PCAD & CATPAC2011

Brittani Wolanin & Mike Kurtz

PCAD 2000

Psychiatric Content Analysis Diagnosis Program that performs content analysis of

input text on scales developed by Louis A. Gottschalk, Goldine Gleser.

Numerous studies have confirmed the validity and reliability of the scales in measuring affect, including cross-language and cross-cultural studies.

Developed to eliminate extensive training of human scorers and decrease the time required to analyze samples.

How it Works

The program assigns scores on the user-selected Scales to each clause in the input sample

At the user’s option, reports score summaries for each scored Scale with comparisons to established norms for the subject’s demographic group

Provides an analysis of the score profile, and suggests possible diagnoses drawn from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)

Scales

Anxiety Death Mutilation Separation Guilt Shame Diffuse or Non-

specific Anxiety

Hostility Scales Hostility directed

outward Hostility directed

inward Ambivalent

Hostility

Scales Continued

Social Alienation-Personal Disorganization Scale

Cognitive and Intellectual Impairment Scale Hope Scale Depression Scale

Hopelessness Self-Accusation Psychomotor Retardation Somatic Concerns Separation Depression Hostility Outward

Scales Continued

Human Relations Sale

Achievement Strivings Sale

Dependence Strivings Scale

Health-Sickness Scale

Quality of Life Scale

How to use

The material being analyzed must be at least 80-90 words and put into .SAM or .TXT format.

Was difficult to figure out .SAM

.TXT was simple and worked properly

.TXT is recommended

Start>All Programs>CATA>PCAD>Scoring

Copyright information> Click OK

Guide Me

Sample Selection

If you saved files as .txt then switch the menu from .sam to .txt

Select the sample you want to analyze

PCAD’s Default is to run on all scales It is possible to choose specific scales

Select> select and/or deselect desired Scales

Select Output Options Printer File (Summary) output to spreadsheet

Charlie Sheen Interview

An interview with Charlie Sheen on “The Alex Jones Show”

Interview Recorded on Feb 24, 2011  Based in Austin, Texas, airs via the

Genesis Communication Network over 60 AM, FM, and shortwave radio stations across the United States and on the Internet. His websites include Infowars.com and PrisonPlanet.com.

Analysis Results> Scored Clause

Analysis of Charlie Sheen Int.

Analysis on each scale

Summary analysis

Diagnoses

Schizophrenia, Disorganized (295.1x), Paranoid ((295.3x), undifferentiated (295.9x)

Schizophreniform disorder (295.40) Psychiatric disorder (Atypical psychosis)

not otherwise specified (298.90) Schizoaffective disorder (295.70) Child

Autistic disorder (299.00)

CATPAC

CATPAC

Created by Joseph K. Woelfel Can identify the most important words in

a text and determine patterns of similarity

As a part of the Galileo suite the analysis can be turned into perceptual mapping in Thought View

Also provides interactive clustering through Oresme

How does it work?

Catpac moves a window of n words throughout the text

Words that appear through window making connections and associating words that appear close together within the text

Running the Program

Files need to be formatted into txt files in order to be analyzed

Analysis can be run for an output of either 25 words or 50 words (25 words were used in this analysis due to the size of the documents)

Opening CATPAC

Start > All Programs > CATA > Galileo Suite > CATPAC

Input .txt file into Catpac

• File > Open > Input File

•If you do this under “NEW”, it will crash the program.

Select File to be Analyzed

• Select your .txt file here.

Make Dendrogram (Dendogram)• File > Run > Make Dendogram (Really Dendrogram)

What it looks like

Excluding for Common Words• Maximize the tab labeled “DEFAULT.EXC”

• Then File > Open > Input File in this opened document.

Save as .CRD (Crud)

File > Save As > CRD File

Open Thought View

Start > All Programs > CATA > Galileo Suite > TV32

Thought View / Concept Clusters

Poem Analysis

Shakespeare Lanyer Charles Dickens

Results

Dickens

Demonstration

Demonstrating the capabilities of Thought View