1. Narrative and Sequential Approaches to Content Data Special Forms of Qualitative Analysis

2. Quantitative Analysis Logic Question is RELATION between VARIABLES  Assume independence and linear relation  Relations are between whole variables  Test hypotheses about variables Find correlations between variables Find correlations between variables Build from simple to complex models Build from simple to complex models Infer causation to invisible processes Infer causation to invisible processes

3. Problems with this Approach  Variables are active; Actors are passive  Interactions between variables problematic  Categorical variables awkward  Difficulties handling time and sequences  Chops up experience and loses context

4. Narrative and Sequential Approaches  Ask different questions  Based on different kinds of theories  Think about data in different ways  Build logic from people and events  Treat each case as a holistic story

5. When Narrative & Sequential Methods Fit  Theory and research question Are you studying a process? Are you studying a process? Are you studying interaction sequences? Are you studying interaction sequences? Are you studying sequential patterns or forms? Are you studying sequential patterns or forms?  Does the data tell a “story” Action unfolds over time or sequentially Action unfolds over time or sequentially Sequence or unfolding is of interest Sequence or unfolding is of interest Factors at each point might affect outcome Factors at each point might affect outcome  Start with natural complexity and simplify

6. What story does each case tell Are there similar patterns to these stories? Is there an internal sequence or order? Is there an internal sequence or order? How much can the sequence vary? How much can the sequence vary?  Do clusters of features appear together? When in the sequence do they appear? When in the sequence do they appear? Do clusters affect outcome? Do clusters affect outcome?  Is there an interaction process? Who interacts and how? Who interacts and how? Does the interaction have a sequence? Does the interaction have a sequence?

7. Several Different Approaches Andrew Abbott: sequential analysis, optimal matching David Heise: event sequence analysis (ETHNO, ESA) Roberto Franzosi: narrative analysis, graphing networks Charles Ragin: qualitative comparative analysis

8. Abbott’s Optimal Matching  Designed to study “careers”  Uses metric methods from biology  Data are simple lists of sequential events  Calculate “optimal match” between lists How many changes are needed How many changes are needed To convert one sequence into another? To convert one sequence into another?  Only works for straight linear sequences  Programs exist to do the matching

9. Heise’s Event Sequence Analysis  Codes sequence of events within a case  Work from narrative to event sequence Construct one sequence from many sources Construct one sequence from many sources Or compare sequences from different sources Or compare sequences from different sources Program diagrams the sequence for you Program diagrams the sequence for you  Free Online Program, also can download ESA Website http://www.indiana.edu/~socpsy/ESA/ ESA Website http://www.indiana.edu/~socpsy/ESA/ ESA Website ESA Website

10. My Use of ESA to Compare Accounts

11. Code properties of each event  Properties to be Coded (Event Frame): Actions, Objects, Instruments, Actions, Objects, Instruments, Alignments, Settings, Products, Alignments, Settings, Products, Beneficiaries, Action associations Beneficiaries, Action associations  Very labor intensive, forces attention  Reveals associations within events  Can also do this in Access ESA does it for “events” within one sequence ESA does it for “events” within one sequence Program produces simple text files to save work Program produces simple text files to save work

12. Franzosi’s Narrative Analysis  Similar to Heise conceptually  Uses “semantic grammar” to code narratives subject-action-object, plus modifiers subject-action-object, plus modifiers adapts categories to type of data adapts categories to type of data grammar defines relation between codes grammar defines relation between codes  Then analyzes the sets of relations recodes categories to reduce detail recodes categories to reduce detail graphs major sets to show interaction patterns graphs major sets to show interaction patterns

13. Applications of Franzosi  Labor-intensive coding from narratives  Uses any relational database to store data  Works with complex narratives or many cases  Power is because data unit is one “semantic triplet” with modifiers data unit is one “semantic triplet” with modifiers coded data contains “grammatical” structure coded data contains “grammatical” structure preserves relationships between elements preserves relationships between elements large dataset permits quantification of patterns large dataset permits quantification of patterns strong relations can be diagrammed as networks strong relations can be diagrammed as networks

14. Ragin’s QCA  Method for qualitative comparisons  Used to build and test qualitative theory  Based on comparison of a set of cases Works with words and concepts, not numbers Works with words and concepts, not numbers Can be small N, analyzes across cases Can be small N, analyzes across cases Uses logic to find configuration patterns Uses logic to find configuration patterns Then tests possibilities with clear criteria Then tests possibilities with clear criteria To produce verbal equations To produce verbal equations ethnic political mobilization=large*growing + fluent*wealthy

15. How Procedure Works  Select a set of comparative cases for the study  Identify configurations of properties in the cases  Create a matrix of all possible configurations (+/-)  Identify which configurations exist, assign cases  Assign outcome variable to cases by configuration  Reduce configurations to logical minimum criterion is “sufficiency” of elements in the configuration criterion is “sufficiency” of elements in the configuration strict requirement for small N strict requirement for small N probabilistic with exact probability test for more cases probabilistic with exact probability test for more cases  Express results in equation form  Build theoretical explanation from the findings