EDUC 7011 - Introduction to Quantitative Educational Methods

North Terrace Campus - Semester 1 - 2014

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.

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Course Details

Course Code	EDUC 7011
Course	Introduction to Quantitative Educational Methods
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
Corequisites	EDUC 7001 Educational Inquiry
Assessment	Practical portfolio, Group Presentation, Report

Course Staff

Course Coordinator: Dr Igusti Darmawan

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.

Course Learning Outcomes

Foster students’ understanding of the researcher’s work (model)
Introduce students to statistics and tests used in educational research
Introduce students to statistics programs
Promote students’ competence and confidence in using descriptive statistics
Promote students’ competence and confidence in using inferential statistics in general and to the use of significance in testing hypotheses in particular
Familiarize students with some more complicated statistical tests
Develop students’ ability to understand and master the handling of data and employ proper analyses
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, 5, 6
The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner.	7, 8
An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems.	1, 2, 3 ,4 ,5 , 6, 7
Skills of a high order in interpersonal understanding, teamwork and communication.	8
A proficiency in the appropriate use of contemporary technologies.	3
A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life.	7, 8
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, 7, 8
An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities.	2, 5, 6, 8

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 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.

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 Assessments and Requirements Introduction to Statistics	Data manipulation
Week 2	Describing Sets of Observations and Distributions	Frequencies Bar charts Histograms
Week 3	Mesures of Central Tendency of a Set of Scores	Descriptive Statistics part 1
Week 4	Measures of Spread of a Set of Scores	Descriptive Statistics part 2
Week 5	Experimentation The mean procedure - comparison of Groups	One-Sample t-test Independent-Samples t-test
Week 6	Analysis of Variance	Paired-Samples t-test One-Way
Week 7	Probability and inference Categorical Data Analysis of Two-way Tables	Cross tabulation & Chi-square analysis
Week 8	Correlation and the Association between Two Variables	Scatter plots Bivariate correlations
Week 9	Linear Regression and Least Squares Analysis	Simple Linear Regresion
Week 10	Multiple regression analysis	Multiple Linear Regression
Week 11	Sampling and Estimating Error	Use of AM
Week 12	Presentations

Specific Course Requirements

N/A

Small Group Discovery Experience

In small 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. Finally, you and your team will present your findings. Use your research to justify and support the strategies used in tackling the problem.

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 Summary

Assignment 1 : Practical portfolio
Type : Formative and Summative (Individual)
Due Date : at the beginning of each session
Weighting : 20%
Learning objectives : 1, 2, 3, 4, 5, 6

Assignment 2 : Group Presentation
Type : Summative (Group)
Due Date : Week 12
Weighting : 30%
Learning objectives : 3, 7, 8

Assignment 3 : Report
Type : Summative (Individual)
Due Date : Week 14
Weighting : 50%
Learning objectives : 1, 2, 3, 4, 5, 6, 7, 8

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 Detail

Assessment 1: Practical Portfolio
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
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
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.
In addition, students are expected to discuss the results obtained from their analyses.

Submission

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.

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
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator