LinChiat Chang, Ph.D. is an independent consultant providing advanced solutions in data science and research design. She works with a wide range of organizations, from early-stage startups with fewer than five employees to prominent foundations funding ambitious programs worldwide. Her expertise spans pattern discovery and predictive modeling in population health information systems and legacy big-data environments; iterative feature engineering and algorithm optimization for large-scale data streams; and statistical and machine learning methods for forecasting, classification, clustering, association rule mining, anomaly detection, and more.

Dr. Chang develops data models and research designs grounded in a strong background in social psychology and quantitative research methods, including factorial experimental design, psychometrics, and survey sampling. She evaluates the validity and reliability of research findings, supports causal inference through original study designs and machine learning approaches, quantifies uncertainty in population projections, and identifies contingencies that constrain predictive chains or limits the generalizability of observed effects to national and regional populations.

She supports the development and launch of new programs and services in global health and evaluates the impact of interventions over both short- and long-term horizons. Her research has been published in peer-reviewed journals such as Public Opinion Quarterly, Psychology & Marketing, Military Psychology, Sociological Methodology, and Field Methods. Dr. Chang holds a Ph.D. in Psychology from The Ohio State University and completed postdoctoral research at Stanford University. She founded her solo consulting practice in San Francisco in 2010, and has worked globally as a digital nomad since 2020.

 

My work generally falls into one of two areas - Data and Methodology. My work in Data runs the gamut from data discovery, data evaluation, data transformation, to data analytics and visualizations grounded in statistical learning algorithms spanning both predictive and exploratory models. My services in Methodology includes survey design, sample design, experimental design, psychometrics, and quantitative aspects of program evaluation.

If you are looking for guidance on research methods or advanced analytics, I can help you. Regardless of industry sector, the same fundamental principles apply if you wish to obtain the most valid and reliable findings possible, within budgetary constraints. You can be assured at the outset that I maintain total transparency in data sourcing and modeling; so all steps will be clearly documented to withstand the scrutiny of your intended audience.

I have studied the methodological challenges underlying every step of end user research, starting from survey sample design and weighting, coverage and nonresponse errors, psychometric tool development, cognitive biases in recall and response across multiple modes of data collection, and techniques to assess data quality and veracity. I am also adept at factorial experimental designs, including discrete choice models, embedded within probability-based survey sample designs, thus assuring both valid causal inference as well as generalizability of research findings.

Let's talk if you want valid and reliable research output.