EDUC 7021 - Quantitative Approaches to Research
North Terrace Campus - Semester 1 - 2020
General Course Information
Course Code EDUC 7021 Course Quantitative Approaches to Research Coordinating Unit School of Education Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 2 intensive blocks of 15 hours each plus structured online activities Available for Study Abroad and Exchange Y Assumed Knowledge EDUC 7065 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, multiple regression, path analysis, factor analysis, cluster analysis, partial least squares path analysis, and structural equation modelling using SPSS, AMOS and MPlus. 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)
1,2,3,4,5 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
1,2,3,4,5 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
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
2,3,4,5 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
1,6 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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
Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2018) Multivariate Data Analysis: Pearson New International Edition (8th edition), England: United Kingdom, CENGAGE
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.The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Contact time : 30 hours
Non-contact time : 100 hours (readings, home works, and assignments)
Learning Activities Summary
Please note Intensive 1 will be held on Friday 28 February 2020 and Saturday 29 February 2020 9 am - 5 pm.
Class Day Topic Practical 1 Day1: Session 1 Introduction to Multivariate and Multilevel Analysis
Correlational Procedures in Data Analysis
Aggregation and disaggregation effects on Descriptive Statistics and Correlation coefficients 2 Day1 : Session 2 Handling of missing values SPSS:
3 Day 1: Session 3 Least Square Analysis vs Maximum Likelihood
The use of AMOS
Introduction to AMOS
4 Day 2: Session 1 Cluster Analysis SPSS: Cluster Analysis 5 Day 2: Session 2 Exploratory Factor Analysis SPSS EFA 6 Day 2: Session 3 Confirmatory Factor Analysis AMOS: CFA 7 Day 3: Session 1 Path Analysis 1 SPSS: Path Analysis 8 Day 3: Session 2 Path Analysis 2 AMOS: Path Analysis 9 Day 3: Session 3 Structural Equation Modelling AMOS: SEM 10 Day 4: Session 1 Hierarchical Linear Modelling 1 HLM 11 Day 4: Session 2 Hierarchical Linear Modelling 2 HLM 12 Day 4: Session 3 Growth Modelling HLM
Specific Course RequirementsN/A
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.
Assessment SummaryAssignment 1 : Practical portfolio
Type : Formative and Summative (Individual)
Due Date : The following session
Weighting : 20%
Learning objectives : 1, 2, 4, 6
Assignment 2 : Report 1
Type : Summative (Individual)
Due Date : After Intensive 1
Weighting : 40%
Learning objectives : 1, 3, 5, 6
Assignment 3 : Report 2
Type : Summative (Individual)
Due Date : After Intensive 2
Weighting : 40%
Learning objectives : 1, 3, 5, 6
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 2 and 3
You are required to show competence in analysing data using at least two data analysis procedures. 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:
• 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
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