Things in Common Quiz

How the Scales are Structured
Who Usually Takes This Test?
See How You Compare
Below is a preview of how scores are typically distributed across each scale.
Example Questions From the Test
- Each borders Lake Victoria.
- Each has a coastline on the Indian Ocean.
- Each borders the Red Sea.
- Each is crossed by the Tropic of Capricorn.
- They were written by the Brontë sisters.
- They are novels by Jane Austen.
- They are plays adapted by William Shakespeare.
- They are novels by George Eliot.
- Reptiles
- Mammals
- Amphibians
- Birds
Things in Common Quiz - Description and methodology of the test
For readers who enjoy a knowledge challenge spanning several subjects, Things in Common Quiz offers a compact way to test broad knowledge. It works as a what do they have in common quiz, asking what different examples have in common across several subjects, making the result useful for deciding which connections deserve further reflection. It suits anyone who enjoys a focused exercise without committing to one subject.
What This Assessment Explores
The questionnaire asks whether you can identify what unites examples across subjects. Prompts cover literature, geography, biology, mathematics, language, flags, astronomy, music, government, and minerals. It is a common knowledge quiz requiring relational recognition, not recall alone.
All three question themes contribute to the single Things in Common Knowledge total, not separate subscores:
- Category membership questions show whether examples fit a shared scientific, cultural, linguistic, or artistic class.
- Common-property questions show whether a rule or feature links objects, places, or concepts.
- Indirect-connection questions show whether specialized knowledge or less-obvious relationships are detected.
The score (0-16) reflects how many common links you identify correctly. Starting Point (0-6) indicates easier recognition of familiar links; Developing Connector (7-12) indicates many shared features with room to strengthen less-familiar associations; Strong Connector (13-16) indicates consistent detection of direct and less-obvious relationships across topics.
Methodology and Scientific Basis
Each item presents a prompt with four answer choices. The respondent selects the option identifying a category, rule, origin, visual feature, or other shared attribute. Together, varied prompts require identifying the relationship uniting each set of examples. The result reflects the number of common links identified correctly.
Conceptually, the questionnaire is consistent with the Test of Relational Reasoning (TORR), which examines how people recognize and coordinate relations. It also overlaps with the BEFKI GC-K approach to crystallized knowledge and with Rusche and Ziegler’s work on domain-specific knowledge, since success depends on learned facts from several fields. This is a conceptual relationship, not an adaptation or standardized administration of those instruments.
This shared knowledge test profiles performance on mixed-domain prompts. A strong result may reflect broad factual knowledge and efficient connection-making; a lower result may reflect unfamiliar content or difficulty identifying the intended relation.
Interpretation is limited by education, cultural exposure, language, and concentration. The displayed bands are descriptive catalogue categories rather than population norms or clinical cutoffs; a single score cannot distinguish relational performance from gaps in background knowledge.
- Alexander, Patricia A., Denis Dumas, Emily M. Grossnickle, Alexandra List, and Carla M. Firetto. “Measuring Relational Reasoning.” The Journal of Experimental Education, vol. 84, no. 1, 2016, pp. 119-151. 10.1080/00220973.2014.963216
- Schipolowski, Stefan, Oliver Wilhelm, Ulrich Schroeders, Anastassiya Kovaleva, Christoph J. Kemper, and Beatrice Rammstedt. “BEFKI GC-K: A Short Scale for the Measurement of Crystallized Intelligence.” Methods, Data, Analyses, vol. 7, no. 2, 2013, pp. 153-181. 10.12758/mda.2013.010
- Rusche, Marianna Massimilla, and Matthias Ziegler. “Measuring Domain-Specific Knowledge: From Bach to Fibonacci.” Journal of Intelligence, vol. 11, no. 3, 2023, article 47. 10.3390/jintelligence11030047
Updated: