Data and Statistics

From raw collection
to publishable result.

Nine services covering the full data lifecycle in academic research, from structuring heterogeneous databases to advanced statistical modeling. Operations run on R, Python, JASP and Mplus, with output ready for the results section — tables, publication-quality figures, and technical prose.

Database Construction

Research begins before the first test — when raw data becomes an analyzable database.

Structuring raw data into analyzable format with documented variable dictionary. Aria receives questionnaires, medical records, heterogeneous spreadsheets, scanned PDFs, API exports or legacy system extracts, and returns a consolidated database in Wide or Long format depending on the intended analysis. Encoding treatment, standardization of categorical variables, harmonization of units, and detection of input inconsistencies. Includes dictionary with type, range, coding rule and source for each variable.

Survey Data Processing

Between collection and analysis, the silent work that decides whether the study is defensible.

Tabulation, open-ended response coding, missing value treatment, preliminary cross-tabulations, preparation for analysis. For researchers who collected via Google Forms, SurveyMonkey, REDCap, Qualtrics or paper, and need to transform raw output into an analyzable base. Includes coding of open questions with auditable codebook, decision logic for missing values (listwise, pairwise, imputation), and technical report — every decision documented for reproducibility.

Statistical Analysis

Statistical tests do not decorate results — they decide whether the hypothesis holds.

Parametric and non-parametric tests, ANOVA with factors and interactions, linear and logistic regression, exploratory factor analysis, correlations with multiple comparison corrections, post-hoc tests, survival analysis, time series. Report with tables formatted for the target journal, publication-quality figures in high resolution, and technical prose ready for the results section — describing sample, verified assumptions, executed tests, and interpretation of findings.

Instrument Validation

Before measuring the phenomenon, the instrument must prove it measures what it claims.

Psychometric analysis of questionnaires and scales with Cronbach's alpha, McDonald's omega, exploratory and confirmatory factor analysis (EFA/CFA), convergent and discriminant validity via AVE and HTMT ratio, composite reliability. For researchers who developed an original instrument, translated an international one, or are validating a scale in a new population. Output includes modeling in lavaan or Mplus, with complete fit indices, interpreted modification indices, and a technical report defensible under peer review.

Structural Equation Modeling

When hypotheses involve multiple latent variables in sequence, isolated regression is not enough.

Specification, estimation and evaluation of structural models — path analysis, simple and multiple mediation, moderation, measurement models combined with structural models. Analysis in lavaan, Mplus or AMOS depending on the target journal's preference. Output includes complete fit indices (CFI, TLI, RMSEA, SRMR), publication-quality path diagrams, decomposition of direct and indirect effects, bootstrapping for mediation confidence intervals, and a technical report covering specification decisions, modifications tested, and substantive interpretation.

Web Scraping and Data Collection

When the data exists somewhere on the web, but not in analyzable form.

Structured collection from public websites, government portals, social networks via API, scientific journals, patent databases, e-commerce platforms. Pipelines in Python (BeautifulSoup, Scrapy, Selenium, Playwright) with handling of pagination, anti-bot, rate limiting, and multiple heterogeneous sources. Delivery: clean dataset in analyzable format, ethical procedure report covering source terms of use, GDPR/LGPD compliance when applicable, and reproducible documentation. Documented code available as optional add-on.

Data Visualization and Dashboards

A publishable chart is different from a pretty chart — it requires rigor of visual encoding and journal compliance.

Publication-quality figures in high resolution following technical norms of target journals — typography, color palette, axis encoding, significance annotation, peer-review-friendly readability. Composition in ggplot2, matplotlib, plotly or similar, with vector output for print and screen. Includes review of existing figures with redesign suggestions when current representation does not communicate the finding. Interactive dashboards in Streamlit, Dash or Shiny available as add-on for researchers who need data exploration beyond the paper.

Transcription and Qualitative Data

Raw audio is not data; it is raw material until it becomes auditable transcription.

Audio and video transcription (interviews, focus groups, podcasts, ethnographies) with human review after an initial automated pass. Initial thematic coding following Braun & Clarke or the methodology indicated by the client, categorization of meaning units, export to Atlas.ti, NVivo, MAXQDA or Dedoose. For researchers who collected dense qualitative material and need to transform it into analyzable corpus before substantive coding.

Bibliometric Analysis

When the question is about the field itself — who produces, with whom, on what, since when.

Quantitative mapping of scientific production in a field. Extraction from Scopus, Web of Science and Dimensions with replicable queries, application of bibliometric laws (Lotka, Bradford, Zipf), co-authorship and co-citation networks, temporal mapping of emerging fronts, keyword analysis with clustering. Visualizations in VOSviewer, CiteSpace and bibliometrix. Technical report with interpretive synthesis — not just a map, but substantive reading of what the map reveals about the field.