*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.
This course is designed for those students (or any researchers) who plan to conduct studies targeting the measurement of latent variables (i.e. variables that cannot be measured directly). The course consists of two major parts: First, the introduction of survey instrument design and development; Second, the introduction of statistical technques for estimating relationships among variables within causal systems. Statistical methods include exploratory/confirmatory factor analysis and structural equation modeling including its directed acyclic graphical notation as a means to estimate assumed causal relationships in the presence of mediators and moderators. 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.
After completing this course the student will be able to:
This is an online course delivered in Moodle. The Moodle login page explains how to login to Moodle. Contact Moodle Support at email@example.com if you have problems accessing Moodle. If 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 662-4201 (SP17).
The Practice of Survey Research
Authors: Ruel, E., Wagner, W. E., & Gillespie, B.
A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition)
Authors: O'Rourke, N. & Hatcher, L.
Publisher: SAS publishing
All assignments and projects are expected to follow APA styles (6th edition). Make sure you have one APA publication manual for this course.
IBM SPSS Statistics
SPSS is also required for this course to complete all analytic tasks. The most updated version of SPSS is version 24 (you can use easier version like 22 or 23 as well). You will need the Premium version because we will use AMOS for Structural Equation Modeling later in the course. (version comparisons)
Buying one-year grad pack (Premium for students) is a recommended option because SPSS might be updated once or twice a year. You might consider one of the following websites:
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.
|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|
*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
Knowledge & Skills
|Creating||2, 3, 4, 5, 6, 7, 8, 9||
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||2, 3, 4, 5, 6, 7, 8, 9||2, 3, 4, 5, 6, 7, 8, 9|
|Accessing/Evaluating||1, 2, 3, 4, 5, 6, 7, 8, 9||2, 3, 4, 5, 6, 7, 8, 9||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||1, 2, 3, 4, 5, 6, 7, 8, 9||2, 3, 4, 5, 6, 7, 8, 9|
|Diversity of Learners||2, 3, 4, 5, 6, 7, 8, 9|
Final grades are based on the following scale.
|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.
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.
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 firstname.lastname@example.org 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.
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.
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.
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.
Please adhere to the following guidelines:
Please do your own original work for each project. Projects that were created for other classes may not be submitted for credit in this course. Each project may only be submitted for credit one time by the person who created it. The BSU Student Code of Conduct states: "Academic dishonesty also includes submitting substantial portions of the same academic course work to more than one course for credit without prior permission of the instructor(s)."
All projects and other assignments should be composed in original text that is written by the student who is submitting it. The exception to this is the use of small amounts of quoted material that is properly cited. Copying and pasting from other websites or projects (including the instructor's examples) is not permitted.
The practice of copying answers from classmates is strongly discouraged. It is best to complete all course actvities on your own.
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.).
Incompletes are not guaranteed. However, when they are given incompletes adhere to Boise State University
guidelines 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
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).
Readings & Videos
Module 1: Introduction to the Course (1/9 - 1/18)
- Syllabus and calendar
|Module 2: Review of Concepts (1/16 - 1/25)|
- Survey Research Ch1 & 2
|Module 3: Exploratory Factor Analysis (1/23 - 2/8)|
- Survey Research Ch3 & Ch4
|Module 4: Assessing Scale Reliability (2/6 - 2/22)|
- Survey Research Ch5 & 6
|Module 5: Path Analysis (2/20 - 3/1)|
- SEM Ch4
|Module 6: Survey pretest and pilot (2/27 - 3/15)|
|Module 7: Confirmatory Factor Analysis (3/13 - 4/5)|
- Survey Research Ch7 &8
|Spring Break (3/20 - 3/26)|
No class during the break
|Module 8: Structional Equation Modeling (I) (4/3 - 4/19)|
- Survey Research Ch9 & 10
- SEM Ch6
|Module 9: Structional Equation Modeling (II) (4/17 - 4/26)|
|Module 10: Final Project (4/24 - 4/30)|
Refer to the Boise State Academic Calendar for University dates and deadlines (e.g., the last day to drop).
Graduate Catalogs for present and prior academic years can be found online at:
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.
The Department of Educational Technology is a diverse and international network of scholars, professional educators and candidates who:
Lead research and innovations in online teaching and learning
Model, promote, manage, and evaluate digital-age work and learning resources in educational environments
Inspire creativity and expertise in digital media literacies
Design and develop imaginative learning environments
Empower learners to be evolving digital citizens who advocate cultural understanding and global responsibility
Promote and pattern participatory culture, professional practice, and lifelong learning
McDonald, R.P., & Ho, M.-H., R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64 - 82.
Kline, R.B. (2011). Principles and practice of structural equation modeling, 3 ed. New York: The Guilford Press.
Raykov, T., & Marcoulides, G.A. (2006). A first course in structural equation modeling, 2 ed. nd Mahwah, NJ: Erlbaum.
Bollen, K.E. (1989). Structural equations with latent variables. New York: Wiley.
Pugesek, B., Tomer, A. & von Eye, A. (Eds.)(2003). Structural equation modeling. Applications in Ecological and Evolutionary Biology. Cambridge, UK: Cambridge University Press.
von Eye, A., & Clogg, C.C. (Eds.)(1994). Latent variables analysis - Applications for developmental research. Newbury Park, CA: Sage.
Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling. Multilevel, longitudinal, and structural equation models. Baton Roca, FL: Chapman & Hall.
Curran, P. (2003). Have multilevel models been structural models all along? Multivariate Behavioral Research, 38, 529 - 569.
Bartholomew, D.J., & Knott, M. (1999). Latent variable models and factor analysis. London: Arnold.
Bollen, K.A., & Curran, P.J. (2006). Latent curve models. A structural equation perspective. Hoboken, NJ: Wiley.
Preacher, K.J., Wichman, A.L., MacCallum, R.C., & Briggs, N.E. (2008). Latent growth curve modeling. Los Angeles: Sage.
Fuller, B.E., von Eye, A., Wood, P.K., & Keeland, B. (2003). Modeling manifest variables in longitudinal designs - a two-stage approach. In B. Pugesek, A. Tomer, & A. von Eye (Eds.), Structural equation modeling: applications in ecological and evolutionary biology (pp. 312 - 351). Cambridge, UK: Cambridge University Press.
Mun, E.Y., von Eye, A., & White, H.R. (2009). An SEM approach for the evaluation of intervention effects using pre-post-post designs. Structural Equation Modeling, 16, 315 - 337.
von Eye, A., Spiel, C., & Wagner, P. (2003). Structural equations modeling in developmental research - concepts and applications. Methods of Psychological Research - online, 8, 75 -112