Chapter 1 Course Syllabus

Course Description

EDRE 361 will cover the following statistical methods: multiple linear regression, principal components analysis, exploratory factor analysis, confirmatory factor analysis, cluster analysis, discriminant analysis, and logistic regression.

Course Rationale

The course addresses the Assessment and Reporting Domain of the Philippine Professional Standards for Teachers (DO 42, s. 2017) which expects teachers to be able to “collaborate with colleagues in the conduct and application of research to enrich knowledge of content and pedagogy.”

Learning Outcomes

Upon completion of the course, the students must be able to:

  1. familiarize themselves of different statistical methods to address a variety of research objectives one may want to consider;
  2. properly characterize the type of data that one needs to obtain in order to apply the appropriate data analytic techniques;
  3. understand the capabilities, assumptions, and limitations of the different statistical methods;
  4. implement various statistical techniques using a software application.

Mode of Delivery The course will be delivered remotely. There will be synchronous and asynchronous sessions for this course. With the consent of all students in the class, our synchronous sessions will be recorded, and their links will be posted in our UVLE course site.

Communication Plan

The following are the various means of communicating with me regularly:

Online consultation session

We can hold an online consultation session via any online platform convenient to the students on a schedule agreed upon by both parties. This session can be conducted on an individual, pair, small group, and/or whole class basis.

Email

You can reach me through my UP email for any concern. My alternative email address is .

Mobile Number/Viber

In case of unstable/no internet connection, you may contact me at \(09175370810\) for any EDRE 361-related concern.

Course Materials

The main references for this class are the Applied Linear Regression Models (1996) by John Neter, Michael Kutner, William Wasserman and Christopher Nachtsheim and Applied Multivariate Techniques (1996) by Subhash Sharma.

Assignments/quizzes and other learning materials will be uploaded in our UVLE course site. The links of the recordings of our classes will be uploaded as well.

NOTE: Materials for this class should be for your personal use ONLY. They should not be shared with others who are not enrolled in this class. Do NOT upload them in any website without my consent.

Course Requirements

There will be three major course requirements in this class, namely, assignments/quizzes, midterm exam and project. For the entire semester, there may be 4-6 assignments/quizzes. Some of them may be done in small groups. Submission will be done via UVLe. The midterm exam is scheduled on October 25 (Monday) during class hours. Further details about the exams will be given later. Although not graded, I expect that everyone will be attentive and participative during class discussions.

Grading System

The 30% of your grade is based on your assignments, quizzes, and report. The midterm exam weighs 30% as well of your final grade. The class project is 40% of your final grade in this course.

In submitting class requirements, I highly recommend that you use Camscanner (or any similar application) to scan your handwritten work and save it as PDF. Enhance the scanned pages and make sure they are easy to read.

This is a sample book written in Markdown. You can use anything that Pandoc’s Markdown supports, e.g., a math equation \(a^2 + b^2 = c^2\).

The bookdown package can be installed from CRAN or Github:

install.packages("bookdown")
# or the development version
# devtools::install_github("rstudio/bookdown")

Remember each Rmd file contains one and only one chapter, and a chapter is defined by the first-level heading #.

To compile this example to PDF, you need XeLaTeX. You are recommended to install TinyTeX (which includes XeLaTeX): https://yihui.name/tinytex/.