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

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

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.

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

Preparation/Formatting cont’d

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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*

Preparation/Formatting cont’d

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

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

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

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

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

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

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.}

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!

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.

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

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.

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

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

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!}