Business decisions nowadays are guided by “what user wants?” and hence rely heavily on data. UX Surveys are the best way to get that data. With UX Surveys you can gather insights, guide strategy, prove or disprove hypotheses, and back unique and fresh ideas.
However, the data that is gathered from these surveys is not always accurate. In this blog, we’re going to address some biases, the UX surveys processes, and some UX survey best practices.
Content and Design
Survey branding, prototypes, assumptions, and biases are some factors to consider that impact a UX Survey. You need objectivity in your surveys and that is established by the content, design, and development itself.
Identifying any biases in survey design is relatively simple. You can go through questions and eliminate logos, branding, and lofty assumptions that aren’t of any value. Even when you remove all the fluff from your survey design, surveys based on instant data will always pose a challenge of going stale as you process the research and present the results as data goes redundant pretty quickly. Political and COVID surveys are apt examples of this.
Participant selection is crucial for the quality of the survey. When selecting participants, you need to take into account several biases like sampling, demand characteristic, extreme responding, and non-responsive. The right audience ensures good quality and is time-efficient, giving you better results to back original ideas. Getting the right people is more important than quantity.
Create criteria for qualifying participants and stick to them. It could be to disqualify anyone who completes the survey way before the estimated time or the respondent who selects the same option for more than 60% of the questions.
The quality of data won’t matter if the interpretations are baseless. Remember you’re not the participant. Look at the data in terms of what it shows and not the reasoning behind it. Just look at the numbers the very first time you take a look at the dataset, avoid reading too much into it at once. Focus on one thing at a time.
We have addressed survey accuracy at participant, content, design, and analysis level. We now need to determine how the results look while calculating errors concerning population values.
Research accuracy = True population plus/minus any errors.
To put it differently, survey accuracy is defined with its closeness to the truth with repeated measurement. The more repeat surveys we do for similar audiences, the closer we get to this perceived “True population”. However, repeated surveys are difficult and impractical so we consider confidence intervals. The most common measurement of sampling error in surveys is a 95% confidence interval. This implies that if you repeat the survey, the accuracy of answers will be 95% every time.
Using the confidence intervals, precision and the number of sampling errors can be calculated. This will ultimately give you an idea of closeness to true population value. Even after all this, we can’t completely say that the results are accurate and the biases eliminated. Then again the aim is to identify biases and minimize them instead of assuming that objective surveys consist of no biases at all.
A different look at the Data
Before you go on to convert your data into insights, here are a few things that you should consider:
- Revisit the objectives of the survey before your draw any insights
- Identify segments and respective respondents
- Exclude bias and anomalies before analysis
To sum up we have covered three stages for data analysis and getting better results out of your surveys.
- Cleaning the data – Filtering participants and responses
- Analyzing the data – Number based analysis
- Getting the insights – Revisiting objectives to compare the findings
These stages are different and require a different level of commitment, concentration, and engage different parts of the brain. So it’s advised that every single one of these stages is addressed separately and one at a time. It’s crucial for the quality of results you’re seeking and the impact it will have on user experience.
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