EDUC 7021 - Quantitative Approaches to Research

North Terrace Campus - Semester 1 - 2024

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.

  • General Course Information
    Course Details
    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 Two intensive blocks of 15 hours each plus structured online activities
    Available for Study Abroad and Exchange Y
    Assumed Knowledge EDUC 7065
    Assessment Practical portfolio 30%, Report 1 35%, Report 2 35%
    Course Staff

    Course Coordinator: Dr Igusti Darmawan

    Name Dr. I Gusti Ngurah Darmawan
    Location Room 831, Level 8, 10 Pulteney Street
    Telephone 8313 5788
    Course Website
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

    Intensive #1
    Friday 23 February 2024 in Nexus10, 228, Computer Suite 9am - 5pm
    Saturday 24 February 2023 in Nexus10, 228, Computer Suite 9am - 5pm 

    Intensive #2
    Friday 24 May 2024 in Nexus10, 228, Computer Suite 9am - 5pm 
    Saturday 25 May 2024 in Nexus10, 228, Computer Suite 9am - 5pm
  • Learning Outcomes
    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)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.


    Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency

    Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    No Specific text book is required.
    Recommended Resources
    Keeves, 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 Learning
    Each 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 Modes
    A 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:
    Single Imputation
    Multiple Imputation
    3 Day 1: Session 3 Least Square Analysis vs Maximum Likelihood
    The use of AMOS
    Introduction to AMOS
    SPSS: Regression
    AMOS: Regression
    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 Requirements
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assignment 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 Detail
    Assessment 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.
    1. Students must retain a copy of all assignments submitted.
    2. All individual assignments must be attached to an Assignment Cover Sheet which must be signed and dated by the student before submission.
    3. 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.
    4. 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).
    5. 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.
    Course Grading

    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.

  • Student Feedback

    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 ( 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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