1. Minnesota Contextual Content Analysis
MCCAlite
Minnesota
Contextual
Content
Analysis
Jeff Spicer & Matthew Egizii

2. MCCAlite Created by Donald McTavish & Kenneth Litkowski
Department of Sociology, University of Minnesota
Full version only operates on a Control Cyberdata 174 computer at the University of Minnesota

3. MCCAlite Used to analyze: Scripts Transcripts Screenplays
Open-ended items
Potentially Likert type scales
Conceptual Dictionary
116 categories used to organize word meaning
Examine patterns of emphasized ideas in text as well as the social context or underlying perspective reflected in the text.

4. Preparation/Formatting
Choose an Input file
As usual, .txt is preferred
If you use the full version, it can also take .sav (its own file type)
{MCCALite cannot save output!}
[Correction: it has been updated to give output, but our lab does not have this version!]
Frequent Words File
Default is FREQWDS.TXT
This is used to exclude certain words from analysis
Not recommended by the programmers!
Throws off weightings

5. Preparation/Formatting cont’d

6. Preparation/Formatting cont’d

7. Preparation/Formatting cont’d

8. Preparation/Formatting cont’d
Text Separator
$ means the end of a body of text.
This is really important.
MCCALite can’t process E-/C-Scores without it!
Text Title
Marks the word/text as a unit of analysis.
Bracket the word/text with “=“
For example:
= Horatio =
would track every time Horatio speaks
ALSO VERY IMPORTANT
Can be time consuming…*sigh*

9. Preparation/Formatting cont’d

10. Word Analysis Word Accounting Total # of Words Total Words Categorized
% of unique words
% of categorized unique words
Word Length
Mean
Standard Deviation
Low/High

11. Words by Category/Frequency
Select a text group from the drop down to examine their word use.
This shows ONLY that text group.
right is “SLUG LINE”
Frequency can be set to a minimum in the drop down box above.
right set to {5}

12. KWIC Key Words In Context
Lines up concordances in the text to observe them in context.

13. C-Scores Measures social context across 4 main categories Traditional
Focus on norms and expectations
Practical
Focus on successful (efficient) goal accomplishment
Emotional
Focus on personal involvement, comfort, enjoyment or leisure
Analytic
Focus on objectivity, curiosity, or interest

14. C-Scores cont’d Scores (Weighted)
Scaled for each text with values of –25 to +25

15. C-Scores cont’d
Plots (Weighted)
Helps compare the profiles visually

16. C-Scores cont’d Scores (Raw)
Un-normalized scores, sum to 0, but aren’t scaled.
Plots (Raw)
As with weighted, these help visualize the scores.
{Not shown, we figure you get the picture.}

17. C-Scores cont’d Distance Matrix
Euclidian distance between C-scores of each pair of text groups in the input file.
Texts that are similar have smaller values.
E-scores have this table too!

18. E-Scores
Show emphasis on groups of idea categories formed from the 116 individual categories.
Scores are “Normed” against expected usage.
This demonstrates over- emphasis and under-emphasis.
It is a computed score based on probability.

19. E-Scores cont’d High-Categories
23 super-categories grouped together from the initial 116

20. E-Scores cont’d Selected Plots
Allows you to choose a specific category’s emphasis to plot, by Text Group.
Not the plot of the High Categories, but the individual ones.

21. E-Scores cont’d Difference Analysis
Indicates how different the selected Text Group is from all other Text Groups (or characters, etc.)
Listed by category

22. E-Scores cont’d Diagnostic Groups 43 combinations of categories
Set up as scales for further analysis
{pretty sure about that}
This is the interesting bit

23. The Searchers
We decided to demonstrate this software using this film:
Characters:
Ethan Edwards (Wayne) & 5 others
Also, created “SLUG LINE”
Scene heading, actions, transitions, etc.
Plot:
“As a Civil War veteran spends years searching for a young niece captured by Indians, his motivation becomes increasingly questionable.”
Length:
Approximately 8 pages of screenplay.
Reason for excerpt:
Contained dialog and more than one character.
This was entirely exploratory
{remember, data can’t be saved!}