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• CPD 186-Sampling I

  • Start
    6 Feb 2025
  •  
    12:00 PM
  • End
    3 Apr 2025
  • Location
    Online- Weekly 1 hour meetings taking place Thursdays
  • Spaces left
    8

  Registration is closed

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

  1. Introduction; Course Perspectives

  2.  Simple Random Sampling, Sampling Frames, and Introduction to Clustering

  3. Stratified Sampling I

  4. Stratified Sampling II, Systematic selection

  5. Cluster Sampling

  6. Unequal-Sized Clusters I

  7. Unequal-Sized Clusters II

  8. Variance Estimation

  Timeframe:

  February 6 – April 3, 2025. Weekly Meetings taking place Thursdays, 12:00 – 1:00 pm AEDT

  
  

  Course Objectives

   By the end of the course, participants will… ▪ understand the basic ideas, concepts and principles of probability sampling from an applied perspective ▪ be able to identify and appropriately apply sampling techniques to survey design problems ▪ be able to compute the sample size for a variety of sample designs ▪ understand and be able to assess the impact of the sample design on survey estimates ▪ be able to estimate the precision of the survey statistics using different estimation techniques

  
  

  Your instructor: Raphael Nishimura, PhD 

  Raphael Nishimura is the Director of Sampling Operations in Survey Research Operations at the Survey Research Center (SRC) in the Institute of Social Research of the University of Michigan. He has been working with sampling and survey statistics for over ten years. He holds a PhD in survey methodology from the University of Michigan and a bachelor’s degree in statistics from the University of São Paulo. His main research interest includes sampling methods, survey nonresponse and adaptive/responsive designs. Nishimura is also the director of the Sampling Program for Survey Statisticians of the SRC Summer Institute for Survey Research Techniques
  

  
  

  Prerequisites

   The course is presented at an intermediate statistical level. While we will not develop mathematical aspects of sampling theory, statistical notation and outlines of some algebraic proofs will be given. A sound background in applied statistics, proficiency in mathematics, including basic algebra, is necessary, since some algebraic derivations will be presented (although little emphasis will be placed on the derivations). A thorough understanding of the notation and algebraic results will be required.

  
  

  
  

  Grading will be based on:

  ▪ 8 Homework assignments (50% of grade)

  ▪ 8 Quizzes (one at the start of each online session; 15% of grade)

  ▪ Participation in discussion during the weekly online meetings, submission of questions in the weekly discussion forum, demonstrating understanding of the required readings and video lectures, and positive contributions on the Discussion Forum, see below (10% of grade)

  ▪ A final open-book online exam (25% of grade)

  Early Bird Deadline
  Please book before 6 January 2025 to take advantage of the Early Bird Deadline.

  Prices include GST.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.
  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.
  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy
  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• Mixed Models in R

  • Start
    10 Feb 2025
  •  
    9:00 AM
  • End
    14 Feb 2025
  •  
    12:30 PM
  • Location
    Online for 3 mornings: Mon/Wed/Fri

  This course, conducted by the Statistical Consulting Centre, is suitable for researchers who need to fit mixed models to their data. Mixed models are also known as multi-level models or hierarchical models, and include cluster randomised trials in medicine, unbalanced designs in agriculture, hierarchical structures in education, repeated measures in the social sciences, and nested factors in ecology. Topics include fixed and random effects; variance components; nested and crossed factors; comparison and adjustment of means; models with categorical and continuous predictors; using R to fit mixed models; interpretation of R output.

  To register:

  https://scc.ms.unimelb.edu.au/statistics-courses/course-listing/mixed-models

  
  

  Show details
• Logistic Regression 5 Day Masterclass

  • Start
    24 Feb 2025
  • End
    28 Feb 2025
  • Location
    Deakin University’s Melbourne CBD campus 727 Collins Street, Docklands VIC 3008

  Deakin Epidemiology is pleased to offer a summer Masterclass focused on Logistic regression to be delivered by arguably the world’s most famous teacher of this statistical technique – Prof. Stanley Lemeshow. In years past, Lemeshow together with Ken Rothman offered back-to-back masterclasses in Biostats and Epi in Tasmania which were a bit of an institution, with many epidemiologists and biostatisticians building their knowledge and networks by heading south for a healthy dose of upskilling or as a refresher. Stan has agreed to offer this program onshore once again in Australia, this time at Deakin University’s Melbourne CBD campus 727 Collins Street, Docklands VIC 3008.

  This 5-day course (Feb 24-28, 2025) will provide theoretical and hands-on practical knowledge and skills in statistical modeling with an in-depth focus on logistic regression analysis – the standard method for regression analysis of binary, multinomial and ordinal response data in health research. Each day comprises a 4-hr class in the morning and a 2-hr practical session in the afternoon and opportunities to network with fellow health and medical practitioners and researchers.”

  Places are limited, so get in early! For more information click here!”

  
  

  Show details
• Di Cook Award: Promoting Open Source Statistical Software Development

  • When
    25 Feb 2025
  •  
    12:00 PM - 1:00 PM
  • Location
    Online

  Register

  Statistical Computing and Visualisation section proudly presents the Di Cook Award webinar, promoting open-source statistical software development. 

  There were many excellent submissions in this round. Come along to hear from this year's winner Xiaolei (Adam) Wang for his work on the bsvarSIGNs package. This software implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions.

  We will also be hosting some lightning talks from other notable entries!

  Show details
• CPD 193-A Roadmap for Your Research Story: Making Communications your Companion for a Smoother Journey

  • When
    26 Feb 2025
  •  
    11:00 AM - 1:00 PM
  • Location
    Online

  Register

  The SSA proudly presents: A Roadmap for Your Research Story: Making Communications Your Companion for a Smoother Journey presented by Tim Macuga.

  This workshop empowers participants to recognise and overcome the barriers and biases that may discourage them from communicating their work effectively. Through thought-provoking discussions, participants will explore how strong communication skills are integral to achieving their professional and personal goals.

  By the end of the session, attendees will leave with practical, easy-to-implement techniques that enable them to confidently share their research with diverse audiences.

  Topics Covered:

  • There is one reason why we all communicate. When you realise what that is, the need to communicate effectively takes on new importance.

  • There is a long list of reasons why some people avoid communicating their research or their research story. We explore some of these barriers and biases and how we might move past them.

  • Sometimes the hardest step is to just get started. We introduce some new and easy-to-use tools and techniques that will get you going and wanting to do more.

  Key message:

  There is no one way to get where you’re going. But being able to communicate your research story will benefit you greatly.

  Bio-Tim Macuga:

  
  

  Tim is a seasoned Science Communicator with extensive experience in journalism and a passion for bridging the gap between complex research and broad audiences. Before joining the Statistical Society of Australia, Tim spent a decade leading communications for the Australian Data Science Network (ADSN), the QUT Centre for Data Science, and the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).

  In these roles, he specialised in empowering researchers to share their work effectively, fostering deeper connections between their discoveries and the wider community. Originally from the United States, Tim enjoyed a distinguished 25+ year career in television news, working across three states. For more than 15 years, he served as Executive News Producer at a leading TV station in Phoenix, Arizona, where he managed the evening newscasts in one of the nation’s fastest-growing markets.

  Target audience:

  • PhD students and early career researchers in academia
  • Professionals in technical roles who may not consider communication skills as vital to their work

  Cancellation Policy:

  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.

  Cancellations received prior to two weeks before the event will be refunded, minus the Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20.

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

  For any questions, please email events@statsoc.org.au.

  Show details
• Introduction to R and Reproducible Research

  • Start
    27 Feb 2025
  • End
    28 Feb 2025
  • Location
    Peter Hall Building, University of Melbourne

  This workshop, conducted by the Statistical Consulting Centre, covers the tools needed to efficiently work with data using R, particularly focusing on importing, rearranging, describing and visualising data. It is suitable for beginners and those familiar with R who are looking to improve their skills and strategies for working with data in a reproducible manner.

  This workshop covers the tools needed to efficiently work with data using R, particularly focusing on importing, rearranging, describing and visualising data. It is suitable for beginners and those familiar with R who are looking to improve their skills and strategies for working with data in a reproducible manner.

  The workshop is presented by Cameron Patrick and Sandy Clarke-Errey in person at the University of Melbourne over two days, with a mixture of lecture presentations and practical work.

  The course covers the following topics:

  • the basics of R and RStudio;
  • using R Markdown to tie together your R code, output and analytical decisions;
  • the benefits of a reproducible approach to data analysis;
  • concepts relating to types of data and how to best organise the data you collect;
  • importing data from commonly used file formats including Excel and CSV;
  • practical data-cleaning tasks to get your original data ready for analysis;
  • methods for summarising and describing data;
  • producing high-quality graphics with the ‘ggplot’ package;
  • presenting results from statistical analyses in tables and graphs.

  More details and registration here: https://scc.ms.unimelb.edu.au/statistics-courses/course-listing/research-and-r

  
  

  Show details
• Personal Journey Of Len Cook: A Webinar by Early Career & Student Statisticians Network and History Standing Committee of SSA

  • When
    11 Mar 2025
  •  
    12:30 PM - 1:30 PM
  • Location
    online

  Register

  Personal Journey Of Len Cook: A Webinar by Early Career & Student Statisticians Network and History Standing Committee of SSA.

  This is the fourth in the series of webinars that focus on the personal journey of older Australian statisticians who have made a difference. 

  This is the first in the series that focuses on the personal journey of a New Zealand statistician. The interview of Len, for up to 40 minutes, will be conducted by Dennis Trewin, former SSA President and a member of its History Committee. There will then be a Q&A led by Muskaan, the facilitator for the New Zealand branch of the ECSSN.

  

  Bio-Len Cook

  After working in Statistics New Zealand in a variety of roles starting in methodology in 1971, He became Government Statistician of New Zealand, from 1992 to 2000. , From 2000 to 2005 He was the National Statistician of the United Kingdom and Registrar-General of England and Wales. Len Cook joined the Department of Statistics after he graduated in Mathematics and Statistics from Otago in 1971.

  While working in Statistics NZ in his early years, He became a secretariat member of a Ministerial Task Force on Tax Reform (1981), and then a member of, the NZ Royal Commission on Social Policy (1987-88).  In 2008, Len jointly led the preparation of a ten-year development plan for a Pacific statistical system, and for some 5 years after that, he met regularly with Pacific Statisticians overseeing its implementation. Between 2015 and 2018 he was Families Commissioner and Chair of the board of the Superu evaluation agency. He has been a member of the Remuneration Authority until 2021. He has also been involved in medical workforce planning, as well as public sector and university review panels.

  Some recent professional roles include Vice-President, of the International Statistics Institute (2005-2009), and President of, the Institute of Public Administration NZ (2009-13). He is an elected Companion of the Royal Society of New Zealand.

  His longstanding interests are in the areas of population change and public policy, public administration, retirement provision, imprisonment patterns, official statistics, and the place of science in policy.  His interests include fly fishing.

  
  

  Why Attend?

  Learn from Experience: Len’s career path offers invaluable lessons for aspiring statisticians and educators.

  Contribute to History: This webinar will be recorded, with a transcript prepared to support the History Standing Committee's efforts in documenting the evolution of statistics education in Australia.

  Who Should Attend?

  ·        Early career statisticians

  ·        Students aspiring in statistics and related fields

  ·        Anyone interested in the history and development of statistics education

  
  

  Show details
• WA Branch AGM 2025 (Ming Dong, Minitab Pty Ltd)

  • When
    11 Mar 2025
  •  
    5:15 PM - 7:00 PM
  • Location
    Blakers Lecture Theatre, The University of Western Australia

  Register

  The first meeting for 2025 is the Annual General Meeting (AGM) of the WA Branch of the Statistical Society of Australia.

  
  

  Date: Tuesday, 11 March 2025

  Time (AGM): 5:15PM to 6:00PM (AWST)

  Time (Webinar): 6:00PM to 7:00PM (AWST)
  

  Location: Blakers Lecture Theatre, The University of Western Australia.

  
  

  Non-members of the SSA WA Branch are welcome at the AGM but cannot vote on Branch matters. Following the AGM, Mr Ming Dong, Senior Analytics Consultant at Minitab Pty Ltd, will deliver a webinar.

  
  

  The AGM will not be streamed or recorded but the webinar will be streamed via Zoom to the in-person venue and to any online participants.
  

  
  

  Please register if you wish to attend the webinar.
  

  
  

  Annual General Meeting
  

  The AGM agenda, proxy forms, and other relevant documents are emailed to members directly. Please contact the WA Branch Secretary if you have not received these by the Monday preceding the AGM.

  The election of the 2025 WA Branch Council will occur at the AGM.
  

  WEBINAR

  Enhancing Decision-Making with analytics in the Government and Mining sector

  
  

  Ming Dong, Senior Analytics Consultant, Minitab Pty Ltd.

  Join our exclusive webinar to explore how Minitab is revolutionizing decision-making in Government and Mining sectors through powerful statistical and data analysis applications.

  1. Discover how governments and politicians can leverage statistical analyses to improve service delivery and public satisfaction.
  2. Understand how machine learning and data analysis are transforming predictive and preventive maintenance in the mining industry.

  This webinar is perfect for professionals looking to leverage data analysis for smarter decisions, better resource allocation, and enhanced performance.

  About the Speaker
  

  Ming Dong is one of only a handful of practitioners across the Asia Pacific Region to be a certified trainer for Minitab’s flagship product, Minitab Statistical Software. The training certification process is the culmination of years of statistical training with real-life application of data analysis in the industry.

  Ming’s formal qualifications include an undergraduate degree in Industrial Engineering Management, a Masters of Computer Science from the University of New South Wales, and a Masters of Applied Statistics from Macquarie University. Ming has a unique and profound understanding of the application of statistics in the business world.

  In addition, Ming has broad business development experience, developed through his work in senior technical roles for large organisations such as Toshiba, OzEmail and National Computing Systems Singapore. As a university research officer at Macquarie University, he had several papers published in the field of Bioinformatics.

  His extensive business development and industry experience are the reason why Ming has been engaged by some of the world’s largest mining, banking and medical devices companies, to provide consulting services in the application of data analysis.

  Refreshments and Dinner

  Members and visitors are invited to mingle over wine and cheese from 5:00PM onwards at the venue. Following the meeting all are invited to dine at a nearby restaurant. Visitors are welcome.

  Meeting Directions

  The Blakers Lecture Theatre is located on the ground floor of the Mathematics building at The University of Western Australia. Its entrance is on the northern side of the building. See: UWA Maps, Google Maps.

  Parking is free on the UWA Crawley campus after 5:00PM. A convenient place to park is Car Park 18 accessible from Fairway Entry 1.

  For further information please contact the WA Branch Secretary (ssa.wa.secretary@gmail.com).

  Show details
• Venables Award Webinar

  • When
    18 Mar 2025
  •  
    12:00 PM - 1:00 PM
  • Location
    Online

  Register

  Statistical Computing and Visualisation section proudly presents the Venables Award webinar. The goal of this award is to encourage new open source software development from the Australian community with a view to support efforts to develop and share data science and statistics methodology. It is particularly for software development work from early to mid-career researchers or developers.

  Two developers shared this year’s award. Come along to hear both winners talk about their exciting work:

  - Alex Lee for his software whereabouts. This is a light-weight Python package for bulk geocoding and address standardization.

  - Rob Moss for his pypfilt (Particle filters for Python). The software implements several online inference methods that have been used in many scientific fields for real-time forecasting, with the COVID-19 pandemic being one of the most high-profile examples

  
  

  Show details
• CPD 192-Using Psychology to Maximise the Accuracy of Your Measurement

  • When
    3 Apr 2025
  •  
    9:00 AM - 12:00 PM
  • Location
    Hybrid (in-person and online) Sydney, NSW: Venue: TBC

  Register

  The Social Research Centre, in collaboration with the Statistical Society of Australia, is proud to sponsor this unique opportunity to learn from one of the world’s most respected experts in survey methodology, social psychology, and political science.

  Since the beginning of quantitative social science, a great deal of research has been done using questionnaires. The structuring, wording, and ordering of questions have traditionally been viewed as an art, not a science, best guided by intuition. But in recent years, it has become clear that this is an antiquated and even dangerous view that does not reflect the accumulation of knowledge throughout the social sciences about effective question-asking.

  Intuition often leads us astray in the questionnaire design field, as becomes clear when putting intuitions to the test via scientific evaluation. A large body of relevant scientific studies has now accumulated, and when taken together, the findings point to a series of formal rules for how best to design questions. Yet the vast majority of contemporary questionnaire design fails to follow these rules, because they are not yet widely understood.

  During the past 25 years, Jon Krosnick has discovered the emergence of general principles of optimal questionnaire design that are often at variance with common practices in questionnaire construction. In many cases, these departures in practice have reduced the accuracy of collected data, even though other question forms could easily have been employed instead that would have avoided bias and improved precision of measurement.

  Goals of the workshop

  The workshop would serve many purposes. First and foremost, it would educate participants about the optimal techniques for questionnaire design, for guarding against measurement artifacts, and for analyzing data in order to overcome the biasing impact of such artifacts. And because the huge accumulated literature on questionnaire design does not provide guidance to researchers about how to handle every decision they will face, the second and equally important goal of the workshop is to teach participants a new way of thinking about questionnaire design, rooted in psychology.

  The aim is to get researchers into the heads of their respondents in a way that few have done before. By gaining insight into how respondents approach their tasks of interpreting questions, searching their memories for information, integrating that information into judgments, and expressing those judgments in words, workshop participants will begin to develop skills that will help them to mange design issues for which there are not yet formal rules.

  Workshop Topics

  • 1. Introduction: The Survey Response Process

  • 2. Question Types

  • 3. Formatting and structuring questions

  • 4. Wording and ordering of questions and response frames

  • 5. Other considerations in questionnaire design

  • 6. Summary and conclusions

  Bio-Jon A. Krosnick, Professor of Communication, Political Science, Sustainability, and Psychology Stanford University

  
  

  Winner of the lifetime career achievement award from the American Association for Public Opinion Research and the Nevitt Sanford Lifetime Achievement Award from the International Society of Political Psychology, Jon Krosnick is Frederick O. Glover Professor in Humanities and Social Sciences, and Professor of Communication, Political Science, Sustainability, and Psychology at Stanford University, Director of Stanford’s Political Psychology Research Group, and Research Psychologist at the U.S. Census Bureau.

  He has expertise in questionnaire design and survey research methodology, voting behaviour and elections, and American public opinion. He has taught courses for professionals on survey methods for decades around the world and has served as a methodology consultant to government agencies, commercial firms, and academic scholars. He is a world-recognized expert on the psychology of attitudes, especially in the area of politics and has been co-principal investigator of the American National Election Study, the nation's preeminent academic research project exploring voter decision-making.

  Target Audience

  1. Statisticians and Data Analysts

  • Professionals working on survey design, data analysis, and improving measurement accuracy.

  2. Researchers and Academics

  • Experts in psychology, social sciences, or survey methodology.

  • University researchers and graduate students focused on surveys and data collection.

  3. Industry Professionals

  • Market researchers, consultants, and corporate professionals.

  • Individuals from government, non-profits, or private sectors utilising survey data.

  4. Students and Early Career Professionals

  • Those seeking to develop skills in survey design and measurement techniques.

  Cancellation Policy

  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.

  Cancellations received prior to two weeks before the event will be refunded, minus the Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20.

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

  For any questions, please email events@statsoc.org.au. 

  Show details
• CPD 187-Sampling II

  • Start
    22 May 2025
  •  
    11:00 AM
  • End
    12 Jun 2025
  • Location
    Online- Weekly 1 hour meetings taking place Thursdays
  • Spaces left
    15

  Register

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

  1. Multistage Sampling

  2. Multistage Sampling

  3. Multistage Sampling & Area Sampling

  4.    Multiphase Design

  Timeframe:

  May 22 – June 12, 2025. Weekly Meetings taking place Thursdays, 11:00 am – 12:00 pm AEST.

  Course Objectives
  By the end of the course, participants will… ▪ understand the principles of multistage sampling and its components ▪ be able to design and implement multistage sampling with different sampling techniques ▪ be familiar with area sampling and how to use it in various contexts ▪ understand multiphase design and be able to implement it in various survey context

  Your instructor: Raphael Nishimura, PhD 

  Raphael Nishimura is the Director of Sampling Operations in Survey Research Operations at the Survey Research Center (SRC) in the Institute of Social Research of the University of Michigan. He has been working with sampling and survey statistics for over ten years. He holds a PhD in survey methodology from the University of Michigan and a bachelor’s degree in statistics from the University of São Paulo. His main research interest includes sampling methods, survey nonresponse and adaptive/responsive designs. Nishimura is also the director of the Sampling Program for Survey Statisticians of the SRC Summer Institute for Survey Research Techniques
  

  Prerequisites

  Sampling I or equivalent knowledge. Some experience with the R statistical computing software is helpful.

   Grading will be based on:

  ▪ 4 Homework assignments (50% of grade)

  ▪ 4 Quizzes (one at the start of each online session; 15% of grade)

  ▪ Participation in discussion during the weekly online meetings, submission of questions in the weekly discussion forum, demonstrating understanding of the required readings and video lectures, and positive contributions on the Discussion Forum, see below (10% of grade)

  ▪ A final open-book online exam (25% of grade)

  Early Bird Deadline

  Please book before 31 March 2025 to take advantage of the Early Bird Deadline.

  Prices include GST. There is also a discount of $150 for Sampling 2 if registering at the same time as Sampling 1 or have completed Sampling 1 in the past.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.

  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.

  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy

  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• CPD-188-Item Nonresponse and Imputation

  • Start
    24 Jun 2025
  •  
    5:00 PM
  • End
    15 Jul 2025
  • Location
    Online- Weekly 1 hour meetings taking place Tuesdays
  • Spaces left
    15

  Register

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

  1. Introduction & Missing Data Mechanisms

  2. Default Strategies of (Not) Dealing with Missing Data and Their Implications

  3. Common Misconceptions Regarding Imputation & Basic Imputation Methods

  4. More Advanced Imputation Methods & Multiple Imputation

  Timeframe:

  June 24 – July 15, 2025. Weekly Meetings taking place Tuesdays, 5:00 – 6:00 pm AEST

  Course Objectives

   By the end of the course, participants will… ▪ understand why the default way of dealing with missing data as implemented in most statistical software is often problematic. ▪ realize that it is better not to account for the missingness instead of applying simplistic imputation methods such as mean imputation or last-observation carried forward. ▪ know what is meant by a missing data mechanism and understand the implication of the different mechanisms. ▪ be familiar with the principle ideas and concepts of multiple imputation.

  Your instructor: Instructor Prof. Dr. Jörg Drechlser  

  Current positions:

  ▪ Interim head of the Department for Statistical Methods at the Institute for Employment Research, Germany

  ▪ Associate Research Professor, Joint Program in Survey Methodology, University of Maryland ▪ Honorary Professor, University of Mannheim

  Teaching engagements:

  • Instructor for three different courses in the International Program in Survey and Data Science

  • Regular short courses for various institutions and universities on data confidentiality and synthetic data

  Research areas:

  ▪ Missing data, imputation, data confidentiality

  Prerequisites

   ▪ Students should be familiar with generalized linear models and basic probability theory.

  ▪ The statistical software R will be used for illustrations and for (some of) the homework assignments. Thus, basic knowledge of R is required to be able to complete the assignments.

   Grading will be based on:

  ▪ 2 online quizzes

  ▪ 2 homework assignments

  ▪ Participation in weekly meetings & engagement in discussion forums

  ▪ Final online exam

  Early Bird Deadline
  Please book before 30 April 2025 to take advantage of the Early Bird Deadline.

  Prices include GST.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.

  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.

  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy
  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• Spatial Statistics 2025: At the Dawn of AI

  • Start
    15 Jul 2025
  •  
  • End
    18 Jul 2025
  •  
  • Location
    NH Leeuwenhoorst, Noordwijk, The Netherlands

  During the Spatial Statistics 2025 conference in Noordwijk, the Netherlands, specific attention will be given to the opportunities, including challenges to be addressed, that Artificial Intelligence (AI) opens up and how spatial statistics can be developed further with AI.

  The latest developments in spatial statistics will be presented, emphasising their contributions at the dawn of AI, now and in the future. The optimal use of collected data, predicting in space and time, object recognition and segmentation, and transferability in the presence of spatial and temporal correlations are typical, but not exhaustive examples.

  For more information on the conference:

  https://www.elsevier.com/events/conferences/all/spatial-statistics

  
  

  Show details
• CPD 189-Step-by-Step in Survey Weighting

  • Start
    14 Aug 2025
  •  
    4:00 PM
  • End
    4 Sep 2025
  • Location
    Online- Weekly 1 hour meetings taking place Thursdays
  • Spaces left
    15

  Register

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

   1. Basic Steps in Weighting 

  2. Basic Steps in Weighting (continued) 

  3. Calibration and Other Uses of Auxiliary Data in Weighting

  4.  Calibration (continued) and Replicate Weights

  Timeframe:

  August 14 – September 4, 2025. Weekly Meetings taking place Thursdays, 4:00- 5:00 pm AEST.

  Course Objectives

   

  By the end of the course, participants will have learned… ▪ The role of survey weights in population inference. ▪ Steps in weighting, including computation of base weights, nonresponse adjustments, and uses of auxiliary data. ▪ Nonresponse adjustment alternatives, including weighting cell adjustments, formation of cells using classification algorithms, and propensity score adjustments. ▪ Weighting via poststratification, raking, general regression estimation, and other types of calibration. ▪ How to assess if weights are not needed.

  Your instructor: Anna-Carolina Haensch 

  Current positions:

  ▪ Assistant Research Professor at University of Maryland

  ▪ Lecturer (Akademische Rätin) at LMU Munich

  Teaching engagements:

  ▪ Review of Statistical Concepts (IPSDS)

  ▪ Introduction to Machine Learning Techniques (GESIS Summer School)

  ▪ Introduction to Statistics and Data Science (LMU)

  Research areas:

  ▪ Synthetic Data

  ▪ Survey Sampling

  ▪ Missing Data

  Prerequisites

  ▪ Sampling theory,Applied sampling.

  ▪ Some experience with variance estimation and statistical analysis using survey data will be helpful.

  ▪ Intermediate knowledge of the R statistical computing software

   Grading will be based on:

   ▪ 4 Homework assignments(50% of grade)

  ▪ A take-home final exam (30% of grade)

  ▪ Class Participation (20% of grade) in discussion during the weekly online meetings and posting questions to the weekly discussion forum (deadline: 24 hours before class) demonstrating understanding of the required readings and video lectures

  Early Bird Deadline
  Please book before 30 May2025 to take advantage of the Early Bird Deadline.

  Prices include GST.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.

  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.

  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy
  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• CPD 190-Introduction to Big Data & Machine Learning

  • Start
    26 Aug 2025
  •  
    9:30 AM
  • End
    16 Sep 2025
  • Location
    Online- Weekly 1 hour meetings taking place Tuesday
  • Spaces left
    15

  Register

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

    1. Overview of Big Data; Working with Big Data, Classical Statistical Approaches vs. Statistical Machine Learning

  2. Model Evaluation/Validation, K-Means Clustering

  3. Nearest Neighbors, CARTS

  4.   Random Forests

  Timeframe:

  August 26 – September 16, 2025. Weekly Meetings taking place Tuesdays, 9:30 - 10:30 am AEST.

  Course Objectives

   

  This course covers… ▪ an overview of key Big Data terminology and concepts ▪ an introduction to common data generating processes ▪ a discussion of some primary issues with linking Big Data with Survey Data ▪ issues of coverage and measurement errors within the Big Data context ▪ inference versus prediction ▪ general concepts from machine learning including signal detection and information extraction ▪ potential pitfalls for inference from Big Data ▪ key analytic techniques (e.g. classification trees, random forests, conditional forests) to process Big Data using R with example code provided

  Your instructor: Prof. Trent Buskirk 

  Current positions:

  ▪ Novak Family Professor of Data Science and Chair of the Applied Statistics and Operations Research Department at Bowling Green State University

  ▪ Adjunct Research Professor at the University of Michigan

  Dr. Buskirk is a Fellow of the American Statistical Association. His research includes the areas of Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and the use of big data and machine learning methods for health, social and survey science design and analysis. His research has been published in leading journals such as Cancer, Social Science Computer Review, Journal of Official Statistics, and the Journal of Survey Statistics and Methodology.

  Prerequisites

  No prerequisites.

  We recommend a good understanding of the material typically taught in undergraduate statistics courses and some familiarity with regression techniques. While not a prerequisite, familiarity with the R software package (base R or R using Rstudio) is strongly encouraged.

   Grading will be based on:

  ▪ 4 online quizzes (worth 5% each)

  ▪ Participation in discussion during the weekly online meetings and submission of questions to the weekly discussion forums demonstrating understanding of the required readings and video lectures (20% of grade).

  ▪ 3 homework assignments (worth 20% each)

  Early Bird Deadline

  Please book before 30 June.2025 to take advantage of the Early Bird Deadline.

  Prices includes GST.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.

  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.

  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy
  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• CPD 191-Machine Learning II

  • Start
    14 Oct 2025
  • End
    2 Dec 2025
  • Location
    Online- Weekly 1 hour meetings taking place Tuesday
  • Spaces left
    15

  Register

  The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

  Places are limited, please register early to take advantage of early bird discounts and secure a place.
  

   Topics:

   1. Intro: Bias-variance trade-off, cross-validation (stratified splits, temporal cv) and model tuning (grid and random search)

  2. Classification: Performance metrics (ROC, PR curves, precision at K) and class imbalance (over- and undersampling, SMOTE)

  3. Ensemble methods I: Bagging and Extra-Trees

  4. Ensemble methods II: Boosting (Adaboost, GBM, XGBoost) and Stacking

  5. Variable selection: Lasso, elastic net and fuzzy/ recursive random forests

  6. Support Vector Machines

  7. Advanced unsupervised learning: Hierarchical clustering and LDA

  8.  Interpreting (Variable Importance, PDP, ...) and reporting ML results

  Timeframe:

  October 14 – December 2, 2025. Tentative Weekly Meetings as follows (exact dates and times TBC):

  ▪ Week 1: Tuesday, October 14, 9:00 – 10:00 am AEDT

  ▪ Week 2: Tuesday, October 21, 6:00 – 7:00 pm AEDT

  ▪ Week 3: Tuesday, October 28, 6:00 – 7:00 pm AEDT

  ▪ Week 4: Tuesday, November 4, 6:00 – 7:00 pm AEDT

  ▪ Week 5: Tuesday November 11, 11:00 am – 12:00 pm AEDT

  ▪ Week 6: Tuesday November 18, 11:00 am – 12:00 pm AEDT

  ▪ Week 7: Tuesday, November 25, 11:00 am – 12:00 pm AEDT

  ▪ Week 8: Tuesday, December 2, 9:00 – 10:00 am AEDT

  Course Objectives

   By the end of the course, participants will… ▪ have a profound understanding of advanced (ensemble) prediction methods ▪ have built up a comprehensive MLtoolkit to tackle various learning problems ▪ know how to(critically) evaluate and interpret results from ''black-box'' models

  Your instructor: Prof. Trent Buskirk 

  Current positions:

  ▪ Novak Family Professor of Data Science and Chair of the Applied Statistics and Operations Research Department at Bowling Green State University

  ▪ Adjunct Research Professor at the University of Michigan

  Dr. Buskirk is a Fellow of the American Statistical Association. His research includes the areas of Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and the use of big data and machine learning methods for health, social and survey science design and analysis. His research has been published in leading journals such as Cancer, Social Science Computer Review, Journal of Official Statistics, and the Journal of Survey Statistics and Methodology.

  Your instructor: Prof. Christoph Kern

  Christoph Kern is Junior Professor of Social Data Science and Statistical Learning at the Ludwig-Maximilians-University of Munich and Project Director at the Mannheim Centre for European Social Research (MZES). He received his PhD in social science (Dr. rer. pol.) from the University of Duisburg-Essen in 2016. Before joining LMU Munich, he was a Post-Doctoral Researcher at the Professorship for Statistics and Methodology at the University of Mannheim and Research Assistant Professor at the Joint Program in Survey Methodology (JPSM) at the University of Maryland. His work focuses on the reliable use of machine learning methods and new data sources in social science, survey research, and algorithmic fairness
  

  Prerequisites

  Topics covered in Introduction to Machine Learning and Big Data (ML I), i.e.:

  ▪ Conceptual basics of machine learning (training vs. test data, model evaluation basics)

  ▪ Decision trees with CART

  ▪ Random forests

  Familiarity with the statistical programming language R is strongly recommended. Participants are encouraged to work through one or more R tutorials prior to the first-class meeting.

  Some resources can be found here:

  ▪ https ://rstudio.cloud/learn/primers

  ▪ http ://www.statmethods.net/

  ▪ https ://swirlstats.com/ ▪ https ://www.rcommander.com

   Grading will be based on:

   ▪ 4 homework assignments (10% each)

  ▪ 8 online quizzes (5% each)

  ▪ Participation in discussion during the weekly online meetings (20% of grade)

  Early Bird Deadline

  Please book before 15 August 2025 to take advantage of the Early Bird Deadline.

  Prices include GST. There is also a discount of $150 for Machine Learning II if registering at the same time as Introduction to Big Data & Machine Learning or have completed Introduction to Big Data & Machine Learning in the past.
  

  Disclaimer
  
  Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.

  The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.

  If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.
  
  Group bookings
  
  For group bookings, please email events@statsoc.org.au with the names, email addresses, and telephone numbers  of the participants in the group.
  
  Cancellation Policy
  Occasionally courses have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.
  
  Cancellations received prior to two weeks before the event will be refunded, minus the  Stripe processing fee (1.75% + $0.30 per transaction) and an SSA administration fee of $20. 

  From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.
  
  For any questions, please email events@statsoc.org.au

  Show details
• Biometrics in the Bush Capital 2025 - external event

  • Start
    24 Nov 2025
  • End
    28 Nov 2025
  • Location
    Canberra

  Come join the International Biometrics Society Australasian Region's biannual conference in the bush capital Canberra!

  https://biometricsociety.org.au/conference2025/

  Show details
• ASC 2025

  • Start
    1 Dec 2025
  • End
    5 Dec 2025
  • Location
    Perth, WA

  Mark your calendars! The Australian Statistical Conference (ASC2025) is coming to Perth, WA, from 1–5 December 2025. This year’s conference promises to be an unmissable event, bringing together leading minds in statistics and data science from across Australia and beyond.

  Join us for an exciting week of cutting-edge presentations, engaging workshops, and invaluable networking opportunities with professionals, academics, and industry leaders.

  Stay tuned for more details, including keynote speakers, program highlights, and how you can get involved.

  
  

  Show details