know if anything else is needed to assess the project.
02
Receive Your Assessment
Your statistically trained Research Coordinator reviews the project and confirms the required analysis, deliverables, timeline, fixed project price, and any additional materials needed before work begins.
03
Work Completed
Once you confirm the scope, an in-house statistician prepares and analyzes the data, completes the agreed diagnostics and interpretation, and delivers the analytical files and outputs.
Researcher Voice
What Researchers Say
“I had a fairly complex dataset with several scales and multiple predictors, and I was not confident the original analysis plan was the best fit. The analysis was clearly structured, the assumptions and model results were explained in a way I could follow, and the final tables and interpretation made the findings much easier to present in my research.”
FREQUENTLY ASKED QUESTIONS
Questions About Quantitative Data Analysis
What happens when I request an assessment, and how long does it take?
Assessment requests are normally reviewed the same day. You can submit your research question, current stage, and any available documents through the assessment form, with no account required. We then confirm the proposed scope, timeline, deliverables, and fixed project price by email. There is no obligation to proceed.
How does pricing work?
We provide one fixed project price after reviewing the study and confirming the work required. The price reflects the complexity of the project and the agreed deliverables. You receive the scope, timeline, and price together before deciding whether to proceed.
What files do you need before the analysis begins?
The required materials depend on the project, but typically include the dataset, research questions or hypotheses, variable definitions, study methodology, and any existing analysis plan. Codebooks, questionnaires, protocols, or previous statistical outputs can also be useful where available.
What if I do not know which statistical test or model I need?
You do not need to specify the statistical method before contacting us. We review the research questions, study design, variables, outcome structure, sample characteristics, and data properties before determining the appropriate analytical approach.
Can you work with an analysis I have already started?
Yes. Existing analyses can be extended where additional models, variables, subgroup analyses, sensitivity analyses, or diagnostics are required. If the primary need is to independently verify work that has already been completed, Independent Statistical Review is usually the more appropriate service.
What happens if I need revisions after delivery?
If you receive comments from a committee, journal reviewer, collaborator, or research team after delivery, we review them against the agreed scope and update the analysis where appropriate. Where the requested changes require new analyses or data outside the original scope, we confirm this with you before proceeding.
How do you protect my research data and confidentiality?
We handle research data, project materials, and client identity confidentially throughout the engagement. We do not publish client names, institutions, datasets, or project details, and an NDA is available where required. Materials submitted for assessment are deleted if you decide not to proceed.
How does statistical consulting fit with academic authorship?
Statistical consultancy is an established part of research practice. We provide the agreed statistical and methodological work, while responsibility for the research questions, scholarly decisions, discussion, conclusions, and intellectual authorship remains with the researcher (We do not claim authorship or ownership of your work) Where an institution or publication has disclosure requirements, the researcher determines how those requirements apply.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Your Research. Analyzed. Interpreted.
Publication-Ready
Share your dataset and research questions, and we will assess the statistical analysis required, the expected deliverables, timeline, and fixed project fee before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014