Scope and Customization
Articul's quantitative research practice covers the full range of methods that fall under a rigorous scientific approach to data, fully customized to the research question, data environment, and decision context of each engagement. Method selection, study design, and output format are determined by the problem, not by a fixed service menu.
Study Design and Measurement
Work begins at the design stage: specifying the research question, selecting the appropriate study design (experimental, quasi-experimental, or observational), and defining the measurement framework. Where primary data collection is required, we build the instruments including psychometric scales, structured interviews, survey protocols, and behavioral experiments. Where existing data is used, we assess its structure and reliability before analysis proceeds.
Analytical Methods
Analysis draws from the full quantitative toolkit: classical statistical methods, time series and forecasting models, causal and inferential modeling, segmentation and classification, predictive and prescriptive modeling, and machine learning approaches where complexity warrants it, including NLP for unstructured text data. Feature engineering, model training and evaluation, and model optimization are applied as standard components of any ML-integrated engagement.
Implementation and Deployment
Outputs are implemented in Python or R and delivered in the format most useful to the client: statistical reports, interactive dashboards, Streamlit applications, or API-integrated models. RAG architectures and LLM-based components are incorporated where analysis extends into unstructured data environments.
Output Types
Deliverables are classified by their functional role: predictive models that generate probabilistic forecasts, inferential models that establish direction and magnitude of effects, and prescriptive models that recommend specific courses of action. Every output is calibrated to a defined decision point within the client's operational context.