LinChiat Chang, Ph.D. is an independent consultant offering solutions in data science and market analytics. She builds machine learning data models and research designs with a strong background in social psychology and quantitative research methods. She works with diverse organizations spanning brave young start ups with fewer than 5 employees, to powerhouse foundations funding ambitious programs around the world. She assesses the validity and reliability of research findings, supports causal inference with original research designs and innovative machine learning algorithms, quantifies uncertainty around population projections, and reveals contingencies that limit predictive chains, as well as limits on generalizability of observed effects to national and regional populations. She helps develop or inform launch of new programs and services, and evaluates the impact of interventions in proximal and long term time frames. Her research is published in peer-reviewed journals including the Public Opinion Quarterly, Psychology and Marketing, Military Psychology, Sociological Methodology, Field Methods, and more. She holds a doctorate in Psychology from Ohio State University, and did post-doctoral research at Stanford University. She founded her solo consulting practice in San Francisco, California in 2010, and is now a digital nomad based out of Cape Town, South Africa 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 methodology 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 sample designs, thus assuring both valid causal inference as well as generalizability of research findings.
Let's talk if you are seeking guidance on valid and reliable research.