You’ve got survey results! It’s exciting. It’s empowering. It’s…a little overwhelming.
But before you start to worry, remember that you already set goals for your survey—and from your goals, you formed your response data analysis plan.
A data analysis plan is a roadmap for how you’re going to organize and analyze your survey data—and it should help you achieve three objectives that relate to the goal you set before you started your survey:
When you were planning your survey, you came up with general research questions that you wanted to answer by sending out a questionnaire. Remind yourself of your objectives when you start your data analysis plan.
Let’s say you held a conference for educators, and you wanted to know what the attendees thought of your event. Your survey goal was to get feedback from the people who attended your conference. And in order to achieve that goal, you came up with general research questions you’d like to get the insights on:
Conference Feedback Survey Goal: To get feedback from the people who attended my education conference. (I want feedback from attendees so I can assess my event’s strengths and weaknesses—and make targeted improvements accordingly.)
By going back to your goal and research questions, you should have your objectives fresh in your mind—and you’ll be ready to plan out how you’re going to organize your survey data.
Typically a data analysis plan will start with the questions in your survey that ask respondents to respond directly to your primary research question. In the case of your education conference, it will be these two questions:
From these two questions, you’ll know whether your conference was a success. When you report back to your boss or decide whether to hold the conference again next year, this is the information you’ll look to, and it’s the cornerstone of your topline results.
However, overall ratings don’t tell you anything about why attendees liked your conference or how you can make it even better.
Because you want to gain a more insightful understanding of what your data means, organize your thoughts by attributing your specific survey questions to each general research question. So when it comes to creating an effective final report, you’ll know exactly which data you need to answer your bigger questions.
If it helps, organize your questions in a table format:
|Research question||Survey question(s)|
|How did attendees rate the event overall?||1. Overall, how satisfied were you with the conference?|
2. How useful was this conference compared to other conferences you have attended?
|What parts/aspects of the conference did attendees like the best?|
What parts/aspects of the conference need to be improved?
|3. How would you rate the difficulty of the workshop?|
4. Overall, do you think the conference provided too much, too little, or about the right amount of networking?
5. In general, how would you rate the food at the conference?
6. Do you feel the temperature in the conference building was too hot, too cold, or just right?
|Who are the attendees and what are their specific needs?||8. Are you a teacher, student, or administrator?|
9. How large is your school?
10. How old are you?
Now, for example, when you want to answer the larger question, “What parts/aspects of the conference need to be improved, you know that you should draw on responses to survey questions 5 and 6.
You performed an event feedback survey because you wanted to know where you need to make improvements so you can host better future events. But one of the most important parts of understanding the significance of your data—and figuring out what you need to do to improve—is identifying different demographic groupings by segmenting your respondents.
To get a handle on who’s taking your survey, make sure to include demographic questions at the end of your survey, such as age, gender, job role, institution, and more. But why should you do this?
When you’re writing your data analysis plan, think about which groups you want to compare. You should plan to take into account who is taking your survey (and how many of them there are) so you can slice and dice the data in a meaningful way that will inform any improvements you make.
For example, what if your overall satisfaction scores are low, but you see that all the students at your conference loved it? You need to see how different demographic groups answered your survey questions. It’s possible that attendees over 60 didn’t enjoy events that required a deep knowledge of computers. And if enough of them took your survey, they may have lowered your overall scores.
But don’t fret—students were happy with your conference, so you know that your entire event wasn’t awful. Filtering your results by different demographic groups helps you gain perspective—and turn your data into valuable, actionable results.
Now that you know that writing an effective analysis plan involves starting with topline results, organizing your survey questions, and figuring out how you want to segment your survey population into subgroups, you’re ready to start analyzing the data!