Survey

Using the topic and research question you developed in week 1, you will design a quantitative instrument that could potentially answer your topic/research question if it were to be applied to a quantitative study. Keep in mind, this may take some stretching if you wrote your question leaning quantitatively. The purpose here is not to box you in but to ensure that you have a solid understanding of both methodologies. This assignment functions similar to 3.1 but in a quantitative format. Finally, view the rubric and examples to make sure you understand the expectations of this assignment.

 

 

 

 

 

 

 

 

 

 

 

 

35 years
5+ years
I am _______. *Mark only one oval.
Male
Female
Other:
Instructional Coach Impact Survey
1. I serve as instructional coach at the ________ level. *Mark only one oval.
Elementary
Middle
High
2. I currently have ________ years of experience in education. *Mark
only one oval.
05 years
610 years
1120 years
2130 years
30+ years
3. I currently have ________ years of experience as instructional coach.
*Mark only one oval.
02 years
4.
5. I provide pre- and post-conferences within the coaching cycle for my
new and at-risk teachers in my building. * Mark only one oval.
Strongly Disagree Strongly Agree
6. I provide monthly new teacher meetings with agenda/schedule/sign-in
sheet. * Mark only one oval.
Strongly Disagree Strongly Agree

 

 

 

 

 

7. I provide examples of feedback to teachers as follow-ups to classroom observation. * Mark
only one oval.
Strongly Disagree Strongly Agree
8. I have evidence of modeling or co-teaching with teachers in my building. * Mark only one
oval.
Strongly Disagree Strongly Agree
9. I have documentation aligning my work with new teachers specific to their needs. * Mark
only one oval.
Strongly Disagree Strongly Agree
10. I have evidence of teacher requested assistance with follow up. * Mark only one oval.
Strongly Disagree Strongly Agree
11. I have evidence of administration requested assistance with follow up. * Mark only one
oval.
Strongly Disagree Strongly Agree
12. I have evidence of student intervention plans for the most at-risk students in our building. *
Mark only one oval.
Strongly Disagree Strongly Agree
13. I have evidence or data team meeting agendas and schedules for students. * Mark only one
oval.
Strongly Disagree Strongly Agree
14. I have sample data of a student in my building making academic progress and sample data
of a student in my building not making academic progress. * Mark only one oval.
Strongly Disagree Strongly Agree

 

 

 

 

 

 

15. I have fidelity monitoring documentation for students I conducted this school year. * Mark
only one oval.
Strongly Disagree Strongly Agree
16. I have weekly intervention documentation from my academic interventionists. * Mark only
one oval.
Strongly Disagree Strongly Agree
17. I have evidence of working with a priority professional learning community or other
professional learning community in my building. * Mark only one oval.
Strongly Disagree Strongly Agree
18. I have evidence of fidelity monitoring for standards-based intervention being completed in
my building. * Mark only one oval.
Strongly Disagree Strongly Agree
19. I have evidence of assistance given to teacher(s) for data analysis of Tier I data. * Mark
only one oval.
Strongly Disagree Strongly Agree
20. I have documentation of resources shared with staff members. * Mark only one oval.
Strongly Disagree Strongly Agree
21. I have documentation of school improvement goals based on TVAAS and achievement
data in my building. * Mark only one oval.
Strongly Disagree Strongly Agree
22. I have evidence of PDs I organized during the school year based on specific needs in our
building. * Mark only one oval.
Strongly Disagree Strongly Agree

 

 

 

23. I have evidence of needs assessments completed by teachers in my building. * Mark only
one oval.
Strongly Disagree Strongly Agree
24. I have evidence of PDs I have attended this school year based on our building needs. *
Mark only one oval.
Strongly Disagree Strongly Agree
25. I have evidence of collaboration with other instructional coaches or district staff during this
school year. * Mark only one oval.
Strongly Disagree Strongly Agree
26. I have evidence of parent/family communication this school year. * Mark only one oval.
Strongly Disagree Strongly Agree
27. I have evidence of universal screening schedules, fidelity monitoring schedules, and
progress monitoring schedules being completed in a timely manner. * Mark only one oval.
Strongly Disagree Strongly Agree

 

 

 

 

 

 

 

 

Research queston
The research queston used “Can machine learning aid in preventng cybersecurity atacks in healthcare”,
data will be collected by the following: surveys will be sent to Chief Informaton Security Ofcers (CISO)
and Chief Informaton Ofcers (CIO) or any designee for cybersecurity related maters.
Collecton Instrument
Survey with closed ended questons will be used and as follows:
Dependent Variable:
Dependent variable will be do “do you currently have machine learning sofware (sometmes called AI) to
prevent cyber-atacks. (yes or no)
Independent Variables:
Did you experience a cyber-atack? (yes or no)
What type of atack did you experience? (Select the type of atack. Multple selectons permited)
Type of atack:
Brute force password atack
DDoS/DoS
malware
ransomware
phishing
suspected insider threat
other (write in type of atack)
Did intrusion detecton work? (yes or no)
What year did you experience the atack?
Year of atack (2015 – 2020) (select the year, Check box)
Not Contained in the Study
Size of insttuton will not be accounted for

 

 

 

 

 

 

 

 

 

Do you currently have machine
learning sofware (sometmes
called AI) to prevent
cybersecurity atacks?
Yes
No
Did you experience a cyberatack
Yes
No
What type of atack did you
experience? (Select the type of
atack. Multple selectons
permited)
Brute force password atack
DDoS/DoS
Malware
Ransomware
Phishing
Suspected insider threat
Other (write in type of atack)
Did intrusion detecton work? Yes
No
What year did you experience 2015
the atack?
2016
2017
2018
2019
2020

 

 

 

 

 

 

 

 

 

 

1
Research Question
The survey respondents for this research was to understand whether health care security
is more important as a concern or to focus on the problem. For this data collection, the research
question was to answer How machine learning can improve healthcare cybersecurity and what
ML techniques can protect health care data? (Zerka et al., 2020). The purpose of this study is to
measure machine learning methods to improve healthcare cybersecurity. Machine learning
techniques are used for cybersecurity analysis to identify potential threats and potential
vulnerabilities in future systems and infrastructure to protect business systems and assets of
Healthcare systems. This focus on the problem of cybersecurity in healthcare using cyber
security rating as an indicator to identify current threats and potential vulnerabilities in various
(Zerka et al., 2020).
Data Measure
The security events of health care data will be collected from electronic health care
records EHR to find insights of malicious activity by analysing large data set for malicious
activity. This data is generated for medical diagnosis information and provides unique
healthcare information including diagnosis codes, patient characteristics, and medical treatment
information (Zerka et al., 2020). For healthcare security it is important to evaluate the amount
of information used to find new threats and other breaches by looking at security events related
to healthcare. This is possible if thus study understand the underlying security issues of
healthcare and security awareness of users of the products, especially care of healthcare (Zerka
et al., 2020).
The dependent measure is the rate of breaches in terms of healthcare data by the
respective technologies. Data set for Healthcare Health care events will be generated based on a
survey of over 20,000 consumers. The purpose of this study is to gather data about electronic

 

 

 

 

 

 

 

 

 

 

2
health care records ( EHRs) of over 20,000 consumers within a period of two to three years.
The sample size is from over 50,000 in case of healthcare data and also from over 25,000 in
case of medical diagnostics information. It will assess the security data by analysing the source
information of EHR data.
However, some of the aspects of this research for data collection are as following
 Patients health data.
 Electronic health records.
 Healthcare security rating.
 Information about cyber risk.
 Personal Health care information PHI.
 Telemedicine data.
 Social media data.
To collect data for the study, the participants will gather from private health care
companies on a daily basis. After the data gathering, it is stored in a computer located at their
home address. The data is stored using the machine vision that is in use and the data is analyzed
in order to identify specific ML techniques used to achieve more secure health care data. At
some stage of the research, the sample of participants will also be analyzed with ML techniques
to identify what will be the most important health care security events for EHRs and to gather
additional data. The final stage of this research project is to identify ML techniques which will
be used to improve health care data security and the study of ML techniques related to ML
techniques related to ML techniques related to security issues.
However, the ML techniques used in this research are following
 Reinforcement Learning RL.
 Convolutional neural networks CNNs.
 Deep Learning DL.

 

 

 

3
 Supervised Learning.
 Unsupervised Learning.
 Semi-supervised Learning.
In health care cyber security machine learning techbiques are used to create a profile of
the health care providers, the patients, the doctors and the patients’ habits and relationships
(Boukerche, & Coutinho, 2020). Data are collected from these individuals and the data are
analyzed in order to identify what the health care providers do, the health care events that they
will engage in, and the types of events that they will be using health care security tools for. At
some stage of the research, the health care is analyzed with ML techniques to identify what the
health care providers use in order to protect health care services, and to gather additional data
(Boukerche, & Coutinho, 2020).

 

 

4
References
Boukerche, A., & Coutinho, R. W. (2020). Design Guidelines for Machine Learning-based
Cybersecurity in Internet of Things. IEEE Network.
Zerka, F., Barakat, S., Walsh, S., Bogowicz, M., Leijenaar, R. T., Jochems, A., … & Lambin, P.
(2020). Systematic review of privacy-preserving distributed machine learning from
federated databases in health care. JCO clinical cancer informatics, 4, 184-200.

 

 

 

 

 

 

 

 

 

Research Queston
The influence of educators and universities on first – time, freshman students with low
GPA and ACT scores is more prevalent than ever. Teaching students to be successful in college
courses, specifically those with low standardized test scores is essential. One would create a mock
dissertation on this topic, by studying proactive, motivational study skills to students who enter
college with lower academic entrances. The research question is “What is the benefit of enrolling
students who lower than average composite standardized test scores into study and life skills
courses?”. The purpose of this study will be to examine the effectiveness of teaching a particular
cohort using T-test and linear regressions to determine the impact on those who receive the service
and those who do not.
Data Measure
The success of these courses will be based on grade point average, degree completion, and
time of degree completion. This data is housed in many universities registrar office; however, it
can also be found at Kentucky Council on Postsecondary Education. These organizations collect
all students who have been enrolled in a determined course in Kentucky, their standardized test
scores, demographic characteristics, high school test scores and more. Those who obtained a
degree will be found from the University and the year that they began at the university to the year
they completed a degree. Grade point average will be available through individual universities that
the student attended. The information regarding the course, those enrolled, the rate that continued
each year, and more will be available through the Kentucky Council on Postsecondary Education.
Attainment of Associates degrees (typically at a community, two-year university) versus a
Bachelor’s degree, can be found from the Kentucky Council on Postsecondary Education.

 

 

 

 

 

 

 

 

 

 

DATA COLLECTION 1
Research Question and Purpose
The research question for this study is: “Does higher educational leadership have an impact
on student persistence from first-year to second-year in a public four-year institution?”
This study will be looking at a public four-year institution in the Midwest that is accredited by the
regional accrediting agency Higher Learning Commission (HLC). The institution has undergone a
complete change of leadership four years ago. With this change, we will be evaluating if there was
an impact on first-year to second-year student retention.
Collection of Archival Data
Each year, the university is required to disseminate a climate survey as a part of the
regional accreditation requirements. The climate survey is solely focused on the leadership of the
institution and the support they provide. The survey is a Likert scale, the university archives the
survey results for accreditation purposes and for assessing trends. These past and current results
will be gathered and utilized from the period before and after the leadership change. The survey
results are housed in the Office of Institutional Effectiveness and Assessment and are readily
accessible with a request submission. The study will analyze the survey results for four years prior
to the leadership change and the results for the four years since the change.
Additionally, the Office of Institutional Effectiveness and Assessment will be able to
provide first-year to second-year retention rates for the corresponding years. With both sets of data,
the study will evaluate if there is a correlation using a Chi-Square test. Furthermore, enrollment
and retention information for the institution can be accessed from Integrated Postsecondary
Education Data System (IPEDS) in order to gather a complete picture of enrollment and retention
trends during the eight-year period the study will be examining.

 

 

 

 

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 2
Research Question:
What is the Influence of Corporate Structure and Vision Statements on the Organizations
Financial Success in the Period of January 2019 Through January 2021?
In looking at the topic of the influence of visionary leadership on change management and
implementation, the period immediately preceding and through the current peak of Covid-19 is a
time of unprecedented change. This period also encompasses a highly contested political election
for the president that exacerbates the requirements for change management and implementation for
an organization to remain successful. Organizational structure has a significant impact on how
information moves within a company and the speed at which it can be assimilated and reacted
upon (Krishnan, 2018). The leader is primarily responsible for implementing the corporate
structure and articulating and communicating its vision statement. The prevalence of an articulated
and well-communicated vision statement within various organizational structures can be correlated
to financial performance metrics such as free cash flows (FCF) and the weighted average cost of
capital (WACC) to form statistical correlations. This will be contrasted with the same
organizational structure without a well-articulated and accessible vision statement with the
expected outcome that the same financial metrics will suffer. It is also expected that more
decentralized structures will enhance financial performance during periods of significant change.
A combination of survey and archival data will be utilized. The survey will be emailed to a
random selection of HR departments of 217 of the Fortune top 500 companies for distribution to
front line and middle managers (Krejcie, 1970). Archival data from publicly available financial
statements will be used to determine FCF and WACC.

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 3
Data Measure
A survey instrument will capture the independent variables of demographic information,
corporate structure, and vision data.
Demographic Information
1. Gender (check one): ________Female _________Male
2. Geographic Location of the Company (Check one):
_________Northeast _________Southeast
_________Northwest _________Southwest
_________Midwest
3. What industry do you work in? _______________________________________
4. Approximately how many people are employed by your organization? ______________
Corporate Structure (Bolea & Atwater 2016).
1. How would you define your corporate structure?
A. Functional: Centralized, people are organized based on similar job skills. Tasks are
defined and structured.
B. Divisional/Organic: Organized by business function (region, unit, product).
Decentralized, with a focus on a specific part or region of the business, but typically
one responsible supervisor.

 

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 4
C. Matrix: A combination of functional and divisional. Necessary when there rapidly
changing business environments. Combines structured functions with function-based
work teams with dual or multiple departmental supervisors.
Corporate vision will be measured on a Likert scale utilizing the following closed-ended
questions utilized in previous studies, pending approval from the authors (Carsten, 2006).
Corporate Vision
1 = Strongly Disagree, 2 = Disagree, 3 = Neither Agree Nor Disagree, 4 = Agree, 5 = Strongly
Agree.
1. I am aware of my company’s vision 1 2 3 4 5
2. I would feel comfortable explaining my 1 2 3 4 5
company’s vision to a new co-worker.
3. Measures have been taken to make sure 1 2 3 4 5
I understand the vision.
4. There is a commonality of purpose in my 1 2 3 4 5
organization.
5. There is total agreement on our organizational 1 2 3 4 5
vision across all levels, functions, and divisions.
6. The vision is aligned with my company’s 1 2 3 4
overreaching goals.
5

 

 

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 5
7. The vision reinforces my company’s 1 2 3 4 5
guiding purpose.
8. The vision is aligned with my company’s 1 2 3 4 5
core philosophy.
9. Attempts to make things better at my company 1 2 3 4 5
will not produce good results.
10. Plans for future improvement are likely to 1 2 3 4 5
change things for the better.
11. Suggestions on how to solve problems 1 2 3 4 5
are likely to produce real change.
12. Employees were involved in creating our 1 2 3 4 5
company’s vision.
13. Top management asked employees to 1 2 3 4 5
participate in the visioning process.
14. The vison was produced entirely by 1 2 3 4 5
top management.
15. Employees were asked to provide input on the 1 2 3 4 5
content of the vision statement.
Thinking about your department or work group, please indicate whether you
agree with the following statements using the scale from 1 (strongly disagree) to 5
(strongly agree).

 

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 6
16. The vision helps to guide the goals or 1 2 3 4 5
Objectives of my department/work group.
17. The vision has an influence on the decisions 1 2 3 4 5
that are made by my department/work group.
18. My department/work group is not influenced 1 2 3 4 5
by the vision.
19. My department/work group plays an essential 1 2 3 4 5
role in achieving the vision.
Now, Thinking about your specific job, please indicate the degree to which you
agree with each statement by circling a number between 1(strongly disagree) and 5
(strongly agree).
20. My work directly contributes to carrying out 1 2 3 4 5
the vision of my organization.
21. The vision helps guide my work activities. 1 2 3 4 5
22. I don’t understand how the vision impacts 1 2 3 4 5
my particular job.
23. The vision helps me understand the purpose 1 2 3 4 5
of my work.
24. Generally speaking, I am very satisfied 1 2 3 4 5
with my job.

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 7
25. I am interested in my work. 1 2 3 4 5
Corporate financial data will be obtained from archival data found on the 10-Q (quarterly)
reports filed with the Securities Exchange Commission (SEC) and publicly available on their
website (www.sec.gov). Corporate value, the dependent variable, can be determined as follows
(Brigham & Ehrhardt, 2020):
Value = FCF1/(1+WACC)1 + FCF2/(1+WACC)2
+ FCF3/(1+WACC)3
+ …
FCF = EBIT(1- Tax Rate) – (Present year Operating Capital – Previous year Operating Capital)
Corporate earnings before income taxes (EBIT) and tax rate will be obtained on the SEC website
in the condensed consolidated statement of income. Operating capital will be obtained from the
condensed consolidated balance sheet as the sum of net property and inventory.
After exploring the difficulty in obtaining the WACC from public financial records, and its
anticipated limited impact in the low interest rate environment during the period of interest (2019-
2020), corporate value will be approximated as the sum of the future cash flows (FCF’s).
Value ≈ FCF1 + FCF2 + FCF3 + …

 

 

 

 

 

Running Head: 3.1 Week 3 Discussion Forum 8
References
Bolea, A., & Atwater, L. E. (2016). Applied Leadership Development: Nine Elements of
Leadership Mastery. New York: Routledge, Taylor & Francis Group.
Brigham, E. F., & Ehrhardt, M. C. (2020). Corporate finance: a focused approach. (7th ed.).
Boston, MA: Cengage.
Carsten, M. K. (2006). Vision in focus: Investigating follower processes that mediate vision
articulation and organizational outcomes (Order No. 3233754). Available from
ABI/INFORM Global. (305357922). Retrieved from
https://search.proquest.com/dissertations-theses/vision-focus-investigating-followerprocesses/docview/305357922/se-2?accountid=10378
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed
methods approaches. Thousand Oaks, CA: SAGE Publications.
Krejcie, R. V. & Morgan, D. W. (1970). Determining sample size for research activities.
Educational and Psychological Measurements, 30, 607-610.
Krishnan, R. R. (2018). Organizational change readiness: Effects of organizational structure and
leadership communication in organizational change (Order No. 10791024). Available from
ProQuest Dissertations & Theses Global. (2055262240). Retrieved from
https://search.proquest.com/dissertations-theses/organizational-change-readiness-effectsstructure/docview/2055262240/se-2?accountid=10378
Teachers’ Sense of Efficacy Scale1
(long form)
Teacher Beliefs How much can you do?
Directions: This questionnaire is designed to help us gain a better understanding of the g
Very Little
Quite A Bit
kinds of things that create difficulties for teachers in their school activities. Please indicate
your opinion about each of the statements below. Your answers are confidential.
me
Great
Nothin
So
A
1. How much can you do to get through to the most difficult students? (1) (2) (3) (4) (5) (6) (7) (8) (9)
2. How much can you do to help your students think critically? (1) (2) (3) (4) (5) (6) (7) (8) (9)
3. How much can you do to control disruptive behavior in the classroom? (1) (2) (3) (4) (5) (6) (7) (8) (9)
4. How much can you do to motivate students who show low interest in school (1) (2) (3) (4) (5) (6) (7) (8) (9)
work?
5. To what extent can you make your expectations clear about student behavior? (1) (2) (3) (4) (5) (6) (7) (8) (9)
6. How much can you do to get students to believe they can do well in school work? (1) (2) (3) (4) (5) (6) (7) (8) (9)
7. How well can you respond to difficult questions from your students ? (1) (2) (3) (4) (5) (6) (7) (8) (9)
8. How well can you establish routines to keep activities running smoothly? (1) (2) (3) (4) (5) (6) (7) (8) (9)
9. How much can you do to help your students value learning? (1) (2) (3) (4) (5) (6) (7) (8) (9)
10. How much can you gauge student comprehension of what you have taught? (1) (2) (3) (4) (5) (6) (7) (8) (9)
11. To what extent can you craft good questions for your students? (1) (2) (3) (4) (5) (6) (7) (8) (9)
12. How much can you do to foster student creativity? (1) (2) (3) (4) (5) (6) (7) (8) (9)
13. How much can you do to get children to follow classroom rules? (1) (2) (3) (4) (5) (6) (7) (8) (9)
14. How much can you do to improve the understanding of a student who is failing? (1) (2) (3) (4) (5) (6) (7) (8) (9)
15. How much can you do to calm a student who is disruptive or noisy? (1) (2) (3) (4) (5) (6) (7) (8) (9)
16. How well can you establish a classroom management system with each group of (1) (2) (3) (4) (5) (6) (7) (8) (9)
students?
17. How much can you do to adjust your lessons to the proper level for individual (1) (2) (3) (4) (5) (6) (7) (8) (9)
students?
18. How much can you use a variety of assessment strategies? (1) (2) (3) (4) (5) (6) (7) (8) (9)
19. How well can you keep a few problem students form ruining an entire lesson? (1) (2) (3) (4) (5) (6) (7) (8) (9)
20. To what extent can you provide an alternative explanation or example when (1) (2) (3) (4) (5) (6) (7) (8) (9)
students are confused?
21. How well can you respond to defiant students? (1) (2) (3) (4) (5) (6) (7) (8) (9)
22. How much can you assist families in helping their children do well in school? (1) (2) (3) (4) (5) (6) (7) (8) (9)
23. How well can you implement alternative strategies in your classroom? (1) (2) (3) (4) (5) (6) (7) (8) (9)
24. How well can you provide appropriate challenges for very capable students? (1) (2) (3) (4) (5) (6) (7) (8) (9)
Teachers’ Sense of Efficacy Scale1
(short form)
Teacher Beliefs How much can you do?
Directions: This questionnaire is designed to help us gain a better understanding of
the kinds of things that create difficulties for teachers in their school activities. Please
indicate your opinion about each of the statements below. Your answers are
confidential.
Nothing
Very Little
Some
Quite A Bit
A Great Deal
1. How much can you do to control disruptive behavior in the classroom? (1) (2) (3) (4) (5) (6) (7) (8) (9)
2. How much can you do to motivate students who show low interest in school (1) (2) (3) (4) (5) (6) (7) (8) (9)
work?
3. How much can you do to get students to believe they can do well in school (1) (2) (3) (4) (5) (6) (7) (8) (9)
work?
4. How much can you do to help your students value learning? (1) (2) (3) (4) (5) (6) (7) (8) (9)
5. To what extent can you craft good questions for your students? (1) (2) (3) (4) (5) (6) (7) (8) (9)
6. How much can you do to get children to follow classroom rules? (1) (2) (3) (4) (5) (6) (7) (8) (9)
7. How much can you do to calm a student who is disruptive or noisy? (1) (2) (3) (4) (5) (6) (7) (8) (9)
8. How well can you establish a classroom management system with each (1) (2) (3) (4) (5) (6) (7) (8) (9)
group of students?
9. How much can you use a variety of assessment strategies? (1) (2) (3) (4) (5) (6) (7) (8) (9)
10. To what extent can you provide an alternative explanation or example when (1) (2) (3) (4) (5) (6) (7) (8) (9)
students are confused?
11. How much can you assist families in helping their children do well in school? (1) (2) (3) (4) (5) (6) (7) (8) (9)
12. How well can you implement alternative strategies in your classroom? (1) (2) (3) (4) (5) (6) (7) (8) (9)
Directions for Scoring the Teachers’ Sense of Efficacy Scale1
Developers: Megan Tschannen-Moran, College of William and Mary
!!!!!!!!!!!!!!!!!!!!!!!!!Anita Woolfolk Hoy, the Ohio State University.
Construct Validity
For information the construct validity of the Teachers’ Sense of Teacher efficacy Scale, see:
Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing and
elusive construct. Teaching and Teacher Education, 17, 783-805.
Factor Analysis
It is important to conduct a factor analysis to determine how your participants respond to the
questions. We have consistently found three moderately correlated factors: Efficacy in Student
Engagement, Efficacy in Instructional Practices, and Efficacy in Classroom Management, but at
times the make up of the scales varies slightly. With preservice teachers we recommend that the
full 24-item scale (or 12-item short form) be used, because the factor structure often is less
distinct for these respondents.
Subscale Scores
To determine the Efficacy in Student Engagement, Efficacy in Instructional Practices, and
Efficacy in Classroom Management subscale scores, we compute unweighted means of the items
that load on each factor. Generally these groupings are:
Long Form
Efficacy in Student Engagement:
Efficacy in Instructional Strategies:
Efficacy in Classroom Management:
Items
Items
Items
1, 2, 4, 6, 9, 12, 14, 22
7, 10, 11, 17, 18, 20, 23, 24
3, 5, 8, 13, 15, 16, 19, 21
Short Form
Efficacy in Student Engagement:
Efficacy in Instructional Strategies:
Efficacy in Classroom Management:
Items
Items
Items
2, 3, 4, 11
5, 9, 10, 12
1, 6, 7, 8
Reliabilities
In Tschannen-Moran, M., & Woolfolk Hoy, A. (2001). Teacher efficacy: Capturing and elusive
construct. Teaching and Teacher Education, 17, 783-805, the following were found:
Long Form Short Form
Mean SD alpha Mean SD alpha
OSTES 7.1 .94 .94 7.1 .98 .90
Engagement 7.3 1.1 .87 7.2 1.2 .81
Instruction 7.3 1.1 .91 7.3 1.2 .86
Management 6.7 1.1 .90 6.7 1.2 .86
1 Because this instrument was developed at the Ohio State University, it is sometimes referred to
as the Ohio State Teacher Efficacy Scale. We prefer the name, Teachers’ Sense of Efficacy
Scale.

 

 

 

 

 

 

 

 

 

 

 

DATA GATHERING INSTRUMENT 1
Mock Dissertation Topic- Updated
Has the COVID-19 pandemic changed teacher perspectives on Professional Learning
Communities and collaboration?
If so, how can school administrators expand upon this changed perspective as a
transformational moment for increased collaboration?
I started preliminary research on this topic, in a broad sense, for the Learning in
Adulthood course, but based everything on my opinion rather than hard evidence. I would
appreciate the opportunity to see if my opinions will be supported within survey results
and other researchers conclusions. Also, there has been renewed pushes on
Professional Learning Communities within my school, but much of the focus has
concerned administrative paperwork, data analysis, etc. rather than on relationships,
collaboration, etc. I feel this research could be applicable to the school’s administrative
team as the purpose of PLC’s may expand beyond pre-pandemic status.
Data Gathering Instrument
The Tennessee Department of Education in partnership with Vanderbilt University
conducts a yearly Educator Survey, and historical data is available to establish trends.
The survey results are detailed at the State, District, and School level, if 45% of the
teachers respond. Unfortunately with March 2020 COVID-19 closures, only 22% of
teachers participated in the 2020 Tennessee Educator Survey at LaVergne High School. I
would like to focus on those questions which relate to collaboration and Professional
Learning Communities to gather information. I was able to track trends within State and
District data, but would like to go further into the school level to direct administrative
action in my building concerning PLCs, collaboration, and teacher support related to

 

 

 

 

 

 

 

 

 

 

 

DATA GATHERING INSTRUMENT 2
retention. The questions from the Tennessee Educator Survey related to collaboration
include:
1. Our school staff is a learning community in which ideas and suggestions for
improvement are encouraged. (TN Educator Survey 2020, 2019, 2018)
2. On average, how many hours per week do you spend creating or sourcing
materials to use for classroom instruction including planning time during and
outside of school hours? (TN Educator Survey 2020, 2019)
3. What percentage of this total time is spent on collaborative instructional planning?
(TN Educator Survey 2020)
I would also add specific questions concerning teacher burnout inspired from the RAND
Teacher Survey including:
1. To what extent is each of the following a concern for you right now? Feelings of
Burnout (RAND 2020)
My data gathering instrument will be a survey to LaVergne High School teachers based
on the questions from the Tennessee Educator Survey and other prominent surveys
including RAND, AIR, and TALIS. The TALIS data on fostering collaboration to improve
professionalism is in depth and I am still taking time to fully read all the components of the
research to see how it fits into my topic since it provides international information.
There is emerging research concerning the impact of COVID-19 on educator
perspectives from the University of Maryland’s College of Education, but much of the
research completed by groups like RAND and the American Institutes for Research, has
focused on student subgroup support (English-Language, Economically Disadvantaged,
Special Education, etc.) and District/Administrative response from an organizational

 

 

 

 

 

 

 

 

DATA GATHERING INSTRUMENT 3
standpoint. It appears there is a focus on how schools mismanaged the pandemic
response, and I would like my focus to surround increased collaboration as a positive
effect of the pandemic.
I set up a preliminary SurveyMonkey Survey based on those questions I would like
to focus on related to PLCs and collaboration. I may have to explore other platforms
based on the limits within the free version of SurveyMonkey. I know faulty may be more
honest in a platform not connected to school login information. You can see the survey at
this link- https://www.surveymonkey.com/r/6PLFQLF I realize I will also need to add
demographic style questions too to analyze the data appropriately and cite the questions.
Resources
American Institutes for Research. (2020). Teaching in the time of COVID-19.
https://www.air.org/resource/teaching-time-covid-19
Hamilton, L., Grant, D., Kaufman, J., Diliberti, M., Schwartz, H., Hunter, G., Messan
Setodji, C., and Young, C. (2020. COVID-19 and the state of K–12 schools: results
and technical documentation from the spring 2020 American educator panels
COVID-19 surveys. https://www.rand.org/pubs/research_reports/RRA168-1.html.
OECD (2020), TALIS 2018 Results (Volume II): Teachers and School Leaders as Valued
Professionals, TALIS, OECD Publishing, Paris, https://doi.org/10.1787/19cf08dfen.

 

 

 

 

DATA GATHERING INSTRUMENT 4
Patrick, S.K., & Newsom, U. (2020). Teaching through a global pandemic: COVID-19
insights from the Tennessee educator survey.
https://peabody.vanderbilt.edu/TERA/files/TES2020_COVID_Brief_FINAL.pdf
Tennessee Department of Education (TDOE). (2020). 2020 Tennessee educator
survey. https://www.tn.gov/education/data/educator-survey.html
Tennessee Department of Education (TDOE). (2019). 2019 Tennessee educator
survey. https://www.tn.gov/education/data/educator-survey/2019-tn-educatorsurvey.html
Tennessee Department of Education (TDOE). (2018). 2018 Tennessee educator
survey. https://www.tn.gov/education/data/educator-survey/2018-tennesseeeducator-survey.html
Tennessee Department of Education (TDOE). (2020). Tennessee educator survey
2020 overview: A report from the Tennessee Department of Education.
https://www.tn.gov/content/dam/tn/education/data/2020-survey/Combined_Briefs.p
df
University of Maryland’s College of Education. (2020). The impact of COVID-19 on
teachers. https://education.umd.edu/research-college/impact-covid-19-teachers