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The Method Book. Chapter 3: Exploring the problem with data

In Chapter 2, we took a big picture view of the problem by looking at it through a systems lens. In Chapter 3, we introduce some common methods for analysing data to better understand the problem. They cover a mix of simple and complex analysis techniques, as well as audience segmentation approaches.

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One of the best ways to understand a specific problem and identify 'who needs to do what, when and where' is to look at the data. Practitioners often have more data than they realise, or may not use it in a way that really helps them explore the problem. Assumptions can also be made about the scale or cause of an issue, but this understanding may be inaccurate or biased. Numbers have a great way of taking assumptions out of a problem. Here we use real-world case studies to show how and when data can illuminate solutions. Click on the image to go to the Monash Bridges publications platform.

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Tabachnick, B. G. and L. S. Fidell (2019). Using multivariate statistics. Boston, Pearson.

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.

Field, A. Miles, J. & Field, Z. (2012). Discovering statistics using R. Sage Publications

Howell, D. (2019). Fundamental statistics for the behavioral sciences. Cengage.

Work with us

Need more help using tools within The Method? We offer a wide range of research services.

Case study
Making Australia a digitally-inclusive society

To help close the 'digital divide' between Australians who use the internet and those who do not, Australia Post engaged BehaviourWorks to conduct formative research on this technological divide. We identified five profiles of internet users.

Case study
Are we making progress on social inclusivity?

Inclusive Australia is a powerful alliance of organisations wanting to see a more coordinated approach to inclusivity. To support its activities and provide a benchmark of progress, BehaviourWorks created the first ever Social Inclusion Index.

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Case study
Tackling the problem of illegal dumping

To reduce the high levels of illegal dumping outside charity stores and investigate any underlying factors that could be influencing the problem, we analysed waste-disposal and store-specific data from hundreds of stores in Victoria.

Opinion piece
Why looking at the average can be misleading: The argument for segmentation

In this article, chapter co-author Kun Zhao and Luke Smillie (University of Melbourne) argue that personality science can help us better understand, measure and capitalise on individual differences when it comes to behaviour change.

Blog post
Overturning assumptions about behavioural problems with data

Why bother looking at the numbers and investing the time to collect data, conduct analyse and interpret the findings? In this post, we show how intuitions and assumptions are not always reliable guides for designing behavioural interventions.

Academic article
Digital inclusion and online behaviour: Five typologies of Australian internet users

Associated with our work with Australia Post (left), is this this *paid* article in Behaviour and Information Technology, 4, 367-380. Borg, K. & Smith, L. (2018)

Academic article
The practice of cluster analysis

In the *paid* Journal of Classification, 23(1), 3-30 . Kettenring, J. (2006).