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Running Head: DATA ANALYSIS PROJECT 1
DATA ANALYSIS PROJECT 2
Data Analysis Project: Analysis of Social Behavior and
Education
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Abstract
This research examined social life with respect to how society
is optimistic; sources of stress to the population under study as
well analysis of social behavior across various level of
education completed by the respondents. Participants for this
study (n = 439) were randomly selected respondents of the legal
age. The respondents represented two major age groups, age
group three that consists of 18-29, 30-34 and 45 plus while the
other age group, five, were categorized into the cohorts; 18-
24,25-32, 33-40,45-49 and 50 plus. This data was analyzed to
determine the nature social behaviors and habits across all the
age groups and their levels of education. Correlation and

regression analyses were also utilized to determine whether or
not there was an association between the total life optimism and
self esteem. Findings showed that self esteem is a factor that
explains all the social life habits. Results are discussed in light
of previous research, including limitations of the study.
Implications to put into practice and future recommendations to
the findings of this research are presented.
Introduction
Social behavior has interesting and a vast forms and its study is
never limited to some aspects yet a study could still concern a
section of society and analyze among many, the common
behavior and social life habit within a society. Social behavior
could be determined in terms of sex, level of education or even
age. According to South African Supplement to Social
Psychology3e (2009), in a wider scope, social behavior stems of
instincts. Social behavior is also attaché to personal traits.
Some of these personal traits are prejudice and authoritarianism.
In this respect the habits shown at a time and within a people of
a specific group varies widely. The sources of communal stress
and reasons for satisfaction in life also vary from group to
group. Just as widely segregated as the social life is, the social
behavior is also as distinct according to these social lines.
In the study and analysis of social life, there are however some
common habits and social perspectives that cuts across the
social lives. Habits such as cigarette smoking or taking alcohol
are such habits that cut across social boundaries. There is often
a reason towards a particular social behavior. In some instances
class and education level contributes a lot while at other time
peer pressure and influence contribute towards a particular
social orientation. Moreover, stress satiations may lead to a
population assuming some habits for example the smoking as a
way to deal with stress or even using hard drugs.
Problem Statement
The phrase social behavior has been used in many contexts.
Social behavior is defi8nable with respect to a particular society

without which it would pose more problems than tend to find
solutions. It is a combination of social values with particular
attention to social uniformities of the society (Harsanyi,
2012).Social behavior of a people is an area that poses a
problem in its analysis since it is wide and extensive.
Parameters such as optimism in life and stress people face in
life are just a few ways to study social behavior. The human
social habit is interrelated and therefore there is need to find
more common and consistent social habit among people of a
given population to help predict how they will act on different
stimuli. Such a common social aspect is the self esteem of
various people. Depending on how an individual feels about
themselves, they are bound to respond to life and feel about life
in a particular way. Stress is also a social perspective that is
mostly related to self esteem. If the social problems that regard
self esteem are solved and each member of the society feels
better about them, stress and other social dissatisfaction could
be overcome and people could be more positive about life.
Literature Review
Explaining social behavior by analyzing some individual
feelings and perspectives has had a lot of study around it but
some of this mostly entailed analysis of Psychiatric social
behavior. Some studies on this topic compare high and low
frequently dating among college students. In this study there
emerged two groups those that are driven by anxiety. Aside
from anxiety this group is driven by fear, distress or avoidance
of negative evaluation in the eyes of others. The other study
was based on peer ratings to the behavior of dating in a frequent
manner and social conflicts as well as behaviors. The study
results showed that those who date frequently had shorter
latencies. Their utterances were relatively longer but the study
found no significance in their verbal supports such as gestures
and other non verbal cues (Singleton, 2013).
Extensive research has also been done that relates a number of
topics to job stress and offer solutions on how to deal with

stress at work place. A common source of theory for
organizational psychology has over time been Social
Psychology and it is a key source of work stress studies on the
sources of stress in the society aside from work (Terry, 2010, p.
213). Also, past studies have used negative affectivity to study
occupational stress. This is related to the mood of an individual
and the study found significant results to how it relates to work
stress (Terry, 2010, p. 215).
Purpose of the Study
The intent of this study is to analyze a section of social life of
randomly selected respondents. It explains the common
emotions individual always have and to what degree they
experience such emotions. The paper intents to explain the
trends of stress situations that might be common and those that
are rare among the respondents. Further the study intents to find
out if these emotional states are tied to other social perspectives
such as education level, marital status. The paper by extension
determines the proportion of the people that smoke cigarettes as
this is tied to results of emotional outcomes of person’s
feelings.
Deductions are made to show any relationship among the social
life choices and emotional feelings the respondents have. If
there is any kind of emotion that is consistent with a people of a
particular social class, very educated, moderately educated or
not educated at all. Cigarette smoking is also associated with
class and the study will determine which class of respondents
that has high prevalence of smoking. Also the habit of smoking
is related to the kind of emotions people always find themselves
in. In this perspective the study determines which emotion has
the highest number of cigarette smokers.
Research Questions
The following are the research question that guides the study:
· Is there any common social behavior within a given age group
or within a particular gender? Comment by Nouf Wajidi: can
you be more spesifice Comment by Nouf Wajidi: can you
relate this to the total life optimism and self esteem

Comment by Nouf Wajidi: and relate the result to it.
· Is there any relationship among various forms of social
perspectives and how best can one behavior predict the presence
of another among the respondents? Comment by Nouf Wajidi:
can you make it is simple
· Is there a common and most popular source of stress that cuts
across the respondents of all social walks or it is confined to a
particular age group or respondents with the same level of
education ?
Methods
Participants: 439 respondents were randomly selected to
participate in this survey. They were interviewed on a
completely voluntary basis and all the respondents were of a
legal age. Respondents were not coerced to state their names
and those who provided names on a voluntary basis due to
privacy reasons. The research based solely on administered
questionnaires to which every respondent recorded their
feelings at will and as best as they understood the questions.
However for those who could not read for any special reasons,
the questions were read to them as they appear on the
questionnaire paper.
The respondents of this study were men and women of all legal
ages. There were fewer males than females who took part in the
study. 42.1% of the respondents were males while 57.9% were
females. These proportions are represented in the chart below;
Figure1: Pie chart showing the proportion of female to male
respondents
The study only targeted the respondents of the legal age with
the average age of all the respondents being 37, a youngest
person was 18 and the oldest person interviewed was 82 years
old. For one to have participated in the interview, age was
mandatory to determine if they have attained a legal age.
Instruments: Questionnaire booklet was used to carryout the
interviews. Respondents used a pen or a pencil and any other
materials that could be used to record responses to write down

their responses and feeling regarding every question.
Validity and reliability of the questionnaires: The
questionnaires administered to the respondents passed criteria
of face validity since they appeared at face value to measure
what they are designed for, social behavior among the
respondents. In this case a measure of face validity is that an
instrument appears to measure what it is supposed to measure
(Sharon & William, 2008).
. On the other hand, to satisfy content validity, it has to be
comparable to known perspectives. For example the question on
the sources of stress has well known variables and so is other
questions used in this survey. The instrument here hence fulfills
content validity. This extends to satisfy criterion validity and
even construct validity. On the question of reliability, test retest
reliability could not be achieved because the test on each
individual was done only once, however consistency was
evident in the results from the respondents. This is because for
any measure to satisfy reliability it should give a consistent
outcome and if repeated it should give the same outcome. A
questionnaire must therefore satisfy conditions for internal
consistency reliability such as there should be as many multi
object scales as possible and they are supposed to be organized
in such away that they act together (Zoltan, & Tatsuya, 2009).
Data Analysis
The initial step in the analysis was to generate frequencies for
all qualitative variables to present an accurate reasoning of the
participants on various issues of the study. The qualitative data
involved whether or not the respondent smoked, the sex of
respondents, the respondents optimism about life and the
respondents marital status. This helps to inform the exact
proportions that were associated with a particular social
behavior and their family background or societal orientation.
This information was used to draw conclusions about the
consistency of the social behaviors that respondents exhibited.
Next, descriptive statistics were run on each of the quantitative
variables to provide more information about the individual

portions of the quantitative variables. Among the quantitative
variables are the age of the respondents, total number of
cigarettes smoked per week and total social life perspectives
mentioned. Finally, a linear regression analysis was conducted
to evaluate how well levels of education explain use of
cigarettes or stress situations explain use of cigarettes. The
results from these analyses follow along with a discussion of
the findings.
Results Comment by Nouf Wajidi: ( can you relate the
question of the optimism to the result)
The participant’s information was compiled on seven qualitative
variables; sex, marital status, whether or not the respondents
have children, highest level of education, whether or not the
respondents smoked, life optimism and feelings about life.
Further life optimism was categorized into six categories on
which respondents were to strongly agree or strongly disagree
as the two extremes or choose any point on a scale of one
through to five accordingly. Also the feelings about life were
also listed for the respondents to choose as they see fit
according to their life experience and emotions.
Of 439 respondents the proportion that participated reveals
more female than men. The female contributed 57.9% while the
males contributed 42.1%. this is generated from all the sampled
data points indicating that no respondent ignored their gender.
Table1: Frequency of Participants by Sex Comment by
Nouf Wajidi: make with frequency ( mean, mod median, st
deviation) Comment by Nouf Wajidi: and explain it Comment by
Nouf Wajidi: apply it for all frequencies
sex
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
MALES

185
42.1
42.1
42.1
FEMALES
254
57.9
57.9
100.0
Total
439
100.0
100.0
There is a larger number of females that smokes, 52 females, in
relation to the number of men,33 males.
Table2: The Population that smokes by gender.
sex * smoker Crosstabulation
Count
smoker
Total
YES
NO
sex
MALES
33
151
184
FEMALES

52
200
252
Total
85
351
436
Of all the respondents, the highest proportion of the population
was in first time marriages, 43.1%. This was followed by the
single respondents, 23.9%. The fewest sampled respondents
were the widowed at 1.6% followed by separated at 2.3%. Those
respondents in a steady relationship and those that live with
partner were at 8.4 percent each. The rest were the remarried
and divorced at 6.8% and 5.5% respectively.
Table3: Frequency of Participants by marital status
There are 42.2% respondents who were interviewed with
children while 57.8% did not have children. However there was
only one person that did not indicate whether or not they have
children.
Table4: Frequency of Participants with Children
child
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
YES
185
42.1
42.2
42.2

NO
253
57.6
57.8
100.0
Total
438
99.8
100.0
Missing
System
1
.2
Total
439
100.0
Only 0.5% of the respondents had primary school as their
highest level of education while the highest education level
attained by the respondents was completed undergraduate at
28%. It is also worth noting that a good percentage also
completed post graduate, 12.8%. 12.1% of this population
completed some secondary school while 19.4% completed High
school. These figures shows that the population interviewed
were a learned population. The data is represented in a
frequency table as shown below.
Table5: Frequency of Participants at various Levels of
Education
highest educ completed

Frequency
Percent
Valid Percent
Cumulative Percent
Valid
PRIMARY
2
.5
.5
.5
SOME SECONDARY
53
12.1
12.1
12.5
COMPLETED HIGHSCHOOL
85
19.4
19.4
31.9
SOME ADDITIONAL TRAINING
120
27.3
27.3
59.2
COMPLETED UNDERGRADUATE
123
28.0
28.0
87.2
POSTGRADUATE COMPLETED

56
12.8
12.8
100.0
Total
439
100.0
100.0
With respect to smoking, most of the respondents were not
smokers, 80%. Only 19.4% reported that they were smokers.
There were however three respondents that did not indicate
whether they smoke or not, 0.7%
Table6: Frequency of Participants that smoke
smoker
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
YES
85
19.4
19.5
19.5
NO
351
80.0
80.5
100.0
Total

436
99.3
100.0
Missing
System
3
.7
Total
439
100.0
The Pearson correlation between the age of the respondents and
their habit of smoking is positive 0.103. This indicates that
smokers form that habit as they accumulate more years from the
time they become of legal age. It is however noticeable that this
is a weak correlation hence the conclusion that age does not
explain why a respondent smokes.
Table7: Pearson Correlation between age and smoking habit
Correlations
age
smoker
age
Pearson Correlation
1
.103*
Sig. (2-tailed)
.031

N
439
436
smoker
Pearson Correlation
.103*
1
Sig. (2-tailed)
.031
N
436
436
*. Correlation is significant at the 0.05 level (2-tailed).
It was found out that those who had stress about work were
the population that smokes the most. However among those who
experienced stress due to work, the majority still do not smoke.
Stress due to money and finances also represented a greater
population of smokers but still the trend remains the same. It is
therefore accurate to conclude that the habit of smoking is
slightly dependent on the kind of stress situations respondents
were in.
Table8:Ccross tabulation for source of stress and smoking habit
source of stress * smoker Crosstabulation
Count
smoker
Total
YES
NO
source of stress

WORK
45
178
223
SPOUSE OR PARTNER
1
11
12
RELATIONSHIPS
1
11
12
CHILDREN
3
21
24
FAMILY
1
26
27
HEALTH/ILLNESS
4
16
20
LIFE IN GENERAL
5
27
32
MONEY/FINANCES

20
34
54
TIME (lack of time, too much to do)
2
14
16
Total
82
338
420
Considering the level of education and habit of smoking, the
level with the highest smokers is those who completed some
additional training with the majority within this group also not
smokers. Those who completed only primary school did not
have any smokers at all. The second category with the highest
number smoking were those completed undergraduate followed
by those who completed post graduate. Since these levels shows
the lifestyle of individuals it can be concluded that smoking
habit is associated with lifestyle of the respondents but not lack
of knowledge about the effects of tobacco.
Table9: Ccross tabulation for highest Education Completed and
smoking habit
highest educ completed * smoker Crosstabulation
Count
smoker
Total
YES
NO

PRIMARY
0
2
2
SOME SECONDARY
7
45
52
COMPLETED HIGHSCHOOL
12
73
85
SOME ADDITIONAL TRAINING
33
85
118
COMPLETED UNDERGRADUATE
22
101
123
POSTGRADUATE COMPLETED
11
45
56
Total
85
351
436
A negative correlation of -0.011 between the rank given to
education level and the number of cigarettes each respondents

takes in a week. This shows that as the number of cigarettes
taken per week increases, the education rank lowers. This is
true because it reveals the distribution of most smokers that are
at the level that completed some training downwards. this shows
that the number of cigarettes taken by the least learned person
per week is very high. This is perhaps due to the unawareness of
the health risks or perhaps they experience the greatest
emotional distress.
Table10:Correlation between the level of education completed
and the number of Cigarettes smoked per Week.
Correlations
smokenum
Pearson Correlation
1
-.011
Sig. (2-tailed)
.833
N
439
360
smokenum
Pearson Correlation
-.011
1
Sig. (2-tailed)
.833
N

360
360
Regression analysis of the social life perspective generates an R
squared value of 0.501 since the adjusted R square value is
0.501, which is value higher than 0.05 which is the significance
level, the model expressly describes the total self esteem is
significantly explained with the rest of the social aspects. The R
square value, 0.510, if it is 100% all the variability of the total
esteem has its mean explained by the model, and when it is 0%
none of them is explained by the model. On the statistical
significance, no variables are statistically significant from the
coefficients table since none of the values in the variable
significance column is 0.05. Since the closest significance value
is .053, but close does not count in significance test.
Table11-13: Bivariate regression Summary Statistics table for
the relationship between self esteem and the rest of the social
perspective.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.714a
.510
.501
3.775
a. Predictors: (Constant), Total PCOISS, Total social
desirability, Total life satisfaction, Total negative affect, Total
positive affect, Total Optimism, Total Mastery, Total perceived
stress
ANOVAa
Model

Sum of Squares
df
Mean Square
F
Sig.
1
Regression
6116.579
8
764.572
53.660
.000b
Residual
5870.314
412
14.248
Total
11986.893
420
a. Dependent Variable: Total Self esteem
b. Predictors: (Constant), Total PCOISS, Total social
desirability, Total life satisfaction, Total negative affect, Total
positive affect, Total Optimism, Total Mastery, Total perceived
stress
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients

t
Sig.
B
Std. Error
Beta
1
(Constant)
20.494
2.698
7.596
.000
Total Optimism
.272
.056
.224
4.870
.000
Total Mastery
.154
.066
.113
2.348
.019
Total positive affect
.061
.031
.081
1.942

.053
Total negative affect
-.090
.036
-.120
-2.513
.012
Total life satisfaction
.110
.034
.139
3.215
.001
Total perceived stress
-.116
.052
-.127
-2.245
.025
Total social desirability
-.048
.097
-.018
-.499
.618
Total PCOISS
.071
.021
.159
3.309
.001

a. Dependent Variable: Total Self esteem
The scatter plot below is an example of a distribution of
the various social life views. From the plot, we realize no
outliers hence the data has no abnormities. Also, the variables
are well distributed. This shows goodness of fit hence the
conclusion that each of the social life aspects is interrelated
with one another and one leads to the other in most cases.
Figure2: Scatter plot for the total positive effects and total
negative effect. Comment by Nouf Wajidi: we need also
histogram and bie chart and box plot
Finally the most significant cause for stress among the
respondent is work. This is because those who have work are
dissatisfied or the once that do not have are really straining to
have one.
Figure3: Proportions of the Sources of Stress among the
respondents.
The education level that is most popular among the respondents
in the age group 18-29 was undergraduate education. On the
other hand the number of respondents that only completed
primary school is least across all the age groups. The age group
30-44 has its majority members completed some additional
training while those in the age group above 50 completed some
secondary school.
Table14-15: Cross Tabulation for the Highest Education Level
Completed with Each Age Group.
Discussion and Conclusions
Most of the respondents completed undergraduate and still many
completed post graduate. Therefore analysis shows that the
majority of the population is learned. Some of the respondents
completed high school while only two reported to have
completed primary. There is a habit of smoking as a common

behavior among the elite, which shows a consistently low
amount of cigarette consumption per week greatly low.
However, the least learned have the least number with smoking
habit but they are the ones that smoke relatively the highest
amount of cigarettes per week. Smoking is another common
social life found to be consistent with people at a particular
social rank. Another social category are the undergoing some
kind of stress. The first research question that guided this study
seeks to find out a common habit consistent with a particular
sex. In this perspective, the gender that recoded the highest
number of smokers was the females. It is clear that smoking
comes up as a social behavior common among the stressed
population that derives their stress from work or lack of it.
Other social perspectives like the optimism in life are evenly
interdependent with each other. Among the respondents, they
derive strong will for hope in life depending on the social
cycles they are. Most of the people who completed post
graduate recorded three to five levels of the scale as their
optimism in their social life. The highest percentage of those
who completed undergraduates is stressed about work and they
are the most affected by lack of time as the main source of
stress. Those who completed primary only have two sources of
stress, health and children. The population that completed post
graduate are also the one affected more with work stress after
the undergraduate guys but are least stressed by life in general
after those that only finished primary. Therefore level of
education affects social life orientation as revealed from the
analysis.
The third question that guided this study concerns the general
source of stress among the respondents. The most popular
source of stress reported among the respondents was work. The
rest of the stress sources varied from one group to the next
interviewed. The population of interest is one that consists of
educated people and most of the economies have high rate of
unemployment or there are employees dissatisfied with their
current job. Still some of the employed might have felt unhappy

at the job place which resulted to stress.
Implications of the Study
The findings of this study beg for discussions on why work
among the employed and the unemployed is mentioned as the
main source of stress for a people. Therefore the following
recommendations are worth noting. The data clearly shows that
three quarters of the respondents that had completed
undergraduate are citing work as a common source of stress.
This implies that the government needs to come up with a
policy for creation of more jobs both in the public and private
sector. This is achievable by ensuring that the cost of
establishing a business entity lower and also entry into the
business industry is relatively free. Entry into business by new
and small firms should be enhanced by reduced or by offering
government grunts and subsidies. This will ensure more
companies and healthy business environment that encourages
emergence of new firms and at the same time more job
opportunities might emanate from this. Also the college and
middle level curriculums should not rely on theory but there is
need to train self reliant student, learning should focus on the
real job market so that the graduating students could open their
own firms. This aspect calls for government empowerment and
credit institutions to come up with collateral policies those
suites the graduating population.
The study also reveals that most of the people who used the
most number of cigarettes per week were the least learned. This
requires that the public be sensitized and public awareness
campaigns should be done to help inform the public that is not
yet informed on the dangers of tobacco. It is also found from
the study that the greatest number with smoking habit is
actually women. This requires more sensitization and public
education with regards to the fact that they risk contracting
breast cancer. Also the population should be encouraged to go
for cancer screening to help detection at early stages. Finally,
self esteem from the analysis appears to hold more weight as a
factor that influences the way respondents optimism about life

is and it dictates how the respondents approach the rest of
social perspectives. Recommendations are made so that the
society should learn how to appreciate the self so that each
member would have high self esteem.
Limitations of the Study
There are hardly some limitations to the present study that
worth noting. While work and the self esteem stands out to be
social factors that dictate social life orientation, there are still
some other factors that could be examined for example family
poverty or health of the respondents. Moreover, variations of
social behaviors those results from peer influence should also
be looked into. Though questionnaire is a good method to
collect this kind of information, its reliability with respect to
test re-test criteria is not met. There are still other forms of
collecting data on social life such as face to face interview and
recording or observation and recording which are more
effective.
Suggestions for Future Studies
Above recommendations allows room for more future studies on
this topic. One such study that could impact this area greatly
would be Determination of Reasons Why Women Smoke and
Diseases found among the Women with Smoking Habit. In this
respect, the Research questions could include the following:
· Is there an association between smoking and any diseases
found among women with smoking habits?
· Is smoking among women a good determinant of any diseases
such as breast cancer in women?
Answering these questions might support the implementation of
a program to discourage women from use of tobacco and device
other methods to help detect complications associated with
tobacco in time. Another potential study would be to research
the accuracy of questionnaires for validity and reliability. To
determine whether they measure what they are supposed to
measure. Data should be gathered from past surveys that used
questionnaires to conduct survey and results measured against
known reality to deduce their reliability.

Conclusion
Smoking is a common social habit and most people associate it
with class and wealth. Others still smoke as a result of stress
they face in life. From the analysis, most of the respondents
smoke because they associate it with class. On the proportion of
the females that smoke, most of them does it due to the stress
situations they find themselves in. Most of them that smoke are
found to have a major source of stress as work yet a few
mentioned general life as the source of stress. It is therefore
prudent to conclude that work or lack of it among the smoking
population is the reason they smoke to make them at least feel
better.
Self esteem is the social perspective that emerged to shape the
rest of the social perspectives discussed in this project. Most of
the respondents who had higher regard for themselves felt
positive about life and they recorded great optimism in life.
Most of these people were the most learned and they fall in the
post graduate category, some college training and those that
completed undergraduate. This is because they are the most
potential population and they seek to achieve a lot with their
social life. It is therefore valid to conclude that education level,
self esteem and work are the most common social perspectives
that guide a social life and optimism people have in their life.
References
Harsanyi, J., C. (2012). Essays on Ethics, Social Behaviour,
and Scientific Explanation. New York, NY: Springer Science &
Business Media.
Singleton,W., T. (2013). Analysis of Social Skill. New York,
NY: Springer Science & Business Media.
Terry, A. (2014). Psychological Stress in the Workplace
(Psychology Revivals). New York, NY: Routledge.
Sharon, A., & William, C. (2008).Criterion-referenced Test
Development: Technical and Legal Guidelines for Corporate

Training. Hoboken, NJ: John Wiley & Sons.
Zultan, D. & Tatsuya, T. (2009). Questionnaires in Second
Language Research: Construction, Administration, and
Processing. New York, NY: Routledge.
Pearson South Africa. (2009). South African Supplement to
Social Psychology 3e. Johannesburg, GP: Pearson South Africa.
Image (4)Image (5)Image (6)Image (7)Image (8)Image (9)
B1.docx
Data Analysis Project

Table of Contents
Abstract
Introduction
Problem Statement
Literature Review
Research Questions
Variables

Independent Variables
Dependent Variable
Methods
Participants.
Instruments.
Procedures.
Data Analysis.
Results
Qualitative Variables
Quantitative Variables
Graphs utilizing the appropriate data
Discussion
Conclusions
Limitations of the study
References
Appendix
B2.pdf
Copy of the questionnaire used in survey.sav
On the pages that follow, I have included a portion of the actual
questionnaire used to
collect the data included in the survey.sav file. The first page
includes the demographic
questions, followed by the Life Orientation Test (6 items) and
the Positive and Negative
Affect Scale (20 items).

Sample questionnaire:
1. Sex: male (please tick whichever applies)
female
2. Age: _________ (in years)
3. What is your marital status? (please tick whichever applies)
1. single
2. in a steady relationship
3. living with partner
4. married for first time
5. remarried
6. separated
7. divorced
8. widowed
4. Do you have any children currently living at home with you?
(please tick)
yes
no
5. What is the highest level of education that you have
completed? (please tick the
highest level you have completed)
1. primary school

2. some secondary school
3. completed high school
4. some additional training (apprenticeship, TAFE courses etc.)
5. undergraduate university
6. postgraduate university
6. What are the major sources of stress in your life?
_____________________________________________________
________
7. Do you smoke? (please tick)
yes
no
If yes, how many cigarettes do you smoke per week?
____________
Please read through the following statements and decide how
much you either agree
or disagree with each. Using the scale provided write the
number that best indicates
how you feel on the line next to each statement.

strongly disagree 1 2 3 4 5 strongly agree
1. _____ In uncertain times I usually expect the best.
2. _____ If something can go wrong for me it will.
3. _____ I'm always optimistic about my future.
4. _____ I hardly ever expect things to go my way.
5. _____ Overall I expect more good things to happen to me
than bad.
6. _____ I rarely count on good things happening to me.
Source: Scheier, Carver & Bridges, 1994.
This scale consists of a number of words that describe different
feelings and emotions.
For each item indicate to what extent you have felt this way
during the past few weeks.
Write a number from 1 to 5 on the line next to each item.
very slightly or not
at all
1
a little

2
moderately
3
quite a bit
4
extremely
5
1. ______ interested
2. ______ upset
3. ______ scared
4. ______ proud
5. ______ ashamed
6. ______ determined
7. ______ active
8. ______ distressed
9. ______ strong

10. ______ hostile
11. ______ irritable
12. ______ inspired
13. ______ attentive
14. ______ afraid
15. ______ excited
16. ______ guilty
17. ______ enthusiastic
18. ______ alert
19. ______ nervous
20. ______ jittery
Source: Watson, Clark & Tellegen, 1988.
Codebook for survey.sav
Full variable name SPSS variable
name

Coding instructions
Identification number id subject identification number
Sex sex 1 = males; 2 = females
Age age in years
Marital marital 1 = single; 2 = steady relationship; 3 = living
with
a partner; 4 = married for the first time; 5 =
remarried; 6 = separated; 7 = divorced; 8 =
widowed
Children child 1 = yes; 2 = no
Highest level of education educ 1 = primary; 2 = some
secondary;
3 = completed high school;
4 = some additional training;
5 = completed undergraduate;
6 = completed postgraduate.
Major source of stress source 1 = work; 2 = spouse or partner;
3 =
relationships; 4 = children; 5 = family; 6 = health /
illness; 7 = life in general

Do you smoke? smoke 1 = yes; 2 = no
Cigarettes smoked per week smokenum Number of cigarettes
smoked per week
Optimism Scale
op1 to op6 1=strongly disagree , 5=strongly agree
Mastery Scale mast1 to mast7 1=strongly disagree , 4=strongly
agree
PANAS Scale pn1 to pn20 1=very slightly, 5=extremely
Life Satisfaction Scale
lifsat1 to lifsat5 1=strongly disagree , 7=strongly agree
Perceived Stress Scale pss1 to pss10 1=never, 5=very often
Self esteem Scale sest1 to sest10 1=strongly disagree ,
4=strongly agree
Marlowe-Crowne Social
Desirability Scale

m1 to m10 1=true, 2=false
Perceived Control of Internal
States Scale (PCOISS)
pc1 to pc18 1=strongly disagree, 5=strongly agree
Total scale scores included in survey.sav
Full variable name SPSS Variable
name
Coding instructions
Total Optimism Toptim reverse items op2, op4, op6
add all scores op1 to op6
range 6 to 30
Total Mastery Tmast reverse items mast1, mast3, mast4, mast6,
mast7
add all items mast1 to mast7
range 7 to 28
Total Positive affect Tposaff add items pn1, pn4, pn6, pn7, pn9,

pn12, pn13,
pn15, pn17, pn18
range 10 to 50
Total Negative affect Tnegaff add items pn2, pn3, pn5, pn8,
pn10, pn11, pn14,
pn16, pn19, pn20
range 10 to 50
Total Life Satisfaction Tlifesat add all items lifsat1 to lifsat5
range 5 to 35
Total Perceived Stress Tpstress reverse items pss4, pss5, pss7,
pss8
add all items pss1 to pss10
range 10 to 50
Total Self-esteem Tslfest reverse items sest3, sest5, sest7,
sest9, sest10
add all items sest1 to sest10
range 10 to 40
Total Social desirability Tmarlow reverse items m6 to m10
(recode true=1, false
=0)
add all items m1 to m10
range 0 to 10
Total Perceived Control
of Internal States

Tpcoiss reverse items pc1, pc2, pc7, pc11, pc15, pc16
add all items pc1 to pc18
range 18 to 90
New Education
categories
educ2 recoded the categories primary, some secondary
into one group because of small numbers in each
group.
1=primary/some secondary, 2=completed
secondary, 3=some additional training,
4=completed undergraduate university,
5=completed postgraduate university.
Age group 3 categories Agegp3 1=18-29yrs, 2=30-44yrs,
3=45+yrs
Age group 5 categories Agegp5 1=18-24yrs, 2=25-32yrs, 3=33-
40yrs, 4=41-49,
5=50+yrs.
B3.sav
B4.pdf
Copy of the questionnaire used in survey.sav

On the pages that follow, I have included a portion of the actual
questionnaire used to
collect the data included in the survey.sav file. The first page
includes the demographic
questions, followed by the Life Orientation Test (6 items) and
the Positive and Negative
Affect Scale (20 items).
Sample questionnaire:
1. Sex: male (please tick whichever applies)
female
2. Age: _________ (in years)
3. What is your marital status? (please tick whichever applies)
1. single
2. in a steady relationship
3. living with partner
4. married for first time
5. remarried
6. separated
7. divorced
8. widowed
4. Do you have any children currently living at home with you?
(please tick)

yes
no
5. What is the highest level of education that you have
completed? (please tick the
highest level you have completed)
1. primary school
2. some secondary school
3. completed high school
4. some additional training (apprenticeship, TAFE courses etc.)
5. undergraduate university
6. postgraduate university
6. What are the major sources of stress in your life?
_____________________________________________________
________
7. Do you smoke? (please tick)
yes
no
If yes, how many cigarettes do you smoke per week?
____________

Please read through the following statements and decide how
much you either agree
or disagree with each. Using the scale provided write the
number that best indicates
how you feel on the line next to each statement.
strongly disagree 1 2 3 4 5 strongly agree
1. _____ In uncertain times I usually expect the best.
2. _____ If something can go wrong for me it will.
3. _____ I'm always optimistic about my future.
4. _____ I hardly ever expect things to go my way.
5. _____ Overall I expect more good things to happen to me
than bad.
6. _____ I rarely count on good things happening to me.
Source: Scheier, Carver & Bridges, 1994.
This scale consists of a number of words that describe different
feelings and emotions.
For each item indicate to what extent you have felt this way

during the past few weeks.
Write a number from 1 to 5 on the line next to each item.
very slightly or not
at all
1
a little
2
moderately
3
quite a bit
4
extremely
5
1. ______ interested
2. ______ upset
3. ______ scared
4. ______ proud
5. ______ ashamed

6. ______ determined
7. ______ active
8. ______ distressed
9. ______ strong
10. ______ hostile
11. ______ irritable
12. ______ inspired
13. ______ attentive
14. ______ afraid
15. ______ excited
16. ______ guilty
17. ______ enthusiastic
18. ______ alert
19. ______ nervous
20. ______ jittery
Source: Watson, Clark & Tellegen, 1988.

Codebook for survey.sav
Full variable name SPSS variable
name
Coding instructions
Identification number id subject identification number
Sex sex 1 = males; 2 = females
Age age in years
Marital marital 1 = single; 2 = steady relationship; 3 = living
with
a partner; 4 = married for the first time; 5 =
remarried; 6 = separated; 7 = divorced; 8 =
widowed
Children child 1 = yes; 2 = no
Highest level of education educ 1 = primary; 2 = some
secondary;
3 = completed high school;

4 = some additional training;
5 = completed undergraduate;
6 = completed postgraduate.
Major source of stress source 1 = work; 2 = spouse or partner;
3 =
relationships; 4 = children; 5 = family; 6 = health /
illness; 7 = life in general
Do you smoke? smoke 1 = yes; 2 = no
Cigarettes smoked per week smokenum Number of cigarettes
smoked per week
Optimism Scale
op1 to op6 1=strongly disagree , 5=strongly agree
Mastery Scale mast1 to mast7 1=strongly disagree , 4=strongly
agree
PANAS Scale pn1 to pn20 1=very slightly, 5=extremely
Life Satisfaction Scale
lifsat1 to lifsat5 1=strongly disagree , 7=strongly agree

Perceived Stress Scale pss1 to pss10 1=never, 5=very often
Self esteem Scale sest1 to sest10 1=strongly disagree ,
4=strongly agree
Marlowe-Crowne Social
Desirability Scale
m1 to m10 1=true, 2=false
Perceived Control of Internal
States Scale (PCOISS)
pc1 to pc18 1=strongly disagree, 5=strongly agree
Total scale scores included in survey.sav
Full variable name SPSS Variable
name
Coding instructions
Total Optimism Toptim reverse items op2, op4, op6
add all scores op1 to op6

range 6 to 30
Total Mastery Tmast reverse items mast1, mast3, mast4, mast6,
mast7
add all items mast1 to mast7
range 7 to 28
Total Positive affect Tposaff add items pn1, pn4, pn6, pn7, pn9,
pn12, pn13,
pn15, pn17, pn18
range 10 to 50
Total Negative affect Tnegaff add items pn2, pn3, pn5, pn8,
pn10, pn11, pn14,
pn16, pn19, pn20
range 10 to 50
Total Life Satisfaction Tlifesat add all items lifsat1 to lifsat5
range 5 to 35
Total Perceived Stress Tpstress reverse items pss4, pss5, pss7,
pss8
add all items pss1 to pss10
range 10 to 50
Total Self-esteem Tslfest reverse items sest3, sest5, sest7,
sest9, sest10
add all items sest1 to sest10
range 10 to 40

Total Social desirability Tmarlow reverse items m6 to m10
(recode true=1, false
=0)
add all items m1 to m10
range 0 to 10
Total Perceived Control
of Internal States
Tpcoiss reverse items pc1, pc2, pc7, pc11, pc15, pc16
add all items pc1 to pc18
range 18 to 90
New Education
categories
educ2 recoded the categories primary, some secondary
into one group because of small numbers in each
group.
1=primary/some secondary, 2=completed
secondary, 3=some additional training,
4=completed undergraduate university,
5=completed postgraduate university.
Age group 3 categories Agegp3 1=18-29yrs, 2=30-44yrs,
3=45+yrs
Age group 5 categories Agegp5 1=18-24yrs, 2=25-32yrs, 3=33-
40yrs, 4=41-49,
5=50+yrs.

A4.doc
PAGE
1
EDPS 621: FALL - 2015
Data Analysis Project (DAP) Directions
Statistics are used to answer many of our pressing questions in
the world through analytic techniques that explore data and test
hypotheses. Our questions in quantitative research are very
specific and intimately linked to the analytic technique needed
to answer the question. For example, “Are there more females
or males in the Fall 2015 session of EDPS 621?” is answerable
by counting and then comparing the number of female to male
students in our class (in statistical language we’d call this a
frequency analysis). Another question, “Do GRE scores predict
EDPS 621 test scores?” is answerable using regression
(something we’ll study later in the semester). Finally, other
questions such as “Do females and males perform differently on
exams in the Fall 2015 session of EDPS 621?” are answerable
using a “t-test” (an analysis we’ll cover next semester in EDPS
651).
The purpose of the DAP to provide a platform for you to
demonstrate your statistical prowess in an authentic “real-life”
context. The hope is the authenticity of the activity will
facilitate the transfer of our “book” and “lecture” knowledge to
your life outside of class. Each topic we’ll cover in class,
univariate and bivariate graphs and analyses, will be utilized in
your final DAP. Selecting and running the appropriate graph or
numeric analysis is the first part of being a proficient
statistician; the second (and most important) is being able to
accurately interpret the results. The DAP structure mimics a
journal article, with an introduction (what is the problem, why

is it important, what have others done to help solve the problem,
and what are the questions being investigated in this study), a
description of the methods (sample, instruments, and
procedures) that will be used to answer the question, the results
(using each of the ways we’ve discussed in class), and a
discussion on how your results reframe what was known before
the study was conducted. In the conclusion, you will describe
the limitations of the study and the real-world implications of
the results.
Data Sources
You are welcome to use whatever data sources (including files
provided in class) you’d like for your DAP. If you use your own
data it must include at least 100 cases and at least two
qualitative and three quantitative variables. If you choose your
own data source from outside of class you’ll need my approval
before moving forward with your project. THIS SHOULD BE
DONE BY OUR OCTOBER 12th CLASS. My interest in not in
being the arbiter of what you study, but instead to ensure the
data will not inhibit your ability to demonstrate your
proficiency in course topics. If you chose this route also let me
know whether you’ll be taking 651 in the Winter semester to
ensure you’re able to use the data for both classes.
Formatting
· Follow APA guidelines for text (including headings,
subheadings, writing style, and citations), tables, graphs
(figures in APA language), and the reference list. Guidelines
are available in the 6th edition of the APA publication manual,
or on-line at: https://owl.english.purdue.edu/owl/section/2/10/
or http://www.stylewizard.com/
· Papers should be double spaced, 12-point font, with 1”
margins, & no spaces between sections.
· Include a running header describing the topic in a few words,
your name, and the page number on the top right corner of each
page (limit of 50 characters).

· On the title page include: Study Title, Course Number & Title,
My Name, Your Name, Your Department, Eastern Michigan
University, Date of submission, and Semester.
· Key tables and figures need to be integrated into the text for
full credit. At least one of each type of table and figure required
(listed under “results” below) need to be included in the text
and properly formatted. Make sure a table or figure is
completed on the same page.
· The results in each table should be described and interpreted
as part of the text of the paper.
· There should be separate pages for the Title page, Abstract,
References, and Appendices
· Staple your final projects (no binders, covers, folders, etc.)
· Sample APA formatted papers can be found at:
https://owl.english.purdue.edu/media/pdf/20090212013008_560.
pdf or
http://www.apastyle.org/manual/related/sample-experiment-
paper-1.pdf
Content
Each paper should include five parts: 1) abstract, 2)
introduction, 3) methods, 4) results, and 5) discussion. Written
components should be presented in paragraph form, where each
new paragraph is indented and APA style throughout. The paper
should follow standard rules for good grammar, spelling,
punctuation, and written communication. Tables and graphs
should be integrated into the text with appropriate APA
formatting (See “APA formatting” PowerPoint for the basics).
Each section is described in detail below.
Title. Your title should be complete in giving the reader a clear
idea of the contents of the paper and include the following:
· The population of interest
· The major variables
Abstract. The abstract provides a summary of the study and is
typically written once the rest of the paper is complete and

should be no longer than 150 words (yes, I will count how many
words are in your abstract).
1) Clearly stated problem addressed by the study (one sentence)
2) The number and basic characteristics of the subjects (one
sentence)
3) Major variables (one sentence)
4) Measurement instruments used (one sentence)
5) Brief procedures (one sentence)
6) Major results (one or two sentences)
7) Conclusions/implications (one sentence)
Introduction. The introductory section of the paper frames and
provides context for your study. There are typically four
important elements of an introductory section in a quantitative
research study: 1) a statement of the problem, 2) literature
review, 3) the purpose of this study, and 4) the research
questions.
Problem statement. At the beginning of your paper you want to
clearly state the problem that your study will focus on. The
problem is typically broad and is the driving issue that led you
(and your reader) to commit the time and energy necessary to
study the topic. For example a problem statement could be,
Michigan legislators are about to pass a bill requiring all third
grade students in the state pass the reading portion of the M-
Step before moving on to fourth grade without examining
consequences of the decision.
Review of the literature. Once the problem is identified then the
paper should explore what other researchers have done to study
aspects of the issue. For the example for the problem above one
might focus on the effect of retention on future student
academic performance or the impact of using a standardized test
as a gateway for promotion to the next grade (perhaps even
graduation). This is where you identify the variables of interest
and the specific relations between those variables. For the DAP
we’ll only utilize a couple research articles in the literature

review as the focus of the class is on analysis and
interpretation.
Purpose of the study. This section should tie together the
problem statement, summarize the key elements of the
literature, and identify gaps in the literature that the study will
address. These gaps lead to the purpose of the current study.
Research questions. The research questions focus the inquiry
(which should be to fill the gap in the literature identified in the
previous section) and are answered in the results section. For
the DAP the questions must be answerable by using one of the
analyses covered in class. Good questions should follow
logically from the statement of the problem and include the
following:
· Clearly and specifically stated (no broad or nebulous
questions, this is not a philosophy class)
· The key variables (i.e., independent and dependent variables)
· The expected relationship or difference among variables
Methods
Participants.Describe the major characteristics of the sample
(i.e., overall numbers, group numbers, anything that would help
the reader understand who was studied). This section describes
the “who” of your study. Remember, the minimum sample size
is 100 for a descriptive study (which we’re doing in this class).
You’ll find other recommended sizes for other types of studies
(e.g., 50 for a correlational study or 30 in each group for
experimental and causal-comparative studies), but given we’re
in a descriptive statistics class your minimum sample size must
adhere to the 100 minimum. Also include an explanation of how
the sample was selected. Please note if you are using one of my
data sets you will make up this section because you did not
collect the data yourself.
Instruments. Identify the instrument(s) used to collect the data
and include a brief discussion of the validity and reliability of
each (if available). This section describes the “what” of your
study. Also include a rationale for the selection of each of the
instruments (why do you think they were chosen?). Finally,

describe the content of the instrument.
Procedures.Explain, in detail, how the study was conducted
providing enough information to allow another researcher to
replicate the study. This is the “how” of the study and again
will need to be made up if you are using one of my data sets.
Data Analysis. Describe the statistical procedures YOU used to
answer each of YOUR research questions.
Results
The results section is key to the DAP. Please note the number of
points and the level of detail needed for full points in this
section. Make sure to AT LEAST include the following in your
results section (not the appendix):
· A frequency table and an explanation of key findings for all
qualitative variables
· A descriptive table and an explanation of key findings
(particularly “center” and “spread”) for all quantitative
variables
· A bar graph, pie chart, histogram, boxplot, and scatterplot for
the appropriately scaled variables. Make sure to include an
explanation of what each figure shows to demonstrate your
proficiency in analyzing graphs in a quantitative paper.
· A table that includes at least THREE Pearson correlations with
an explanation and interpretation of each result (i.e., the
direction, strength, and meaning).
· One linear regression that includes: the raw regression
equation, all appropriate output, explanation, and interpretation
of the results (i.e., the direction, strength, and meaning).
Discussion
This section should synthesize the results from your study,
connect these results to the literature described in the
introduction, and explain the implications of the study. Be sure
to include each of the following elements:
1) Answers to the questions: Summarize the results for each

question
2) How these answers fit findings fromprevious research:
Connect what YOU found to the findings of the two (or more)
studies presented in the introduction.
3) Implications: Both the theoretical and practical implications
of the findings
4) Limitations of the study: Identify areas that potentially
compromise the findings from the study. Typically the
limitations center around threats to internal validity (e.g.,
possible effects of uncontrolled variables, plausible alternative
hypotheses, sampling bias, etc.).
5) Suggestions for Future Studies
References
Be sure to include AT LEAST two references that are empirical
research articles. Pay particular attention to ensure they are
properly formatted using APA guidelines.
Appendix
Ancillary documents that might help the reader (me) understand
the paper are included in an appendix. You may include SPSS
output not used in the paper, a copy of the instrument(s) used to
collect the data, or other pertinent information.
DAP Scoring Rubric
Element
Points
Title
1) Complete, clear idea of the contents
0.5
2) Population and the major variables.
0.5
Abstract
1)150 words in a separate page

0.5
2) Clearly stated problem
0.5
3) Number & type of subjects
0.5
4) Major variables
0.5
5) Measuring instrument(s) used
0.5
6) Brief procedures
0.5
7) Major results
0.5
8) Conclusions/implications
0.5
Introduction
Problem statement
Overview of the issue
1
Variables of interest & the specific relation
1
AT LEAST TWO empirical articles
1
Significance of the problem
1
Research Questions
Follow logically from problem
1.5
Clearly & specifically stated

1.5
Independent/dependent variables included
1.5
Expected relationship included
1.5
Methods
Participants
Size & major characteristics of the sample
4
Explanation of how the sample was selected and the population
it represents
1
Instruments
Instrument(s) used and a brief discussion of the validity and
reliability of each
0.5
Rationale for selection
0.5
Content of the instruments
0.5
Procedures
Explain basic procedures for data collection
0.5
Data Analysis
The analytic technique used to answer each question is included
1
Element
Points
Results

Qualitative Variables
Frequency table for all
5
Description and explanation of table
5
Quantitative Variables
Descriptive table for all
5
Description and explanation of table
5
Graphs utilizing the appropriate data
Bar Chart
1
Pie Chart
1
Histogram
1
Boxplot
1
Scatterplot
1
Accurate and complete description of each
5
Three correlations
Table, description, and explanation for all three
10
One linear regression
Raw Regression Equation
3
Table, description, and explanation
7

Discussion
Conclusions
Summarize results for each question
3
Connect results to literature review
3
Describe theory/practice implications
3
Limitations of the study
1
1
References
Two quantitative references
2
APA Style
3
Overall
Clear, concise, professionally written
8
APA style followed throughout
3
A5.pdf

Running head: GIFTED AND TALENTED EDUCATION
1
Gifted and Talented Identification at One District:
An Analysis of Testing Results
EDPS 621: Statistical Applications in Educational Research
Fall 2014
GIFTED AND TALENTED EDUCATION

2
Abstract
This study details the testing process and analysis of testing
results for admitting students to the
ACT program – a program for gifted and talented students at
Johnsonville Public Schools.
Participants for this study (n = 181) were students in the second
through fifth grades that were
nominated for the program. The students represented six
different schools within the district.
Gender, school, grade, teacher, and involvement in Spanish
Immersion data from scores on the
Cognitive Abilities Test (CogAT) and the Renzulli Scales for
Rating the Behavioral
Characteristics of Superior Students (SRBCSS) was analyzed to
determine the nature of testing
within the district and whether or not there were major
differences between the schools.
Correlation and regression analyses were also utilized to
determine whether or not there was an
association between the SRBCSS, performance on the CogAT,
and admittance to the ACT
program. Findings showed a slight, positive correlation

between the Renzulli Scales and the
aforementioned variables. The findings are discussed in light of
previous research, including
limitations of the study. Implications for practice and future
research are presented.
3
Introduction
Gifted education is a field that is continually evolving.
Understanding its many facets
can be overwhelming, especially for parents of gifted children
or for those educators new to the
field. Chief among the extraordinary changes and advances in
the field of gifted education are
the expanding conceptions of giftedness and how we come to

understand gifted learners. Even a
cursory review of the research on gifted and talented education
reveals that gifts and talents
among the “gifted” vary widely. While, at the turn of the 20
th
century, giftedness was equated
with high IQ (Jolly, 2008), over the years, researchers have
come to define giftedness in terms of
multiple qualities, not all of which are related to intelligence
(Wallace & Pierce, 1992; Sternberg
& Davidson, 1986).
With the works of Renzulli (1978), Gardner (1983), and
Sternberg and Davidson (1986),
we now know that motivation, creativity, and task-commitment
are also reflected in the
behaviors of above-average students. Even the United States
Department of Education utilizes a
multifaceted approach to gifted education viewing gifted youth
as those:
Children and youth with outstanding talent [who] perform or
show the potential for
performing at remarkably high levels of accomplishment when
compared with others of

their age, experience, or environment. These children and youth
exhibit high performance
capability in intellectual, creative, and/or artistic areas, possess
an unusual leadership
capacity, or excel in specific academic fields. They require
services or activities not
ordinarily provided by the schools. Outstanding talents are
present in children and youth
from all cultural groups, across all economic strata, and in all
areas of human endeavor.
(U.S. Department of Education, 1993, p. 26)
4
Because of their unique abilities, gifted children are students
who require differentiated
educational programs and/or service beyond that which
normally is provided by the traditional
school program in order to realize their contribution to self and
society. If a school district
operationalizes this concept, it fundamentally changes the way
it does business. It means that

teachers differentiate instruction and assignments within their
classrooms. It means that various
levels of course work will be made available to all students
when appropriate. It also means that
options for acceleration and alternative programming will be
present.
However, though many schools adopt a multifaceted definition
of giftedness to serve as
the foundation for their philosophy and programming choices,
the identification process for
gifted programs is still a very complex and controversial topic.
While it is easy to measure IQ
and academic achievement, it is certainly much more difficult to
measure innovative thinking,
creative potential, and task-commitment. This potentially
leaves some students with gifts and
talents unrecognized and underserved (Lemons, 2011). While
there may not be one right answer
as to how best identify students for participation in gifted and
talented programs, it is certainly
very important to review the literature and to stay up-to-date on
the research related to this issue.
It is along this venue that this analysis is based.
Changes in Gifted and Talented Testing and Identification

Though the testing and identification of gifted students has
changed drastically over the
years, many educators working in the field credit Terman (1931)
and his research as being the
catalyst for studying students with above-average abilities. As
a faculty member at Stanford
University, Terman notably adapted Binet’s intelligence test for
use in the United States resulting
in the Stanford-Binet Intelligence Scales (Terman et al, 1917).
What’s more, in addition to his
construction of mental tests, Terman was also well known for
his longitudinal study of gifted
5
elementary-school-aged children (Terman, 1961. This became a
defining feature of his career.
Entitled the Genetic Study of Genius, the basic purpose of the
study was to determine how gifted
children vary compared to children of average intelligence
(Jolly, 2008). Though Terman’s
study relied on a definition of giftedness that was significantly

linked to intelligence, his work
was one of the first of its kind in the United States. What’s
more, as Sally M. Reis, President of
The National Association of Gifted Children, states, “Some of
this ‘legacy’ survives to the
present day as giftedness and high IQ continue to be equated in
some conceptions of giftedness
(Reis, n.d.).
While Terman’s work was essential and helped to begin the
conversation in terms of
studying gifted students, the way we conceptualize “giftedness”
has drastically changed. Soon
after Terman’s work, other researcher posited that intelligence
could be conceptualized as more
than just one factor but could, instead, be related to many
different skill sets (Thurstone, 1938;
Cattell, 1952; and Guilford, 1956).
The intent of this study is to analyze the testing procedures and
results of the gifted and
talented programming at a small, school district – Johnsonville
Public Schools. Johnsonville
Public Schools has long been recognized for excellence in

education and academic
programming. In 1985, administrators from the district worked
alongside a dedicated group of
Johnsonville parents to create a program for students
demonstrating evidence of high intellectual
and creative achievement capability. The ACT Program (the
acronym a variation of
ACademically Talented) was the result of their hard work. The
ACT class was designed as an
academic enrichment pullout program specifically tailored for
gifted and talented
children. Meeting each school week, the curriculum promotes
advanced academics in the core
6
subject areas of Math, Science, Language Arts and Social
Studies. Third through sixth grade
students from Johnsonville Public Schools, Johnsonville
Christian Schools, or home-schooled
students in the Johnsonville area who qualify for the ACT
program are provided learning

experiences that encourage them to apply their abilities,
creativity, and task commitment to solve
personally meaningful problems they encounter in their schools,
community, and world.
Just as the field of gifted education has evolved, so, too, has
the educational
programming and, in particular, the identification process for
students admitted to the ACT
Program at Johnsonville. The question of identification and
admittance has by far been one of
the most difficult to answer. How to test, what testing materials
to use, and what standards to use
have been recurring questions throughout the history of the
ACT Program. As the program’s
new Director and Teacher, I wish to review the testing data
from the spring of 2014 in order to
help make informed decisions for the future.
Research Questions
The following research questions guide this study:
1. How does student performance on the Cognitive Abilities
Test (CogAT) differ between
the various schools in the district? Are there certain buildings
or grade levels in which

students perform better?
2. Is there an association between students’ scores on the
Renzulli Scales for Rating the
Behavioral Characteristics of Superior Students (SRBCSS),
performance on the
Cognitive Abilities Test, and acceptance to the ACT Program?
3. How well do students’ scores on the Learning Characteristics
portion of the SRBCSS
predict performance on the Cognitive Abilities Test?
7
Methods
Participants
The participants for this study were students in grades two
through five that were
nominated by their parents to be evaluated for the ACT
Program. Due to special circumstances
prearranged with district administration, one first grader also

participated in the testing process.
In years past, nomination forms were sent to the parents of all
Johnsonville Public Schools’
students in the aforementioned grades through student folders
that are taken home at the end of
each school week. However, this practice was changed when
district administration recognized
the need for parents to understand the purpose of the ACT class
prior to nominating their child.
Now, parents are notified via school newsletters about the ACT
program and are instructed to
pick up nomination forms in their school’s office. What’s
more, in some instances, parents
speak with their child’s teacher about nominating. This most
often happens around Parent-
Teacher Conferences. At other times, classroom teachers may
speak with the parents of their
high-ability students about the potential benefits of such a
program and may encourage parents to
nominate their child for testing.
The parent nomination form is a modified version of the
Renzulli Scales for Rating the
Behavioral Characteristics of Superior Students (SRBCSS)
(Renzulli, Smith, White, Callahan,

Hartman, & Westberg, 2002) that is explained further below (to
view the entire nomination form,
see Appendix A). Upon receiving the parent nomination forms,
separate Renzulli Scales are then
sent to classroom teachers for all of the nominated students.
Prior to filling out the scales,
teachers are required to attend a short meeting explaining how
the Renzulli Scales work and how
to fill them out. Using this format for the 2013-2014 school
year, the number of students
8
nominated for the program was one hundred eighty-one
(n=181). Of these participants, there
were more female testers (56.9%, n=103) than male testers
(43.1%, n=78). Participants from
each grade level were represented as follows:

It is important to note that this is not a random sample; thus, it
is not representative of the entire
second through fifth grade population. Rather, this group of
students represents those parents
who feel the ACT Program may be beneficial for their child.
Nomination can happen for many
reasons. There are parents who have the knowledge and a sound
understanding of the traits of
giftedness and academic talent to provide rationale for
nominating their child. However, parents
nominate for other reasons, and this reasoning can vary greatly.
Based on conversations with
district administrators and former directors of the ACT
program, parents will also nominate their
child because an older sibling was in the program. Other
reasoning includes the fact that the
child wants to be in the ACT Program, the child’s friends are in
the program, or because the
parents simply believe that their child is gifted and could
qualify.
Instruments

Cognitive Abilities Test (CogAT). Cognitive ability tests that
measure verbal,
quantitative, and nonverbal reasoning skills are widely used by
schools to provide valuable
information to teachers hoping to differentiate instruction to
their students’ cognitive strengths
(Lohman, 2009; Lohman & Hagen, 2001b). All students
nominated for the ACT program take
9
the Cognitive Abilities Test (CogAT) (CogAT, Form 6; Lohman
& Hagen, 2001a). Issaquah
School District 411 in Washington administers the CogAT to
test students for admittance into
their Highly Capable programs. In a section about the CogAT
on their website, the district states,
The CogAT is a test of reasoning skills. It's not like a spelling
or a math test where if you
know the words or the facts you can get 100%. There is no
defined curriculum for the
CogAT. It is a norm-referenced test and the national average is

50th percentile (Issaquah
School District, n.d.).
Currently, JPS uses Form 6 of the CogAT written by Lohman
and Hagen. To identify students
for the ACT Program, Johnsonville Public Schools administers
advanced versions of the CogAT
to the nominated students as students are tested one full year
ahead.
There are three parts to the CogAT: Verbal Battery,
Quantitative Battery, and Non-
Verbal Battery. The figure below illustrates the breakdown of
each test battery on CogAT
(Lohman & Hagen, 2002).
Figure 1. Breakdown of Cognitive Abilities Test.
10
Each section of the multilevel battery (for grades 3-12) is timed
and students are required to fill
in their answers on a bubble sheet. There are also common
directions, thus, students from

various grades could take the test at the same time, if necessary.
The CogAT is scored and performance is reported in a number
of ways. The raw score is
the number of correct items a student got on any particular
section of the test. This raw score can
then be converted to a Universal Scale Score (USS). This USS
allows test administers to map
the number correct on different levels of the test onto a single,
common developmental scale.
11
Figure 2. Illustration of how raw scores from various test levels
can map onto the same,
universal scale (USS).
USS Scale

A
B
D
C
12
The USS scores are used to determine a tester’s grade percentile
rank on each portion of the test.
What’s more, Johnsonville Public Schools averages these
percentile ranks to obtain an overall
CogAT average score.
Reliability & Validity. For years, the Cognitive Abilities Test
has been one of the most
trusted and widely used group abilities tests in the United States
and abroad (Lohman, 2011). As
described by Lohman (2011), author of the Cognitive Abilities
Test Forms 6 & 7, and one of the
leading voices in gifted education, “Each battery has subtests
that make use of three different test

formats. Measuring with multiple formats rather than the same
format on all items increases
both the fairness and the validity of the scores students obtain”
(Lohman, 2011, p. 1). These
batteries were also found to provide strong predictive validity
for achievement in non-ELL
populations (Lohman & Hagen, 2002).
Renzulli Scales for Rating the Behavioral Characteristics of
Superior Students. The
Cognitive Abilities Test has been the primary means for
acceptance into the ACT program.
However, starting in 2005, some changes were made in the
testing process. One such change
was the addition of using the Renzulli Scales for Rating the
Behavioral Characteristics of
Superior Students (SRBCSS) (Renzulli, Smith, White, Callahan,
Hartman, & Westberg, 2002) in
tandem with the Cognitive Abilities Test. The Renzulli Scales
are designed to obtain teacher
estimates of a student's characteristics in the following areas
(those denoted with an asterisk are
new to the latest version of the scales):

13
Characteristics*
A description on the Prufrock Press website about the Renzulli
Scales states,

Research shows that gifted children tend to exhibit certain
observable behaviors, such as
using advanced vocabulary, grasping underlying principles, and
making generalizations from
complex information. The Renzulli Scales asks teachers to rate
children in comparison to
their peers on a host of these observable behaviors. The
children who score high on the
scales are more likely to be gifted. Using a tool like the Scales,
a school can narrow the
number of students who will be fully evaluated for a gifted
program (Prufrock Press, n.d.).
Appendix A displays a sample copy of the Renzulli Scales for
each of the areas that Johnsonville
Public Schools uses in the process for identifying potentially
gifted and talented students
(learning, creativity, motivation, and leadership). As seen in
the sample instruments, each scale
contains multiple items relating to student characteristics that
are rated using a Likert-type scale.
14

Reliability & Validity. School districts have utilized teacher
rating scales such as the
SRBCSS for many years to help with the identification process
and to make decisions regarding
the range of services provided to high ability students (Renzulli,
Siegle, Reis, Gavin, & Reed,
2002). Joseph Renzulli first developed the first three scales in
the SRBCSS series – learning,
motivation, and creativity, in 1978 (Renzulli, 1978). As the
definition and conceptions of
giftedness changed and began to encompass a range of talent
areas in addition to cognitive
abilities, the other scales were then created. A manual that
accompanies the scales includes
teacher training activities in an effort to ensure that teachers
understand how to fill out the scales,
thus increasing the reliability of the scores (Renzulli et al.,
2002b). What’s more, as noted by
Renzulli and colleagues, “Each scale was subjected to a
standard instrument development
research process to ensure technical characteristics such as
validity, reliability, objectivity, and
practicality” (Renzulli et al., 2002b, p. 86).

Procedures
Currently, all students nominated by their parents are tested,
and teachers fill out the
Renzulli Scales for each student in his or her classroom who
will be taking the CogAT. Prior to
testing, teachers attend a small informational session in which
directions on how to fill out the
SRBCSS are given. Testing for the ACT program is completed
at each of the five elementary
buildings in April. Participants from Johnsonville Christian
School are bused to Poplar Grove
Elementary in order to take the Cognitive Abilities Test.
Students from grades two and three
(levels A & B) are tested in the morning, while students from
grades four and five (levels C & D)
are tested in the afternoon on the given testing day. The district
hires a proctor for the test, this
individual used to be a former principal in the district. After
testing is completed, the proctor
then scores the tests and sends the information to Central
Administration where the data is

15
entered into a spreadsheet. The current spreadsheet weights
various pieces from the testing:
CogAT Avg is weighted at 50%, while each of the Renzulli
Scales are weighted at 10% (for a
total of 40% of the final, overall score). The parent profile (See
Appendix A) and a separate
Student Application are also each weighted at 5%. This
produced an “Overall Score” that was
used for admittance purposes.
Data Analysis
The first step in the analysis was the generation of frequencies
for all qualitative variables
to present an accurate view of the testing participants within the
various schools in the district.
This information was used to draw conclusions about the
consistency of the nominating and
testing process and to inform any changes for the future. Next,
descriptive statistics were run on
each of the quantitative variables to provide more information
about the performance on the
individual portions of the CogAT and SRBCSS. Correlation
coefficients were calculated for

each of the Renzulli Scales with the admittance to the program
and performance on the CogAT
variables to see if there was an association. Finally, a linear
regression analysis was conducted
to evaluate how well students’ scores on the Learning
Characteristics portion of the SRBCSS
predict performance on the Cognitive Abilities Test. The results
from these analyses follow along
with a discussion of the findings.
16
Results
Information about the participants was collected on six
qualitative variables: gender,
grade, building, participation in Spanish Immersion, classroom
teacher, and whether or not the

student was accepted to ACT. Students in grades two through
five tested for the program,
including one first grade student. Additionally, there are six
categorical options for building:
Scenic Knoll, Red Cedar, Poplar Wood, Birchwood, Burbank,
and Johnsonville Christian.
Table 1
Frequency of Participants by Gender
Frequency Percent Valid Percent Cumulative Percent
F 103 56.9 56.9 56.9
M 78 43.1 43.1 100.0
Total 181 100.0 100.0
Table 2
Frequency of Participants by Grade
1 1 .6 .6 .6
2 65 35.9 35.9 36.5
3 50 27.6 27.6 64.1
4 40 22.1 22.1 86.2

5 25 13.8 13.8 100.0
Total 181 100.0 100.0
Table 3
Frequency of Participants by Building
Birchwood 29 16.0 16.0 16.0
Burbank 48 26.5 26.5 42.5
JC 10 5.5 5.5 48.1
Poplar Wood 29 16.0 16.0 64.1
Red Cedar 34 18.8 18.8 82.9
Scenic Knoll 31 17.1 17.1 100.0
Total 181 100.0 100.0
17
Table 4
Frequency of Participants in Spanish Immersion

No 157 86.7 86.7 86.7
Yes 24 13.3 13.3 100.0
Total 181 100.0 100.0
Table 5
Frequency of Students Accepted to Program
No 95 52.5 52.5 52.5
Yes 86 47.5 47.5 100.0
Total 181 100.0 100.0
Table 6
Frequency of Female Students Accepted to Program
No 57 55.3 55.3 55.3
Yes 46 44.7 44.7 100.0
Total 103 100.0 100.0
Table 7

Frequency of Male Students Accepted to Program
No 38 48.7 48.7 48.7
Yes 40 51.3 51.3 100.0
Total 78 100.0 100.0
As depicted in Table 1 above, the number of female testers
(56.9%, n=103) was greater than the
number of male students who tested for the program (43.1%,
n=78). All grade levels permitted
to test for the program were represented, including one student
from first grade who tested under
special circumstances. A little more than one-third of the
participants represented came from
second grade (35.9%, n=65). The other grade levels were
represented as follows and are
18
depicted in Figure 3: First Grade—0.6%, n=1; Third Grade—
27.6%, n=50; Fourth Grade—

22.1%, n=40; Fifth Grade—13.8%, n=25). The greatest number
of testers came from Burbank
Elementary (26.5%, n=48) as illustrated in Figure 4. The
number of participants in Spanish
Immersion was 13.3% (n=24), as shown in Table 4. Finally,
descriptive Tables 5, 6, and 7 show
that eighty-six of the one hundred eighty one students who
tested were accepted to the program.
Of these, forty-six were female (25.4% of the total number of
testers) and forty were male
(22.1% of the total number of testers). This further indicates
that almost half of the students
(47.5%) who tested for the ACT Program during the 2013-2014
school year were accepted.
Figure 3. Percent of testers from each grade.

19
Figure 4. Number of testers per school building.
In addition to collecting information on qualitative variables,
data was also gathered from
various quantitative variables. As mentioned earlier, the
Cognitive Abilities Test measures
students’ learned reasoning abilities in the three areas most
linked to academic success in school:
Verbal, Quantitative, and Nonverbal. Students’ performance on
these three test batteries is
represented in Table 8 in two forms – the Universal Scale Score
(USS) and percentile rank.
The USS scores for the CogAT provide a continuous measure of
cognitive development from
Kindergarten through Grade 12. These scores are the means by
which raw scores from different
levels of the test are placed on a common scale. This makes it
possible to compare performance
on one level of a test battery with performance on any other
level of that test battery. The USS
scores were converted to percentile ranks for each of the test
batteries. The percentiles from

each portion of the test were then averaged to create another
variable called CogAT Avg. This
overall score is what is used for admittance purposes. Finally,
data collected from the Renzulli
0
10
20
30
40
50
60
Birchwood Burbank JC Poplar
Wood
Red Cedar Scenic Knoll
20
Scales for Rating the Behavioral Characteristics of Superior
Students were locally normed
resulting in percentile ranks in the areas of learning, creativity,

leadership, and motivation.
Table 8
Descriptive Statistics of Quantitative Variables
N Range Minimum Maximum Mean Median Std. Deviation
Verb USS 181 123 129 252 190.44 192.00 20.924
Verbal Percentile 181 98 1 99 69.56 76.00 23.419
Quant USS 181 127 139 266 190.05 189.00 21.555
Quantitative Percentile 181 87 12 99 65.85 69.00 21.700
Nonverbal USS 181 116 143 259 200.27 201.00 20.190
Nonverbal Percentile 181 92 7 99 69.29 75.00 23.882
CogAT Avg 181 93 4 97 68.16 71.00 19.000
Learning Percentile 181 94 5 99 74.76 83.00 21.163
Creativity Percentile 181 98 2 100 71.83 80.00 25.496
Leadership Percentile 181 100 0 100 69.29 78.00 26.397
Motivation Percentile 181 94 4 98 71.52 79.00 22.790
The ranges for the percentile scores on the various portions of
the CogAT were as follows:
st

percentile to 99
th
percentile (range of 98)
th
percentile to 99
th
th
percentile to 99
th
th
percentile to 97
th
The arithmetic mean of each of each of these scores is lower
than the median score for each as
shown listed below:

.16, Mdn = 71.00
Thus, some participants scoring rather low on the various
portions of the test negatively skew the
21
distribution of each. Figures 5 and 6 on the next page show the
distribution of the participants’
average Cognitive Abilities Test score. It is very easy to see
that this distribution has a strong
negative skew just by looking at the Stem-and-leaf plot and the
histogram of the data.
A boxplot and means comparison of the CogAT Average scores
by building illustrates
how the various buildings compare to one another. Figure 7 and
Table 9 depict this and show
that the average performance of students on the Cognitive
Abilities Test at Bursley is lower than
the performance of students at other schools within the district.
Though each building had

outliers, the lower range of scores at Bursley Elementary was
certainly greater than those at the
other buildings. This might suggest that there are some students
at Bursley who are nominated
for testing purposes for which the ACT Program may not truly
be the best fit – this will be
addressed further in the discussion portion of this study.
2 . 0 1 1
2 . 6 7 9
3 . 1 3
3 . 6 6 8 9 9 9
4 . 1 1 1 2 2 3
4 . 5 5 5 6 7 8 8 9
5 . 0 0 1 2 2 2 2 3 3 3 4
5 . 5 5 5 6 6 6 6 7 7 8 8 9 9
6 . 0 1 1 1 1 2 3 3 3 3 3 4 4
6 . 5 5 6 6 7 7 7 8 8 8 9 9 9 9
7 . 0 0 1 1 1 1 1 1 1 1 2 2 2 3 3 4 4 4 4 4
7 . 5 5 5 5 5 5 6 6 6 6 6 6 7 7 8 8 8 9 9 9 9

8 . 0 0 0 1 1 1 1 1 2 2 2 3 3 3 4 4 4 4 4
8 . 5 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9
9 . 0 0 0 0 0 0 1 2 2 2 3 3 4 4 4
9 . 5 6 6 6 7
Figure 5. Stem & Leaf plot of CogAT Avg
22
Figure 6. Distribution of participants’ Cognitive Abilities Test
Score Average
23
Figure 7. Boxplot of CogAT Avg by Building.

Table 9
Comparison of Means
CogAT Avg
Building Mean N Std. Deviation
Birchwood 69.62 29 22.611
Burbank 62.96 48 22.635
JC 69.40 10 18.506
Pine Wood 69.10 29 15.896
Red Cedar 69.21 34 15.573
Scenic Knoll 72.42 31 14.635
One of the purposes of this study was to review the addition of
using the Renzulli Scales
as part of the testing procedures for the ACT class. The review
of the literature and common
experience might suggest a positive association between the
scores on these scales and either

24
performance on the Cognitive Abilities Test or admittance into
the program. In order to see if
this is truly the case, correlation coefficients were calculated
for each of the Renzulli Scales and
the aforementioned variables. The Learning Characteristics
scale correlation coefficient was
calculated with the CogAT Average variable (See Table 10)
along with a scatterplot to help
picture any possible association (Figure 8).
Table 10
Correlation between Learning % and Acceptance to Program
CogAT Avg Learning %
CogAT Avg Pearson Correlation 1 .252
**
Sig. (2-tailed) .001
N 181 181
Learning % Pearson Correlation .252
**
1

N 181 181
**. Correlation is significant at the 0.01 level (2-tailed).
Figure 8. Scatterplot depicting the association between
Learning % and CogAT Avg
25
The other scales, Motivation, Creativity, and Leadership were
calculated with acceptance to the
program, as shown in Tables 11, 12, and 13.
Table 11
Correlation between Motivation % and Acceptance to Program
Accepted to Program Motivation %
Accepted to Program Pearson Correlation 1 .200
**
N 181 181

Motivation % Pearson Correlation .200
**
1
N 181 181
Table 12
Correlation between Creativity % and Acceptance to Program
Accepted to Program Creativity %
**
N 181 181
Creativity % Pearson Correlation .219
**
1
N 181 181

Table 13
Correlation between Leadership % and Acceptance to Program
Accepted to Program Leadership %
*
N 181 181
Leadership % Pearson Correlation .177
*
1
N 181 181
*. Correlation is significant at the 0.05 level (2-tailed).
26

A positive correlation coefficient (r = +.252) was found for the
association between
students’ percentile ranking on the Learning Characteristics
portion of the SRBCSS and
performance on the CogAT. While the correlation was
significant at the 0.01 level, the strength
of the relationship was low. Positive correlations were also
found for Motivation (r = +.200),
Creativity (r = +.219), and Leadership (r = +.177), and each
were significant, though the strength
of the correlations were also low. A linear regression analysis
was conducted to evaluate how
students’ scores on the Learning Characteristics portion of the
SRBCSS might predict
performance on the Cognitive Abilities test. As shown in the
scatterplot in Figure 8, the
regression equation for predicting performance on the CogAT is
CogAT Avg = +51.22 + (+0.23)Learning %
Table 14 describes the linear relationship between CogAT Avg
and Learning %.
Table 14
Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .252
a
.064 .058 18.436
a. Predictors: (Constant), Learning %
ANOVA
a
Model Sum of Squares df Mean Square F Sig.
1 Regression 4139.476 1 4139.476 12.178 .001
b
Residual 60842.878 179 339.904
Total 64982.354 180
a. Dependent Variable: CogAT Avg
b. Predictors: (Constant), Learning %
Coefficients
a
Model
Unstandardized Coefficients

Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 51.221 5.044 10.155 .000
Learning % .227 .065 .252 3.490 .001
a. Dependent Variable: CogAT Avg
27
The correlation between Learning % and CogAT Avg was
+.252. Approximately 6.4% (R
2
=
.064) of the variance of CogAT Avg was accounted for by its
linear relationship with scores on
the Learning Characteristics portion of the SRBCSS. The model
is statistically significant
(F1,179 = 12.178, p =.001), but it does not really explain a
whole lot of the variability in the
scores on the CogAT. The little bit it does explain (6.4%), it
explains well. In interpreting this

information, there must be other explanatory factors that may be
worth investigating that could
better explain the variability in students’ scores on the
Cognitive Abilities Test. This will be
discussed further below.
Discussion
The findings from the current study are certainly very
interesting and will help to guide
future decisions within the Johnsonville Public Schools district
in terms of how to best admit
students to the ACT program. The first research question
guiding this study attempted to
ascertain differences between students testing from various
buildings and grade levels
throughout the district. Based on the findings, it is clear to see
that the number of students
testing decreases with each grade level. The majority of student
testers represented in the sample
came from second grade or third grade, while there were fewer
students represented for testing at
fifth and sixth grade. The reasons for this may vary, but a few
possibilities follow. First, the
option to have students tested becomes available to families at

the end of second grade. Thus,
after hearing about the ACT program from classroom teachers,
district publications, or other
families within the district, there is probably quite a bit of
interest generated and parents may
look to have their child test for the program. This in turn, acts
as a system to “weed out”
students from testing in subsequent years. Often, when parents
receive the rejection letter or see
that their child’s performance on the test was quite low, they
choose not to test again in
28
subsequent years. This finding might indicate that we work to
educate parents and classroom
teachers at our lower grade levels more about gifted and
talented students and the ACT program,
in general.
Findings from this study also showed differences between the
individual school
buildings. Almost one-third of the total students tested (26.5%)

came from Burbank Elementary
School. Yet, Burbank had lowest mean score of the average
percentile ranking on the Cognitive
Abilities Test (M = 62.96). From discussions with the ACT
program’s previous director, this
pattern seemed to occur each year, though further analysis of
past data would serve as evidence
of this. Thus, it might be worthwhile to ensure that all
buildings have a consistent understanding
of how to nominate students.
Analysis of the data also revealed that a number of the students
who tested for the
program had very low test scores – some even as low as the
fourth percentile. This was certainly
quite revealing as the participants were tested for admittance
into a program for gifted and
talented students. Though there might be a few factors that
contribute to these lower scores, such
as the fact that the test is timed, the novelty of a “bubble test,”
and, quite possibly, test anxiety,
this finding does indicate that some of the students testing for
the program would most likely not
be considered academically gifted and talented. To ameliorate
this problem, a better screening

process may need to be implemented so as to not test students
unnecessarily.
The second and third research questions that guided this study
sought to evaluate the
Renzulli Scales for Rating the Behavioral Characteristics of
Superior Students and determine if
there might be an association between the various scales and
admittance to the program. What’s
more, a linear regression was conducted to evaluate whether or
not the Learning Characteristics
Scale on the SRBCSS served as a good predictor of student
performance on the CogAT. The
29
Learning Characteristics Scale (See Appendix A) asks teachers
to rate student behaviors of
student learning tendencies, such as having “a large storehouse
of information about a variety of
topics” or “keen and insightful observations” (Renzulli et al.,
2002a). The findings showed that
there really was not a strong relationship between the Learning

Characteristics Scale and
performance on the CogAT. In fact, there was a weak
relationship between all of the other
Renzulli Scales – Motivation, Creativity, & Leadership – and
admittance the ACT program, too.
This is somewhat contrary to the literature and what we would
think would be the case for these
students. Most educators would probably assume that ratings on
the Learning Characteristics
Scale of the SRBCSS would mean that a student would perform
well on the CogAT. Because
there was not truly a relationship found in this study, it begs the
question whether or not the
Renzulli learning scale accurately measures students’ general
reasoning abilities. It seems
somewhat hard to believe that an eleven-item scale could
capture a child’s capabilities in the
areas most linked to success in school. What’s more, because
the scales are based on teacher
impressions, it may not be as valid as we might think. Renzulli
et al. (2002b) have definitely
brought to light an important idea about the giftedness – it is
most likely more than just pure
intellect. However, the degree to which the Scales are taken

into consideration during
identification processes, and how they might be used in tandem
with other instruments, warrants
further exploration. We know there are exceptions to every
rule. Some students may perform
poorly on the CogAT, but do well in school because of pure
grit. Other students may perform
well on the Cognitive Abilities Test but, because they lack
social skills or intrinsic motivation,
they may perform poorly in school. Further multi-factor
analysis will need to be completed to
help understand this dynamic more. What’s more, as the ACT
program’s new director, I will
need to establish my own philosophy in order to guide the
direction of the program and to help
30
determine what kinds of students will fit best.
Conversations with the previous two directors of the program
help to understand different
philosophies for admitting students. One director, who only

used scores from the CogAT, most
likely had many bright students, but it could probably be
inferred that not all of the students were
highly motivated or showed strong leadership skills. The other
director of the program sought to
expand the definition of giftedness to include other traits, such
as leadership, motivation, and
creativity, as consistent with Renzulli and his colleagues
(Renzulli et al., 2002b). With the
findings from this study, the challenge going forward will be to
determine the appropriate
direction to take in terms of what students should be admitted,
what instruments to us to find
those students, and how best to prepare administrators, teachers,
and parents for any changes
that may result.
Implications of the Study
The findings from this study help to open the discussion for
what course of action to take
when trying address the very difficult and controversial topic of
finding the best system to
identify students for admittance to the ACT program at
Johnsonville Public Schools. The

following recommendations will help serve as a catalyst to
ameliorate some of the problems with
the current system and to stimulate conversation about the
future steps. First, the data clearly
shows that more work needs to be done up front to ensure that
the students who take the
Cognitive Abilities Test genuinely demonstrate traits of
giftedness. Though our conceptions of
what it means to be gifted have greatly changed since the early
20
th
Century (Reis, n.d.), the
ACT program’s foundation was built on serving students who
needed academic acceleration and
enrichment. Thus, those students testing for the program should
demonstrate evidence of high-
intellectual and creative achievement capability. One way to
assist with this would be to set up a
31
multi-phase screening process. After being nominated, those
individuals on the admittance

committee could use students test scores, such as students’
scores from the Discovery Education
Assessment. By analyzing student performance on this test,
educators at Johnsonville might be
able to make more refined selections for the students that
should actually take the Cognitive
Abilities Test.
Another implication of this study remains in the fact that there
seems to be a need to
work with all stakeholders – building administrators, classroom
teachers, and parents – so that
there is a better understanding of the traits of giftedness, what
the Johnsonville ACT Program
actually is, and how to recommend or nominate students. When
a parent asks questions about
whether or not they should nominate their child for the ACT
program, it can certainly be a
difficult or somewhat uncomfortable conversation, especially if
the student may not demonstrate
all of the qualities of someone who would fit best in the
program. However, these conversations
should happen so as not to confuse families of student ability or
provide false hope to students

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