4632001 Tests and Measurements

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46-320-01Tests and Measurements

• Intersession 2006

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Course Highlights
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Course Outline

• The Course Outline is available through the Class
  Notes website
• There is a course website
• http//web2.uwindsor.ca/courses/psychology/hall6/i
  ndex.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

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Class Notifications

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

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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.)

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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, 700 PM) 40

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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 700 P.M. to 1000 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

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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.

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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., physicians 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

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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
  Registrars Office

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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

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Course Outline Highlights

• Assignment
• Due AT THE BEGINNING OF CLASS on Wednesday June
  21st
• Assignments received after 630 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

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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

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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)

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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 26 700 - 1000 P.M. Final Exam

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Introduction and Definitions

• Test
• Psychological Test
• Scales
• State vs Trait
• Administration Individual vs Group
• Test Battery
• Standardization Sample
• Standard Conditions
• Representative Sample

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More Testing

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

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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)

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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

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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

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Example

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

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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

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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)

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Standard Deviation

• The square root of the average deviation from the
  mean

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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

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Z-scores

• Z-Scores (or standard scores) are a way of
  expressing a raw scores place in a distribution

• Z-score formula

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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

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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

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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

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Z-scores

• z 0 represents the mean score (which would be
  89 in this example)
• z lt 0 represents a score less than the mean
  (which would be less than 89)
• z gt 0 represents a score greater than the mean
  (which would be greater than 89)

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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

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

• Appendix I, Part II (p. 635)
• Lets 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

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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
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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
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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

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McCalls T

• Transforms raw scores to a distribution with mean
  50, s 10
• Standard scores, not normalized score

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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

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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

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Norms

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

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Criterion-Referenced Tests

• Comparison of test performance with a specified
  set of criterion skills
• Mastery of material

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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

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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
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Correlation
WEIGHT
HEIGHT
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Degrees of linear correlation
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Degrees of linear correlation
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Characteristics of r

• r has two components
• The degree (magnitude) of relationship
• The direction of relationship
• r ranges from 1.00 to 1.00