Welcome to RESD-720
Doctoral Course on Multivariate Research Methods

This research course is designed to assist doctoral students develop their advanced multivariate research methods skills. Students will conduct hands-on sessions in the Advanced Multivariate Lab using IBM's SPSS and PLS-SEM tools. This advanced multivariate doctoral research course includes on-campus, in-lab sessions, recorded MP3 lectures, and independent research assignments.

RESD-720 - Multivariate Research Methods (4 credits)
Fall 2017 - August 21, 2017 - December 10, 2017
Mobile Computing Laboratory (MCL) - Room 4032
DeSantis Building
Cluster: Fri & Sat (Cluster Sessions) 8am - 12pm




Dr. Yair Levy
Professor of Information Systems and Cybersecurity


Nova Southeastern University
College of Engineering and Computing (CEC)
The DeSantis Building, room 4058
3301 College Avenue
Ft. Lauderdale, FL 33314


levyy@nova.edu (please send all correspondence via e-mail)


954-262-2006 (for faster respond, send me an e-mail...)



Prof.'s Web Site:


Levy's CyLab:


Class Web Site:

In BlackBoard via https://sharklearn.blackboard.com/

Office Hours: 

By appointment only via e-mail (on-campus or phone).




Send me all correspondence to levyy@nova.edu. When sending me e-mail, please make sure to:

  • Send me e-mail from your NSU e-mail address ONLY -- this is CEC policy! (Also note that e-mails sent from non-NSU e-mail address maybe detected as spam and will not be received or answered!)
  • Type "RESD-720" in the subject line.
  • Type your full name in the message.
  • Type your BlackBoard username in the message.
  • Type your NSU e-mail address in the message.

E-mails will usually be answered within 24 hours on weekdays and within 48 hours on weekends or official holidays, although in most cases, I will answer you even earlier. If I'm out of town and have posted a note to the site about it, you will get an automated response and I will answer it when I get back or have access to the Internet from that location. 


1. Fundamental knowledge of statistics (See Terrell, 2012 below if needed)
2. A passing grade in RESD-705 (or DISS-700), Introduction to Research Methodology for Information Systems.


This advanced multivariate research methods course builds on the content learned in the doctoral course RESD-705, Introduction to Research Methodology for Information Systems. This data-driven doctoral course will provide the skills needed to perform advanced multivariate data analysis by incorporating currently used techniques. Topics covered will include assumptions and limitations, multivariate data collection, pre-analysis data screening, factorial and multivariate analysis of variance and covariance, linear and non-linear multiple regressions, path analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation models (SEM). Students will be provided with data-sets for data analyses of the multivariate methods discussed in course along with scholarly articles that make use of the multivariate methods discussed. Students will be introduced to the use of SPSS, PLS, and other advanced multivariate tools.


The overall goal of this course is to provide the student with an understanding and hands-on skills needed to perform multivariate research. Specifically, a student completing this course will be able to:

  1. Understand the assumptions and limitations of multivariate research
  2. Conduct multivariate data collection
  3. Understand the need for and be able to conduct pre-analysis data preparation
  4. Understand factorial analysis of variance, analysis of covariance, as well as multivariate analysis of variance and covariance, interprete the results, and reporte in a scientific way
  5. Conduct multiple regression (linear & non-linear), interpret the results, and report in a scientific way
  6. Conduct path analysis, interpret the results, and report it in a scientific way
  7. Conduct exploratory factor analysis using Principal Competent Analysis (PCA), interpret the results, and report in a scientific way
  8. Conduct confirmatory factor analysis – Structural Equation Models (SEM), interpret the results, and report in a scientific way.


Mertler, C. A., & Vannatta, R. A. (2013). Advanced and Multivariate Statistical Methods (5th ed.): Practical Application and Interpretation. Glendale, CA: Pyrczak Publishing.
(Newer or prior editions are also OK)

ISBN#: 978-1936523-09-2


Hair, J. F., Hult, J. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publication.

ISBN-10: 1452217440 or ISBN-13: 978-1452217444


APA (2009). Publication Manual of the American Psychological Association (APA)  (6th ed.).

ISBN#: 978-1-4338-0561-5

Foundational Statistics Book (if need additional background)  

Terrell, S. R. (2012). Statistics Translated: A Step-by-Step Guide to Analyzing and Interpreting Data. New York, NY: The Guilford Press.

ISBN-10: 1462503012 or ISBN-13: 978-1462503018

Additional articles, Internet resources, data-sets, and notes will be provided in class or via the BlackBoard site assigned to this course. Please check the BlackBoard site regularly!


This course will utilize BlackBoard and GoTo Meeting as supplements for in-class activities. Assignments, projects, and class discussions will take place during our on-campus meetings and in the BlackBoard site assigned to this doctoral course. Also, we will be using the following applications and tools during the term. I recommend getting these installed during the first week of the term to ensure you have them available and – ready to go!

  1. Statistical Package for the Social Sciences (SPSS, an IBM company) SPSS Statistics – For a reduced rate for students, click here! ***REQUIRED ver 17 or up ***
  2. SmartPLS – Partial Least Squares (PLS) - Path Modeling and Model Exploration (Free tool)
  3. SPSS-AMOS - Structural Equations Modeling (SEM) (Model Testing) – For a reduced rate for students, click here! ***NOT REQUIRED***
  4. Microsoft Excel (for pre-analysis data preparation)
  5. Microsoft Word (to write the assignment reports) with Equation editor!

*** Access to virtual SPSS and AMOS via NSU's Virtual Machine will be provided to students during the term, so if you are OK with running these online and not locally, there is no need to purchase even the reduced fee.


There will be four major assignments in this course. Additional information will be provided during the class meetings as noted in the calendar. Moreover, additional information on each assignment is also provided under each of the assignment guidelines in the "Course Content" section of the course's BlackBoard site. All assignments should be uploaded into the Dropbox area in BlackBoard. Additional information on the uploading process will be provided in our first class meeting.

Note: Please allow yourself enough time prior to due date to upload your assignment to the BlackBoard's dropbox.

Assignment Due Date Grade Weight
Student Introduction/Bio Post 08/24 2%
Assignment #1 – MANOVA/MANCOVA via SPSS 09/18 20%
Assignment #2 - Multiple Regression (Linear & Non-Linear) via SPSS 10/08 20%
Assignment #3 – Exploratory Factor Analysis (EFA)/Principal Component Analysis (PCA) & Factor Reliability Analysis 11/05 25%
Assignment #4 – Confirmatory Factor Analysis (CFA) - Structural Equation Models (SEM) 11/26  25%
Advanced Multivariate Lab Participation - Attendance and full participation are mandatory on all on-campus sessions to get these points! - 8%

Grading Scale:

[93-100] =A   [83-86) =B   [73-76) =C
[90-92) =A-   [80-82) =B-   [70-72) =C-
[87-89) =B+   [77-79) =C+   Below 70 =F
  • Mutual respect and courtesy.
  • Professional quality in the organization, completeness, neatness, and timeliness of any material submitted will be expected.
  • Late assignments will not be accepted! However, the professor realizes that exceptional situations (such as justified emergencies or medical situations) do occur. In such cases, please inform your professor via e-mail to obtain special permission for late submission, prior to the deadline.
  • A student may not do additional work or repeat an examination to raise a final grade.
  • All papers and assignments should include a certificate of authorship signed by the student.
  • The professor is not obligated to communicate with students via e-mail or telephone about the course or assignments after final grades have been submitted. However, official Challenge of Course Grade and Student Grievance Procedure, as outlined in the graduate catalog, will be processed.
  • Students should be aware that any submitted work for this course may be subjected to detection of breach of copyright
  • No incompletes will be provided unless the student compleated at least 75% of the course assignments.


Although some sections above are parts of this course's syllabus, this is not the course syllabus. The purpose of this page is to allow students and prospective students to gain understanding on the nature of this course. The course syllabus will be provided via Blackboard and will be available for all students who register for this course.

Looking forward to seeing you in my class!

Yair Levy, Ph.D. (levyy@nova.edu)
Professor of Information Systems and Cybersecurity
Director, Center for e-Learning Security Research (CeLSR)
College of Engineering and Computing
Nova Southeastern University
Copyright ©  - Dr. Yair Levy, all rights reserved worldwide.
Modified  August 16, 2017