Sampling and Units of
Analysis
Making the Basic Decisions for
a Content Analysis Project
Basic Considerations
 Content


analysis requires sufficient data
desired content may be scattered thinly
analysis may require large volume of data
 Absence


need to compare presence with absence
need to understand context of presence
 Finding


is as important as presence
the right balance
how much data the research requires
what is feasible for one person to do
When and Why to Sample
 each
unit of data source is fairly small
 data
source extends over a long time
 data
source contains way too much
 you
are interested in particular aspects
 you
need material from multiple sources
When Not to Sample

you have only a limited amount of data

the data source is complete in itself

you need the entire set to make the case

there is sufficient internal variability

the data set is unique or has special properties

you will do primarily qualitative analysis

it is feasible to include the entire set
Two Content Analysis Strategies
 Traditional



procedure (hypothesis testing)
develop codes on a sample
throw out that sample
apply fixed codes to the rest of the data
 Contemporary



approach (exploratory)
start with small sample for familiarity
expand gradually but use all the material
develop codes and analysis iteratively
 Usually
need to begin with exploratory
 Usually not testing a clear hypothesis
Get Started with a Test Sample
 purpose
is to become familiar with data
 find out what is POSSIBLE




 to
what content does it contain?
what questions could you answer with it?
how can you extract relevant content?
how much effort does it take?
plan a feasible research project
 start with a few cases of the text data
Sampling Unit vs. Unit of Analysis
 Sample
the form the data source provides
 Unit of Analysis can be smaller




sampling texts, using sentence or paragraph
sampling films, using scenes
sampling events, using phases, relations, etc.
sampling interactions, using exchanges
 Unit
of analysis CONTAINS what you want
 Unit of analysis defines N or denominator
Determining Units of Analysis
 Level


how does it appear in the material?
what context is needed to interpret it?
 Are




there already natural units to the data
does it come in small pieces already?
are there clear internal divisions?
are larger units appropriate to the task?
 Will

of the phenomenon of interest
the volume of data be appropriate
will you have enough “cases” to analyze?
can you manage that much coding?
Multiple and Nested Units
 Counting


can count every incident and sum for unit
can count presence/absence in larger unit
 Flexible


incidence
units such as time periods
code in individual data units
can combine units later to clarify patterns
 Comparison


between sets of data
code units for two or more sets of data
combine data by set for analysis
Unit of Analysis vs Coding Unit
 Unit



of Analysis
what you code WITHIN
what you compare in the analysis
you can combine but not divide units later
 Coding



Units
what you actually code for each unit of analysis
level at which something is described
you can combine but not divide codes later
 Scale
of these two determines coding time
Three Basic Principles
 Make


for the type of coding you will do
for the type of analysis you will do
 Code




everything you need for every case
code characteristics of the units as context
code at the level you can see in the data
 You

units only as small as necessary
can combine later but you cannot divide
Units of analysis can be combined easily
Codes can also be combined easily
Dividing requires going back and starting over