Count Data

Presenter(s): Vernon Gayle

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Examples of social science data that take the form of positive counts are legion. For example, how many burglaries take place in a neighbourhood, how many women under twenty gave birth last year, or how many cases of a disease were diagnosed? Indeed, the question how many 'any things' will usually be answered with a count. Despite the prevalence of count data in the social sciences, most social scientists know very little about analysing count data. In reality social science data analysts tend to know more about analysing either binary (i.e. 0,1) outcomes or continuous (i.e. metric) measures. This resource provides information on analysing count data.

Count Data

Count data - a tale of Poisson and predicting football results This resource includes a 30 minute video on analysing count data. It introduces the Poisson distribution. A sporting example is used to exemplify the concept of expected outcomes. Some social science examples are provided and some alternative models for count data are introduced.

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About the author

My work involves the statistical analysis of large-scale and complex social science datasets. These datasets include both social surveys and administrative data resources. The analysis of longitudinal (i.e. repeated contacts) data is an area in which I specialize. The main substantive focus of my work is social stratification. I have particular interests in the sociology of youth and youth transitions, education and sport. I also have interests in demography, with a focus on migration, and to a lesser extent fertility. I have also undertaken work in the area of digital social research. My methodological research focuses on a range of challenges, which include topics such as quasi-variance estimation, missing data and multiple imputation, and the graphical representation of data. I am attempting to promote the 'Public Awareness of Social Statistics'.

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