A case study involves in-depth analysis of a single or small number of subjects, such as an individual, organization, or event. This document provides guidance on conducting case studies, including how to plan a case study, establish validity, analyze data, and report results. It emphasizes using multiple sources of evidence, detailing protocols, testing for interrater reliability, examining causal relationships, addressing rival explanations, and comparing data to predictions to strengthen internal validity. Effect sizes should be reported to evaluate the significance of any differences observed in a single-case study. Pattern matching and explanation building are suggested techniques for analyzing case study data.

1. When you can't or
won't run an
experiment (and still
want to study atypical
language development)
David Saldaña

2. Case studies: why do them
• A case study is not just the study of a case
• What makes a case:
– Critical cases for a theory
– Unique or rare cases
– Representative cases
– Longitudinal case – time series

3. Plan your study!
GOOD AND BAD CASE STUDIES

4. Construct validity
• Your measures should reflect what you say
you are measuring
– Multiple sources of evidence
– Others review draft report
– Chain of evidence
Yin (2009)

5. Chain of evidence
Paper
Database
Protocol
Questions

6. External validity
• Where can your findings be generalized?
– Use theory
– Use replication logic across cases
Yin (2009)

7. Reliability
• Would someone else
find the same results
with the same
operations
– Detail a protocol
– Make sure you test
interrater reliability
Yin (2009)
© Nevit Dilmen via Wikimedia Commons

8. Internal validity
• Establish causal relationships, distinguished
from spurious relationships
– Pattern matching
– Explanation building
– Addressing rival explanations
– Using logic models
Yin (2009)

9. ANALYZING THE DATA

10. Pattern Matching
•
•
•
•
•
•
Put the info into different arrays
Make a matrix of categories
Graphically analyze the data
Count!
Do stats
Use time

11. Pattern matching (more)
• Test predictions against your data – looking at
the DV:
– Predicted should happen
– NOT predicted should NOT happen
• Test independent variables against your data –
looking at potential ID:
– Predictor should be there
– NON predictor should NOT be there
• Find the simplest

13. Single-case studies:
• single-case studies typically employ a large
number of measures,
• more often than not these measures are
expressed on different metrics,
• there is an emphasis on examining the profile
(i.e., the relative strengths and weaknesses)
• it is not uncommon for single-case studies to
employ more than one control sample
Crawford et al., 2010

14. Effect sizes
Crawford et al., 2010

15. What should you report? At least
• the mean and standard deviation for controls
on the task
• the raw score of the single case
• the effect size for the difference between the
case and controls
Careful with inflation of type 1 error!
Crawford et al., 2010

16. Some software
http://homepages.abdn.ac.uk/j.crawford/pages/
dept/psychom.htm

19. Thank you
http://institucional.us.es/dcptea
http://personal.us.es/davsalsag/inicio.html