As the field of data analytics becomes more popular across all channels, people are increasingly becoming aware of the importance of valid results. Whether you’re posting a friendly poll on Facebook or conducting a widespread market study for your employer, you need to be able to get accurate data from the survey results. This means using one of the many tools available today.
Understanding the Data Collected
Before you can decide which statistical tool to use, you must first understand the data being collected. Surveys are often in questionnaire form, with answers varying from multiple choice to open-ended. Statisticians can also use sampling, which allows them to take a subset of a larger population, choosing to assume that the sample represents the whole. Data collectors must also take variables into consideration, since results can be skewed by choosing participants who already have certain features in common. If you’re trying to pinpoint a link between certain regions and lung cancer prevalence, for instance, you would want to factor in features like smoking habits.
There are also two major types of statistics: descriptive and inferential. Descriptive statistics look for similarities between all members of a population, while inferential statistics make assumptions about a population based on trends seen in the data. With inferential statistics, often the survey starts with a hypothesis. For instance, “Depression is more prevalent among seniors who live alone than seniors who share a home.” That hypothesis would then be applied to the answers from the sample collected and used to determine whether or not that hypothesis is true.
Familiarity With the Tools
Once all of the answers from a survey have been collected, statisticians must find a way to organize it in a way that it can be studied. Simple studies could be collated in an Excel spreadsheet, with rows set up to represent each answer. But basic spreadsheets have their limitations, which is why more advanced statisticians veer toward statistical tools specific to collecting and analyzing data.
Often the software used to collect survey data can also be used to manage it. Tools like SurveyMonkey will display the results as percentages and numbers, also allowing you to cross-reference responses based on the types of people responding. If a survey reaches out to a sampling of men and women, for instance, the data can be segmented by age, gender and geographic location, as long as you collected this data at the time you surveyed participants.
Learning to create and use basic surveys is easier than ever, thanks to technology. However, advanced data analytics are often collected by professionals who specialize in statistical work, and they use intensive tools like MATLAB and SAS Business Intelligence. These tools can be pricey and come with a severe learning curve, often requiring specialized training in order to use them.