About Surveys

Automating processes is the new assembly line of the twenty first century.  Using computers and machines we take simple mind numbing tasks that humans dislike doing over and over again and we do them automatically with little or no human intervention.  When we automate surveys by programming them and putting them online we eliminate the need for humans to administer these surveys.  This decreases costs and increases accuracy for the data collected. 

While you will always find bias, for an online survey which is automated it is easier keep it unbiased.  This is because there is nobody actually giving the survey who could possibly allow their biases (even subconsciously) to affect the respondents.  Each survey will be presented in exactly the same way for each respondent.  The randomization of questions, answers or whole question scenarios becomes a breeze.   And the good part is that it has become very easy to make these surveys.  You no longer need to know how to do computer programming to make them.  These automated online surveys can be made online from a variety of companies (I suggest Qualtrics) in a very short time.

Hurray for modern technology!

Max-Diff is a method of scaling in which respondents are asked to identify the most important attribute and least important attribute for a set of attributes.  It counts down significantly on the amount number of questions that need are asked in comparison to the Paired Comparisons technique.  It is based on a measure of customer choice and trade-off, instead of typical rating scale responses.  It can be used to generate importance or preference scores for multiple items such as brands, concepts, or attributes.

Basically, it works like this:

A respondent is shown a set of attributes;

      A  B  C  D

And are asked to identify the most important attribute and the least important attribute (They say A is most importand and D is least important).  From this one question we know five of the six paired comparisons:

         A>B, A>C, A>D, B>D, C>D.

The analysis of Max-Diff can be done using a number of different algorithms and from these we can estimate utility functions.  Probably the most commonly used algorithm to analyze Max-Diff is using a Hierarchal Bayesian procdure.  Hierarchal Bayes is beneficial because it allows for borrowing across the data.

 Qualtrics surveys is developing this functionality.

Multivariate data analysis is the analysis of multiple variables at the same time. This type of analysis is used to find how a set of variables explain one or more other variables. For example, sets of variables may explain one overall variable (brand loyalty) or may differentiate between key market segments. Similarly, a set of brand attributes may be used to map relationships to the key brands competing in the marketplace, thereby showing the strengths and weaknesses of each brand.

Some typical applications of multivariate data analysis are:

• Quality optimization (food, beverages, consumer products, insurance).
• Optimization of brand attributes.
• Multi-item Scale Development.
• Optimization of scale measures and methods.
• Classification of respondent and market segments.
• Development of new advertising and promotional materials.