1. The use of electronic books (eBooks) in social science research Richard Parker* Danius Michaelides† Huanji Yang† Alex Frazer† Luc Moreau† Camille Szmaragd* ZhengZheng Zhang* Christopher Charlton* William Browne* *Centre for Multilevel Modelling, University of Bristol, UK †School of Electronics & Computer Science, University of Southampton, UK

2. Template Dataset Stat-JR: to re-cap…

5. Stat-JR writes commands, etc., to perform requested function Template Dataset Stat-JR prompts user for input Function performed (If applicable) external software opened, run, then closed, with results returned to Stat-JR. E.g… Results of function produced (If applicable) results outputted as dataset to be fed back in… myModel<- glm(normexam~ Summary(myModel) plot(myModel,1) Select Open Worksheet Select datafile.dta Select Equations from Fi EquationsMacrosScriptsPoint & click instructions Results Model: DIC: 9766.506 Parameters: Beta1: 0.594 Charts Results tables

6. Stat-JR’s eBook interface: DEEP Documents with Embedded Execution and Provenance Embeds Stat-JR’s statistical functionality within a ‘traditional’ notebook…

7. …so we first import one… No eBooks loaded yet…

13. Navigate through pages of eBook Hierarchical table of contents (can be expanded / collapsed at each node)

20. Behind the scenes… The eBook author (me) has specified which Stat- JR template to associate with this region of the eBook… …and has chosen one which creates plots via (“PlotsViaR”). Templates require input, from a user, before they can go ahead & perform the function appropriately… …the eBook author can pre-specify inputs (by writing them into the eBook code); any that are not pre-specified are then left to the eBook reader to complete.

21. Stat-JR writes commands, etc., to perform requested function Template Dataset Stat-JR prompts user for input Function performed (If applicable) external software opened, run, then closed, with results returned to Stat-JR. Results of function produced (If applicable) results outputted as dataset to be fed back in… myModel<- glm(normexam~ Summary(myModel) plot(myModel,1) Select Open Worksheet Select datafile.dta Select Equations from Fi EquationsMacrosScriptsPoint & click instructions Results Model: DIC: 9766.506 Parameters: Beta1: 0.594 Charts Results tables Stat-JR: to re-cap…

35. Behind the scenes… …the eBook author has associated relevant model-fitting Stat-JR templates with this region of the eBook… …and has pre-specified all of the inputs, bar the explanatory variables, which are therefore the only ones left to eBook reader to specify. Author has also specified what / where / when the output resulting from a template’s execution will be presented in the eBook…

36. Stat-JR writes commands, etc., to perform requested function Template Dataset Stat-JR prompts user for input Function performed (If applicable) external software opened, run, then closed, with results returned to Stat-JR. Results of function produced (If applicable) results outputted as dataset to be fed back in… myModel<- glm(normexam~ Summary(myModel) plot(myModel,1) Select Open Worksheet Select datafile.dta Select Equations from Fi EquationsMacrosScriptsPoint & click instructions Results Model: DIC: 9766.506 Parameters: Beta1: 0.594 Charts Results tables Stat-JR: to re-cap…

43. Content of text returned is conditional on value of results

50. Stat-JR’s DEEP system: Summary of features Built on Stat-JR’s powerful & flexible data- analytical engine. Embeds inputs and outputs of Stat-JR’s executable statistical functions within contextual information. Tailoring & specificity: e.g. associating carefully- chosen templates; pre-specifying inputs. Log / recording tool: behind-the-scenes, a comprehensive record is kept of each execution.

51. Stat-JR’s DEEP system: Teaching of quantitative social science research methods (including inter-operating software). Communicating principles / theories / inviting exploration of quantitative social science topics. Reports: transparency (e.g. access to embedded dataset / analytical methods, etc); facilitates multi-authored preparation. Tailored analytical techniques: pre-specifications allow user to ‘cut to the chase’ and/or circumvent software-specific learning curve. Benefits for the social science researcher?

52. …so how do I use them? See http://www.bristol.ac.uk/cmm/research/estat/downloads/index.html …http://www.bristol.ac.uk/cmm/research/estat/downloads/index.html

54. …so how do I use them? See http://www.bristol.ac.uk/cmm/research/estat/downloads/index.html …http://www.bristol.ac.uk/cmm/research/estat/downloads/index.html for:  Software download & installation instructions;  eBook reading & authoring guidance;  Example eBooks. eBook authoring: is it easy?  Depends what you want to do! …eBook-writing tool can help set-up basic eBook structure …although more sophisticated functionality will take time to implement.  User Guides, and the example eBooks (which can open & look ‘under the bonnet’), are there to help.

55. Any questions? richard.parker@bristol.ac.uk