| Variable | Description |
|---|---|
| pid | Participant ID number |
| Attendance | Total attendance (in days) |
| Conscientiousness | Conscientiousness (Levels: Low, Moderate, High) |
| Time | Time of ulass (Levels: 9AM, 10AM, 11AM, 12PM, 1PM, 2PM, 3PM, 4PM) |
| OnlineAccess | Frequency of access to online course materials (Levels: Rarely, Sometimes, Often) |
| Year | Year of study in university (Levels: Y1, Y2, Y3, Y4, MSc, PhD) |
Categorical predictors: Study brief
Data Analysis for Psychology in R 2
Conduct and report on an analysis that addresses the research aims.
The data is contained in two datasets available at: https://uoepsy.github.io/data/DapR2_S1B2_PracticalPart1.csv and https://uoepsy.github.io/data/DapR2_S1B2_PracticalPart2.csv
Study background and aims
The data used for this write-up exercise are simulated. They draw on a meta-analysis that explores the association between student characteristics and grades. The simulated data are loosely based on the findings of this work, and they expand on the methods and results reported in the paper:
Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80(2), 272-295. https://doi.org/10.3102/0034654310362998
NOTE: You are not expected to write an introduction, so you do not have to read this article.
Method and procedure
The current study was split into two parts.
In the first part, researchers were interested in what might predict attendance in university courses. They collected information from 397 students across all years of study (Year; i.e., UG (Y1–Y4), MSc, and PhD), and recorded their class attendance across the academic year (Attendance), their level of Conscientiousness (Conscientiousness categorized as Low, Moderate, or High), the frequency of which they accessed online course materials (OnlineAccess categorized as Rarely, Sometimes, or Often), and the timing of class (Time categorized as 9AM, 10AM, 11AM, 12PM 1PM, 2PM, 3PM, 4PM).
In the second part, researchers were interested in how attendance across the year is associated with final course grades. They collected data from 200 students, recording their class attendance across the academic year (Attendance) and their final course grade (Marks, ranging from 0–100).
Research aim and questions
Research Aim
Explore the associations among academic outcomes, student/course characteristics (e.g., class time, online access), and attendance.
Research Questions
- RQ1: Does conscientiousness, frequency of access to online materials, and year of study in university predict course attendance?
- RQ2: Is there a difference in attendance between those with early/late classes in comparison to those with midday classes?
- RQ3: Is class attendance associated with final grades?
Data dictionary
Data dictionary: Dataset 1
The data in DapR2_S1B2_PracticalPart1 contain six attributes collected from a simulated sample of \(n=397\) hypothetical individuals. It includes:
Data dictionary: Dataset 2
The data in DapR2_S1B2_PracticalPart2 contain two attributes collected from a simulated sample of \(n=200\) hypothetical individuals. It includes:
| Variable | Description |
|---|---|
| Marks | Final grade (0 to 100) |
| Attendance | Total attendance (in days) |