Dr Martin Schweinberger
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Languages & Cultures School
This course introduces basic statistical techniques used in quantitative research in applied linguistics. The rationale and assumptions for various techniques will be presented and practice given in applying them to data using statistical software packages.
This course introduces students to quantitative methodology with a focus on data analysis techniques used in second language acquisition and applied linguistics. In addition to providing students with the theoretical and conceptual basis for understanding quantitative methods, the course teaches how to implement and perform basic but also very versatile and flexible statistical methods. Students will acquire skills relating to data management and analysis as well as gain experience in basic computation and programming.
While the focus of this course is placed on engaging students in exciting analyses of language-related data and methods used in applied linguistics and the language sciences, the course will be of interest to anyone who wants a better understanding of data management, processing, and analysis.
The course has three main aims. It will
1. allow students to develop an understanding of the basic logic and scope of quantitative reasoning
2. provide students with exciting opportunities to learn about and explore basic but flexible statistical techniques
3. endow students with in-depth experience with computation and learning basic programming in R and RStudio.
After passing this course students will have acquired skills that are relevant for and applicable to many domains ranging from academic research and teaching over professional development to private sector enterprises.
Course requirements
Assumed background
No background in statistics or mathematics is assumed.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
SLAT7806
Course staff
Course coordinator
Lecturer
Dr Martin Schweinberger
Mr Quy Pham
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Students will be advised of any changes in advance. If classes fall on a public holiday, dates and times for make-up classes are made available to students in advance via Blackboard.
If a due date falls on a public holiday, the next workday automatically becomes the due date.
Aims and outcomes
The course has three main aims. It will
1. allow students to develop an understanding of the basic logic and scope of quantitative reasoning
2. provide students with exciting opportunities to learn about and explore basic but flexible statistical techniques
3. endow students with in-depth experience with computation and learning basic programming in R and RStudio.
After passing this course students will have acquired skills that are relevant for and applicable to many domains ranging from academic research and teaching over professional development to private sector enterprises.
ﾠ ﾠLearning outcomes
After successfully completing this course you should be able to:
LO1.
Understand and apply basic statistical tests in applied linguistics and second language acquisition.
LO2.
Understand the role of quantitative evidence in describing and explaining language learning and use.
LO3.
Read quantitative research in these areas in a more critical and productive manner.
LO4.
Use statistical software to analyse and present data.
LO5.
Write up results for a dissertation or publication.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Online Study Task
|
20% |
Week 3 OST - 5/08/2024 1:00 pm Week 4 OST - 13/08/2024 1:00 pm Week 5 OST - 19/08/2024 1:00 pm Week 6 OST - 26/08/2024 1:00 pm Week 7 OST - 2/09/2024 1:00 pm Week 8 OST - 9/09/2024 1:00 pm Week 9 OST - 16/09/2024 1:00 pm Week 10 OST - 30/09/2024 1:00 pm Week 11 OST - 8/10/2024 1:00 pm Week 12 OST - 14/10/2024 1:00 pm |
| Presentation |
Dataset Description and Summary
|
30% |
18/09/2024 4:00 pm |
| Paper/ Report/ Annotation |
Statistical Analysis Report
|
50% |
6/11/2024 4:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Online Study Task
- Hurdle
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 20%
- Due date
Week 3 OST - 5/08/2024 1:00 pm
Week 4 OST - 13/08/2024 1:00 pm
Week 5 OST - 19/08/2024 1:00 pm
Week 6 OST - 26/08/2024 1:00 pm
Week 7 OST - 2/09/2024 1:00 pm
Week 8 OST - 9/09/2024 1:00 pm
Week 9 OST - 16/09/2024 1:00 pm
Week 10 OST - 30/09/2024 1:00 pm
Week 11 OST - 8/10/2024 1:00 pm
Week 12 OST - 14/10/2024 1:00 pm
- Learning outcomes
- L01, L02, L03
Task description
Every week from Week 3 to Week 12, there will be an Online Study Task consisting of 10 questions that you need to answer and submit in preparation for that week.
These tasks are available 7 days before the due date and must be submitted by Monday at 1 pm (or Tuesday if Monday is a public holiday).
The questions will cover the lecture content and required readings for that week. For example, the Week 3 Online Study Task will be about the Week 3 lecture and readings, and you need to submit it by 1 pm on Monday of Week 3.
Each Online Study Task is worth 2% of your final grade. You have 60 minutes and one attempt to complete each task.
After all ten Online Study Tasks are completed, your total study task score will be calculated. Please note that tasks not completed and awarded 0 points are included in the average calculation. The percentage of the maximum achievable points will determine your final grade for this assessment.
The link to each week's Online Study Task will be available on Blackboard.
Marking Information
The weekly Online Study Tasks must be accessed, completed, and submitted via Blackboard.
Answers will be assessed automatically, and results will be made available to students within the week of submission. Answers to questions will be rated as correct or incorrect. For each correct answer, students will receive 1 point. Each Online Study Task consists of 10 questions, and there are 10 tasks in total, meaning students can earn up to 100 points if they answer all questions correctly. The marks for the Online Study Tasks follow the marking scheme of the School of Language and Cultures.
Statement on Gen AI & MT
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
You must attempt all four assessments in order to pass the course.Submission guidelines
Submission via Blackboard/UQ Learn.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
No extensions will be granted as all students will have the results to the quiz once the due date has passed.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Dataset Description and Summary
- Hurdle
- Online
- Mode
- Product/ Artefact/ Multimedia
- Category
- Presentation
- Weight
- 30%
- Due date
18/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The purpose of this assignment is to give you experience in finding, assessing, describing, and visualizing appropriate datasets for research projects. For this task, you need to find a dataset consisting of at least 3 columns of which at least one must contain numeric values. You then need to record a 10-minute presentation in which you describe, summarise, and visualise the dataset. In addition, you need to motivate and formulate at least one research question that you could answer based on the dataset. The motivation for answering the research question should be based on a gap in the existing body of research. Finally, we want you to elaborate on how you would proceed to answer, in detail, the research question (be specific!).
Below, you will find some pointers on what questions should be answered in your presentation:
- Source: where does the data come from or how was it compiled?
- Overview: What variables does the data set consist of and what do the variables represent? How many observations does the dataset contain?
- Variables: What are the levels of categorical variables? What are mean, standard deviation, range, etc. of numeric variables?
- Distributions: What are the distributions of the variables in the data set? (you can use tables and visualisations to address this)
- Research question: What is a research question, that extends existing research, could be answered based on this dataset?
- Possible research: How would one have to proceed to answer the research question based on the dataset?
Prepare slides for your presentation and record yourself and the slides (we suggest you use Zoom and share your screen). The entire presentation should be between 9 and a maximum of 11 minutes (recordings must not be longer than 11 minutes). Your answers should be presented as a coherent, clear, logical, and engaging manner. Any sources and citations mentioned in your presentation need to be added in a reference list at the end of your presentation (which should also be formatted according to APA7).
Your slides and your presentation should be engaging and informative. Your slides should not be too text heavy, and the slides should contain the main aspects of what you say/present. Use appropriate language without errors both on the slides and in your presentation. You are welcome to use visualisations and tables created by yourself and/or from the articles. Your face needs to be visible during the presentation.
Statement on Gen AI & MT
Artificial Intelligence (AI) and Machine Translation (MT) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task.. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
You must attempt all four assessments in order to pass the course.Submission guidelines
Submission via TurnitIn.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Maximum extension length allows assessors to give timely feedback before subsequent tasks are due.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Statistical Analysis Report
- Hurdle
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 50%
- Due date
6/11/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The Statistical Analysis Report assessment serves as a practical application of the knowledge and skills acquired throughout the course. It offers an opportunity to engage with a real-world, language- or culture-related data set chosen by the student, honing your abilities to describe, summarize, visualize, and quantitatively analyse data. By undertaking this assessment, you will gain insights into the approaches and practices employed by data analysts in the field.
For the Statistical Analysis Report, you are required to write a concise report, limited to 2000 words (excluding R code, R output, and references). It is crucial that you also include the R code you use in the analysis. The report should encompass the following key aspects:
- Motivation and Research Question: Begin by providing a clear rationale for your research question, explaining its significance and relevance to the field of study.
- Hypothesis Formulation: Develop a hypothesis that corresponds to your research question, demonstrating a hypothesis-driven approach to your analysis.
- Data Description, Tabulation, and Visualization: Thoroughly describe the dataset utilized in your analysis, employing appropriate tables, figures, and visualizations to present the data in a comprehensive manner.
- Statistical Analysis Description: Describe the statistical techniques and methods employed to analyse the data. Provide a detailed explanation of the chosen approaches, justifying their relevance to the research question.
- Results Reporting: Present the results of your analysis using a combination of prose, tables, and figures. Ensure that your findings are clear, concise, and effectively support your hypothesis.
- Critical Evaluation: Conduct a critical evaluation of your analysis, highlighting any potential issues or limitations that may impact the validity or generalizability of your results. Offer thoughtful insights into how these shortcomings could be addressed or mitigated.
- References: Include a reference section following APA7 format, acknowledging any external sources used in your report.
By completing the Statistical Analysis Report, you will demonstrate your proficiency in data analysis, hypothesis formulation, result interpretation, and critical evaluation. This assessment provides a valuable opportunity to apply your knowledge in a practical context and gain hands-on experience in the field of data analysis.
Statement on Gen AI & MT
Artificial Intelligence (AI) and Machine Translation (MT) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task.. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
You must attempt all four assessments in order to pass the course.Submission guidelines
Submission via TurnitIn.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
This is an end of semester assessment during exam period. Approved extensions will be rescheduled based on assessors' availability.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 24 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
ﾠ
Total score: ﾠData analysis ﾠ& presentationﾠ percentage score = 25 points + ﾠMastery quiz (I)ﾠ percentage score = 25 pointsﾠ+ Mastery quiz (II)ﾠ= 25 pointsﾠ+ Final assessmentﾠ= 25 points = 100 points
All assessment items must be attempted for a final grade of 4 or higher.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
All resources will be made available on Blackboard/UQ Learn.ﾠ
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lecture: Quantitative Methods in Applied Linguistics Lecture: The lecture consists of a one-hour pre-recorded session available one week in advance. Students need to watch the lectures before the corresponding week. For example, the Week 3 lecture needs to be watched before Monday of Week 3 at 1 pm. The online lectures cover the following topics: Week 1: Introduction to the course and to quantitative thinking Week 2: Basic concepts 1 Week 3: Basic concepts 2 Week 4: Getting started with R and Rstudio 1 Week 5: Getting started with R and Rstudio 2 Week 6: Descriptive Statistics Week 7: Introduction to Data Visualization Week 8: Basic Inferential Statistics Week 9: Regression Analysis 1: Simple and Multiple Regression Week 10: Regression Analysis 2: Model Diagnistics Week 11: Regression Analysis 3: Types of Regression Week 12: Regression Analysis 4: Mixed-Effects Models Week 13: Tree-Based Models, revision, summary, and outlook Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorials: Quantitative Methods in Applied Linguistics Tutorials: During the two-hour onsite tutorials, we will discuss required readings and lecture content, engage in activities and group discussions, and explore concepts mentioned in the lectures. We will also practice what was demonstrated in the lecture. Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.