EDUC 7021 - Advanced Quantitative Educational Research
North Terrace Campus - Semester 2 - 2014
General Course Information
Course Code EDUC 7021 Course Advanced Quantitative Educational Research Coordinating Unit School of Education Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Prerequisites EDUC 7001 Educational Inquiry, EDUC 7011 Introduction to Quantitative Educational Research Course Description The course introduces the use, interpretation and application of various types of basic empirical research designs and statistical techniques utilized in education and related policy analysis. It examines the use of quantitative methods in educational research, and seeks to develop an awareness of the assumptions and perspectives that underlie a quantitative approach to research, and in particular the utilization of regression techniques such as multiple regression and path analyses. The course will be taught in the computer laboratory where students will work through a set of exercises using the SPSS and WesVar programs.
Course Coordinator: Dr Igusti Darmawan
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
1 Foster students’ understanding of the researcher’s work (model) 2 Introduce students to procedures for collecting and storing of data in educational research 3 Introduce students to procedures for analysis of multivariate and multilevel data 4 Promote students’ competence and confidence in using computer based procedures for the data analysis 5 Develop students’ ability to understand and master the handling of data and employ proper analyses 6 Develop students’ understanding of output derived from statistical procedures and to converting such output to understandable statements in English
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1, 2, 3, 4 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 5, 6 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1, 2, 3, ,4 ,5 ,6 Skills of a high order in interpersonal understanding, teamwork and communication. 6 A proficiency in the appropriate use of contemporary technologies. 4 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1, 5, 6 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 1, 2, 3, 4, 5, 6 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 1, 6
Required ResourcesNo Specific text book is required.
Recommended ResourcesKeeves, J.P. (ed.) (1997) Educational Research, Methodology, and Measurement: An International Handbook. (2nd Edn) Oxford: Pergamon
Online LearningEach week, the instructor will assign readings of selected chapters from statistic textbooks, which will be made available online via MyUni.
Learning & Teaching Activities
Learning & Teaching ModesA balance between ‘student centred’ and ‘teacher centred’ approaches to learning with emphasis on fostering an engaging learning pedagogy will be used in this course. Lectures will be supported by discussions and problem-solving practicals using statistical programs which will require active participation from students.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Contact time : 24 hours (12 hours lectures, 12 hours practicals)
Non-contact time : 120 hours (readings, home works, and assignments)
Learning Activities Summary
Schedule Week 1 Introduction to Multivariate and Multilevel Analysis
Correlational Procedures in Data Analysis
Week 2 Linear and Multiple Regression
Least Square estimates
Week 3 Cluster Analysis Week 4 Multidimesional Scaling Week 5 Exploratory Factor Analysis and its use Week 6 Confirmatory Factor Analysis Week 7 Introduction to Path Analysis Week 8 Partial Least Square Path Analysis Week 9 Structured Equation Modelling 1 Week 10 Structured Equation Modelling 2 Week 11 Hierarchical Linear Modelling 1 Week 12 Hierarchical Linear Modelling 2
Small Group Discovery ExperienceStudents are required to show competence in working with multivariate and multilevel data. There will be small group hands-on activities every week, and students are required to submit their works by the beginning of the next class.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assignment 1 : Practical portfolio
Type : Formative and Summative (Individual)
Due Date : The following session
Weighting : 30%
Learning objectives : 1, 2, 4, 6
Assignment 2 : Report 1
Type : Summative (Individual)
Due Date : Week 8
Weighting : 35%
Learning objectives : 1, 3, 5, 6
Assignment 3 : Report 2
Type : Summative (Individual)
Due Date : Week 14
Weighting : 35%
Learning objectives : 1, 3, 5, 6
Assessment Related Requirements
- Students are required to attend all practicals.
- Criteria that will be used to assess students’ work will be distributed and discussed in class.
- To gain a pass, a mark of at least 50% must be obtained on ALL assessed components as well as a total of at least 50% overall.
Assessment DetailAssessment 1: Practical Portfolio
Students are required to show competence in working with multivariate and multilevel data. There will be hands-on activities every week, and students are required to submit their works by the beginning of the next class.
Assignments 2 and 3: Reports 1 and 2
Your are required to show competence in analysing data using at least two data analysis procedures (one procedure in each report). You can use your own dataset or one of those made available in the course, or with special permission, a dataset of your choosing. You will need to address the following in each of your reports:
- Formulate one or more research questions to address
- Specify hypotheses that you will test empirically
- Identify statistical methods appropriate for your data and analysis
- Conduct the analyses
- Interpret the results of your statistical analyses in terms of the research questions and hypotheses you defined at the onset of the study.
- Students must retain a copy of all assignments submitted.
- All individual assignments must be attached to an Assignment Cover Sheet which must be signed and dated by the student before submission.
- All group assignments must be attached to a Group Assignment Cover Sheet which must be signed and dated by all group members before submission. All team members are expected to contribute approximately equally to a group assignment.
- Markers can refuse to accept assignments which do not have a signed acknowledgement of the University’s policy on plagiarism (refer to policy on plagiarism above).
- Requests for extensions will be considered only if they are made three days before the due date for which the extension is being sought. Students must apply to the lecturer concerned on the ‘Application for Extension’ form at the back of the Academic Program Handbook
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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