Deck 10

Question 1

case study research design

Answer 1

intensively study a few research units to gain a very good understanding. use it to get understanding of a phenomenon in a specific case

Question 2

phenomenon

Answer 2

anything that you think is interesting. you want to understand something that is complex. multiple sources of data/multiple methods are used

Question 3

case

Answer 3

case is where the phenomenon takes place and is bound by time and space. the case is the research unit

Question 4

sampling in case studies

Answer 4

1. define the theoretical and operational population of cases
2. specify a frame and select cases
3. define theoretical and operational population of observation units within cases
4. for each population, select a relevant frame and units

Question 5

selection of a case is based on:

Answer 5

• typicality: how typical is it?
• insensity: how intense does phenomenon occur?
• stage: where on the empirical cycle does the use of the case fall?
• instrumentality: extent to which generalisation is relevant

Question 6

single and multiple case studies

Answer 6

single: only one case is investigated
multiple: covers several cases
- parallel: all cases are selected before the study
- sequential: the selection of the next case depends on the previous case

Question 7

picking multiple cases: what dimensions do you pay attention to?

Answer 7

heterogeneity: you want some variation in the cases, extent to which cases are variable on analytically relevant dimensions
iterativity: the extent to which you reference one case in selecting the next ( idk what is important when first selecting cases, so i start out by picking 1 or 2. when i gain info i pick more cases on relevant units/characteristics
stages: the extent to which you reference one case in moving to other stages of the empirical (or regulative) cycle. e.g. my 1st case is about generating theory, and my 2nd case is on theory testing

Question 8

saturation

Answer 8

when do you stop collecting data/information? once you learn nothing new and relevant anymore. this is a risk when you have a bad sampling procedure (e.g. snowballing in small circles)
eliminate these risks by critically evaluating your data and searching for info contrary to what you found