Going beyond basic questionnaire construction and survey fielding to include advanced research techniques can transform the integrity and depth of your findings. In this blog post, we'll explore a range of sophisticated strategies for elevating your quantitative surveys.
While not exhaustive, the list below can help elevate your survey and heighten the caliber of your research outcomes.
Conduct Rigorous A/B Testing
Implementing A/B testing methodologies (also known as split testing) allows for comparing two versions of an asset, such as packaging design or advertising copy. The respondent pool is split into two groups, where the control asset (version A) is shown to one group of respondents, while the test asset (version B) is shown to the other group. Based on the responses to each asset, researchers can determine which version will most likely perform better in the real world.
Example: An educational content platform hypothesizes that younger users (under 25 years old) will respond more positively to innovative, dynamic content, while older users (25 years and older) might prefer more straightforward, informative content.
To design this test, Group A will receive messaging emphasizing innovative aspects of the platform (e.g., cutting-edge tools), and Group B will receive a message emphasizing practical outcomes (e.g., getting ahead in your career). Survey participants will randomly be assigned to either Group A or Group B within their segments.
The expected outcome is to identify which message resonates best with each demographic. This data will help inform the educational platform's content strategies going forward.
Utilize Implicit Association Tests (IAT)
Incorporating IAT principles can offer a nuanced understanding of unconscious biases. By crafting questions that indirectly assess attitudes or preferences without explicitly asking for them, you can reveal underlying biases and diminish the inclination toward socially desirable responses.
Example: An environmental attitudes study wants to measure subconscious preferences or biases toward renewable vs non-renewable energy sources across participants of varying demographics. In this case, the IAT would be designed to assess automatic associations between concepts (renewal energy, non-renewable energy) and attributes (positive, negative).
In the warm-up round, participants first categorize just the energy sources and then just the attributes, to familiarize them with the task. In the test rounds, the IAT mixes these categories. In some rounds, Renewable Energy and Positive words/images are paired on the same response key, while Non-Renewable and Negative are paired on another. In other rounds, these pairings are switched. Participants must respond as quickly as possible. The data is then reviewed for speed and accuracy, with an analysis comparing the average reaction times between critical pairings to uncover subconscious bias. Different demographics (age, location, educational background) can also be analyzed for stronger biases among certain groups.
Integrate Eye-Tracking Technology
Analyzing respondents' visual attention patterns during survey interactions reveals valuable insights into where and how participants focus their attention while answering survey questions or interacting with survey content. This method can reveal insights into user behavior and preferences that are not always apparent through self-reported data.
Example: A grocery store wants to understand how consumers interact with different store layouts to optimize product placements. Survey participants are shown several virtual layouts of the grocery store through an interactive online interface. Eye-tracking technology is used to track their gaze as they navigate through a virtual tour of the layout. The technology records where they look, how long they fixate on specific items, and their eye movement paths across the store layout.
Deploy Machine Learning Algorithms
Leveraging machine learning algorithms in online surveys can help predict outcomes, segment users, and personalize survey content based on participant responses.
Example: A company wants to personalize content for a market research survey about smartphone preferences. To optimize engagement, they will use a machine learning algorithm to adapt the questions in real-time based on participant responses. The machine learning algorithm will use a decision tree classifier to handle categorical data and be trained on pre-collected data from previous surveys including demographics, previous smartphone feature preferences, and engagement metrics.
After passing the screener, respondents will be asked initial questions about their current smartphone brand and primary uses, such as gaming or photography. Based on those responses, the decision tree algorithm predicts which smartphone features (e.g. camera quality, battery life, processing power) the participant might be most interested in. The survey dynamically adapts, focusing subsequent questions on predicted areas of interest. As the survey progresses, the model’s predictions continue to adapt the survey in real-time.
Embrace Real-Time Response Monitoring
Implementing robust systems for real-time response monitoring allows researchers to swiftly detect and address issues as they arise, such as high dropout rates or patterns of inconsistent responses. As the example below illustrates, real-time response monitoring can also facilitate post-survey respondent interactions based on the feedback received.
Example: A retail company wants to measure customer satisfaction after an online purchase. The survey includes various questions about product satisfaction, shipping speed, shipping cost, and customer service interactions. Each question uses a Likert scale (1 to 5) and open-ended responses where necessary. If patterns of dissatisfaction are detected, the survey can offer an immediate feedback form with an alert sent to the customer service team for review and follow-up.
Leverage the Power of Cognitive Interviewing
Conducting cognitive interviews with a select group of respondents pre-launch affords researchers valuable insights into how individuals interpret and respond to survey questions. This process helps pinpoint cognitive biases, comprehension hurdles, and response biases, allowing you to refine question wording and structure for enhanced clarity and accuracy prior to survey distribution.
Example: A university wants to gather detailed feedback from students on campus facilities, such as the school library. After designing the survey instrument, they select a diverse group of students to conduct cognitive interviews.
Upon asking the question, “Do you find the library resources adequate?” students may provide feedback that “resources” mean different things to them. Some might consider a broad definition that includes books, seating, meeting rooms, and computers, while others might think only of books when they read the question. To clarify, the question could be split into specific attributes, such as “Rate the adequacy of seating availability in the library.”
Consider Multimodal Survey Distribution Channels
By designing your survey for flexible distribution channels, you can increase respondent participation while minimizing bias associated with specific survey modalities.
Example: A company is preparing to launch a new smartphone model and wants to interview current customers for feedback. Knowing that these customers are already smartphone users, they choose three distribution channels: an email campaign targeting previous buyers and subscribers, an SMS text message to customers who have opted in to receive text messages from the company, and a push notification via their mobile app.
In Conclusion
With these advanced research techniques, you can elevate your quantitative research to generate more meaningful and actionable insights. If you would like guidance on implementation, IntelliSurvey's team of seasoned market researchers and survey programmers can help. To learn more about our services and our partnership approach to online studies, please contact us today.
