EDUC 7009 - Exam of Info & Analysis of Freq & Count Data

North Terrace Campus - Semester 1 - 2014

This course is designed to develop skills in the use of computer-based procedures for the storage and systematic examination of information obtained from published sources, extended interviews on the use of detailed observation schedules, particularly of learning and teaching in classroom situations. In some studies this leads to the discipline interpretation of the information, while in other studies this leads to the development of explanatory models that can be tested with frequency and count data. The first stage of the topic involves an introduction to storage and extraction procedures, and the sorting and shifting of the extracted information, while the second stage involves the analyses of contingency tables, configural frequency analyses, correspondence analyses, log-linear modelling, mobility tables and Markov chains. The emphasis in this course is on the unity of educational research across different disciplines and different methods of inquiry.

  • General Course Information
    Course Details
    Course Code EDUC 7009
    Course Exam of Info & Analysis of Freq & Count Data
    Coordinating Unit School of Education
    Term Semester 1
    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, EDUC 7020 Qualitative Approach to Educational Research
    Course Description This course is designed to develop skills in the use of computer-based procedures for the storage and systematic examination of information obtained from published sources, extended interviews on the use of detailed observation schedules, particularly of learning and teaching in classroom situations. In some studies this leads to the discipline interpretation of the information, while in other studies this leads to the development of explanatory models that can be tested with frequency and count data. The first stage of the topic involves an introduction to storage and extraction procedures, and the sorting and shifting of the extracted information, while the second stage involves the analyses of contingency tables, configural frequency analyses, correspondence analyses, log-linear modelling, mobility tables and Markov chains. The emphasis in this course is on the unity of educational research across different disciplines and different methods of inquiry.
    Course Staff

    Course Coordinator: Dr Igusti Darmawan

    Name                 : Dr. I Gusti Ngurah Darmawan
    Location             : Room 834, Level 8, 10 Pulteney Street
    Telephone          : 8303 5788 (work)
    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
    This course is designed to:
    1. Foster students’ understanding of the researcher’s work (model)
    2. Introduce students to procedures for the storage and systematic examination of informationin educational research
    3. Introduce students to procedures for analysis of frequency and count data
    4. Promote students’ competence and confidence in using computer based procedures for the examination of information, using NVivo in particular, and various statistical test using SPSS for the analysis of frequency and count data
    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
  • 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.

    Miles, M.B. and Huberman, A.M. (1994 ) Qualitative Data Analysis, Thousand Oaks, CA: Sage.
    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.

    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 the conduct of inquiry
    Collecting information through interview and questionnaire
    Getting started in NVivo
    Week 2

    Information storage procedures

    Week 3 Early step in data analysis: Explaining, coding and indexing information
    Week 4 Identifying themes
    Week 5 Attributes
    Week 6 Searching for patterns
    Week 7 Video, Audio, and Images
    Week 8 Summarizing frequency and count data Contingency tables and identification of interaction
    Fisher Exact Test 
    Week 9 Contingency Table
    Week 10 Cluster Analysis
    Week 11 Correspondence Analysis
    Week 12 The unity of educational research and the need for explanation
    Group Presentation
    Specific Course Requirements
    N/A
    Small Group Discovery Experience
    In a small group, students are required to explore and show competence in working with information storage procedures using NVivo and data analysis in addressing a particular educational 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
    Assignment 1 : Group Presentation
    Type : Summative (Individual)
    Due Date : Week 12
    Weighting : 20%
    Learning objectives : 1, 2, 3, 4, 5, 6

    Assignment 2 : Report1
    Type : Summative (Individual)
    Due Date : Week 14
    Weighting : 40%
    Learning objectives : 1, 2, 3, 4, 5, 6

    Assignment 3 : Report2

    Type : Summative (Individual)
    Due Date : Week 14
    Weighting : 40%
    Learning objectives : 1, 2, 3, 4, 5, 6
    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: Group Presentation
    In a small group, students are required to explore and show competence in working with information storage procedures using NVivo and data analysis in addressing a particular educational problem.

    Assignment 2 and 3: Report1 and Report2
    You are required to show competence to analyse information and data using NVivo and various 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.

    Three optional assignments for assessment are proposed for this course. Each option involves the preparation of a report of around 6,000 words.

    The three options include:
    1. One major assignment on Descriptive analysis and NVivo includes
    a. Descriptive analysis and NVivo Stage 1: 3,000 words, and
    b. Descriptive analysis and NVivo Stage 2: 3,000 words

    2. One major assignment on Statistical analysis and count models includes
    a. Any two of statistical procedures listed below with 3,000 words each (6,000 words in total)

    3. Two minor assignments on both Descriptive analysis and Statistical analysis include
    a. Descriptive analysis and NVivo Stage 1 (3,000 words), and
    b. Any one of statistical procedures listed below (3,000 words).

    Options:

    A. Descriptive analysis and NVivo

    Stage 1
    Introduction
    Statement of problem
    Design of investigation
    Choice of questions
    Interview protocol
    Using NVivo for descriptive analysis
    Data under examination
    Data analysis
    Coding
    Creating Node
    Refining Node (add nodes, change nodes, delete nodes)
    Coding documents
    Profile coding

    Stage 2
    Data analysis
    Searching for patterns
    Groups’ similarity and differences (e.g. gender differences, educational background differences,   Australian vs others, etc which are basically links between nodes and attributes)
    Proximity search (too see how close a construct to others, links between nodes and other nodes)
    Model building
    Summary

    B. Statistical analysis and count models

    Statistical procedures:
    Fisher Exact Test
    Contingency Table
    Cluster Analysis
    Correspondence analysis
    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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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