Instructor Information

Andy HungAndy Hung, Ed.D.
Office Hours: by appointment
Phone: (208) 426-5542
Adobe Connect:

*Email is the best way to reach me. If I haven't responded within 48 hours, please email me again. Contact me in other ways as needed.

Course Description

The course covers statistics and data analysis for many of the most commonly used study designs in educational studies with a strong focus on applications. We will cover descriptive and inferential statistics and their use in studies about means, variances and slopes (correlation and regression) including the descriptive analysis, analysis of variance, regression, and analysis of covariance. The primary approach to the course will be analytical/logical. It is important to understand why and how we use the methods rather than just being able to get the correct answer (which, of course, is also important!). Some mathematics, however, will be necessary to understand course content. Heavy emphasis will be placed on understanding how to select appropriate methods/approaches for data analysis and result presentation. There are multiple assignments and expectations for this course. Please remain aware of due dates and subsequent penalties. The content builds upon previously learned information so it is necessary to keep up with the course progress. If you are having any difficulty, please do not hesitate to email me ahead of time instead of missing an assignment.

Course Outcomes

After completing this course the student will be able to:

Course Location and Login Information

This is an online course delivered in Moodle. The Moodle login page explains how to login to Moodle. Contact Moodle Support at if you have problems accessing MoodleIf you have forgotten your password, click the link below the login box, "lost password?" and you will be able to reset it. When you login Moodle look for a link to EDTECH 652-4201 (SP17).

Course Materials

Required Textbook


The practice of survey research Introduction to Statistics (free ebook)
Authors: Lane, D. M., Scott, D., Hebl, M., Guerra, R., Osherson, D. & Ziemer, H
Date: 2013
Publisher: Lane, D. M.


The eletronic version can be found from



Introduction to StatisticsAdvanced Statistics in Research: Reading, Understanding, and Writing Up Data Analysis Results
Authors: Hatcher, L.
Date: 2013
Publisher: Saginaw, MI: Shadow Finch Media LLC.
ISBN-10: 0985867000

Two places to purchase the textbook: Boise State University Bookstore or Amazon.


Reference Books

HTML & CSS All assignments and projects are expected to follow APA styles (6th edition). Make sure you have one APA publication manual for this course.






Required Software

IBM SPSS Statistics

SPSS is also required for this course to complete all analytic tasks. The most updated version of SPSS is version 24. Please ensure to get the Standard, or Higher version because we will introduce Linear Regression, Logistic Regression, ANOVA, and ANCOVA in this course (version comparisons)


Buying six-month or 12-month grad pack (Standard version for students) is a recommended option because SPSS might be updated once or twice a year. You might consider one of the following websites:

Assignment Policy and Grading Scale

Assignment Information

All projects in this course are scenario-based. They involve use of the methods learned in class to analyze, interpret, and explain data from various research scenarios. In general, you will be given research scenarios along with a data set (or sets) that corresponds to the scenario(s). You will be asked to answer the research questions by running the correct analyses, interpreting the results, and writing up your results in a format suitable for a journal article (i.e. APA format).


Detailed information about each assignment is posted in Moodle. Assignments are always due on Wednesdays. Check Moodle and your Boise State email regularly each week; announcements and course updates can be posted at any time.


  Assignments Points
1 Assignment 1 (Module 2) 50
2 Assignment 2 (Module 3) 50
3 Assignment 3 (Module 4) 50
4 Assignment 4 (Module 5) 50
5 Project 1 (Module 6) 100
6 Assignment 5 (Module 7) 50
7 Assignment 6 (Module 8) 50
8 Assignment 7 (Module 9) 50
9 Project 2 (Module 10) 100
10 Participation in Online Discussions (Modules 2 - 10) 50
  Peer Support (extra credit)* (Modules 2 - 10) 50
  Course Evaluation (extra credit)** 20
  Total Points 600

*Earn up to 50 points extra credit for helping fellow classmates with technical & analysis problems.
**Earn 20 points extra credit by filling out the course evaluation.


The assignments in this course are aligned to the AECT standards

This table lists the assignments by number from the previous table and the associated standards

  Standard 1
Standard 2
Standard 3
Standard 4
Knowledge & Skills
Standard 5
Creating       2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9
Using       2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9
Accessing/Evaluating     2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9
Managing       2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9
Ethics       2, 3, 4, 5, 6, 7, 8, 9  
Diversity of Learners       2, 3, 4, 5, 6, 7, 8, 9 2, 3, 4, 5, 6, 7, 8, 9


Final grades are based on the following scale.

Grade Points Required
A+ 580 - 600
A 560 - 579
A- 540 - 559
B+ 520 - 539
B 500 - 519
B- 480 - 499
C+ 460 - 479
C 440 - 459
C- 420 - 439
F 0 - 419

Submitting Assignments: All assignments will be submitted via assignment dropboxs on Moodle by clicking on the submission links.

Asynchronous Discussions: You are encouraged to post questions, course notes, personal experiences, and study resources on discussion forums. In each module you will post your assignment answers online for discussion. Asynchronous discussions are worth 8% of your grade.

Grading Cycle

All assignments are graded together as a group to maintain a higher level of consistency. Grading begins on the first day after a due date and is typically completed before the next due date. You may track your progress through Grades in Moodle.


Feedback varies throughout the course. Because this might be your first network administration course, you are welcome to post questions to clarify concepts or look for further explanations (you are also welcome to answer questions from peers and extra credits will be granted for helping answer questions). I will provide feedback or supplementary resources in the discussion forums so that everyone can benefit from it.

Late Work

Due Dates: All assignment are due on Wednesdays. Assignments must be submitted by 11:59 pm Mountain time on scheduled due dates.

Point Deduction for Late Work: Ten points may be deducted for each day an assignment is late. For example, an assignment that is two days late can lose 20 points as a late penalty.

Emergency Pass: If you have a major event such as a death in the family, illness, hospitalization, or you are out of town, you may turn in one assignment under the emergency pass. This assignment may be up to one week late and still qualify for full credit. After the one week extension has passed ten points per day can be deducted until the assignment is no longer worth any credit.

Your Responsibility with Late Work: If you are going to be late turning in an assignment for any reason, please e-mail the instructor at on or before the scheduled due date. When the assignment is completed you must send a follow-up email to let the instructor know it is ready to grade. This is how the late work penalty is calculated. Failure to notify the instructor could lead to a grade of zero.

Avoid End of Course Late Work: Please note that there are University deadlines for submitting grades at the end of the semester. All work must be turned in at least a week before grades must be posted.

Technical Difficulties

On occasion, you may experience problems accessing Moodle or class files located within Moodle, Internet service connection problems, and/or other computer related problems. Make the instructor aware if a technical problem prevents you from completing coursework. If a problem occurs on our end, such as Moodle failure, then an automatic due date extension is granted.

Reasonable Accommodations

Any student who feels s/he may need accommodations based on the impact of a disability should contact the instructor privately to discuss specific needs. You will also need to contact the Disability Resource Center to schedule a meeting with a specialist and coordinate reasonable accommodations for any documented disability.

The Disability Resource Center is located on the first floor of the Lincoln Parking Garage, on the corner of Lincoln Ave. and University Dr. at Boise State University. They are available Monday through Friday 8:00 a.m. to 5:00 p.m. Mountain Time.


Privacy Inormation

EDTECH courses involves online delivery and for some courses public display of assignments on websites or social media spaces. In the online course, your name, email address, and Moodle profile may be visible to others who have logged into Moodle. You are advised to familiarize yourself with privacy settings on Moodle or social media sites associated with the course. Privacy settings can sometimes be adjusted to restrict certain types of information. Please contact your instructor if you have questions or concerns.

Academic Honesty

Students are expected to create original work for each assignment. Students must follow the Boise State Student Code of Conduct as well as observe U.S. copyright laws in this course.

Please adhere to the following guidelines:

In the event of academic dishonesty, a complaint is filed with the Boise State Student Conduct Office with supporting documentation. This complaint remains on file and actions may be taken against the student (e.g., loss or credit, grade reduction, expulsion, etc.).

Policy for Incompletes

Incompletes are not guaranteed. However, when they are given incompletes adhere to Boise State University
as follows:

Instructors can enter a grade of I?or incomplete?f both of the following conditions are present:

In order to receive an incomplete, you and your instructor must agree to a contract stipulating the work you must do
and the time in which it must be completed for you to receive a grade in the class. The terms of this contract are
viewable on my.BoiseState under Your Student Center To Do List. The contract time varies as set by the instructor
but may not exceed one year. If no grade other than incomplete has been assigned one year after the original
incomplete, the grade of F will automatically be assigned. The grade of F may not be changed without approval of the
University Appeals Committee. You may not remove the incomplete from your transcript by re-enrolling in the class
during another semester. A grade of incomplete is excluded from GPA calculations until you receive a final grade in
the course.

Course Schedule

Detailed information about assignments is posted in Moodle. The instructor reserves the right to make changes to the schedule as needed. Readings and videos have been identified for each module to help you learn the key concepts to successfully complete each assignment.


Students are expected to spend 9-12 hours per week during the regular semester on each EdTech course. However, some students spend more time than others because they have difficulty in understanding statistical jargons and contents. You might watch videos before reading the textbook and welcome to post questions during the learning process. However, please do not skip readings and reply on instructional videos only. You will miss key contents which are required for later sections.

I also recommend completing any reading and videos at the beginning of each module in order to give you enough time to complete each assignment (as well as enough time for any troubleshooting).

Major Assignments

Readings & Videos

Module 1: Introduction to the Course (1/9 - 1/18)

  • Read course syllabus
  • Review important statistical concepts
  • Bio and Introductions


- Syllabus and calendar
- Intro to Statistics Ch1, 4, 5, 6, & 7
- Advanced Statistics Ch1

Module 2: Review of Concepts (1/16 - 1/25)
  • Assignment 1


- Intro to Statistics Ch9, 10, & 11
- Advanced Statistics Ch2 & 4

Module 3: Data Visualization and Exploration (1/23 - 2/8)
  • Assignment 2




- Intro to Statistics Ch2, 3, 8
- Advanced Statistics Ch3
- Handout

Module 4: Data Processing and Transformation (2/6 - 2/15)
  • Assignment 3


- Intro to Statistics Ch13 & 16, 19
- Handout

Module 5: ANOVA (I) (2/13 - 3/1)
  • Assignment 4


- Intro to Statistics Ch15
- Advanced Statistics Ch13
- Handout


Module 6: ANOVA (II) (2/27 - 3/15)
  • Project 1


- Handout



Module 7: Regression (I) (3/13 - 4/5)
  • Assignment 5


- Intro to Statistics Ch14
- Advanced Statistics Ch9 & 10
- Handout

Spring Break (3/20 - 3/26)

No class during the break


Module 8: Regression (II) (4/3 - 4/19)
  • Assignment 6


- Advanced Statistics Ch12
- Handout

Module 9: ANCOVA (4/17 - 4/26)
  • Assignment 7


- Advanced Statistics Ch14
- Handout

Module 10: Final Project (4/24 - 4/30)
  • Project 2



  • Last day to submit discussion posts and final assignments is April 30


Boise State University Academic Calendar

Refer to the Boise State Academic Calendar for University dates and deadlines (e.g., the last day to drop).

Graduate Catalog

Graduate Catalogs for present and prior academic years can be found online at:

College of Education - The Professional Educator

Boise State University strives to develop knowledgeable educators who integrate complex roles and dispositions in the service of diverse communities of learners. Believing that all children, adolescents, and adults can learn, educators dedicate themselves to supporting that learning. Using effective approaches that promote high levels of student achievement, educators create environments that prepare learners to be citizens who contribute to a complex world. Educators serve learners as reflective practitioners, scholars and artists, problem solvers, and partners.

Department of Educational Technology Mission

The Department of Educational Technology is a diverse and international network of scholars, professional educators and candidates who:


Agresti, A. (1984). Analysis of Ordinal Categorical Data. NY: Wiley.
Agresti, A. (1996). An Introduction to Categorical Data Analysis. NY: Wiley.
Andersen, E.B. (1994). The Statistical Analysis of Categorical Data (3rd ed). Berlin: Springer-Verlag.
Andersen, E.B. (1997). Introduction to the Statistical Analysis of Categorical Data. Springer-Verlag.
Bishop, Y.M.M, Fienberg, S.E., & Holland, P.W. (1975). Discrete Multivariate Analysis. Cambridge, MA: MIT Press.
Blasuius, J & Greenacre, M. (Editors) (1998). Visualization of Categorical Data. San Diego, CA: Academic Press.
Christensen, R. (1990). Log-Linear Models. NY: Springer-Verlag.
Clogg, C.C., & Shihadeh, E.S. (1994). Statistical Models for Ordinal Variables. Thousand Oaks, CA: Sage.
Cox, D.R., & Snell, E.J. (1989). Analysis of Binary Data (2nd ed.). London: Chapman and Hall.
Dobson, A.J. (1990). An Introduction to Generalized Linear Models. London: Chapman and Hall.
Edwards, D. (1995). Graphical modeling. In W.J. Krzanowski (ed) Recent Advances in Descriptive Multivariate Analysis, pp 135-156. NY: Oxford.
Edwards, D. (2000). Introduction to Graphical Modeling, (2nd ed.). NY: Springer-Verlag.
Fahrmeir, L, & Tutz, G. (2001). Multivariate Statistical Modeling Based on Generalized Linear Models (2nd ed.). NY: Springer.
Fienberg, S.E. (1980). The Analysis of Cross Classified Categorical Data (2nd ed.). Cambridge, MA: MIT Press. Goodman, L.A., & Kruskal, W.H. (1979). Measures of Association for Cross Classifications. NY: Springer-Verlag. (reprint of articles appearing in the Journal of the American Statistical Association in 1954, 1959, 1963, and 1972.)
Haberman, S.J. (1975). The Analysis of Frequency Data. Chicago, IL: University of Chicago Press.
Hosmer, D.W., & Lemeshow, S. (1989). Applied Logistic Regression. NY: Wiley.
Le, C.T. (1998). Applied Categorical Data Analysis. NY: Wiley.
Liao, T.F. (1994). Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models. Thousand Oaks, CA: Sage.
Lindsey, J.K. (1995). Modeling Frequency and Count Data. NY: Oxford.
Lloyd, C.J. & Lloyd, C.J. (1999). Statistical Analysis of Categorical Data. NY: Wiley.
Lauritzen, S. (1996). Graphical Models. NY: Oxford.
Lindsey, J.K. (1997). Applying Generalized Linear Models. NY: Springer-Verlag.
Long, J.S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage.
McCullagh, C.E., & Searle, S.R. (2001). Generalized, Linear, and Mixed Models. NY: Wiley.
McCullagh, P., & Nelder, J.A. (1983). Generalized Linear Models (2nd ed.). London: Chapman and Hall.
Moldenberghs, G., & Verbeke, G. (2005). Models for Discrete Longitudinal Data. Springer.
Powers, D.A. & Xie, Y (1999). Statistical Methods for Categorical Data Analysis. Academic Press.
Read, T.R.C., & Cressie, N.A.C. (1988). Goodness-of-¯t Statistics for Discrete Multivariate Data. NY: Springer-Verlag.
Sobel, M.E. (1995). The analysis of contingency tables. In G. Arminger, C.C. Clogg,& M.E. Sobel (eds), Handbook of Statistical Modeling for the Social and Behavioral Sciences, pp 251-310. NY: Plenum Press.
van der Ark, L.A., Croon, M.A., & Sijtsma, K. (editors) (2005). New Developments in Categorical Data Analysis for the Social and Behavioral Sciences. Mahwah, NJ: Lawrence Erlbaum.
Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. NY: Wiley.
Wickens, T.D. (1989). Multiway Contingency Tables Analysis for the Social Sciences. Hillsdale, NJ: Lawrence Erlbaum.
Zelterman, D. (1999). Models for Discrete Data. Clarendon Press.
Dobson, A.J. (1990). An Introduction to Generalized Linear Models. London: Chapman and Hall.
Lindsey, J.K. (1997). Applying Generalized Linear Models. NY: Springer-Verlag.
McCullagh, P., & Nelder, J.A. (1983). Generalized Linear Models (2nd Edition). London: Chapman and Hall.
Edwards, D. (2000). Introduction to Graphical Modeling (2nd Edition). NY: SpringerVerlag.
Lauritzen, S. (1996). Graphical Models. NY: Oxford.
Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. NY: Wiley.