This Chapter describes principles of information management for health systems and the need to focus on key data items required to improve individual and population health. It discusses the collection and analysis of relevant, high-quality data and the importance of agreeing on health programme aims before defining the minimum data set. We review the derivation of health indicators, focusing on WHO indicators. Many indicators rely on linking data from different sources, which requires accurate personal identifiers. Data is useless unless reports based on it can be shared and understood, so data analysts should use different visualization techniques to facilitate and support user decisions such as self-service dashboards. We also review the many high quality, open source, free to use data capture, analysis and data sharing tools that can support health systems, concluding that it is rarely necessary to develop an information system from scratch. Finally, while big data analytics, artificial intelligence and machine learning capture many headlines, health system can achieve much using simple tools to capture relevant, high-quality data and turn it into actionable knowledge to support their decision makers.

The questionnaire, one of the most frequently used methods in the study of language attitudes, can be used to elicit both qualitative and quantitative data. This chapter focuses on the questionnaire as a means of eliciting quantitative data by means of closed questions. It begins by examining the strengths of doing this (e.g. the fact that the resulting data can easily be compared and analysed across participants) as well as the limitations (e.g. the fact that issues unforeseen by the researcher usually do not come to the fore). The chapter then discusses key issues in research planning and design: for example, question types, question wording, question order, reliability and validity, and more general issues regarding questionnaire design. The chapter also considers questionnaire distribution. The exploration of data analysis and interpretation focuses on data cleaning and coding, statistical analyses, and some points of caution regarding the interpretation of findings from questionnaire-based studies. A case study of language attitudes in Quebec serves to illustrate the main points made in the chapter. The chapter concludes with further important considerations regarding the context-specificity of findings and the benefits of combining questionnaires with other methods of attitude elicitation.