Aura: "a distinctive but intangible quality surrounding a person or thing"
How often do readers ask things like "Do you understand"? Do cold readers perform in the same manner as mediums? How often do readers state things like "Remember this"? What is the most common cause of death said by readers? What is the most frequent letter said by readers? Are most of the readers male or female? Where are the sitters from? How often do readers ask questions? Do readers in one group perform the same as other readers in that group? What is the most frequent person the sitter desires to contact? Where are the sitters from? What names are said by the readers? How many transcripts and readings satisfying certain criteria can we find? Do readers of a same gender perform similarly? Can we come up with a system to standardize transcript and reading identification? Can we build and maintain a database of readings? Can we write a script to automate as much of the descriptive statistics and graphs as possible? Regarding the main things a reader should pick up on, the person who is deceased and how that person died, what percentage of the time does the sitter's given cause of death match the reader's given cause of death, and what percentage of the time does the sitter's given person who is deceased match the reader's given person who is deceased? Can we say anything about these "hits" (matches) and differentiate between weak (naive) and strong (less naive) ones?

By examining data we can attach tangible quantities to the "intangible qualities" related to readers and readings and get some understanding about these, and other, questions. A necessary first step to approaching more difficult questions is to focus on the simpler ones. There is not much or any actual cumulative data on these subjects.

Up to date, there have been numerous analyses of transcripts, but many of these are of little use for some of the following reasons

This informal article introduces AURA, the Archive of MediUm and Cold Reader DAta. AURA is an Excel file that is a repository of various data of professional mediums and cold readers from their live readings, which will be used to calculate descriptive statistics for the data collected. It is more importantly a request, by example, for an ongoing, structured method to record data relating to professional readers from verifiable live readings.

Errors are bound to creep in when doing tedious work, and they certainly exist here. My goal is to not make them, but when I do, find and correct them. People are welcome to submit their suggestions and calculations, and they will be reviewed, and changes will be made if needed. Also, if you've found some transcripts that meet the quality criteria (live reading, professional reader, verifiable source), please let me know.


What is being studied?

The unit of analysis is a reading, denoted by R. A transcript, denoted by T, is a collection of text from the readings of readers and sitters. A transcript contains at least one reading. A generic transcript looks like

and each transcript will be edited to show the reading numbers. For example, transcript 10, or T10, contains reading 100, or R100, and will have "R100" in the left-hand margin at the start of the reading in the transcript. To refer to this reading, instead of saying "The reading on Larry King Live from September 6, 2002 with John Edward where he talks about a young male, death from an impact, a D connection..." and etc., simply refer to T10R100.

The structure of AURA

The AURA file has reading numbers as rows, and the variables being kept track of for each reading as columns.

Variable descriptions

T1.txt (R1-R5) T2.txt (R6-R27) T3.txt (R28-R48) T4.txt (R49-R50) T5.txt (R51-R55)
T6.txt (R56-R60) T7.txt (R61-R71) T8.txt (R72-R79) T9.txt (R80-R92) T10.txt (R93-R104) T11.txt (R105-R121)
T12.txt (R122-R139) T13.txt (R140-R160) T14.txt (R161-R180) T15.txt (R181-R197) T16.txt (R198-R221) T17.txt (R222-R241)
T18.txt (R242-R257) T19.txt (R258) T20.txt (R259-R273)
T21 coming soon

THANK YOU !  :-)

I'd like to thank the following people for their support: Clancie from the JREF message board.

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