EDUC 7021 - Advanced Quantitative Educational Research
North Terrace Campus - Summer - 2016
General Course Information
Course Code EDUC 7021 Course Advanced Quantitative Educational Research Coordinating Unit School of Education Term Summer Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites EDUC 7011 Introduction to Quantitative Educational Research Assumed Knowledge EDUC 7001/7001NA Educational Inquiry, EDUC 7054/7054NA Research Design Course Description This topic aims to prepare students to select and employ appropriate analytical procedures for the examination of data collected in surveys, quasi-experimental research studies and longitudinal studies as well as to draw appropriate conclusions and interpret the research findings from such studies. The course concentrates on an understanding of and on the use of the analytical procedures of linear regression, path analysis, multiple regression, factor analysis, cluster analysis, analysis of variance and covariance, partial least squares path analysis, and structural equation modelling using SPSS, AMOS and LISREL. In addition, the problems of multilevel analysis are examined and an understanding and experience in the use of the analytical procedure of hierarchical linear modelling is provided both for studies of growth and of school and classroom effects. The HLM and MPlus programs are introduced as appropriate procedures for multilevel analysis. The implications of the choice of a particular multivariate analytical procedure for the design of quantitative research studies in the social and behavioural sciences are considered.
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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
5,6 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
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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