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GRE Psychology: Measurement And Methodology

Subjects : gre, psychology
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • Match each statement with the correct term.
  • Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. 34.13% - 13.59% - 2.02% - 0.26% and - +3 99.74% - +2 97.72% - +1 84.13% - 0 50.00% - -1 15.87% - -2 2.28% - -3 0.26%






2. If it is significant - same finding can be generalized to the population - use test of significant to reject null hypothesis






3. Tests whether at least 2 groups co-vary - can adjust for preexisting differences between groups






4. (Mental age/chronological age)/100 - Highest age = 16

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5. Not IQ - It is unlikely IQ captures all facets of it






6. The age level of a person'S functioning according to the IQ test






7. The process of representing or analyzing numerical data






8. Includes: testable hypothesis - reproducible experiment - operationalized definition (observable and measurable)






9. Measure the extent to which test measures what it intends to; concurrent - construct - content - face






10. Inactive substance or condition disguised as a treatment substance or condition - used to form control group






11. Measure mastery in a particular area (e.g. final exam)






12. Have order - equal intervals and a real zero ex: age






13. Mean of Americans is standardized to 100 - with SD 15 or 16 depending on test; correlates most with IQ of biological parents and socioeconomic status






14. Frequency polygon (continuous variables) - histogram/ bar graph (discrete)






15. Different subjects of different ages are compared - faster - easier






16. For ranks; determining the line that describes a linear relationship






17. Subjects alter behaviour because they are being observed






18. Give descriptive names - No order or relationship among the variables other than to separate them into groups - ex: male-female






19. Cartoons in which one person is frustrating another; asked to describe how the frustrated person responds






20. Every member of the population has an equal chance of being chosen for the sample






21. Measures the extent to which items in a measure 'hang together' and test the same thing






22. How the score are spread out overall






23. Knowing a fact






24. Internal-External Locus of Control Scale






25. Has plotted points connected by lines - used to plot variables that are continuous (categories without clear boundaries)






26. Number of SD a score is from the mean - For normal distribution - (-3 to +3)






27. Organize data by showing it in a meaningful way; do not allow conclusions to be drawn beyond the sample; percentiles - frequency distributions - graphs - measures of central tendency - variability






28. figure out how much each score differs (deviates) from the mean by subtracting the mean from each score - square each of these deviation values (to get rid of negative value) - add all these squared deviations to get the sum of square - divide sum by






29. Intelligence in relation to performance; pioneered development of psychometrics - 'no intelligence is culture-free'






30. Similar to word association - finish incomplete sentences






31. Similar to T-test - but can measure more than 2 groups






32. 31 cards (1 blank and 30 pictures) with interpersonal scenes (2 people facing each other); subject tells story about each which reveals aspects of personality; often measure need for achievement; interpreting terms include needs - press - personology






33. Rosenthal effect; researchers see what they want to see; minimized in double-blind






34. I when incorrectly reject null - thought significant but chance; II when incorrectly accept null - thought chance but significant






35. How stable measure is; test-retest - split-half






36. compares means of 2 different groups to see if the two groups are truly different - analyze differences between means on continuous data - particularly useful with small n - cannot test for difference between more than 2 groups






37. Mean (standard error of mean) - median mode; normal and platykuric: equal; positively skewed: mode - med - mean; negatively skewed: mean - med - mode; bimodal: equal mean and med - 2 modes






38. When relationship inferred when there is none - ex: many people think there is a relationship between physical and personality characteristics - when evidence show there is none






39. Developed concept of IQ and first intelligence test (Binet Scale)






40. Draw a person of each sex and tell a story about them






41. Comparing an individual'S performance on 2 halves of the same test to reveal internal consistency; internal consistency can be increased by item analysis






42. A level of <0.05or <0.01 means that chance that seemingly significant errors are due to random variation rather than to true systematic variance is less than 5% or 1%






43. Measure arousal of sympathetic nervous system - stimulated by lying and anxiety






44. Revised Binet'S version - used with children - organized by age level - Best known predictor of future academic achievement






45. Knowing how to do something






46. Order - variables need to be arranged by order (not necessarily equally spaced) - ex: maranthon finishers






47. Experimenter bias; researchers see what they want to see; minimized in double-blind






48. Attitude change in response to feeling that options are limited; e.g. dislike experiment and intentionally behaving unnaturally - or being set on a certain flavour of ice cream as soon as told it is sold out






49. How much variation there is among n number of scores in a distribution






50. The degree to which an independent variable can predict a dependent variable