This test assesses a candidate’s lateral thinking, problem-solving, and strategic thinking skills that are valuable in the workplace.
Most commonly, it’s used in industries such as technology and engineering, as it helps recruiters better understand which candidates have the sharp minds and critical thinking skills needed to succeed in such fields.
Abstract Reasoning is closely related to fluid intelligence, the ability to quickly reason with information to solve new, unfamiliar problems independent of prior knowledge.
This test is relevant for candidates who are keen at learning new skills, adapting and adjusting their approaches, seeing the big picture, and thinking strategically. It is also one of the cognitive abilities that have been consistently linked to better work performance and leadership effectiveness. It is an assessment that uses shapes and patterns to assess your problem-solving skills and ability to spot logical series.
Testlify’s skill tests are designed by experienced SMEs (subject matter experts). We evaluate these experts based on specific metrics such as expertise, capability, and their market reputation. Prior to being published, each skill test is peer-reviewed by other experts and then calibrated based on insights derived from a significant number of test-takers who are well-versed in that skill area. Our inherent feedback systems and built-in algorithms enable our SMEs to refine our tests continually.
Abstract reasoning is the ability to analyze and solve complex problems by identifying patterns and relationships. This question can help you assess a candidate’s ability to think critically and creatively about challenging problems.
Look for examples of problems that were difficult to solve or required a high level of abstract thinking, as well as the candidate’s process for breaking down the problem and identifying patterns or relationships. The candidate should be able to articulate how they used logic and reasoning to arrive at a solution.
Abstract reasoning is also important for learning and adapting to new information and concepts. This question can help you assess a candidate’s ability to quickly learn and understand complex information.
Look for examples of skills or concepts that were difficult to learn or understand, as well as the candidate’s process for learning them. The candidate should be able to explain how they identified patterns or relationships in the new information, and how they used logic and reasoning to integrate the new knowledge into their existing skillset.
Abstract reasoning is often required to solve problems that don’t have clear-cut answers or solutions. This question can help you assess a candidate’s ability to think creatively and outside of the box when faced with these types of challenges.
Look for examples of problems or scenarios where the candidate had to think creatively or outside of the box to arrive at a solution. The candidate should be able to articulate their process for generating ideas and exploring different solutions, and how they evaluated the pros and cons of each approach.
Abstract reasoning is often required for analyzing and interpreting complex data sets. This question can help you assess a candidate’s ability to use logic and reasoning to identify patterns and trends in data.
Look for examples of data sets that were particularly large or complex, as well as the candidate’s process for analyzing and interpreting the data. The candidate should be able to explain how they identified patterns or relationships in the data, and how they used this information to draw conclusions or make decisions.
Abstract reasoning often involves the ability to represent complex problems or concepts visually or conceptually. This question can help you assess a candidate’s ability to think abstractly and represent complex information in a clear and logical way.
Look for examples of problems or concepts that the candidate has represented visually or conceptually, as well as their process for creating these models. The candidate should be able to explain how they used logic and reasoning to develop the model, and how they used the model to arrive at a solution.
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