title: Application of summative content analysis to a postal questionnaire.
abstract: BACKGROUND
  Content analysis of replies to closed questions in questionnaires can be undertaken to understand remarks that may explain the responses, provide illustrative examples of issues raised in the questionnaire, define new issues or issues of importance that were not covered in the questionnaire and inform the design of new questions in future surveys.
  
  
  OBJECTIVE
  To discuss the usefulness of summative content analysis to free text in postal questionnaires.
  
  
  CONCLUSIONS
  Content analysis provides useful comparative insights between two respondent groups in the case example provided. Five themes emerged: poor understanding of the concept of 'patient lateral transfer work technique' and the direct instrument nursing observation (DINO) instrument's key directions; outcomes of patient transfer; positive responses; manual handling risk; and poor translation into English of DINO.
  
  
  CONCLUSIONS
  Respondents need an opportunity to clarify their responses to questionnaires using free text, to provide insight into their understanding of the question being asked, understanding of the concept or construct being discussed, and data triangulation through the confirmation of item responses and free-text comments.
  
  
  CONCLUSIONS
  Responses to questions in a postal questionnaire and the opportunity for free-text commentary by respondents enable the identification of hidden meanings behind tickbox responses to questions.
authors.affiliation: Swansea University, Swansea, UK.
authors.forename: Thomas Howard
authors.initials: TH
authors.lastname: Griffiths
mesh_terms: D006801:Humans; D015399:Nursing Research / Q000458:organization & administration*; D011177:Postal Service; D036301:Qualitative Research; D012107:Research Design; D013223:Statistics as Topic / Q000379:methods*; D011795:Surveys and Questionnaires
keywords: data analysis.; free text commentary; narrative data; postal questionnaire; questionnaire; summative content analysis; translational validity