Knowledge from practice. Free to use.
Articles on methodology, data protection, practice and product updates — written from 22 years of working with online surveys.
Analyzing online surveys — it starts before you collect data
The most common analysis question is: "The data's here — now what?" The honest answer: by then it's already late. The most important decisions about your analysis aren't made after data collection, but before it — when you think through which practical question your survey is actually meant to answer. Seven steps from research goal to a result you can rely on.
The chi-square test — testing associations between categorical variables
When you want to know whether two categorical variables are associated — gender and brand preference, education level and voting behavior, location and customer satisfaction — the chi-square test is the right tool. Here's the methodology with a concrete example.
Order effects in surveys — what they distort and how to avoid them
Two questionnaires with identical content can lead to different results — purely because of the order of the questions. Order effects are well documented in survey research and affect both dropout rates and the validity of the answers. Which effects exist, and how to counter them.
The t-Test — comparing means the methodologically clean way
The t-test checks whether a measured mean differs from an expected value, or whether two means differ from each other — methodologically clean and with a manageable amount of calculation. Here are the three most important variants, each with a concrete example.