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 ) 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 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  verage = 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 Divided by 5 = 5 Distribution Sum N Mean

Other Members of Central Tendency Mode - The most frequently occurring value in a distribution 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 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 none to mild depression Scores mild to moderate Scores moderate to sever Scores 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)

Variability and Depression - Continued First it is helpful to arrange the scores. We can then easily find the following descriptive statistics Mean = Median = 20 Mode =

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 = 32 (Exclusive Range) + 1 = 33 (The Inclusive Range)

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 = = = = = = = = = = = = The sum of the squared deviations = The sum of the squared deviations divided by n-1 = Take the square root s = 10.02

Central Tendency, Variability and the Normal Curve 34% 34% 13.5% 13.5% 2.25% 2.25% __|_______|_______|_______|_______|_______|______|__ 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!!