Professor DR. Paul Gerson RIP

09/01/2009

I was shocked, shaken up and sadden when I got an email from a former classmate of mine today informing me of the sudden passing of our professional seminar class professor early last month.

The pro-sem class was a year long, 3 semester class that would guide the student through their thesis report. There was other assignments assigned through out the year, and I will post the other big assignment below. However, he was the hardest professor I ever had. He expected perfection from everyone on every assignment. I got so discourage of his lack of patience and teaching ability when it comes to teaching to someone with a visual impairment. However, after putting the thesis report down for a year, last December, I called him up, told him that I am going to have an English teacher (my high school english teacher) look it over for me and I will send it to him after we have corrected it. I sent it to my English teacher, she sat down with me, pointing out and explaining to me the minor errors she has found. I sent it to Professor Gerson and he still found 5 errors he wanted me to correct. One place I had a double space where there was suppose to have a single space, I put a right perentasy after a period where it should be before, when quoting, after I wrote out all the authors, I did a second time where I am suppose to only write the first name then "et al". Which I have done on other references and there was two more minor errors I had to correct. I felt that a bear size weight was lifted off my shoulders and mind when he called me on my cell phone when I was in NY and he had told me that the report was accepted, than congradulated me..
I thanked him and called my mom and a coworker who also went to school with me and I am sure other people too. That was the last I talked to him.

He mayhave been the hardest professor I have ever had.
However, he was just doing his job in preparing me for if I need to publish an article in an academic journal, this would be the standard they would hold me too and anything less than perfection, they would just toss my article acide.

Thank you for all the extra time you put in to assist me in completing my thesis paper resulting in my graduation with a certificate of advance graduate study's.

Jonathan
Spring 06 Prosem II


1. What are the 4 most important considerations in an Experimental Design? How is this different from Quasi-Experimental Design?

A. An Experimental Design occurs when data is collected and the independent variable is manipulated while the dependant variable is not manipulated.
B. Sample:
1. Random Sample: This is a sample that is taken in such a way that nothing but chance determines which members of the population are selected for the sample. Ideally, any individual member of the population has the same chance as being selected as any other. This type of sample avoids being biased because a biased sample is one that is taken in such a way that some members of the population have a significantly greater chance of being selected for the sample than other members.
2. Stratified Sample: This is a sample that is taken by using the following steps: 1) The relevant strata (population subgroups) are identified, 2) The number of members in each stratum is determined and 3) A random sample is taken from each stratum in exact proportion to its size. This method is obviously most useful when dealing with stratified populations. The general stratum that are controlled for experiments include: gender, age, race, ethnicity and economic class.
C. The experiment must be Replicable. Operational terms must be defined in the methodology section of the report. If the experiment can not be duplicated than the credibility of the experiment can be questioned for its validity.
D. Confounding variables: Two or more variables (explanatory or lurking variables) that are confounded when their effects on a response variable cannot be distinguished from each other.

Quasi-Experimental Design: When an experiment is conducted using pre/posted data. The data is not manipulated but rather just compared. The samples may or may not be represented of the population.

2. Explain the difference between Nominal, Ordinal, Interval, and Ratio Scales of Measurement.

A. Nominal Scale of Measurement: labels observations so that they fall into different categories. Samples are quantitative but not qualitative. Male / female are labels but can not be qualitatively compared.
B. Ordinal scale of measurement: Observations that are ranked in terms of magnitude. Samples are quantitative but not qualitative. First place and second place are ranks. The ranks do not express the differences in the amount between first and second place. Just that one comes before the other.
C. Interval: A scale which represents quantity and has equal units but for which zero represents simply an additional point of measurement. Sea level is one example where 0 does not represent the lowest number.
D. Ratio scale of measurement: A scale that represents quantity and equality as well as having an absolute zero (no numbers exist below the zero). When measuring height, 0 is the lowest number.

3. What is normal distribution? Normal distribution, also called Gaussian distribution, is a theoretical distribution of data where the mean, median and mode are equal. The majority of the data is closest to the mean, the distribution weakens the further away from the mean the data is. This is called a bell shape curve. It is never 100 percent, leaving room for error.

4. Define the 3 measures of central tendency.
Measures of central tendency are ways to represent a list of numbers using one value without losing the information. Mean, Median and Mode are the most frequently used.
A. Mean: The average of all the data in a list.
A. B. Median: The value that separates the list of data. There are equal numbers above this point as there are below this point.
C. Mode: The value that most frequently appears in the list of data. If all data is of different value than there is no mode.

5. Define 3 measures of variability.
When assessing the variability of a data set, there are two key components:
5. 1. How spread out are the data values near the center?
6. 2. How spread out are the tails?
A. The range is the difference between the largest score and the smallest score.
A measure of variability considers every score in its calculation.
There are two measures of variability that do this:
B. Variance: The average of the squared deviation scores.
C. the standard deviation: Square the deviation scores. Average the squared deviations then find the square root of the average squared deviations.

6. Distinguish between: descriptive and inferential statistics. Please include an explanation of the concepts, Population, Parameter, Sample and Statistic.
A. Descriptive Statistic: Describes the data of the population. Descriptive statistic is used to compare characteristics of two or more population with absolute value.
B. Inferential Statistics: Describes a sample of a population. Inferential statistics is used to predict a pattern or to describe a population by using a sample of the population.
C. Population: The entire collection of data. Population is used to explain exact data.
D. Sample: is a percent of a population. Is used to predict behaviors and observations. It is also used to generalize the population.
E. Parameter: a numerical value that describes one of the characteristics of a population.
F. Statistics: A division of math dealing with the collection, interpretation and analysis of numbers.

7. What are the issues regarding validity and reliability as they relate to evaluation research?
Validity and reliability are two general criteria for evaluating the quality of any measurement procedure.
Validity: is the degree to which the measurement process measures the variable it claims to measure.


A. face validity
• The simplest and least scientific definition of validity.
• It is demonstrated when a measure superficially appears to measure what it claims to measure.
• Based on subjective judgment and difficult to quantify.
• E.G. intelligence and reasoning questions on the IQ test
• Problem - participants can use the face validity to change their answers

B. concurrent validity (criterion validity)
• is demonstrated when scores obtained from a new measure are directly related to scores obtained from a more established measure of the same variable
• E.G. new IQ test correlates with an older IQ test.

C. predictive validity
• When scores obtained from a measure accurately predict behavior according to a theory.
• E.G. high scores on need for achievement test predict competitive behavior in children (ring toss game)

D. constructs validity
• is demonstrated when scores obtained from a measure are directly related to the variable itself
• Reflects how close the measure relates to the construct (height and weight example)
• In one sense, construct validity is achieved by repeatedly demonstrating every other type of validity.

E. convergent validity
• is demonstrated by a strong relationship between the scores obtained from two different methods of measuring the same construct
• E.G. an experimenter observing aggressive behavior in children correlated with teacher’s ratings of their behavior

F. divergent validity
• is demonstrated by using two different methods to measure two different constructs
• Convergent validity must be shown for each of the two constructs and little or no relationship exists between the scores obtained from the two different
Constructs when they are measured by the same method
• E.G. aggressive behavior and general activity level in children

Reliability: A measure is considered reliable if it would give us the same result consistently, (assuming that what we are measuring isn't changing.).

A. Inter-Rater or Inter-Observer Reliability
A. • When researchers use more than one observer to rate the same people, events, or places.
• Used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon.
B. B. Test-Retest Reliability
B. • Researchers measure a phenomenon that does not change between two points separated by an interval of time, the degree to which the two measurements are related to each other.
•Used to assess the consistency of a measure from one time to another.
C. C. Parallel-Forms Reliability
D. • Reverse order of response choices and then re-administer questionnaire
E. • Split-halves reliability is when two forms of questions are randomly assigned to half the sample each
•Used to assess the consistency of the results of two tests constructed in the same way from the same content domain.
D. D. Internal Consistency Reliability
• Average inter-item correlation
• Item-total correlation
• Split-half reliability
• Cronbach’s alpha
•Used to assess the consistency of results across items within a test.

8. What does it mean to say that a statistic is “significant”? Please illustrate this with a T-Test explanation.

A statistic is significant when the result of a test is not by chance.

A. Subtract the two means. (The T-value will be positive if the first mean is larger than the second and negative if it is smaller.)
B. takes the variance for each group and divides it by the number of people in that group. C. Add these two values and then take their square root.
D. To test the significance, you need to set a risk level (called the alpha level). The "rule of thumb" is to set the alpha level at .05. This means that five times out of a hundred you would find a statistically significant difference between the means even if there was none, by chance). You also need to determine the degrees of freedom for the test.
In the T-Test, the degrees of freedom are the sum of the persons in both groups minus 2. Given the alpha level, the degrees of freedom, and the T-value, you can look the
t-value up in a standard table of significance to determine whether the T-value is large enough to be significant. (see URL below for the table) If it is, you can conclude that the difference between the means for the two groups is different.
http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm

9. Please explain the Pearson r and the difference between the co-efficient of determination and co-efficient of non-determination.

The correlation between two variables reflects the degree to which the variables are related. The most common measure of correlation is the Pearson Product
Moment Correlation (called Pearson's correlation for short). When measured in a
Population the Pearson Product Moment correlation is designated by the Greek letter rho. When computed in a sample, it is designated by the letter "r" and is sometimes
called "Pearson's r." Pearson's correlation reflects the degree of
linear relationship between two variables. It ranges from +1 to -1. An r of -1.00 means there is a perfect inverse relationship between the two variables. An r of +1.00 means there is a perfect direct relationship. A value of 1.00 or -1.00 means that if you know the value of variable 1, you can exactly know the value of variable 2. An r of 0.00 indicates the complete absence of a relationship. The closer an r is to -1.00 or +1.00, the stronger the relationship. For example, (STUDY THAT SHOWS joggers LIVE LONGER) this study does not mean that if you begin jogging three times a week, you will live longer. Instead, it probably means that people that jog weekly are probably more health-conscious, and are therefore leading healthier lifestyles that would cause them to live longer.

The coefficient of determination (rxy2) indicates the proportion of variance in one variable that can be predicted or explained by the other variable.

The coefficient of non-determination (1 - rXY2) indicates the proportion of variance in one variable that can not be predicted or explained by the other variable.

In simple terms, if there is a direct cause/effect relationship it would be The coefficient of determination. If there is not a direct cause/effect relationship it would be The coefficient of non-determination.

10. What are a Hypothesis, a null hypothesis, and a one-tailed vs. a two-tailed test?
A. Hypothesis: a theoretical prediction of the relationship between the independant variable and dependant variable.
B. null hypothesis: a hypothesis where the independent variable had not change.
C. One tail test: A hypothesis that predicts a single direction. This type has a 0.05 percent chance of prediction being correct.
D. Two tail test: a hypothesis that predicts two directions. Each direction having a 0.025 percent chance of prediction being correct.