EDUC 706 : Measurement and Advanced Statistics

Education and Social Work

2023 Semester One (1233) (30 POINTS)

Course Prescription

Instruction in measurement will cover theories, principles, uses, and techniques for estimating statistical and practical significance, causation, instrument validity, reliability, and error. Principles and methods of factor analysis, structural equation modelling, hierarchical level modelling, missing value analysis, and propensity score analysis will be covered to statistically analyse educational data that are latent, nested, repeated, longitudinal, incomplete, and highly interconnected.

Course Overview

Education data are highly complex; typically nested (e.g., students within classes within schools within districts, etc.); longitudinal (i.e., repeated measures that may or may not be equated); incomplete (i.e., many missing data points); and highly interconnected (i.e., multi-causal, multi-collinear).  Hence, just to read research in the social, psychological, behavioural, and educational spaces, requires some competence with a wide repertoire of sophisticated statistical methods. 
This course exposes students to key principles in data analysis in the first half and then introduces briefly 6 different advanced methods for analysing complex data. 
The goals are to:
  • give students ability read research reports with some sense of whether the statistics and techniques are correct
  • identify the type of statistical methods their own research will need 
  • gain some capability in applying and interpreting statistical techniques with a data set

Course Requirements

To complete this course students must enrol in EDUC 706 A and B, or EDUC 706

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Master of Arts

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and apply statistical methods commonly used in educational, psychological, social science data. (Capability 1.1 and 3.2)
  2. Critically analyse the strengths and weaknesses of statistical methods used to analyse data (Capability 2.1 and 4.2)
  3. Interpret and communicate plausible interpretations about data based on statistical analyses (Capability 2.3, 3.2 and 4.1)


Assessment Type Percentage Classification
10 weekly Tutorial exercises 10% Individual Coursework
4 Quarterly Quizzes 30% Individual Coursework
Article Critique 10% Individual Coursework
Final Data Analysis Assignment 50% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3
10 weekly Tutorial exercises
4 Quarterly Quizzes
Article Critique
Final Data Analysis Assignment

To pass this course students must submit all assessments and achieve at least 50% for the course.

Workload Expectations

This course is a standard 30 point course. On average, students are expected to spend 20 hours per week in each 30 point course that they are enrolled in. the weekly lecture is 2 hours and is followed by a 1-hour tutorial later in the week. Tutorial lab work is begun in the tutorial and completed individually.

A typical semester including the study/exam period totals approximately 15 weeks. This means that for this course you should expect to commit 36 hours to direct contact via on-campus lectures and tutorials.

You can also reasonably expect to commit approximately 240-260 hours to independent learning. This may include reading (and more reading), note-taking, face-to-face and/or online discussion, writing, engaging in collaborative group work, problem solving, undertaking practical tasks, reflecting on learning, accessing learning and study resources, and assignment, test and exam preparation and completion.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including computer labs to complete components of the course.
Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events unless required by emergency restrictions.
Attendance on campus is not required for the tests.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

There is no required text book but if you haven't passed a Stage 1 Probability course you will find this text extremely helpful and enjoyable: Field, A. (2016). An Adventure in Statistics: The Reality Enigma. London: Sage.

There are many online resources for data analysis which will be introduced in class.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

It is anticipated that Prof. Lawrence Zhang will occasionally participate in 2023 to provide diversity of expertise.

Other Information

 This course is ideal for any PG student who considers they will critically read statistical research or conduct research that requires careful analysis of differences in means, the effect of chance, change over time, predictive or causal influences, and so on. Enrolment is not restricted to education students. Previously, students from psychology and computer science have taken the course.
The course presumes no prior knowledge, but will traverse material taught in undergraduate probability and multivariate statistics quite quickly. The course is excellent preparation for doctoral students for their provisional year proposal.
Students will be given assistance to acquire some competence with statistical software in the tutorials. SPSS, JAMOVI, R will be introduced. Students who prefer other statistical software systems are welcome to enrol.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.