This course provides students with an introduction to advanced topics in survey data analysis and will provide several important extensions: (1) dealing with sample selection bias and endogeneity bias in survey data (instrumental variable regression and Heckman selection correction); (2) using panel data to control for unobserved heterogeneity (fixed effects and random effects models); (3) modelling the time to an event (survival analysis) and (4) multi-level modelling for hierarchical or clustered data.  Students will gain experience in using the Stata statistical software package to apply these methods to survey data.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. identify possible issues, impacts, and consequences of sample bias;
2. describe how panel data can be used to control for unobserved heterogeneity (a potential cause of endogeneity bias in cross-sectional data) and apply to real-world data;
3. demonstrate an ability to model the dynamics of cross-sectional populations using survival analysis; and,
4. identify instances of hierarchical/clustered social science datasets and apply multi-level models for working with such data.

Research-Led Teaching

This course has a heavy research focus. Students will be required to replicate existing applied econometric research on real data, and extend this research using their own research questions and ideas.

Required Resources

Students will be given a list of required readings in the first lecture of the course. This will include (a) texts that summarise the main techniques used in the course (available online through the ANU Library) (b) between 6-8 compulsory case-studies of applied econometrics that will be used in lectures and (c) between 6-8 case-studies that will form the basis of student assessment. Computer Labs will be delivered on campus using STATA.

Recommended Resources

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

• Written comments
• Verbal comments
• Feedback to the whole class, to groups, to individuals, focus groups

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Introduction to Course and Continuous Data IDuring this first lecture, we will introduce the content of the course and discuss different types of data that can be used in applied econometrics. We will talk about the concept of an independent and a dependent variable, and different ways to analyse data. We will introduce the main techniques for analysing individual-level data for continuous dependent variables. We will introduce the potential topics for student assignments, and discuss how to access data.No Computer Labs in Week 1	Lecture 1 in Week 1
2	Continuous Data IIDuring the lecture, we will continue the discussion of analysis of continuous dependent variables by looking at univariate and bivariate analysis. We will cover simple linear regression and then extend this to multiple linear regression (when there is one dependent variable and more than one independent variable).	Lecture 2 in Week 1
3	Assumptions in Econometric ModelsWe will discuss the assumptions underlying econometric models (in particular linear regression) in more detail, the different ways in which they can be violated, as well as potential solutions.Computer Lab in Week 2: Introduction to STATA and data management	Lecture 1 in Week 2
4	Categorical Data IDuring the lecture for this week, we will discuss one of the main extensions to micro-econometrics, the use of non-continuous dependent variables. We will begin by looking at binary dependent variables (yes/no) and how to calculate and interpret odds ratios and marginal effects in Logit/Probit regression.	Lecture 2 in Week 2
5	Categorical Data IIWe will extend our analysis of categorical dependent variables by looking at instances of more than two categories. This includes multinomial Logit/Probit; ordered Logit/Probit; and count data such as Poisson regressions.Computer Lab in Week 3: Linear regression and dummy variables	Lecture 1 in Week 3Assessment Task 2 due (Problem Set 1)
6	Time Series IWe will switch the focus of our analysis during this lecture, and look at the analysis of aggregate data, with a particular focus on time series analysis. We will discuss the concepts of (and main techniques for) lags, autocorrelation, trends and stationarity.	Lecture 2 in Week 3
7	Time Series II During this lecture, we will finish off our discussion of time series econometrics, with a focus on commonly used models in time series analysis, including AR, ADL, ARCH/GARCH, VAR and ECM models. Computer Lab in Week 4: Logit/Probit and calculation of predicted probabilities	Lecture 1 in Week 4Assessment Task 2 due (Problem Set 2)
8	Panel Data I We will extend our analysis of 'time', by looking at the main techniques for panel data analysis. That is, when we have more than one observation, for more than one individual. We will begin our discussion of panel data by discussing pooled regression and differencing.	Lecture 2 in Week 4
9	Panel Data IIWe will extend our analysis of panel data by looking at fixed and random effects regression.Computer Lab in Week 5: Multinomial and Ordered Logit/Probit	Lecture 1 in Week 5Assessment Task 2 due (Problem Set 3)
10	Causal InferenceWe will introduce the challenges associated with causal inference and econometric techniques that such Randomized Controlled Trials and Instrumental Variables.	Lecture 2 in Week 5
11	Presenting DataWe will discuss some of the practicalities of summarising data for an academic or policy audience.Computer Lab in Week 6: Introduction to time series analysis	Lecture 1 in Week 6Assessment Task 2 due (Problem Set 4)Assessment Task 3 due
12	Introduction to machine learning for economicsThis lecture will introduce students to machine learning techniques and their role in answering economic problems	Lecture 2 in Week 6
13	Assistance with Case Studies ProjectComputer Lab in Week 7: Time series models, dynamic causal effects and forecasting	Lecture 1 in Week 7Assessment Task 2 due (Problem Set 5)
14	Assistance with Case Studies ProjectComputer Lab in Week 8: Panel data analysis	Lecture 1 in Week 8Assessment Task 2 due (Problem Set 6)
15	Assistance with Case Studies ProjectComputer Lab in Week 9: Assistance with case studies project	Lecture 1 in Week 9Assessment Task 2 due (Problem Set 7)
16	Student Presentations	Assessment Task 4 due
17	Assistance with Case Studies ProjectComputer Lab in Week 11: Assistance with case studies project	Lecture 1 in Week 11
18	Summary and Questions on Case StudyComputer Lab in Week 12: Assistance with case studies project	Lecture 1 in Week 12Assessment Task 5 due

Tutorial Registration

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. https://www.anu.edu.au/students/program-administration/timetabling].

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Participation in Computer Labs	5 %	*	*	3 (see EMET8002)
Problem Sets	10 %	*	*	3 (see EMET8002)
Research Proposal	10 %	30/08/2024	20/09/2024	3 (see EMET8002)
Research Presentation	10 %	07/10/2024	14/10/2024	1,2 (see EMET8002)
Research Report	45 %	25/10/2024	28/11/2024	2,3,4,5 (see EMET8002)
Online Quiz	20 %	11/11/2024	28/11/2024	4,5 (see EMET8002)

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Policy and General Information
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

Lectures for SOCR8010 will be delivered live on campus and ECHO360 Recordings available for lectures. Computer labs will also be on campus but will not be recorded. Details on the delivery of this course and expectations of student participation are outlined in further detail on the Wattle course site in O-week. Attendance at synchronous activities, while not compulsory, is expected in line with “Code of Practice for Teaching and Learning”, clause 2 paragraph (b).

Assessment Task 1

Participation in Computer Labs

Your participation in computer lab discussions each week will be assessable, counting for 5% of your final grade. There are 10 computer labs throughout the semester, participation is expected in the majority of these sessions. Each week the tutor will mark your participation based on your level of engagement in the session and demonstrated understanding of the material discussed. For example:

• higher marks (8-10) will be awarded for consistent demonstration of engagement and demonstration of a high level of understanding of the majority of the material discussed each week;
• 7 is awarded for somewhat consistent demonstration of engagement and demonstration of a reasonable level of understanding of the majority the material discussed each week;
• 6 is awarded for somewhat consistent demonstration of engagement and demonstration of a reasonable level of understanding of the some of the material discussed each week;
• 5 is awarded for somewhat inconsistent demonstration of engagement and demonstration of passable level of understanding of the material discussed each week;
• less than 5 is awarded for inconsistent to little demonstration of engagement and rudimentary to little demonstration of understanding of the material discussed each week.

A partway mark will be provided to you in week 6.

More details will be given in Week 1.

Assessment Task 2

Problem Sets

Students will submit seven problem sets before computer labs in Weeks 3 to 9. Problem sets involve Stata coding and discussion questions that are based on the material covered in preceding lectures. Worked answers to those problem sets will be provided during the computer labs. The best 5 out of 7 problem sets will count 10% towards the final mark.

Assessment Task 3

Research Proposal

Students will submit a 1000-word research proposal based on their independent research. Students will be able to choose one of eight research reports that they will be required to replicate and make a minor extension to. These will be provided via Wattle on or before Week 1 of class. Students can also request to replicate and extend a paper outside of the eight suggested by the lecturers. This proposal should include a brief summary of the main findings, methods and data of the original paper, as well as the proposed extension. Submission will be via turnitin.

More details will be given in Week 1.

Assessment Task 4

Research Presentation

Students will give a five minute presentation on their own independent research. It is highly recommended that students present on the same paper from their Research Proposal. However, students are able to change with permission from the Convenor. Students will give the presentations during Week 10 during the time allocated to the computer lab and lecture. It is expected that students will participate in and comment on the presentations of other students. Presentations will be video recorded, which will enable later validation and verification of assessment if required (in accordance with point 7 in the ANU Student Assessment (Coursework) policy).

More details will be given in Week 1.

Assessment Task 5

Research Report

Students will submit a 4,000- 5,000 word essay based on their independent research. It is highly recommended that students present on the same paper from their Research Proposal and Presentation. However, students are able to change with permission from the Convenor.

More details will be given in Week 1.

Assessment Task 6

Online Quiz

Students will undertake a quiz through Wattle based on the content covered in the entirety of the course. Students will have two weeks to complete the quiz finishing in the final exam period, and can complete at any time during the two-week window. The quiz will consist of short answer questions, as well as STATA analysis and/or a short essay based on an additional set of applied econometric readings (provided at the end of the course through Wattle). Students will be required to explain concepts, interpret econometric results, and undertake basic analysis.

More information will be available in Week 1.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Hardcopy Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations or quizzes.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information. In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service — including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy. If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes. Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students