Data Analysis & Statistics
Whether your data are survey responses, firm panels, interview transcripts or millions of text records, the analysis has to match the question and survive scrutiny. We help you choose methods deliberately, run them transparently and understand every result well enough to defend it yourself.
Overview
This pillar brings together five kinds of analytical support. Quantitative analysis covers the core statistical toolkit — group comparisons, regression, factor analysis, structural equation modeling, mediation and moderation, and multilevel models. Econometrics and finance analytics covers panel and time series models, quasi-experimental causal inference, event studies, efficiency frontiers and valuation for research. Qualitative analysis covers thematic, content, discourse, grounded theory and phenomenological approaches, with or without CAQDAS software. Advanced analysis covers machine learning, text mining, network, spatial and meta-analysis. Software support helps you work confidently in SPSS, Stata, R, Python and other tools.
Across all of them, we treat analysis as a chain of decisions that must be justified: which method fits the research question and data, which assumptions it relies on and how they were checked, how large and how precise the effects are, and what the results cannot show. We write those decisions down, because examiners and reviewers ask about them.
Every quantitative deliverable comes with syntax or code that reproduces it, and every qualitative deliverable comes with an audit trail of coding decisions. You can then re-run, extend or explain the analysis without depending on us.
Who this is for
- Master's and doctoral researchers who must analyze their own data for a thesis or dissertation
- Academics and early-career researchers preparing manuscripts or responding to statistical reviewer comments
- Health, education, business and social science professionals running evaluations or practice-based research
- Research offices, NGOs and public bodies needing defensible analysis of survey, administrative or program data
- Students who want to learn a method or software package through guided, hands-on sessions
Where to start
Pick the situation that sounds most like yours.
- I need to compare groups or test associations and I am not sure which test to useDescriptive & inferential statistics
- I have a conceptual model with survey scales and several connected hypothesesStructural equation modeling
- I want to test whether one variable explains or changes another variable's effectMediation & moderation
- I have data on firms or countries over several yearsPanel data analysis
- I need to show that a policy or program caused a changeCausal inference methods
- I have interview or focus group transcripts to analyzeThematic analysis
- I want to combine results from many published studiesMeta-analysis
- I want to use machine learning to classify, cluster or predict in my researchMachine learning for research
- I am stuck in SPSS and need someone to explain the outputSPSS
- My analysis is done but I cannot turn the output into a clear results sectionResults interpretation & APA reporting
Services by area
Quantitative Analysis
Statistical support for survey, experimental and observational data: describing your sample, testing hypotheses, validating scales and modeling relationships, with assumptions checked and effect sizes reported.
- Descriptive & Inferential Statistics Support: t-Tests, ANOVA, Chi-Square “Help choosing and running the right statistical test and explaining what the p-value means”
- Regression Analysis Support: Linear, Logistic, Ordinal, Count and Hierarchical Models “Help building and interpreting a regression model for my thesis or paper”
- Exploratory & Confirmatory Factor Analysis (EFA and CFA) Support “Help checking whether my questionnaire items measure the constructs they are meant to”
- Structural Equation Modeling (CB-SEM & PLS-SEM) Support “Help testing a model with several connected variables, latent constructs or survey scales”
- Mediation & Moderation Analysis with the PROCESS Macro and Beyond “Help testing whether one variable explains or changes the effect of another”
- Multilevel & Hierarchical Linear Modeling (HLM, Mixed Models) Support “Help analyzing data where people are grouped in classes, teams, clinics or repeated over time”
Econometrics & Finance Analytics
Econometric and finance analytics for economics, finance, management and policy research: models that respect the structure of your data and identification strategies that make causal claims credible.
- Panel Data Analysis: Fixed Effects, Random Effects, Hausman and Dynamic GMM “Help analyzing data on the same firms, countries or people over several years”
- Time Series Analysis: Stationarity, ARIMA, VAR, VECM, Cointegration and GARCH “Help modeling data collected over time, like prices, inflation or exchange rates”
- Causal Inference Methods: IV/2SLS, GMM, Difference-in-Differences, RDD and Matching “Help showing that a policy, program or decision actually caused an effect”
- Event Study Analysis: Abnormal Returns, CAR, BHAR and Significance Tests “Help measuring how share prices reacted to announcements such as mergers or earnings news”
- Efficiency Analysis with Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) “Help measuring how efficiently banks, hospitals, schools or firms turn inputs into outputs”
- Financial Modeling & Valuation Support for Research and Coursework “Help building a DCF or ratio analysis model for my finance project or thesis”
Qualitative Analysis
Support for analyzing interviews, focus groups, open-ended responses and documents systematically, with an approach that fits your research philosophy and a clear audit trail of how interpretations were reached.
- Thematic Analysis Support (Braun & Clarke Reflexive TA) “Help making sense of my interview transcripts and turning codes into themes I can defend”
- Content Analysis & Discourse Analysis Support “Help analyzing news articles, policy documents, social media posts or speeches for my research”
- Grounded Theory Coding Support: Open, Axial & Selective “Help doing grounded theory properly, from open coding to building a theory from my interviews”
- Interpretative Phenomenological Analysis (IPA) Support “Help with IPA for a small interview study about people's lived experience”
- NVivo, ATLAS.ti & MAXQDA Support for Qualitative Projects “Help setting up and using NVivo, ATLAS.ti or MAXQDA for my qualitative data”
Advanced & Computational Analysis
Computational and specialist methods for research questions that go beyond standard statistics: prediction and classification, large text collections, relational and spatial data, and quantitative synthesis of published studies.
- Machine Learning for Research: Classification, Clustering & Prediction “Help using machine learning in my thesis or paper without making beginner mistakes”
- Text Mining, Topic Modeling & Sentiment Analysis “Help analyzing thousands of reviews, tweets or open-ended answers with topic models or sentiment analysis”
- Social & Research Network Analysis Support “Help mapping and analyzing relationships between people, organizations or ideas as a network”
- GIS & Spatial Analysis for Research “Help making maps and analyzing location data for my research using QGIS or ArcGIS”
- Meta-Analysis Support: Effect Sizes, Heterogeneity & Publication Bias “Help pooling the results of studies from my systematic review into a meta-analysis”
Statistical Software Support
Practical help with the tools themselves: running analyses in your chosen software, fixing errors, cleaning and visualizing data, turning output into well-reported results, and making code reproducible.
- SPSS Data Analysis Help for Theses, Surveys and Research Projects “Help running the right tests in SPSS and understanding the output tables”
- Stata Data Analysis Help: Do-Files, Panel Data and Econometric Estimation “Help writing Stata do-files and making sense of regression and panel output”
- R Data Analysis Help: Scripts, Packages and Reproducible Reports “Help getting my R code to work and understanding what the model output means”
- Python Data Analysis Help with pandas, statsmodels and scikit-learn “Help analyzing research data in Python or Jupyter notebooks without getting lost in code”
- SAS, EViews, Excel, MATLAB, JASP & jamovi Analysis Support “Help with my analysis in SAS, EViews, Excel, MATLAB, JASP or jamovi because that is what my course uses”
- Data Cleaning, Coding and Visualization for Research Datasets “Help turning a messy survey export or spreadsheet into a clean dataset with clear charts”
- Statistical Results Interpretation and APA 7 Reporting Support “Help understanding my statistics output and presenting it correctly in APA style”
- Analysis Code Review and Reproducibility Package Preparation “Someone to check my analysis code and get my replication files ready for a journal”
How we approach this work
The research question picks the method
We match methods to your questions, design and measurement levels, not to what a software menu offers, and we suggest a simpler approach when it would answer the question more honestly.
Assumptions are tested and reported
Every model's assumptions — normality, independence, parallel trends, instrument strength, stationarity, saturation in qualitative work — are checked with suitable diagnostics, and failures are handled openly.
Effects, uncertainty and limits
We report effect sizes and confidence intervals alongside tests, control for multiple comparisons where needed and state limits such as correlation versus causation or overfitting.
Reproducible by default
You receive annotated syntax, scripts or project files, and a decision log, so any analyst — including you or your examiner — can reproduce the results.
You stay the author
We deliver and explain analyses; you interpret and write them up, and acknowledge assistance according to your institution's or journal's rules.
Frequently asked questions
How do I know which kind of analysis support I need?
Start from your data and question. Numerical survey or experimental data usually fall under quantitative analysis; data on the same units over time, or questions about causal policy effects, fall under econometrics; transcripts and documents fall under qualitative analysis. If you are unsure, describe your study when you request a quote and we will recommend the right service.
Can you analyze data I collected before my methods were finalized?
Often yes. We assess what your data can and cannot support and suggest the most defensible analysis. Sometimes this means narrowing a research question or reporting some analyses as exploratory, and we will tell you plainly.
Do you only deliver results, or can I learn to do the analysis myself?
Both. Many clients choose guided sessions in which they run the analysis with us, and others prefer a delivered analysis with a walkthrough. The choice affects how we structure the work and the timeline.
How is my data protected?
We ask for anonymized or pseudonymized data wherever possible, use it only for your project and follow any storage and deletion conditions in your ethics approval or data agreement. Tell us about those conditions at the start.
Is it acceptable to get help with my thesis analysis?
Most institutions allow statistical or analytical support, provided the thesis remains your own work and assistance is acknowledged. Rules vary, so check your regulations and, ideally, tell your supervisor. We keep our role transparent so it is easy to acknowledge.
What affects the cost and timeline of an analysis project?
Mainly the number of research questions and models, the complexity of methods, how much data cleaning is needed, the software, and whether you want a delivered analysis or guided sessions. We give a scoped quote after reviewing your materials.
Tell us what you need help with
Describe your project, stage and deadline. We’ll reply with the right service, what we need from you and a quote — no obligation.