Blogs by Audrey Guinn, Ph.D.
Multicollinearity – A Marketing Researcher’s Curse Word by Audrey Guinn, PH.D.
What is Multicollinearity?
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.
Political Divide Deepens Around the Pandemic by Audrey Guinn, Ph.D.
Republicans, Democrats, and Independents seem to be drifting further apart and these differences are noticeable not only within political ideology but also within more mundane aspects of life.
Decision Analyst’s monthly “Consumer Reactions to COVID-19” tracker finds that these divisions exist within beliefs about COVID-19 and the vaccine, feelings surrounding the pandemic, concern about the pandemic, and even comfort levels with gathering in different situations.
Who’s More Likely to Receive the COVID-19 Vaccine? by Audrey Guinn, Ph.D.
Personal characteristics and situational circumstances are potential explanations for why some people receive the vaccine while others do not.
Therefore, we wanted to understand differences in ethnicity, age, political affiliation, income, gender1, area lived in, and occupation with regards to vaccination. To examine these potential demographic differences, we analyzed the data from Decision Analyst’s monthly “Consumer Reactions to COVID-19” tracker.
Motivators for and Barriers to Getting Vaccinated Against COVID-19 by Audrey Guinn, Ph.D.
As of May 20th, 48% of the population has had at least one dose. However, that leaves a little over 50% of the population unvaccinated.
Using the data collected in Decision Analyst’s monthly “Consumer Reactions to COVID-19” tracker, we did examine what impact, if any, beliefs about COVID-19 and its vaccine have on the decision to get vaccinated.
It’s Time to Put Those Negatively Worded Items Behind Us by Audrey Guinn, Ph.D.
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. It may be time for researchers to relinquish negatively worded attributes. So, how can researchers catch cheaters, speeders, and straight-liners if negatively worded attributes are no longer included in the survey?
3 Avoidable Statistical Mistakes by Audrey Guinn, PH.D.
Marketing research is grounded in the scientific method: answering questions by generating a priori hypotheses, collecting data to test hypotheses, and analyzing data to draw conclusions. Adhering to the rules of the scientific method is important to ensure that results are valid and unbiased.
Sometimes marketing researchers are tempted to use undesirable methods, like conducting many single significance tests, performing statistical tests without hypotheses, and rerunning statistical tests until desired results are discovered. Unfortunately, engaging in these methods has unintended, detrimental consequences: namely, an increase in Type I Error.
Suppressors are rarely talked about in the marketing research community. They are viewed as the “red-headed stepchild” of statistics: rejected, neglected, and outcast.
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). What can researchers do when encountering problem suppressors?
When Results Lie: Tips for Overcoming Questionnaire Bias by Audrey Guinn Ph.D.
Biased survey questions wreak havoc on the reliability and validity of the survey which produces junk data.
Biased questions increase respondent confusion which then increases error in their responses. This in turn reduces the strength of the relationships between variables. In worse case scenarios, biased questions can return results that may be untrue which favor a specific outcome. So what can we do to avoid bias in surveys?
Contact Decision Analyst
Audrey Guinn, Ph.D. (email@example.com) is a Statistical Analyst in the Advanced Analytics Group at Decision Analyst. She may be reached at 1-800-262-5974 or 1-817-640-6166.
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