Blogs by Audrey Snowden, Ph.D.


  • B2B Research

    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?


  • 10Feb
    3 Avoidable Statistical Mistakes by Audrey Snowden, PH.D.
    Avoiding Type 1 Error

    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.


  • Strategy Research

    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?

  • Questionnaire Bias

    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 Snowden, Ph.D. ( 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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