46-320-01 Tests and Measurements Intersession 2006 Course Highlights Instructor Imogen E. Hall, M.A. Office Chrysler Hall South Room 181 Office Hours Mondays 4:00 to 6:00 p.m. Phone 253-3000 ext. 2217 or ext. 2218 E-mail hall6@uwindsor.ca Times Mondays and Wednesdays, 6:30 to 9:20 p.m. Location Toldo Health Education Centre, Rm. 104 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 Class Notifications Make sure to check the following website for class notices: http://www.uwindsor.ca/courses/notices 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.) 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% 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 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. 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 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 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 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 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 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) 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 Introduction and Definitions Test Psychological Test Scales State vs Trait Administration: Individual vs Group Test Battery Standardization Sample Standard Conditions Representative Sample More Testing Measuring Human Ability Achievement Aptitude Intelligence Measuring Personality Structured Projective Psychological Testing 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) 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 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 Example Frequency 14 85 58 40 35 16 10 6 4 100 F re q u e n c y Age 18 19 20 21 22 23 24 25 26 80 60 40 20 0 18 19 20 21 22 Age 23 24 25 26 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 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) X x n Standard Deviation The square root of the average deviation from the mean X x 2 s n 1 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 Z-scores Z-Scores (or standard scores) are a way of expressing a raw score’s place in a distribution • Z-score formula: z X 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 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 z X 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 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) 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 Properties of Area Under the Normal Distribution .3413 .3413 .1359 .1359 .0215 .0215 .0013 .0013 z= -3 -2 -1 0 +1 +2 +3 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 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 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 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 McCall’s T Transforms raw scores to a distribution with mean = 50, s = 10 T (10) z 50 Standard scores, not normalized score 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 … 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 % Cases Percentiles Stanine 4 1-4 1 7 5-11 2 12 12-23 3 17 24-40 4 20 41-60 5 17 61-77 6 12 78-89 7 7 90-96 8 4 97-100 9 Norms Based on distribution of sample scores Used to understand raw scores (normreferenced test) Remember representative sample Age-related norms Tracking Gender norms Criterion-Referenced Tests Comparison of test performance with a specified set of criterion skills Mastery of material 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 Graphing Relationships Height/Weight Scatterplot Weight 150 100 50 0 0 2 4 Height r = .77 6 8 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. Correlation WEIGHT HEIGHT Degrees of linear correlation Degrees of linear correlation Characteristics of r r has two components: The degree (magnitude) of relationship The direction of relationship r ranges from –1.00 to +1.00