Title: 4632001 Tests and Measurements
146-320-01Tests and Measurements
2Course Highlights
3Course 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
4Class Notifications
- Make sure to check the following website for
class notices - http//www.uwindsor.ca/courses/notices
5Course 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.)
6Course 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
7Course 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
8Course 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.
9Course 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
10Course 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
11Course Outline Highlights
- The University Calendar explains the regulations
regarding plagiarism and other academic
dishonesty - It is your responsibility to familiarize yourself
with these regulations
12Course 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
13Course 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
14Sign 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)
15Course 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
16Introduction and Definitions
- Test
- Psychological Test
- Scales
- State vs Trait
- Administration Individual vs Group
- Test Battery
- Standardization Sample
- Standard Conditions
- Representative Sample
17More Testing
- Measuring Human Ability
- Achievement
- Aptitude
- Intelligence
- Measuring Personality
- Structured
- Projective
- Psychological Testing
18Stats 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)
19Types 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
20Organizing 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
21Example
- Age Frequency
- 18 14
- 19 85
- 20 58
- 21 40
- 22 35
- 23 16
- 24 10
- 25 6
- 26 4
22Percentile 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
23Mean
- 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)
24Standard Deviation
- The square root of the average deviation from the
mean
25Standard 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
26Z-scores
- Z-Scores (or standard scores) are a way of
expressing a raw scores place in a distribution
27Z-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
28Z-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
29Z-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
30Z-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)
31Z-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
32Properties of Area Under the Normal Distribution
.3413
.3413
.1359
.1359
.0215
.0215
.0013
.0013
z -3 -2 -1 0
1 2 3
33Areas 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
34Example 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
35Example 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
36But 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
37McCalls T
- Transforms raw scores to a distribution with mean
50, s 10 - Standard scores, not normalized score
38Quartiles 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
39Stanine
- 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
40Norms
- Based on distribution of sample scores
- Used to understand raw scores (norm-referenced
test) - Remember representative sample
- Age-related norms
- Tracking
- Gender norms
41Criterion-Referenced Tests
- Comparison of test performance with a specified
set of criterion skills - Mastery of material
42Correlation
- 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
43Graphing 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
44Correlation
WEIGHT
HEIGHT
45Degrees of linear correlation
46Degrees of linear correlation
47Characteristics of r
- r has two components
- The degree (magnitude) of relationship
- The direction of relationship
- r ranges from 1.00 to 1.00