Suppressors are variables that when added to a regression model, change the original relationship between X (a predictor) and Y (the outcome) by making it stronger, weaker, or no longer significant—or even reversing the direction of the relationship (i.e., changing a positive relationship into a negative one).

The term "data weighting" in most survey-related instances refers to respondent weighting (which in turn weights the data or weights the answers). Here are some best practices to keep in mind when you are thinking about weighting survey data.

It's generally a good idea to keep your target universe as broad and inclusive as possible. Here is some advice on dealing with Universe Error, Standard Error, and Sample Screening Error in marketing research surveys.

Adhering to the scientific method, and thus, scientific principles, helps prevent researchers from performing many unnecessary statistical tests that increase Type I Error.

Statistical significance testing is perilous, but there are ways to mitigate the risk. First, understand the potential mistakes that can be made (Type 1 and Type 2 errors).

When you need to compare, rate, or optimize a long list of features or attributes, consider using MaxDiff (Maximum Difference Scaling) instead of a grid. MaxDiff forces respondents to make trade-offs (or choices), like they do in the real world.

Significance testing is philosophically and scientifically based, and it identifies which of many differences between treatments (conditions, interventions, etc.) are worth pursuing. In marketing research, the significance level 95% has been used as a universal benchmark, should it be?

We need to design the choice exercise to allow respondents to make purchase decisions as they would in the marketplace

Structural Equation Modeling (SEM) is a statistical methodology that combines both regression and factor analysis. It is a system of equations based on a priori hypotheses that are modeled simultaneously. The model's configuration is based on assumed relationships between variables derived in advance.

Leading questions are one among many types of biased survey questions. The problem with biased questions, attributes, and response choices are that they create inconsistent, contradicting, and misleading results.

Both 'reliable' and 'valid' are not interchangeable from a scientific perspective, and understanding the difference is important when interpreting research outcomes.

In an effort to catch survey cheaters, researchers use negatively worded attributes placed in groupings of positively worded attributes. This context switching causes respondent confusion, which creates error.

From a research perspective, there are several ways to engage with potential customers to help guide the innovation process. We’ll address three broad research tactics that many companies use to accomplish their goals.

Multicollinearity (also known as collinearity) occurs when two or more variables are very highly correlated. Singularity, a more serious form of multicollinearity, occurs when two or more variables are redundant, where one variable is a linear combination of the others.

In research, understanding cause is often the goal. What is causing a product to sell? The only way to truly determine cause and effect is to control for all extraneous variables.

The Fathers of Marketing Research invented a number of extremely powerful and valuable tools, methods, questions, and concepts that we all use and benefit from every single day.

Calibration of survey-based choice models can make a substantial difference in predicted demand and revenue resulting from price changes.

Choice models are valuable in assisting marketers in decision making. This article explains the benefits choice analysis: Improved experimental design algorithms; Latent Class and Hierarchical Bayes models; and Model calibration.

Outliers can play havoc with the data. At best, these pesky data values can make individual metrics look a bit “off.” At worst, outliers can lead to erroneous business decisions.

A brief summary of the different types of errors that can occur in marketing research surveys and how to avoid them.