Transcript Research Week 1 - University of Saint Joseph

Appraisal and Its Application to
Counseling
COUN 550
Saint Joseph College
For Week # 2
Copyright © 2005 by R. Halstead. All rights reserved.
Importance of Appraisal
In counseling we must always be concerned about
individual similarities and differences.
Why?
Because, in short, similarities and differences usually
have meaning in understanding who the client is and
how best to offer effective counseling services.
Individual differences have been studied and
interpreted through the use of sensorimotor,
physical, mental, and emotional testing.
Importance of Appraisal
It would follow then that the more skilled one is in the
use and interpretation of psychological tests, the
better the counselor will be at providing
comprehensive counseling services.
Why is this important?
ACA Code of Ethics (2014) A.1.a
Welfare of the client
Ethical Considerations
ACA Code of Ethics
Section E: Evaluation, Assessment & Interpretation
American School Counselor Association Code
of Ethics
Legal Considerations – Federal Law
1. Family Education Rights & Privacy Act (PL 93380) and Buckley Amendments
 Parents’ rights to see all information affecting
evaluation, placement, or programming of their
children and stipulates terms of access for others.
2. Perkins Vocational & Technical Act for people
with disabilities and disadvantages
3. ADA 1990 – special needs accommodations &
modifications
Locating and Selecting Tests
Decision Model
Making Decisions and Judgments
Identifying Type of Information Needed
Identifying Information Already Available
Creating Strategies to Obtain Additional
Information
Locating Appropriate Tests
Locating Other Sources of Information on Tests
Using a Compendium of Instruments
Reviewing and Evaluating Tests
The History of Testing
It has been documented that testing can be traced back
to the ancient Chinese.
We, however, need only concern ourselves with only
the last 200 years beginning with the impact that
Charles Darwin’s writings had on the field of
scientific psychology in considering individual
differences.
The early days of the field were most influenced by
Wundt, Galton, Bringham, Cattell, Spearman,
Terman, Woodworth, Binet, and Thorndike.
The History of Intelligence Testing
Wilhem Wundt
Sir France Galton
James Cattell
Alfred Binet
Lewis Terman
Robert Yerkes
Founder of Scientific Psychology
Characteristic of Superior Fitness
Modern “Mental” Testing
Binet-Simon Scale
Stanford-Binet Intelligence Scale
Army Alpha and Army Beta
The History of Aptitude Testing
As new testing and statistical method were developed
along with new trait theories the field of aptitude
testing evolved.
Charles Spearman
T.L. Kelly
Edward L. Thorndike
Aptitude tests measure specific traits an individual
possesses. These traits are then matched with task
needed to perform at job or in an educational
training program.
The History of Achievement Testing
The first standardized achievement was published in
1923. Achievement tests are designed to measure
the breath and depth of knowledge one possesses
and are often used to predict future success.
Edward L. Thorndike
T.L. Kelly
Lewis Terman
The History of Personality Testing
Personality tests aim at measuring qualities of one’s
personality.
During World War I the Woodworth Personal Data
Sheet was developed to screen out seriously
impaired individuals from serving in the military.
This was the first standardized personality inventory
Rorschach (1921)
Thematic Apperception Test (Murray, 1931).
Minnesota Multiphasic Personality Inventory
(MMPI-II)
What am I doing in a statistics class?
Statistics is considered a science. It involves
organizing and analyzing information for the
purpose that the information will be more easily
understood.
Descriptive Statistics are used to organize, summarize,
and describe characteristics of data collected
Inferential Statistics are used to make inferences from a
smaller group of data to a larger one
What is a Statistic?
A statistic is nothing more than a number which some
meaning has been attached.
Example: During the five years that I have taught this
course 106 students have enrolled in it and only two
students that did not pass it on thier first try. This
means that the pass rate for Coun 550 has a 98.2%
pass rate.
Descriptive Test Statistics
Selecting, administering, scoring, and
interpreting test results, means you need to
understand and use statistics
Mean
Median
Mode
Central Tendency
Standard Deviation
Correlation
Standard Scores
Four Scales of Measure
Nominal Scale - Names given to represent
categories that show how members of a group
differ.
Example: Marital status (Never Married, Single,
Married, Divorced)
Ordinal Scale - The rank ordering of individuals
based on some characteristic that has received
a numerical value.
Example: 1st place, 2nd place, 3rd place etc.
Four Scales of Measure
Interval Scale - A scale that differentiates among
levels of attributes and has equal distances
between those levels. Think in terms of equal
intervals between levels of attributes
Example: IQ scores (IQ of 115)
Ratio Scale - A scale that starts at zero and is
continuous.
Example: Time (2.4 seconds)
Distributions of Test Scores
There are two important aspects of test scores with
which we need to concern ourselves.
First, concern is the performance of a whole group
of individuals who have taken a test
Second is how the individual scores relative to the
rest of the group.
To get a handle on both of these aspects, we look at
the whole list of test scores. In statistical
terminology this is referred to as a distribution of
test scores.
What does a Distribution of Scores
Looks Like?
Frequency Distributions - A frequency distribution of data is
helpful in that it allows you to get a representative picture of
what data looks like in terms of how frequently different
scores happen to occur within a group that has been tested.
Graphic Presentations – There are several way that one can
represent the frequency of scores that occur in a distribution
they are as follows:
- Histogram or Bar Graph
- Frequency Polygon or Line Graph
- Smoothed Curve
The Shape of Distribution
The shape of a distribution can tell us a great deal
about the overall group performance.
Symmetry - Symmetric distributions are when a one half
of a graphic representation of that distribution is the
mirror image of the other half.
Skew - A skewed distribution is when there is a greater
number of cases in on one side of the distribution than
the other side. The direction of the tail indicates whether
it is a (-) or (+) distribution. If the tail side of the extends
toward the lower end of scores it is a referred to as a
negatively (-) skewed distribution and visa versa.
Measures of Central Tendency
The Mean - Computing and Understanding the
Average
Average = The Sum of All Values in a
Distribution Divided by the Number of Values
in that Distribution
The next slide examines what the definition
above actually means.
The Mean - A Measure of Central
Tendency
+
3
5
7
2
8
25
Sum
Distribution
Divided by
5
N
=
5
Mean
Other Members of Central Tendency
Mode - The most frequently occurring value in a
distribution
3 3 4 5 6 6 8 8 8 10 15 18 25
Mode
Other Measures of Central Tendency
Median - The physical center of a distribution.
50% of the cases in the distribution are found to
be above the median point and 50% of the
cases are found be below the median point.
1 2 3 4 5 6 7 8 9 10
Median = 5.5
Why Do We Need to Concern
Ourselves with Central Tendency?
Answer: Computing measures of central
tendency is the first step toward understanding
variability.
Why Do We Need to Concern
Ourselves with Variability?
Answer: Understanding how a distribution of
test scores vary helps to describe an aspect the
group we are encountering.
An example may help to make this point clear.
Variability and Depression
Every client that seeks services at a group
practice, as part of the intake, is administered
an instrument that measures depression.
Ten items with a 5 point Likert scale (0 - 4)
Severity of depression is marked by quartiles
Scores - 0-10 none to mild depression
Scores - 11-20 mild to moderate
Scores - 21-30 moderate to sever
Scores - 31-40 sever
Variability and Depression Continued
So lets look at the 13 new clients’ scores that
came to the practice in the month of
December.
There are several things we can do to describe
this group of individuals.
The first thing one could do, given this
distribution is calculate the mean, median, and
mode (for a picture of central tendency).
20 32 5 11 26 33 16 14 20 37 24 18 7
Variability and Depression Continued
First it is helpful to arrange the scores.
We can then easily find the following
descriptive statistics
Mean = 20.23
Median = 20
Mode = 20
5 7 11 14 16 18 20 20 24 26 32 33 37
Variability and Depression Continued
After establishing a picture of central tendency, you
might begin to wonder about the idea of individual
difference - or - how the scores within the
distribution vary.
This is important because obviously not all of the
clients that completed the depression scale achieved
the same score and knowing how the scores vary
may tell us something more about this group of
depressed clients.
Variability and Depression Continued
One measure of variability is the range of distribution. It
is calculated by subtracting the lowest score from the
highest score (sometimes 1 is added so as to be
inclusive)
The Range tells us how much spread there is between
the highest and lowest score in the distribution.
37 - 5 = 32 (Exclusive Range) + 1 = 33 (The Inclusive
Range)
5 7 11 14 16 18 20 20 24 26 32 33 37
Variability and Depression Continued
Although the inclusive range tells us how much
spread there is between the highest and lowest
score in the distribution, it does not tell us
about any of the other scores in the
distribution.
To get an idea about how all the scores in the
distribution differ from one another we need
and additional statistic known as the Standard
Deviation.
Variability and Depression Continued
The standard deviation gives us a more detailed
account of the variability within a distribution
than the range does.
The standard deviation is the average distance
that scores in a distribution deviate from the
mean.
Although you will never need to calculate a
standard deviation by hand the following slide
shows the process.
Variability and Depression Standard Deviation
5 - 20.23 = -15.23
7 - 20.23 = -13.23
11 - 20.23 = -9.23
14 - 20.23 = -6.23
16 - 20.23 = -4.23
18 - 20.23 = -2.23
20 - 20.23 = -0.23
20 - 20.23 = -0.23
24 - 20.23 = 3.77
26 - 20.23 = 6.77
32 - 20.23 = 11.77
33 - 20.23 = 12.77
37 - 20.23 = 16.77
239.95
175.03
85.19
38.81
17.90
4.97
0.05
0.05
14.21
45.83
138.53
163.07
281.23
The sum of the squared
deviations = 1204.82
The sum of the squared
deviations
divided by n-1 = 100.4
Take the square root
s = 10.02
Central Tendency, Variability and
the Normal Curve
34%
13.5%
34%
13.5%
2.25%
2.25%
__|_______|_______|_______|_______|_______|______|__
-3
-2
-1
-+1
+2
+3
X
68.0%
95.0%
99.5%
Variability and Depression Standard Deviation - So What!
The standard deviation allows us to compare
scores from different distributions even when
the means and deviations are different.
Why would we want to do that?
Because it allows us to do a whole host of
cross-test and or sub-test comparisons that
otherwise would not be possible. We will
look at this more in depth as the semester
continues.
To be continued!!