Sampling and Units of Analysis

1. Sampling and Units of Analysis Making the Basic Decisions for a Content Analysis Project

2. Basic Considerations • Content analysis requires sufficient data • desired content may be scattered thinly • analysis may require large volume of data • Absence is as important as presence • need to compare presence with absence • need to understand context of presence • Finding the right balance • how much data the research requires • what is feasible for one person to do

3. 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

4. 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

5. 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

6. Get Started with a Test Sample • purpose is to become familiar with data • find out what is POSSIBLE • what content does it contain? • what questions could you answer with it? • how can you extract relevant content? • how much effort does it take? • to plan a feasible research project • start with a few cases of the text data

7. 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

8. Determining Units of Analysis • Level of the phenomenon of interest • 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 the volume of data be appropriate • will you have enough “cases” to analyze? • can you manage that much coding?

9. Multiple and Nested Units • Counting incidence • can count every incident and sum for unit • can count presence/absence in larger unit • Flexible 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

10. 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

11. Three Basic Principles • Make units only as small as necessary • 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 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