# Employee survey questions

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```					Employee survey questions

In my previous article - Employee Survey Design: Six Key Considerations - we discussed
two broad topics related to survey design - choosing the right topics and creating quality
questions. Survey design (or questionnaire development), however, is not complete until
you can show that all or most of the redundant questions have been filtered out from the
final set. Moreover, this "redundancy" is often only visible through statistical analysis
(i.e., factor analysis, discussed below) conducted after the data have been collected. In
other words, the initial draft of survey questions needs to be treated as just that - an initial
draft. It's only after the first data collection and subsequent revision that survey design
can be said to be complete. Beyond this, additional data should be used - collected on an
annual or biennial basis - for continued refinement of the survey questions.

One common method used to refine survey questions is through factor analysis - a data
reduction technique.

Factor analysis has been around for nearly a century (see Charles Spearman and
intelligence testing); and although the mathematics involved - linear algebra - may seem
intimidating, the concept is simple - it's a technique used to reduce a large number of
variables into a smaller set by examining the interrelationships among the variables.
Fortunately for most of us, understanding how it can be used to improve the quality of
our survey is all that's necessary.

A key premise behind factor analysis is the idea that many can be reduced to few.
Imagine yourself in Munich for their annual Oktoberfest. You would undoubtedly see
thousands people from all walks of life. Now, if I were to "group" these people based on
some meaningful category - e.g., nationality, height, weight, or even the type of beer they
are drinking - the resulting number of groups would be fewer than the thousands of
individuals on which those groups are based. Factor analysis is very similar to this.
Rather than people, however, we're now talking about survey questions.

When you conduct a factor analysis on survey data collected from your employees,
you're asking the program "group" the survey questions in some conceptually meaningful
way. If you're thinking to yourself that survey questions are already organized into
meaningful groups or categories - e.g., training, benefits, supervision, and so on - you're
right. In fact, if the survey was designed properly and the factor analysis done correctly,
you may find that factor analysis results show a perfect match between your survey items
and your survey categories. Unfortunately, this will be rare. More often than not, you will
find that a portion of the survey questions can be omitted, re-categorized or refined.

Bottom line here is that when it comes to employee surveys, factor analysis is an
important tool that can be used to help answer the question - Which questions should I
keep or drop? It is an important step that will help to clarify the conclusions drawn from
results of other advanced analyses typically conducted on survey data.

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 views: 43 posted: 9/29/2010 language: English pages: 2
Description: Employee survey questions
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