psychoinformatics in management

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PSYCHOINFORMATICS IN MANAGEMENT Amrita Vishwa Vidyapeetham DEBDULAL DUTTA ROY, PH.D. (PSY.) PSYCHOLOGY RESEARCH UNIT INDIAN STATISTICAL INSTITUTE, KOLKATA, INDIA [email protected], [email protected] VENUE : VGSOM, IIT., KGP DATE: 11.05, 2015

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Page 1: Psychoinformatics in management

PSYCHOINFORMATICS IN MANAGEMENT

Amrita Vishwa Vidyapeetham

DEBDULAL DUTTA ROY, PH.D. (PSY. )PSYCHOLOGY RESEARCH UNIT

INDIAN STATISTICAL INSTITUTE,

KOLKATA, INDIAddroy@is i ca l .ac . in , dduttaroy@gmai l .com

VENUE : VGSOM, I IT. , KGPDATE: 11 .05 , 2015

Page 2: Psychoinformatics in management

“We have stepped into the new millennium with great hopes and expectations of change. …..The real change must happen within ourselves”.

Sri Mata Amritanandamayi Devi

Page 3: Psychoinformatics in management

W E L C O M E T O I N D I A N S T A T I S T I C A L I N S T I T U T E ( I S I ) , A U N I Q U E  

I N S T I T U T I O N D E V O T E D T O T H E R E S E A R C H , T E A C H I N G A N D

A P P L I C A T I O N O F S T A T I S T I C S , N A T U R A L S C I E N C E S A N D S O C I A L S C I E N C E S .

F O U N D E D B Y P R O F E S S O R P. C . M A H A L A N O B I S I N K O L K A T A O N 1 7 T H

D E C E M B E R , 1 9 3 1 , T H E I N S T I T U T E G A I N E D T H E S T A T U S O F

A N I N S T I T U T I O N O F N A T I O N A L I M P O R T A N C E B Y A N A C T O F T H E I N D I A N

PA R L I A M E N T I N 1 9 5 9 .T H E H E A D Q U A R T E R S O F I S I I S L O C A T E D

I N T H E N O R T H E R N F R I N G E O F T H E M E T R O P O L I S O F K O L K A T A .

A D D I T I O N A L LY, T H E R E A R E T W O C E N T R E S L O C A T E D I N D E L H I A N D

B A N G A L O R E .R E S E A R C H I N S T A T I S T I C S A N D R E L A T E D D I S C I P L I N E S I S T H E P R I M A R Y A C T I V I T Y

O F T H E I N S T I T U T E . T E A C H I N G A C T I V I T I E S A R E

U N D E R T A K E N M A I N LY I N K O L K A T A , D E L H I A N D B A N G A L O R E . O F F I C E S O F

T H E I N S T I T U T E L O C A T E D I N S E V E R A L O T H E R C I T I E S I N I N D I A A R E P R I M A R I LY

E N G A G E D I N P R O J E C T S A N D C O N S U LT A N C Y I N S T A T I S T I C A L Q U A L I T Y C O N T R O L A N D O P E R A T I O N S R E S E A R C H ' .

2 5 0 F A C U LT Y M E M B E R S , 1 0 0 0 S U P P O R T I N G S T A F F S ,

INDIAN STATISTICAL INSTITUTE

It has a long and proud tradition of excellence in training, teaching and research in a number of academic disciplines including statistics, mathematics, computer science, economics, biology, geology, physics and social science. It attracts some of the brightest minds from all over India, and its alumni have made outstanding contributions to academics, governance and industry. – Prof. Bimal K Roy, The Director

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SCIENTISTS OF THIS UNIT CONDUCT RESEARCH ON APPLICATION OF STATISTICS AND MATHEMATICS IN

EXPLAINING DIFFERENT PSYCHOLOGICAL PHENOMENA AND PSYCHOLOGICAL TEST DEVELOPMENT THROUGH

INTERNAL AND EXTERNAL FUNDING PROJECTS, SOMETIMES IN COLLAB ORATION WITH SCIENTISTS OF

OTHER UNITS OF ISI / OR OTHER ORGANIZATIONS. BESIDES, THE FACULTIES OF THE UNIT ARE INVOLVED

IN TEACHING AND TRAINING ACTIVITIES AND ARE PROVIDING PH.D. GUIDANCE TO THE RESEARCH

FELLOWS SELECTED THROUGH ALL INDIA EXAMINATIONS OF THE INSTITUTE.

SCIENTISTS ARE ALSO PROVIDING SERVICES IN STATISTICAL OR PSYCHOMETRIC ANALYSIS OF

PSYCHOLOGICAL DATA.

PSYCHOLOGY RESEARCH UNIT

Page 5: Psychoinformatics in management

Knowledge has to be improved, challenged, and increased constantly, or it vanishes.

-Peter Drucker

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"Knowledge is like a river; its nature is to flow. The dharma of knowledge is to flow to all corners of the

world and nourish the cultures there. We should never stem its flow and thereby turn it into a stagnant pond. It is said that knowledge is the greatest gift one can give,

for knowledge is imperishable. Even if we light a thousand lamps from one flame, the lustre of the first

lamp never diminishes. Similar is the greatness of knowledge. Knowledge does not diminish, no matter how much it is shared. In fact, the more you give, the more it

develops and expands."

-- CHANCELLOR AMMA., Amrita Vishwa Vidyapeetham

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Management complexity

With rapid change in globalization, privatization and liberalization, system of management moves from fixed to variable systems, non-virtual to virtual business, local to transnational, structured to unstructured and their cross variations.

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Knowledge is Non-hypothetical and Randomized change

LOOK, MEDITATE AND FEEL , YOU WILL FIND PATTERN

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Non-virtual to Virtual & Structured to Unstructured Knowledge of Business

UNPREDICTBILITY Local

State

International

Transnational

Non-virtual

Virtual

Structured Unstructured

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Fixed and Variable systems of Management

Fixed Inputs Fixed Transformation (Man-machine interaction)

Fixed Outputs

Variable Inputs

Variable Transformation (Socio-technical process)

Variable outputs

Fixed system of Management

Variable system of Management

Feedback

ddr
In fixed system, organization collects fixed inputs, transforms it with fixed machinery and produces fixed output. For example Jute millBut in Variable system, organization at each every step collects feedback and changes its input, transformation and outputs. For example, software industry
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In Fixed system of management the input, transformation and output are predetermined and fixed. For example one jute factory produced goods by using the same machineries, sources of raw materials, traditional workers and classical management systems.

But in variable system of management organization is giving importance on the feed-back on input, transformation and output. For e.g. one financial institute is giving on and off the job training to its employees before introducing e-marketing business in the organization.

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Flow of electronic data and creation of complexity

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Non-virtual to Virtual & Structured to Unstructured Business

UNPREDICTBILITY Local

State

International

Transnational

Non-virtual

Virtual

Structured Unstructured

Page 14: Psychoinformatics in management

Paradigm Shift in Research

Hypotheses driven research Experimental control : Control of Stimulus, tools,

response. Survey methods : Control over sample, procedure of

data collection, statistical control. Non-hypotheses driven research

Psychoanalysis : Free floating ideas, Word association. Psychoinformatics: Discovery of knowledge by

response pattern recognition based on data mining. Data were retrieved from data warehouse.

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What is Psychoinformatics ?

Psychoinformatics is exploratory research design to retrieve randomized data from digitized or non-digitized data warehouse, to clean and mine the data by appropriate statistical tools, to classify the data for pattern formation and finally knowledge is discovered.

Data (measured response) + Information> Pattern formation >discovery of knowledge.

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Psychoinformatics is the Non-Hypothesis driven Research

Due to rapid change in information archiving system, researcher is getting access to the randomized data ware house.

This is not developed out of researcher’s own intention. For example, in different blogs and virtual social network, regularly many messages are posted. Researcher can mine the data to discover specific variable for research.

It uses computer databases to store, retrieve and assist in understanding psychological information. Data warehouse, data retrieving, data mining, pattern recognition and discovery of knowledge are five basic principles of psycho-informatics.

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Principles of Psychoinformatics

Data Ware House: It is the reservoir of data. It can be in digitized (e.g national sample survey records, NCRB report,) and non-Digitized form (e.g. social network, blogs etc.).

Data Retrieving: It is the process of selecting valuable data, based on query in the data base.

Data Mining: It is an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables.

Pattern Recognition: It is the stage where pattern can be recognized by relating the attributes of the variables. Pattern recognition occurs on the basis of certain model.

Knowledge Discovery: New knowledge about relationship among the variables or the process of analysis is discovered.

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PARADIGM SHIFT IN PSYCHOLOGICAL RESEARCH

HYPOTHESES DRIVEN MODEL It encourages knowing

psychology through responses controlled by the experimenter or test constructor.

Knowing psychology through predetermined hypotheses limits our knowledge to pre-assumed psychological traits.

This causes serious problem to gauge all determinants of individual differences in behaviour.

NON HYPOTHESES DRIVEN MODEL It encourages knowing

psychology through responses uncontrolled by the experimenter or test constructor.

Knowing psychology through response pattern recognition. Pattern finally reflects specific psychological traits.

Psychoinformatics is the non-hypotheses driven model.

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Stages

Data warehouse: Non-Digitized and Digitized.Data retrieval : Using computer resources

through coding.Data mining : data classification, association

using psychological measurement or statistical reasoning, models and tools.

Pattern recognition : Exploring pattern out of statistical distribution.

Discovery of knowledge : Development of theory.

Page 20: Psychoinformatics in management

DATA WAREHOUSE

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Data warehouse

It is the reservoir of digitized or non-digitized data.Digitized ware house: The Digitized warehouses are social

network groups in virtual world. Yahoo groups, Orkut, Facebook, message boards, blogs and Youtube are the Digitized warehouses. In the data warehouse, data are stored in multiple forms like text, numeric, sound, and pictures. Google research, Science direct.com, Pubmed, Emerald and Entrez are the research data warehouse.

Nondigitized data ware house: Answer sheets of board examinations, case history records of patients, absenteeism records of factory workers, foot falls in the shopping malls, National sample survey records are the data warehouse.

Basic assumption of psycho-informatics is that data warehouse is formed by randomly generated data.

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Digitized data warehouse for Sentiment Analysis

Facebook generates random data. Here is a statement: Mother's day is

coming....confused about the gift....any idea ? At 9:42 PM

First response came at 9:51 pm, next 9:52, 9:54, 10:21, P.M., 3:43 am, 7:32 am, 7:34am, 7:36am…contd. This suggests the statement is able to generate data speedily.

If we analyze the content, we will see two principal components- One is tangible (cooking food, coffee mug) and another is intangible (Outing with mother, spending time with mom).

The tangible gift is suggested by unemployed juniors (age ranged from 22 to 25 tears) and intangible is suggested by employed seniors (30-35 years).

Lecture notes: Psychoinformatics by D. Dutta Roy, ISI., Kolkata

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Data Retrieving

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Digitized Data Retrieval

Digitized data retrieval: It is the process of selecting valuable data based on query in the data base.

There are some cross data base search engines like Entrez in the internet. The address of Entrez is ‘http://www.ncbi.nlm.nih.gov/sites/gquery’.

Names Number of citations

Anxiety disorder 2588781

Depression 235173

Conversion reaction 419

Obsessive compulsive disorder 3247

Hypochondriasis 962

Somatoform disorder 2456

Schizophrenia 11128

Manic depressive 11382

Paranoid disorder 6593

Autism 14624

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Non-Digitized data retrieval

Using coding process. Later, coding will be entered into the computer.

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Data cleaning is done before and after data mining.

Before data mining, cleaning is made to fit the data according to the assumptions of specific statistical tools to be used. This includes missing data, unreliable or faulty data (no response wise difference), data entry errors, outlier etc.

After data mining using statistical tools, data are cleaned when data provide unreliable information or information that can affect model validity, e.g., items are removed when it can not be fitted. And rescoring is done after removing the item.

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Data mining

Data mining: Data mining is an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data (Fayyad, Girnistein and Wierse, 2002).

Classification (involves finding rules that partition a given data set into disjoint classes) and clustering (conceptual groups in data on the basis of similarity), prediction are common models (Andrusiewicz and Orlowska, 1997).

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Pattern formation and Discovery of knowledge

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