Advanced survey software solutions, such as Snap Survey Software, include more robust and sophisticated analysis capabilities such as Significance Testing, t-Tests, and Confidence Intervals. Significance Testing provides a measure of how confident we are that the results obtained from the survey reflect the true pattern of responses in the population at large.
Below are some advanced statistical features. When searching for a survey software solution, these advanced analysis features should be available to you.
Significance Testing is necessary where data is collected from a sample population and not from the entire population. Significance Testing tells us how confident we can be that the survey’s sample population accurately displays the views of the entire population. A Significance level is the probability that the result is true and not just a random deviation. T-Tests, Confidence Intervals, and the Chi-Squared Test are all types of Significance Testing.
When sample data is gathered, you may detect that the two groups have different average scores. However, does this represent a real difference between the two populations or just a chance difference in the samples? The t-Test measures the probability that two results being compared could have been found purely by chance. It also compares the Mean value of two (and only two) sets of data. The t-Test is reported as a Confidence Level that the two scores being compared are related in the same way. The higher the Confidence Level, the more assured you can be that there is a deviation in the two groups being tested. For instance, 95% confidence indicates that there is only a 5% chance that such a difference in scores could have been found merely through the effects of sampling.
When calculated, the Confidence Interval reports two values – the Confidence Level and the Confidence Interval. The Confidence Level is the percentage probability that the test was carried out (as shown in the t-Test above). The Confidence Interval is the range of values within which it can confidently state that the true value (for the entire population) is expected to lie based on the value observed in the sample. For instance, if the results of a survey are quoted as 48% +/-5% at the 95% Confidence Level, this indicates that the researcher is 95% confident that the accurate result is between 43% and 53%.
Chi-Squared Test for Independence
The Chi-Squared Test, which is also referred to as Chi-square Test, tests whether there appears to be a relationship between two variables or not. The Chi-Squared Test reports a Confidence Level if there is adequate evidence that a relationship existing, or it reports that a relationship does not exist. A high Chi-Squared value indicates a relationship does exists. In contrast, a low Chi-Squared signifies that there is no apparent relationship. The Chi-Squared Test does not offer any suggestions as to what the relationship is – this is left to the researcher to decipher.
Many survey software solutions, including Snap Survey Software, have the advanced analysis capabilities to calculate Summary Statistics and to perform Significance Testing. Having access to such capabilities enables researchers to comprehend and appreciate the underlying trends in their data, and whether or not the data from their sample is an accurate reflection of the entire population, not just the survey sample.