What are the current trends and innovations in adaptive testing for survey measurement?

1 Types of adaptive testing

Adaptive testing is a type of survey that can be tailored to a respondent or group of respondents. Item response theory (IRT)-based adaptive testing uses a mathematical model to estimate the difficulty and discrimination of each question, and the ability and response pattern of each respondent. Tailored design method (TDM) uses a set of rules and criteria to determine the optimal length, mode, frequency, and content of the survey for each respondent or group of respondents. Adaptive conjoint analysis (ACA) is a specific type of adaptive testing for measuring preferences and trade-offs among different attributes or options, where the algorithm adapts questions and options based on the respondents' previous choices.

2 Benefits of adaptive testing

Adaptive testing can provide several advantages for survey measurement, such as decreasing the number of questions and time required to complete the survey, which can improve the respondents' motivation, attention, and satisfaction while reducing the risk of boredom, frustration, and dropout. Additionally, it can increase the precision and validity of the measurement by avoiding irrelevant, redundant, or too easy or too hard questions and by focusing on the most informative and discriminating questions for each respondent. Furthermore, it can enhance the engagement and interaction of the respondents by providing them with feedback, guidance, or personalized content and by creating a more dynamic and responsive survey experience. Lastly, it can improve the quality and diversity of the data by capturing more nuanced and complex information and by reaching different segments and subgroups of the population.

3 Challenges of adaptive testing

Adaptive testing poses several challenges and limitations for survey measurement, such as designing appropriate algorithms that can handle different types of questions, scales, and data, as well as ensuring comparability and equivalence among respondents. Additionally, it can be difficult to deal with missing data and complex analyses that may arise from the variability and adaptivity of the survey design. Finally, it is important to communicate the logic and rationale of the adaptive testing to respondents, stakeholders, and users of the survey data in order to address any questions or concerns about the validity, reliability, or transparency of the process and results.

4 Examples of adaptive testing

Adaptive testing is being applied and experimented in various fields and domains of survey measurement, such as Education and assessment, Health and psychology, Marketing and consumer research, and Social and political research. In Education and assessment, it can help measure the students' learning outcomes, skills, and competencies, providing them with feedback, guidance, or remediation. In Health and psychology, it can measure the patients' symptoms, diagnoses, treatments, and outcomes; offering support, advice or intervention. For Marketing and consumer research, it can measure the customers' preferences, satisfaction, loyalty and behavior; providing them with recommendations, offers or incentives. Lastly, in Social and political research it can measure the public's opinions, attitudes, values and beliefs; giving them information, education or participation.

5 Tips for using adaptive testing

If you are considering using adaptive testing for your survey measurement, there are several tips to keep in mind. It is essential to define research objectives and questions, and determine which type of adaptive testing is most suitable and feasible. Additionally, it is beneficial to review existing literature and best practices on adaptive testing, as well as consult experts or professionals with experience in the field. Furthermore, it is important to choose or develop a reliable and valid algorithm for your adaptive testing, testing it on a sample or pilot data before implementing it on the full survey. To ensure accuracy, monitor and evaluate the process and results of your adaptive testing, checking for any errors, biases, or anomalies that may impact the quality or interpretation of your data. Lastly, report and document your methods and findings, providing clear and transparent explanations for your decisions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?