Sara Dolnicar was born in Ljubljana (Slovenia), grew up in Vienna (Austria) and now lives and works in Brisbane (Australia). She holds a Masters and PhD degree from the Vienna University of Economics and Business and a Masters degree in Psychology at the University of Vienna. After completing her PhD she worked in the School of Tourism at the Vienna University of Economics and Business where she also served as the Secretary General of the Austrian Society for Applied Research in Tourism. In 2002 she moved to Australia to take up a position in the School of Management and Marketing at the University of Wollongong. Sold on a life in Australia, Sara moved to Brisbane in 2013 where she currently works as the Research Professor in Tourism at UQ Business School.
Sara’s core research interests are the improvement of market segmentation methodology and the testing and refinement of measures used in social science research. Because her key research interests are not tied to any particular application area, Sara has had the luxury to investigate a range of different applied research areas, including sustainable tourism and tourism marketing, environmental volunteering, foster carer and public acceptance of water alternatives and water conservation measures.
To date, Sara has (co-)authored more than 300 refereed papers, including more than 140 journal articles and led a total of twelve Australian Research Council (ARC) grants. In 2011 she took up a prestigious ARC Queen Elizabeth II Fellowship.
Co-Presenters: Kylie Brosnan
Long surveys are problematic because respondents do not like them and get fatigued while completing them. The dislike of long surveys leads to invited respondents refusing to participate in survey studies which reduces response rates, or dropping out and terminating the survey early which reduces completion rates. Tired respondents also provide lower quality responses. A solution to these problems is to strip down item batteries to include only informative items in view of the insights sought.
This study proposes the use of an extended version of principal component analysis to (1) assess the potential of item reduction and (2) identify uninformative survey items which can be dropped. The method is explained using a prototypical artificial data set and illustrated using two empirical survey data sets.