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Cards (19)
Data
Present everywhere and can be analyzed to reach certain
conclusions
and make
decisions
Data
Results
Discussion
Methods for Exploration
1.
__
_____
2. _______
What to look for
1.
__
_____
2. _______
Methods for Validation
1.
_______
2.
_______
3.
_______
4.
_____
__
Based on Your
:
1. _______
2. _______
Data
Cleaning
/
Preparation
Transform data into
manageable
formats
Identify
relevant
and
usable
data
Data Exploration
1.
Chunking
2.
Clustering
3.
Coding
4.
Memoing
Chunking
Breaking down cleaned data and determining
purpose
of parts
Clustering
Classifying
chunks
according to
labels
or basic codes
Coding
Creating
labels
and
categories
that represent data accurately
Moves from
descriptive
to
interpretative
to pattern
Moves from open to
axial
to
selective
Moves from
first
to
second
cycle
Memoing
Taking codes and clusters and adding
notes
to explain or define them
Data Interpretation and Presentation
1.
Narrative
2.
Chronological
3.
Critical Incidents
4.
Thematic
5.
Visual representation
Validity
Verify
or check if data collected is
accurate
and can support proposed discussions
Types of Validity
Content
validity
Criterion-based
validity
Construct
validity
Triangulation
Content Validity
Extent to which questions on
instrument
and
scores
represent all possible questions
Face
Validity - minimum index of content validity, tests degree to which results/instrument measures
concept
Criterion-based Validity
Concurrent
Validity - relates results to
established
/validated scores
Predictive Validity - relates results to
future
criterion to
predict
behavior
Construct
Validity
Convergent
Validity - determines correlation of different results
Discriminant
Validity - determines lack of relationship among certain variables
Triangulation
Uses
multiple validation methods
to verify results
With
different
investigators
With different
research methods (
same
research type)
With
different
research methods (different research types)