EDUC 7011 - Introduction to Quantitative Methods

North Terrace Campus - Semester 1 - 2017

This course will provide students with an introduction to the use of statistics in educational research. Emphasis will be placed on students achieving an understanding of the statistical procedures considered so that they can think critically about suitable procedures for the collection and analysis of data, and about the educational usefulness of calculated statistics. Students will gain experience with using the SPSS package on computers.

  • General Course Information
    Course Details
    Course Code EDUC 7011
    Course Introduction to Quantitative Methods
    Coordinating Unit School of Education
    Term Semester 1
    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 7054
    Corequisites EDUC 7001/EDUC 7001NA or EDUC 7054/EDUC 7054NA
    Course Description This course will provide students with an introduction to the use of statistics in educational research. Emphasis will be placed on students achieving an understanding of the statistical procedures considered so that they can think critically about suitable procedures for the collection and analysis of data, and about the educational usefulness of calculated statistics. Students will gain experience with using the SPSS package on computers.
    Course Staff

    Course Coordinator: Associate Professor Sivakumar Alagumalai

    Name Dr. I Gusti Ngurah Darmawan
    Location Room 834, Level 8, 10 Pulteney Street
    Telephone 8313 5788
    Email igusti.darmawan@adelaide.edu.au
    Course Website www.myuni.adelaide.edu.au
    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course students will be able to:
    1 Explain and discuss the researcher’s work (model)
    2 Elucidate basic statistical concepts and tests used in educational research
    3 Use statistics programs
    4 Demonstrate their competence and confidence in using descriptive statistics
    5 Demonstrate their competence and confidence in using inferential statistics in general and to the use of significance testing in particular
    6 Understand and master the handling of data and employ proper analyses
    7 Use output derived from statistical procedures and convert 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, 6, 7
    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
    7
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    6, 7
    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
    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
    1, 7
  • Learning Resources
    Required Resources
    Green, S.B. and Salkind, N.J. (2010) Using SPSS for Windows and Macintosh: Analyzing and Understanding Data, 6e, Boston, Prentice Hall
    Recommended Resources
    This course also consists of additional readings from a book of notes, of the statistical concepts and tests, prepared by Prof. John P. Keeves and Dr. I Gusti Ngurah Darmawan and of a set of exercises arising from the notes.
    Online Learning
    Occasionally, the instructor may 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.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Workload Total Hours
    1 x 2-hour lecture per week 24 hours per semester
    1 x 1-hour practical per week 12 hours per semester
    4 hours reading per week 48 hours per semester
    2 hours research per week 24 hours per semester
    4 hours assignment preparation per week 48 hours per semester
    Total = 156 hours per semester
    Learning Activities Summary
    Week Lecture Topic
    1 Introduction
    2 Describing set of observations and distributions
    3 Measures of spread of a set of scores
    4 One sample t-test
    5 Paired samples t-test
    6 Independent samples t-test
    7 ANOVA
    8 Analysis of two-way tables (Chi-square analysis)
    9 Correlation
    10 Simple Linear Regression
    11 Multiple Regression
    12 Path Analysis using multiple regression
    Specific Course Requirements
    N/A
    Small Group Discovery Experience
    In small collaborative groups (assignment 2), students will be required to develop an educational policy for a given problem. Initially, as a group, students will need to search for background information to the problem including the availability of the data required and to choose relevant and appropriate statistical procedures. Finally, each team will present their findings. Students should use their research to justify and support the strategies used in tackling the problem.
  • 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
    Assessment Task Task Type Due Weighting Learning Outcome
    Practical Portfolio Formative/Summative Weekly assignments for the first five weeks, Sunday, 5pm 20% 1, 2, 3, 4, 5, 6, 7
    Group Presentation Summative Week 12, during the class time 30% 1, 2, 3, 4, 5, 6, 7
    Report Summative Week 14, Friday, 5pm 50% 1, 2, 3, 4, 5, 6, 7
    Assessment Related Requirements
    1. Students are required to attend all practicals.
    2. Criteria that will be used to assess students’ work will be distributed and discussed in class
    3. 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 Detail
    Assessment 1: Practical Portfolio – 20% weighting
    Each session you are required to respond to a set of questions and utilise discussion board for sharing and critiquing statistical ideas as well as to reflect on what you have done for the day.
    In addition, students are required to show competence in working with statistical programs by preparing a portfolio of assignment from practicals.

    Assignment 2: Group Presentation – 30% weighting              
    In collaborative groups you will be required to develop an educational policy for a given problem. Initially, as a group, you will need to search for background information to the problem including the availability of the data required. You and your group should choose relevant and appropriate statistical procedures. A complete dataset will be provided, but you will need to make a fully informed decision on which part of the data will be used. Finally, you and your team will present your findings. Use your research to justify and support the strategies used in tackling the problem.

    Assignment 3: Report – 50% weighting
    The assignment involves the construction of a data file and the analysis of the data from a school that requires:
    (a) calculation of descriptive statistics
    (b) correlations between variables or a set of variables
    (c) one-way analysis of variance
    (d) multiple regression analysis
    using SPSS or other computer programs.
    Submission
    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 (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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