It takes less effort to lie without numbers, but there are now more numbers and more ways to lie with them than ever before. Poor Reverend Bayes, who understood the true meaning of "evidence".
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.
A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.
Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.
Propensity scores are used in quasi-experimental and non-experimental research when the researcher must make causal inferences, for example, that exposure to a chemical increases the risk of cancer.
Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
Making Sense of Machine Learning
What is Structural Equation Modeling?