Survey Analytics Software: A Review?

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So you've set up a survey, and it was a success! You've received several high quality responses. Now you have a rather large set of raw data what next? Survey analytics software helps you with this step, employing advanced statistical analysis to make sense of your survey data. This software can be incredibly powerful, and you'd be surprised what they have to offer. Here are the unique capabilities of a few of the best survey analytics software.

1. IntegrationSeveral of the best analytics software can actually combine with your existing survey software, working together to provide data. For example, Google Analytics, a software platform generally designed for analytics with regards to website and advertising traffic, can integrate with several survey software packages, such as SurveyMonkey and SurveyGizmo. Once integrated, Google Analytics can track survey conversions, perform path analysis, find sources of survey traffic, and even stratify this data across various versions of the form or survey in question. When looking for survey analytics software, it may well be wise to purchase a software package that isn't designed for surveys per se, since they may integrate powerfully with your current set of software.

2. Imputation of Missing DataMost analysis software lacks the ability to accommodate for missing data normally, the default way to handle missing data involves deleting data points with incomplete data or disregarding them in calculations. Ultimately, this can lead to biased, inefficient, and inconsistent results. Programs such as Amelia II can impute missing data in a single cross-section of the data, such as an individual survey, from a time series of results gathered from previous surveys over time. If you find that your surveys have holes in the data set, you should consider using a software package that will impute missing data for a more complete statistical analysis.

3. Correlation MatricesWhen looking at large, complicated data sets, correlation matrices can provide illumination as to not just the data set as a whole, but to the individual relationships between questions themselves. Understanding correlation matrices allows you to get at the nuances of data, particularly in how to handle strong relationships. For example, if you have a question concerning how much money you make, and another concerning how much your car costs, you can use correlation matrices to determine whether or not income actually correlates positively with vehicle cost that is, whether or not people who make more money drive more expensive cars.

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