Course Information and Reference

This online e-book is the main resource to guide you through the course HE-802 in the MS in HPEd program at MGHIHP in the Spring 2021 semester.

Each chapter contains reading (or links to reading) that you should do as well as an assignment that you should complete and submit by the deadline in the course calendar.

My name is Anshul Kumar and I am the author/preparer of this e-book. You can reach me at with any questions, comments, and/or suggestions for modifications to this e-book.

All of the materials here are available online for anybody to use. Those who are not part of the course HE-802 are welcome to use this e-book however it might be useful. Please e-mail me any feedback you have.

I use a lot of footnotes like the one after this sentence.1 You can read the footnote by clicking on the small-but-tall number in between this sentence and the previous one. Footnotes contain comments from me or extra information that might be helpful. But footnotes in this e-book are never necessary to read. It is fine for you to skip the footnotes and not read them.

0.1 Course Calendar 2021

The calendar below shows when assignments are due and when online video calls (using Zoom) must be scheduled. Keep in mind that this calendar might change during the semester.

Each week starts on a Monday and ends on a Sunday. For example, in the week of January 25, Sunday refers to January 31. You can submit an assignment at any time on the day when it is due.

Each week, please read and follow all instructions in the corresponding chapter. Then complete the assignment at the end of the chapter and submit it in D2L.

Week of Chapter Assignment Due Additional Required
Jan 19 1 Sunday
Jan 25 2 Sunday
Feb 1 3 Sunday
Feb 8 4 Sunday
Feb 16 5 Sunday
Feb 22 6 Sunday Oral exam #1 (Zoom)
Mar 1 7 Sunday
Mar 8 8 Sunday Zoom call, check-in
Mar 15 9 Sunday
Mar 22 10 Sunday Oral exam #2 (Zoom)
Mar 29 11 Sunday
Apr 5 12 Sunday
Apr 12 13 Optional
Apr 19 TBA Final Project, Sunday Oral exam #3 (Zoom)
  • TBA = to be announced
  • Note that the week of April 12 will be the final week in which there is new material given.
  • The final project is due on Sunday April 25 2021.

0.3 Assignments, Grading, and Curriculum

0.3.1 Grade Calculation

Type of Work % of Grade
Weekly assignments 25
Participation 5
Oral exams 45
Final project 25

Your grade will be calculated as shown in the table above.

0.3.2 Description of Curriculum

Here are descriptions of the activities you will do this term:

  • Weekly assignments: Homework assignments will involve applying/practicing the statistical technique(s) that is the focus of the week using a provided dataset. Because of the cumulative nature of the course, these assignments may also involve applying knowledge from previous weeks’ material. Some assignments will help you prepare for your final project. Assignments will be posted within the online e-book and students will submit completed assignments in D2L. Note that if you have data of your own that you are interested in analyzing, you can often use your own data instead of the provided data for the weekly assignments. Please discuss this with the instructors as desired. As long as you are adequately practicing the new skills each week, it doesn’t matter which data you use.

  • Participation: This mostly relates to your participation in mandatory video calls over Zoom. These will include welcome/orientation sessions, oral exams, and occasional group meetings with classmates. Optional office hours or scheduled meetings with instructors will also count towards participation.

  • Oral exams: You must take three oral exams, each occurring approximately during the weeks specified in the calendar. Each exam will be a one-on-one Zoom (online video) meeting between a student and an instructor. During the exam, you will be asked to show your understanding of the concepts we cover and you will be asked to demonstrate data analysis tasks on your own computer while sharing your screen on Zoom. The exams are “open-book,” meaning that you can refer to any notes or course materials during the exam. You are allowed to re-take an exam as many times as you would like, if you are not satisfied with your initial performance. If you want to get your exam dates on the calendar early, just e-mail the instructors and we can definitely do that.

  • Final project: You will develop and execute an abbreviated version of a full quantitative research study. This will include research questions, study hypotheses, sample size/power, gathering or finding the necessary data, and identifying analyses necessary to test hypotheses. Students will then conduct and interpret a full data analysis and write up results, including tables or figures, as appropriate. Some of the homework assignments will help you complete this project. Students who turn in their final project early can receive feedback and then resubmit the project after incorporating the feedback (with the grade of the resubmission being recorded as the final project grade). Details about this project will be posted within the online e-book and students will submit completed projects in D2L.

0.4 Final Project Details and Requirements

This section contains specific expectations and requirements for the final project in this class.

0.4.1 Description

The final project is not meant to be even close to a full quantitative research study. Another way to think about the project is that you will be writing an extended methods section and a condensed results section of an empirical research article, without writing a literature review or anything else.

Note that you can ask for help from instructors as you do the project. It is not like an exam in which you have to do each skill alone. You can send us drafts of your project at intermediate stages and we can give you feedback. We can also meet individually to discuss and advise about your project.

The requirements for the project can be changed for individual students on a case-by-case basis and only with permission from Anshul. If you think that a modification to the goals and/or requirements below may be more productive for your own professional goals and/or your goals in your program at MGHIHP, please discuss this with Anshul.

The final project is due on Sunday April 25, 2021.

0.4.2 Project Goals

The goals of this final project are to…

  1. Present and interpret the results of one quantitatve test or model (such as ANOVA, t-test, linear or logistic regression) that answers a clear and specific research question.

  2. Run, interpret, and appropriately respond to all required diagnostic tests for the quantitative test or model and present the results of all tests.

0.4.3 Project Requirements

Here are the items you must present and tasks you must complete:

  1. Write a clear research question (RQ) that can be solved using one of the quantitative analysis techniques studied in the course. This research question should be a single sentence with a question mark at the end.2

  2. To answer your RQ, various concepts will have to be first measured, recorded in a dataset as variables, and then related to each other quantitatively. Identify a dataset that you will use to answer your research question.3 Clearly describe the dataset, including: a) population from which the data sample was drawn, b) unit of observation, c) all variables that you will use in your analysis and the unit of measurement of each variable, d) background information about the data.

  3. Given the structure of the data and the RQ of interest, explain which type of quantitative test is most appropriate to answer your RQ and why. Also identify at least one other type of quantitative test that could also be used and explain why you instead chose the test that you did.

  4. Present basic descriptive statistics that are relevant to your RQ. You should include at least one table and at least one figure/chart.

  5. Show the code and results of one quantitative test or model that answers your RQ.4

  6. Run and present the results of all diagnostic tests that pertain to the type of test or model you ran. Ideally, your model will pass all of the tests. If your diagnostics show that your model specification violates any of the assumptions of your chosen test, you might be able to fix the problem and run the test again. Please describe all efforts to fix such problems. If you are not able to solve all such problems, it is okay. The key is that you explain what you find and how you went about your methods.

  7. Interpret the results of your test/model that are relevant to your RQ.

  8. Briefly explain any limitations in your analysis.

  9. Include all R code and results in your final submission.

  10. Present all writing in well-written English.

  11. Present everything in an aesthetically pleasing manner. It is recommended that you use an RMarkdown document, but this is not required.

0.4.4 Grading Rubric

The final project will be graded according to the rubric below. Each criterion is worth a maximum score of two points unless otherwise noted.

Criterion Score = 0 Score = 1 Score = 2
Clear RQ Unclear, more than one sentence, not a question. Confusingly presented but understandable. Clear, simply written, single sentence ending with a question mark.
Population and sample Relationship between sample and population is unclear, details about population is omitted. Minor omissions, but overall description of the population is understandable. It is very clear what the population is and how many observations from this population were sampled and then included in the dataset used in the project.
Unit of observation The meaning of each row in the data is not understandable from what is written. Reader can figure out based on context, but a clear explanation is missing. It is very clear what each row of the data means/represents. This is explicitly stated with no ambiguity or confusion.
Variables used The variables used in the analysis are not addressed. Some variables are mentioned but not all. How each variable is measured is not clear. Dependent variable and all independent variables are described in one sentence each. Unit of measure (and any relevant explanation of how a variable is coded in the data) is given for each variable.
Background on data It is not understandable where the data came from and from what context. Few details are given about the data. Clear explanation of where the data came from, when it was collected, who collected it, etc.
Choose test/model No explanation of why the presented quantitative test/model was chosen. No comparison to another test/model. Incorrect selection of model type. An explanation may be there but it might be incorrect, or a comparison to another test/model is missing. Logical explanation of the way the data is structured and how the selected test/model is best suited to that data structure. Clear explanation of why at least one alternative test/model was not used.
Descriptive statistics No or very few statistics presented. Statistics for irrelevant variables or information are presented. Descriptive statistics do not cover all variables and observations relevant to the RQ. Only one of two required charts is included. Descriptive statistics are presented for all variables relevant to the RQ and used in the selected test/model. One well-made figure is presented. One well-made table is presented.
Test/model result Code and/or summary is not shown for test/model. Code does not accomplish the type of test/model that was supposed to be used. Only partial work or result is shown. Type of test/model is unclear. Correct test/model result is shown along with appropriate R code to execute it.
Test/model assumption 1 Assumption not considered. Assumption is mentioned but incorrectly interpreted. Assumption is tested correctly and interpreted correctly.
Test/model assumption 2 Assumption not considered. Assumption is mentioned but incorrectly interpreted. Assumption is tested correctly and interpreted correctly.
Test/model assumption 3 Assumption not considered. Assumption is mentioned but incorrectly interpreted. Assumption is tested correctly and interpreted correctly.
Interpret results Many irrelevant details are given. Research question is not clearly answered. Research question is answered but interpretation of results is not exactly correct. Succinct interpretation of the portion of the test/model output that pertains to the RQ.
Limitations Limitations are not addressed or are completely incorrect given the test/model model used. Limitations are partially addressed. Multiple plausible limitations to the analysis and the conclusions we can draw from it are addressed.
R code included No R code is included Only partial R code for the results presented is given. R code is included (displayed in final document) for all results that were generated using R.
Writing quality (+) Sentences and paragraphs are not formatted according to convention. Full sentences are not used much or well. Minor grammar and/or spelling errors occur throughout, but the main points are understandable. Writing is clear and succinct. It is easy to read quickly and understand the analysis and the results. No grammar or spelling errors.
Aesthetics Project is presented in a confusing manner. Order and flow of requested items is not logical. Unnecessary fonts, symbols, and formatting layout appears. Minor blemishes and errors are visible in the submitted project. Order of all content is clear and logical. Sections and sub-sections are logically and clearly marked. The write-up is easy to read.

Items marked with a (+) in the table above will carry more weight than just two points. All other items have a maximum score of two points.

Your grade on the project will be the number of points achieved divided by the total number of points possible.

If you are not satisfied with your grade on this project, you do have the option of taking an INCOMPLETE grade for the course. Then, you will improve and re-submit your project in the weeks that follow the end of the course. We will re-grade the project and then put your improved final grade for the course into the grading system. Please be sure to communicate with course instructors about this option right away, if you think you might choose it.

0.5 Required Materials

No purchases are necessary (as long as you already have a computer that can run the necessary software). All necessary materials are either a) free and available online or b) provided to you by the Institute for free.

  • Texts/Readings: There is no requirement for students to purchase any texts or readings for this class. All materials are available freely online.

  • Statistical software: All students are required to install R and RStudio on their own computer. R is a free and open-source statistical computing platform and RStudio is a free software that makes R easier to use. Instructions for installing R and RStudio will be provided by the instructor. R and RStudio should run well on computers running Mac, Windows, and Linux operating systems.

  • Videoconferencing software: All students will have access Zoom Pro based on enrollment in the PhD-HPEd program; it should be installed on the student’s local device for group and/or individual meetings with the instructors and classmates.

  • Spreadsheet software: Some exercises or data manipulation in this course may need to be done in Excel or a similar spreadsheet software. Students can complete this work in any spreadsheet software such as Microsoft Excel, Google Sheets within Google Drive (free), or Open Office (free).

0.6 Acknowledgments

  • This course would not be possible without the work that Dr. Annie Fox—the previous instructor of this course—put in to develop it and the materials that she has handed down. A substantial portion of the written text and content in this course was written/curated by her.

  • The building blocks for this e-book are taken from A Minimal Book Example by Yihui Xie, available at https://github.com/rstudio/bookdown-demo. This work would not be possible without this excellent guide from Yihui Xie.

  • Much of the content for this book is influenced by the teaching and research conducted by my colleagues and students.

  • The various efforts of Roger Edwards, Nicole Danaher-Garcia, Grace Ming, Valay Maskey, Tony Sindelar, and all students who have taken the course HE802 at MGHIHP in the past have been particularly instrumental in the development of this course and online textbook.

  1. This is a footnote. You can go back to where you were reading by clicking on the little squiggle right here: ↩︎

  2. There are no exceptions to this requirement.↩︎

  3. If you do not have data of your own that you would like to analyze, you can have a discussion with Anshul, who can provide you with a research question and a dataset in which to study it.↩︎

  4. In reality, you will likely run many tests/models on your own to arrive at the one that fits your RQ and data the best. But you do not need to show all of this work in your final submission. If you do wish to show all of this additional work, you can include it in an appendix to your assignment, but this is not required.↩︎