SLAT7855 Quantitative Research Methods in Applied Linguistics
2024-08-07
Preface
Basic Course Information
Course Description
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.
Assumed Background: No background in statistics or mathematics is assumed.
Course Introduction
This course will examine and apply basic statistical methods used in quantitative research in second language acquisition, applied linguistics and linguistics. The focus will be on quantitative methodology used in language research in applied linguistics and related fields, but the course will be of interest to anyone who wants a better understanding of quantitative methods in language research.
The course has three main aims. It will provide
- grounding in the basic logic and scope of quantitative reasoning (= statistics) in language research
- exposure to the statistical techniques commonly used
- in-depth experience in using and reporting statistical results using R and RStudio.
Statistical methodology in language research is very broad and has different levels of sophistication. The course will endeavor to introduce the conceptual underpinnings, analytical methods and technical tools you need to understand quantitative findings in the research literature and potentially use these techniques in your own research.
Course Changes in Response to Previous Student Feedback
Previous student feedback has been incorporated in this profile.
Aims, Objectives & Graduate Attributes
Course Aims
This course will examine and apply basic statistical methods used in quantitative research in second language acquisition, applied linguistics and linguistics. The course has three main aims. It will provide
- a grounding in the basic logic and scope of quantitative reasoning (= statistics) in language research
- exposure to the statistical techniques commonly used
- in-depth experience in using and reporting statistical results using R and RStudio.
Learning Objectives
After successfully completing this course you should be able to:
Understand and apply basic statistical tests in applied linguistics and second language acquisition.
Understand the role of quantitative evidence in describing and explaining language learning and use.
Read quantitative research in these areas in a more critical and productive manner.
Use statistical software to analyse and present data.
Write up results for a dissertation or publication.
Learning Resources
Required Resources
The script for the course is made available on
https://MartinSchweinberger.github.io/SLAT7855
Lecture recordings and tutorial recordings are made available on Blackboard.
University Learning Resources
It is strongly recommended to attend the following UQ Library Trainings:
- R with RStudio: Getting started
- R reproducible reports with R Markdown and knitr
- R data manipulation with RStudio and dplyr: introduction
- R data visualization with RStudio and ggplot2: introduction
Access to required and recommended resources, plus past central exam papers, is available at the UQ Library website (http://www.library.uq.edu.au/lr/SLAT7855).
The University offers a range of resources and services to support student learning. Details are available on the myUQ website (https://my.uq.edu.au/).
Teaching & Learning Activities
Learning Activities
Recording of Lectures: Please be aware that teaching at UQ may be recorded for the benefit of student learning. If you would prefer not to be captured either by voice or image, please advise your course coordinator before class so accommodations can be made. For further information see PPL 3.20.06 Recording of Teaching at UQ.
Course plan
weeks | content | scriptchapter |
|---|---|---|
Week 1 | Introduction to the course | Preface and Introduction |
Week 2 | Basic concepts 1 | Basic Concepts in Quantitative Research |
Week 3 | Basic concepts 2 | Basic Concepts in Quantitative Research |
Week 4 | Getting started with R and Rstudio 1 | Getting started with R and RStudio |
Week 5 | Getting started with R and Rstudio 2 | Introduction to R and RStudio |
Week 6 | Descriptive Statistics | Descriptive Statistics |
Week 7 | Introduction to Data Visualization | Introduction to Data Visualization with R |
Week 8 | Introduction to Inferential Statistics | Simple and Multiple Regression |
Week 9 | Regression Analysis 1: Model Fitting | Simple and Multiple Regression |
Week 10 | Regression Analysis 2: Testing assumption | Simple and Multiple Regression |
Week 11 | Regression Analysis 3: Types of Regressions | Logistic Regression |
Week 12 | Regression Analysis 4: From fixed to mixed | Mixed-Effects Regression |
Week 13 | Trees and round-up | Tree-based Models |
Assessment
Assessment | DueDate | Weighting |
|---|---|---|
Weekly Online Study Tasks | Weeks 3 to 12 (Aug. 7 - Oct. 16): must be submitted before Monday 1pm of each week | 30% |
Dataset description presentation | Wednesday of week 9 (Sep. 20): must be submitted before 4 pm | 30% |
Statistical Analysis Report | Wednesday two weeks after week 13 (Nov. 8): must be submitted before 4 pm | 40% |
Weekly Oline Study Tasks
Task Description
Weight: 20%
Due date: Week 3 to week 12
Submission: via Blackboard
Weekly Online Study Tasks
Every week - from week 3 to week 12 - there will be 10 questions that you need to answer. This amounts to 10 online study tasks. The questions will be about the content of the lecture and the required reading(s) for that week.
Each online study task is worth 2% of your final grade. You only have 60 minutes and a single attempt to finish each weekly online study task.
The link to the online study tasks for each week will be made available on Blackboard.
Dataset summary presentation
Task Description
Weight: 30%
Due date: Week 9
Submission: via TurnItIn
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 and 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 no longer than 10 minutes. Your answers should be presented as a coherent, clear, logical, and engaging manner. Any 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.
This task has been designed to be challenging, authentic and complex. Whilst students may use Al technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference Al use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of Al tools.
Please refer to the rubic (available via the ECP) for details.
If you are sick/absent, see Section 5.3 Late submission.
Statistical Analysis Report
Task Description
Weight: 40%
Due date: Wednesday two weeks after the end of week 13
Submission: via TurnItIn
The Statistical Analysis Report assessment is a significant component, accounting for 40% of your final grade. This assessment serves as a practical application of the knowledge and skills acquired throughout the course. It offers an opportunity to engage with real-world datasets, 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.
Please refer to the rubic (available via the ECP) for details.
This task has been designed to be challenging, authentic and complex. Whilst students may use Al technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference Al use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of Al tools.
If sick/absent, apply for an extension (see 5.3 Late Submission).
Course Grading
Example criteria for each of the grades can be found in PPL 3.10.02 Assessment Procedures - section 7 Appendix.
Grade X: No assessable work received.
Grade 1, Low Fail: Absence of evidence of achievement of course learning outcomes: Weighted composite score of between 0-24%. No submitted work or submitted work incomplete. The minimum percentage required for a grade of 1 is 0%.
Grade 2, Fail: Minimal evidence of achievement of course learning outcomes: Weighted composite score of between 25-44%. Student shows little understanding of the research process, manifested in consistent lack of application of research techniques to assessment items, inability to formulate a topic and central question, very limited degree of analysis and synthesis of findings from data, and lack of clear and logical presentation of findings in a manner consistent with accepted academic standards. Nil or very poor bibliographic and citation technique.
Grade 3, Marginal Fail: Demonstrated evidence of developing achievement of course learning outcomes: Weighted composite score of between 45-49%. Falls short of satisfying all basic requirements of a pass. Student shows a poor understanding of the research process, manifested in lack of application of research techniques to most assessment items, poor ability to formulate a topic and central question, a limited degree of analysis and synthesis of findings from data, and substantial lapses in clear and logical presentation of findings in a manner consistent with accepted academic standards. Poor bibliographic and citation technique.
Grade 4, Pass: Demonstrated evidence of functional achievement of course learning outcomes: Weighted composite score of between 50-64%. Student shows an adequate understanding of the research process, manifested in application of research techniques to most assessment items, adequate ability to formulate to a topic and central research question, and adequate degree of analysis and synthesis of findings from data, and at times clear and logical presentation of findings (albeit with many incoherencies) in a manner broadly consistent with accepted academic standards. Work contains a notable number of factual errors and grammatical infelicities. Many errors in bibliography and in citations. Sources not consistently or correctly acknowledged.
Grade 5, Credit: Demonstrated evidence of proficient achievement of course learning outcomes: Weighted composite score of between 65-74%. Students shows a good understanding of the research process, manifested in application of research techniques to most assessment items, good ability to formulate a topic and central research question, a good degree of analysis and synthesis of findings from data, and usually clear and logical presentation of findings (albeit with some major incoherencies) in a manner that is mostly consistent with accepted academic standards. Work contains some factual errors and grammatical infelicities. Some obvious errors in the bibliography. Sources not consistently correctly acknowledged.
Grade 6, Distinction: Demonstrated evidence of advanced achievement of course learning outcomes: Weighted composite score of between 75-84%. Students shows a good understanding of the research process, manifested in application of research techniques to most assessment items, good ability to formulate a topic and central research question, a very good degree of analysis and synthesis of findings from data, and almost always clear and logical presentation of findings (some minor incoherencies) in a manner consistent with accepted academic standards. Work may contain rare factual errors and minor grammatical infelicities. Comprehensive bibliography, although with some minor errors. Sources correctly acknowledged.
Grade 7, High Distinction: Demonstrated evidence of exceptional achievement of course learning outcomes: Weighted composite score of between 85-100%. Student shows an excellent understanding of the research process, manifested in consistent application of research techniques to all assessment items, excellent ability to formulate a topic and central research question, coherent analysis and synthesis of findings from data, and consistently clear and logical presentation of findings in a manner consistent with accepted academic standards. Work has no factual errors, few grammatical infelicities and a comprehensive bibliography, with sources consistently and correctly acknowledged.
Late Submission
Extensions
In exceptional circumstances beyond a student’s control, which prevent them from submitting an assessment item by the due date and time, students can apply for an extension.
Extensions can be requested for assessment items such as:
- oral assessment for individuals or groups
- essays
- assignments
- case studies
- laboratory reports
- take-home essays
- tutorial group assignments
Applications to request an extension of assessment due date must be submitted through my.UQ, on or before the assessment item’s due date and time. A request for an extension of the assessment due date must be accompanied by supporting documentation that is truthful, accurate, describes in detail why an extension is required, and demonstrates the impact of the circumstances on a student’s academic performance to justify the entire period they were affected.
Requests for extensions received after the assessment item submission due date and time must include evidence of the reasons for the late request.
In the case of a group assessment item, an extension approved for an individual does not apply to the whole group.
The following penalties apply to late or non-submission of an assessment item:
* A penalty of 10% of the maximum possible mark allocated for the assessment item, or one grade per day if graded on the basis of 1-7, or equivalent penalty if a different grading approach is used, will be deducted for every day for up to 7 calendar days, at which point any submission will not receive any marks. Each 24 hour block is recorded from the time the submission is due;
or
- A penalty of 100% for a late assessment item may be set provided it is academically justified and communicated in the course profile.
Deferred Exams
Deferred examinations are available for the following assessment items, whether they are written, theory-based, practical, or online:
- Mid-semester deferred exam request – applies to exams held at any time other than during the University’s end-of-semester exam block periods.
- End-of-semester deferred exam request – applies only to exams held during the University’s designated end-of-semester exam periods.
Students can apply for a deferred exam:
* if there are exceptional or unavoidable circumstances, or
* as a one-off discretionary request.
Apply for a deferred exam
Submit a ‘deferred examinations request’ through mySI-net. You can save this application and come back to it at any time.
For information on deferred exams and instructions on how to apply please see myUQ information on deferring an exam.
Late applications
Applications should be submitted no later than 5 calendar days after the date of the original exam. However, there may be circumstances that prevent you from submitting on time.
If more than 5 calendar days have passed since the date of the original examination, a Late Notice link will appear on the deferred exam request form in mySI-net. The Late Notice provides instructions about what you can do next.
What to expect after you apply
Your deferred examination request(s) must have a status of “submitted” in mySI-net to be assessed.
- All applications for deferred mid-semester examinations are assessed by the relevant school.
- Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
You’ll receive an email to your student email account when the status of your application is updated.
Approved deferred exam applications
- If your deferred mid-semester examination request is approved, the relevant school will notify you of the scheduled date and time of your deferred examination. Deferred mid-semester examinations are held during a period nominated by the relevant school.
- If your end-of-semester deferred examination request is approved you will receive a timetable the Friday prior to the deferred exam period. You must be available during this time to take your exam.