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46-320-01 Tests and Measurements

46-320-01 Tests and Measurements. Intersession 2006. Course Highlights. Course Outline. The Course Outline is available through the Class Notes website There is a course website http://web2.uwindsor.ca/courses/psychology/hall6/index.htm The site is available through Class Notes

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46-320-01 Tests and Measurements

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  1. 46-320-01Tests and Measurements Intersession 2006

  2. Course Highlights

  3. Course Outline • The Course Outline is available through the Class Notes website • There is a course website • http://web2.uwindsor.ca/courses/psychology/hall6/index.htm • The site is available through Class Notes • All course related material will be posted on this site • Lectures will be placed on the site before class • Check the site often

  4. Class Notifications • Make sure to check the following website for class notices: • http://www.uwindsor.ca/courses/notices

  5. Course Outline Highlights • See the course outline for a full review of the following information • Course Description/Objectives: • An introduction to basic concepts of psychological testing, with a focus on test development, measurement, and test evaluation. Properties of good test items and scales, such as reliability and validity, will be analyzed. Standard tests used to assess personality, achievement, and aptitudes will be surveyed. (Prerequisite: 02-250.)

  6. Course Requirements • Required Textbook: • Kaplan, R. M., & Saccuzzo, D. P. (2005). Psychological Testing: Principles, Applications, and Issues, 6th Edition. Toronto: Wadsworth. • Evaluation: • 1 Midterm Exam (June 5) = 30% • Assignment (due June 21) = 30% • Final Exam (June 26, 7:00 PM) = 40%

  7. Course Outline Highlights • Midterm and Final Examinations: • ONE MID-TERM EXAM: • Monday June 5th(Chapters 1-10 & 18 [pages 512-525] ) • FINAL EXAMINATION: • Monday June 26th from 7:00 P.M. to 10:00 P.M. (Chapters 11-21 [not 18 or 20] ) • Both exams will cover assigned textbook readings and in-class material • The final exam is NOT cumulative

  8. Course Outline Highlights • All exams are “closed-book” format. You may NOT bring any material (e.g., lectures notes or the class textbook) to any exam. The exams will include (but are not limited to) multiple choice questions, fill-in-the blank, definitions, short answer questions, or essays. Further details will be provided in class. • You should bring pens and pencils to both the Midterm and Final exams. You must bring your University of Windsor student ID Card to both exams.

  9. Course Outline Highlights • Missed Tests: You must take the midterm and final exams during the scheduled times • Acceptable reasons • Medical/family emergency or extreme circumstances • Supporting documents (e.g., physician’s note) must be submitted to the instructor within one week following the missed test • Unacceptable reasons • Travel, special occasions, conflicts with other courses, or job-related scheduling conflicts • You will receive a grade of zero for these reasons or if supporting documents are not provided

  10. Course Outline Highlights • Note: The final exam cannot be re-written at another time • If it is missed for a valid reason, the student must apply for aegrotat standing through the Registrar’s Office

  11. Course Outline Highlights • The University Calendar explains the regulations regarding plagiarism and other academic dishonesty • It is your responsibility to familiarize yourself with these regulations

  12. Course Outline Highlights • Assignment: • Due AT THE BEGINNING OF CLASS on Wednesday June 21st • Assignments received after 6:30 P.M. SHARP on the due date without an acceptable, documented reason will be subject to a 5% grade penalty per day late (including weekend days) • Details will be provided soon • Worth 30% of final grade

  13. Course Outline Highlights • You may earn up to two bonus points in this class • You can earn these in two ways: • Participation in research • Completion of a bonus assignment – posted online mid-June

  14. Sign Up for Participant Pool!! • Earn up to 2 bonus points • Sign up on the web (takes less than 5 minutes): • http://uwindsor.experimentrak.net/ • Or access through Psych homepage • You MUST sign up by midnight May 21st to be included (no exceptions)

  15. Course Outline Highlights Important Dates: • May 19: Last day to register for class • May 21: Last day to sign up for Participant Pool • June 5: Midterm Exam (in class) • June 9: Last day to drop class (you will automatically receive a final grade after this date) • June 21: • Assignment due at the beginning of class • Course Evals completed in class • Last lecture • June 26: 7:00 - 10:00 P.M. Final Exam

  16. Introduction and Definitions • Test • Psychological Test • Scales • State vs Trait • Administration: Individual vs Group • Test Battery • Standardization Sample • Standard Conditions • Representative Sample

  17. More Testing • Measuring Human Ability • Achievement • Aptitude • Intelligence • Measuring Personality • Structured • Projective • Psychological Testing

  18. Stats Review: Descriptive/Inferential Statistics • Descriptive Statistics: techniques for organizing, summarizing, representing and extracting information from numerical data • These are used to describe data (e.g., Mean, Standard Deviation) • Inferential Statistics: rules and procedures for inferring the characteristics of populations from sample data (inferring parameters from statistics) • These are used to make inferences about a population (e.g., Correlation)

  19. Types of Measurement • There are 4 types of measurement most often used in statistics • Nominal (categories) • Ordinal (rank order) • Interval (no absolute zero) • Ratio (absolute zero) • They differ on magnitude, equal intervals, and absolute zero

  20. Organizing Data • Frequency Distributions: A frequency distribution is a table which shows the number of individuals or events that occurred at each measurement value • Table/Histogram

  21. Example Age Frequency 18 14 19 85 20 58 21 40 22 35 23 16 24 10 25 6 26 4

  22. Percentile Rank (Pr) • Steps: • Determine how many cases fall below X (B) • N • Divide cases below (B) by N • Multiply by 100 Pr = (B/N)*100

  23. Mean • The mean of a sample of X scores is symbolized as  , which is said as “X bar” • The mean of a population of X scores is symbolized by the Greek letter mu (µ)

  24. Standard Deviation • The square root of the average deviation from the mean

  25. Standard Deviation • Variability: The extent numbers in a data set are dissimilar (different) from each other • The larger the standard deviation, the larger the variability in the data • Standard deviation expresses variability in the same units as the data • The standard deviation of a sample of X scores is symbolized as ‘s’ • The standard deviation of a population of X scores is symbolized by the Greek letter sigma

  26. Z-scores • Z-Scores (or standard scores) are a way of expressing a raw score’s place in a distribution • Z-score formula:

  27. Z-scores • A z-score is a better indicator of where your score falls in a distribution than a raw score • A student could get a 75/100 on a test (75%) and consider this to be a very high score

  28. Z-scores • If the average of the class marks is 89 and the (population) standard deviation is 5.2, then the z-score for a mark of 75 would be: = 89 = 5.2 z = (75-89)/5.2 z = (-14)/5.2 z = -2.69

  29. Z-scores • This means that a mark of 75% is actually 2.69 standard deviations BELOW the mean • The student would have done poorly on this test, as compared to the rest of the class

  30. Z-scores • z = 0 represents the mean score (which would be 89 in this example) • z < 0 represents a score less than the mean (which would be less than 89) • z > 0 represents a score greater than the mean (which would be greater than 89)

  31. Z-scores • A z-score expresses the position of the raw score above or below the mean in standard deviation sized units • E.g., • z = +1.50 means that the raw score is 1 and one-half standard deviations above the mean • z = -2.00 means that the raw score is 2 standard deviations below the mean

  32. Properties of Area Under the Normal Distribution .3413 .3413 .1359 .1359 .0215 .0215 .0013 .0013 z = -3 -2 -1 0 +1 +2 +3

  33. Areas of Normal Distribution • Appendix I, Part II (p. 635) • Let’s say we want to know the area between the mean and z = 0.20: • Look under z = 0.200 (row = .2, column = .00) • The proportion = 0.0793 • Therefore, .0793 (or almost 8%) is the proportion of data scores between the mean and the score that has a z score of 0.20

  34. Example cont. • This means that the area between the mean (z = 0.00) and z = 0.20 has an area under the curve of 0.0793: .0793 .4207 z: 0 0.20

  35. Example cont. • Since the normal curve is symmetrical, the area between the mean and z = -.20 is equal to the area between the mean and z = +.20: .0793 .0793 .4207 .4207 Z: -0.20 0 +0.20

  36. But Why Know This? • Z-scores and percentile • The percentile for a z-score of 0.20 is as follows: (remember distribution symmetry) • .5000 + .0793 • =.5793 • Multiply by 100 = 57.93 percentile • Note: Percentiles and Percentile Rank are not the same thing

  37. McCall’s T • Transforms raw scores to a distribution with mean = 50, s = 10 • Standard scores, not normalized score

  38. Quartiles and Deciles • Quartile: percentage scale divided into 4 groups • Q1: 25th percentile • Q2: median or 50th percentile…. Etc • Interquartile range: middle 50% of distribution • Decile: percentage scale divided into 10 groups • D1: 10th percentile …

  39. Stanine • Transforms raw scores to “standard nine” scores • 1 to 9, mean = 5, s = 2 • Convert data to z-scores • Convert z-scores to percentiles (Appendix 1) • Use table to convert to stanines

  40. Norms • Based on distribution of sample scores • Used to understand raw scores (norm-referenced test) • Remember representative sample • Age-related norms • Tracking • Gender norms

  41. Criterion-Referenced Tests • Comparison of test performance with a specified set of criterion skills • Mastery of material

  42. Correlation • We are often interested in knowing about the relationship between two variables • We are asking whether one variable (X) is related to another variable (Y). Stated differently: Are X and Y correlated? • More specifically: Are changes in one variable reliably accompanied by changes in the other? • Correlation coefficients

  43. Graphing Relationships • When height and weight scores are plotted, we see some irregularity. • We can draw a straight line through these points to summarize the relationship. • The line provides an average statement about change in one variable associated with changes in the other variable. r = .77

  44. Correlation WEIGHT HEIGHT

  45. Degrees of linear correlation

  46. Degrees of linear correlation

  47. Characteristics of r • r has two components: • The degree (magnitude) of relationship • The direction of relationship • r ranges from –1.00 to +1.00

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