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4.3: Quantitative data collection methods: knowledge, attitudes and practice / perceptions surveys

4.3: Quantitative data collection methods: knowledge, attitudes and practice / perceptions surveys

This module gives an overview of how to apply quantitative approaches, specifically knowledge, attitudes, practice/perceptions (KAP) surveys to collect data for community engagement and/or communications activities and wider humanitarian response action.

Learning outcomes

  • Understand the key steps for the use of knowledge, attitudes, practice/perception (KAP) surveys
  • Be familiar with different quantitative data collection survey methods used by social scientists and relevant to community engagement and/or communications activities.

Related resources

Module structure

1.1: Introduction to social science – definition, approaches and role in broader humanitarian action
1.2: Operational social science research in the humanitarian programme cycle
1.3: Advocating for the inclusion of social science in emergency response activities that engage communities
2.1: Understanding context, vulnerability and inequality in public health and humanitarian emergencie
2.2: Understanding behaviour in public health and humanitarian emergencies – models and theories
2.3: Rapid strategies to understand the political, socio-cultural, and economic factors that increase vulnerability
2.4: Understanding the importance of language in social science research
3.1: Ethical principles and approvals for social science research in a humanitarian context
3.2: Promoting the meaningful translation and application of ethical principles
4.1: Localised research: designing operational social science research that is responsive to communities
4.2: Quantitative and qualitative approaches to generate data in operational social science research
4.3: Quantitative data collection methods: knowledge, attitudes and practice / perceptions surveys
4.4: Qualitative data collection methods: in-depths interviews, observation, and focus group discussions
4.5: Quantitative data analysis in operational social science research
4.6: Qualitative data analysis in operational social science research
4.7: Mixing different methods to produce quality evidence
4.8: Triangulation of data: why it is important and how does it work?
5.1: Evidence synthesis for social and behavioural data
5.2: How to transform social science data and evidence into actionable findings
5.3: How to communicate and present research outputs to different audiences
5.4: Feeding back to communities and using findings to support community-level solutions and actions
6.1: Translating social science research into
6.2: Enabling environments for the uptake of social science evidence in emergency response
7.1: Tracking the use or ‘application’ of integrated operational social science outputs: Integrated Outbreaks Analytics and MONITO

Session type

90-120 minutes
Online and offline
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