scale development
Presented by William Bannon, PhD
About the course
This asynchronous 3-week webinar will be held from January 25, 2027 to February 11, 2027. The early registration fee of $250 covers the cost of registration and all course materials. Early registration for this webinar will close Friday, November 6, 2026 (Midnight, EST). After that date, the full registration fee of $300 will apply.
Contact Dr. Bannon at the email address: williambannonassociates@yahoo.com (preferred) or wbannon@ramapo.edu with any issues regarding registration. After registration, you will receive an email message confirming your registration in the webinar class.
Registration
Select a method below to purchase a seat in the next Scale Development on-line webinar class. Please email Dr. Bannon at: williambannonassociates@yahoo.com if you would like to register by check.
Use the button below to register via Paypal or a Credit/Debit Card.
The Thought Process Behind This Course
A multiple-item scale is a measurement tool that utilizes a collection of related questions, statements, or indicators that are combined to create a single composite score that reflects a complex, abstract, and/or multi-dimensional concept. These scales have become integral to conducting effective research because instead of relying on a single question, multiple-item scales use several items to capture the different facets of a single underlying construct (e.g., job satisfaction, needs assessment, or psychological well-being)
In the course of a research study, many researchers are often tasked with developing these types of scales toward effectively measuring a study construct. Subsequently, this 3-week workshop has been designed to instruct attendees on the essential concepts and tools needed to effectively develop multiple-item scales. This course is asynchronous, delivered in three weekly presentations, and includes hands-on exercises toward maximizing learning.
Course Objectives
By the end of this course, attendees will have the knowledge and tools to apply Survival Analysis in their own research. The SPSS software will be used for all statistical analyses. To meet this objective, attendees will be instructed on a number of topics including:
1. How to Prepare and Organize data for Survival Analysis
2. How to Select the Correct Time Axis for Analysis
3. How to Determine When to Use Discrete vs. Continuous Time Methods
4. How Often Independent Variables Should Be Measured
5. How R-squared Is Computed for a Model
6. Addressing Nonproportionality
7. How to Correct for Unobserved Heterogeneity
8. Testing for Sensitivity to Informative Censoring
Course Features
Several resources are provided to meet the objectives, including weekly presentations, personalized attention, and written materials. Specifically, the webinar features include:
1. Weekly Webinar Presentations: This online 3-week webinar class is asynchronous. In terms of delivery, a 3-hour (approximately) webinar presentation will be emailed to participants every Monday during the 3-week duration of the program (emailed to the attendee by 9 am EST). All examples and lecture notes will use SPSS. All presentations are recorded so each may be watched at the viewer’s convenience. Attendees have indefinite access to the presentations during and after the webinar.
2. Q and A Sessions: Learning these materials is often facilitated by the participant being able to present their questions directly to the facilitator. Therefore, participants will be able to email their questions on the material directly to Dr. Bannon every Thursday (a 24-hour Q and A session) during the program and have him respond to them personally.
3. Written Materials: A written chapter (PDF) detailing the materials will be presented to elaborate upon the content presented in each weekly webinar. The combined chapters will produce a manual containing detailed lecture notes, examples of computer printouts, and several other useful features.
4. Practice Database and Quizzes: An SPSS database will be provided for attendees who would like to replicate the statistical procedures illustrated in the class. A series of quizzes will also be provided to test the knowledge of attendees regarding the materials being taught.
5. One-Hour Personal Consultation: Often, participants prefer to present some questions by phone or in person. Subsequently, Dr. Bannon will schedule a 1-hour personal consultation by phone or in person (regularly $225 at the student rate/$300 at the academic rate) with each participant to discuss any topic of the participant's choosing (e.g., webinar course materials, a project that is being developed or worked upon).
Who Should Attend?
This class is designed for individuals who need to analyze longitudinal time to event data. A basic statistical background and knowledge of the basics of multiple regression would benefit attendees.
Course Structure
Learning will occur over the webinar duration in the following 6 week format:
Week 1: The Fundamental Concepts of Survival Analysis
Week 2: Univariate Survival Analysis and The Kaplan-Meier Method
Week 3: Cox Regression and Time-dependent Covariates
Week 4: Competing Risks and Discrete Time Analysis
Week 5: Models for Non-proportional Hazards
Week 6: Repeated Events
Registration
Select a method below to purchase a seat in the next Scale Development on-line webinar class. Please email Dr. Bannon at: williambannonassociates@yahoo.com if you would like to register by check.
Use the button below to register via Paypal or a Credit/Debit Card.
This asynchronous 3-week webinar will be held from January 25, 2027 to February 11, 2027. The early registration fee of $250 covers the cost of registration and all course materials. Early registration for this webinar will close Friday, November 6, 2026 (Midnight, EST). After that date, the full registration fee of $300 will apply.
Contact Dr. Bannon at the email address: williambannonassociates@yahoo.com (preferred) or wbannon@ramapo.edu with any issues regarding registration. After registration, you will receive an email message confirming your registration in the webinar class.