Transcript 46-220-01 Introduction to Adjustment and Personality

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
[email protected]
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