Analytical Tool (bold) / Description	Benefits
(i) Use of questioning Types of questions: Sensitising questions:1 e.g., Who (actors involved), what (issues, problems, concerns), when, where, how (do they define situation) and with what consequences? Theoretical questions:2 What is the relationship of one concept to another? What would happen if..?; How do events and actions change over time? Practical questions:3 Which concepts are well developed, which are not? Where, when and how do I go next to gather the data for my evolving theory? What kinds of permissions do I need? How long will it take? Is my developing theory logical, and if not, where are the breaks in logic? Have I reached saturation point? Guiding questions4	Useful at every stage of analysis Kick starts the process – gets analysts thinking about their data Helps researchers understand the problem from the participants’ perspective Develops provisional answers Think outside the box Become acquainted with the data Helps identify what is not known about a concept
(ii) Making comparisons Types of comparisons: Constant comparisons: Researchers compare each incident in the data with other incidents for similarities and differences. Researchers then place incidents under the same or different codes. In subsequent interviews, researchers compare incidents labelled under the same code for similarities and differences (within-code comparison) to uncover the different properties and dimensions of the code. Theoretical comparisons: Used when analyst is unsure how to classify an incident or is unable to define the incident in terms of its properties and dimensions. Using comparisons brings out properties which, in turn, researchers can use to examine the incident in the data. Analysts can derive the specific incidents they use when making theoretical comparisons from the literature and experience. Analysts look at phenomenon at the property and dimension level.	Helps analysts obtain a grasp on the meaning of events that might otherwise seem obscure Helps sensitise researchers to possible properties and dimensions in the data but which remain obscure due to the lack of sensitivity on the part of researchers Suggests further interview questions based on evolving theoretical analysis Helps analysts move more quickly from the level of description to one of abstraction Counters the tendency to focus on a single case by immediately bringing analysis up to a more abstract level Forces researchers to examine their basic assumptions, their biases, perspectives, and those of participants Forces examination of findings, sometimes resulting in the qualification or altering of initial interpretations Makes it more likely that analysts will discover variation as well as general patterns Ensures the likelihood of a more fluid and creative stance toward data analysis Facilitates the linking and densification of categories
(iii) Drawing on personal experience Used where researchers have life experiences similar to those of the participants Can use experience not as data but as a comparative case to stimulate thinking about various properties and dimensions of concepts	Can use experience to bring up other possibilities of meaning Experience may help confront assumptions about specific data
(iv) Flip-flop technique This technique looks at the opposite or extreme range of a concept to bring out its significant properties and dimensions.	Researchers obtain a different perspective on a phrase or word
(v) Analysis of language Examines how respondents use language Often the terms that they use to express something are so conceptually expressive that researchers can use them as a code. This is called an in-vivo code, indicating that the term comes out of the data	Can generate codes. Language can be rich and descriptive and provide insight into the participants and where they are coming from