Rabindra Sangeet in Psychological counselling

Why Rabindra Sangeet in Psychological counselling  ?
Success of Psychological Counselling depends on situational and dispositional attributions. Sometimes situational attributes become more powerful and client feels less control over it. Again when client attributes self for the illness, he or she is more stressed. Rabindra Sangeet
never attributes self or other/s for illness. Rather it creates illness reasoning and guided imageries to overcome the illness.So illness perception can be changed through the regimen coming from within.

R-Programming

Reading Table data from Desktop 

File name; Testdata
Source:C:\Users\Dr.D.D.Roy\Desktop\Testdata.txt

R command:

1. test <- read.table("C:/Users/Dr.D.D.Roy/Desktop/Testdata.txt", header=TRUE)

Path direction has been changed from | to /. Header indicates name of column variable.

2. list(test)

List command for display of data. The data has been stored in the array named 'test'

> list(test)

[[1]]

  Price Floor Area Rooms Age Cent.heat

1 52.00   111  830     5 6.2        no

2 54.75   128  710     5 7.5        no

3 57.50   101 1000     5 4.2        no

4 57.50   131  690     6 8.8        no

5 59.75    93  900     5 1.9       yes

Reading no of rows and columns


> dim(test)
[1] 5 6
> str(test)
'data.frame':   5 obs. of  6 variables:
 $ Price    : num  52 54.8 57.5 57.5 59.8
 $ Floor    : num  111 128 101 131 93
 $ Area     : int  830 710 1000 690 900
 $ Rooms    : int  5 5 5 6 5
 $ Age      : num  6.2 7.5 4.2 8.8 1.9
 $ Cent.heat: Factor w/ 2 levels "no","yes": 1 1 1 1 2

Help command

Before we get deeper into the use of R, it is good to know how to seek help when we get stuck. Two functions are illustrated here. If you know the name of a function or the topic, you may use the function help(…) with your function name or topic inside the parenthesis. For example, if you are interested in the function plot(), you can type

> help(plot)

and it will pop-up a help manual for plot function. This can be done more quickly by typing a question mark in front of the function in question.

> ?plot

Sometimes, you may want to find something related to a certain keyword. Then you may find help.search() useful. Function help.search() will search for all the functions that have the word you specified in their help document such as name, title, concept, keyword. For example,

> help.search("sort")

will list all functions that have the word 'sort' as an alias or in their title. The dunction help.search() also has a shortcut consisting of two question marks preceding the keyword (e.g., ??sort). If you want to learn more about the help function, you may type help(help). Besides the official help pages, you can also explore the Internet, which has many resources.

More commands

http://www.personality-project.org/r/r.commands.html

Q1. What is R?

Why Use R?

If you currently use another statistical package, why learn R?

It's free! If you are a teacher or a student, the benefits are obvious.

It runs on a variety of platforms including Windows, Unix and MacOS.

It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner.

It contains advanced statistical routines not yet available in other packages.

It has state-of-the-art graphics capabilities.

Obtaining R

R is available for Linux, MacOS X, and Windows (95 or later) platforms. Software can be downloaded from one of the Comprehensive R Archive Network(CRAN) mirror sites.

Feedback

I constantly strive to improve these pages. Feedback and suggestions are always welcome! 

- Rob Kabacoff

CFA parameters

1. CFA by SAS

2. CFA by AMOS

Intellectual Impairment in National AIDS control programme

Relevant sites

1. Barriers to HIV services

2.National Aids Prevention & Control Programme in West Bengal

3. Intellectual Impairment in Patients with Newly Diagnosed HIV Infection in Southwestern Nigeria

Psychological data analytics

GLOSSARY OF PSYCHOLOGICAL DATA ANALYTICS

DATA:   Data are sets of numbers or pieces of information obtained during research studies. Data may be either qualitative (categorical and usually non-numerical) or quantitative (numerical) in nature, but in general, data are numerical pieces of information.

INFORMATION:  Knowledge obtained from investigation, study, or instruction.

DATABASE : Systematically organized or structured repository of indexed information (usually as a group of linked data files) that allows easy retrieval, updating, analysis, and output of data.

DATABASE MANAGEMENT SYSTEM: system that provides users and programmers with a systematic way to create, retrieve, update and manage data.

BIG DATA : Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.

DATA CAPTURE: Retrieval of information from a document using methods other than data entry. The utility of data capture is the ability to automate this information retrieval where data entry would be inefficient, costly or inapplicable.

DATA STORAGE: Archiving data in electromagnetic or other forms for use by a computer or device.

VOLUME: Amount of data.
VARIETY: Number of types of data.
VELOCITY :Speed of data processing.
VERACITY: Biases, noise and abnormality in data.

DATA WAREHOUSE

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. Subject-Oriented: A data warehouse can be used to analyze a particular subject area. Data warehouse is used for storing information and retrieval. It is the systematic process of collecting and cataloging data so that they can be located and displayed on request.

SUBJECT-ORIENTED: A data warehouse can be used to analyze a particular subject area. For example, "sales" can be a particular subject.

INTEGRATED: A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.

TIME-VARIANT: Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transactions system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer.

NON-VOLATILE: Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered. 

DATA RECOVERY : process of salvaging (retrieving) inaccessible, lost, corrupted, damaged or formatted data from secondary storage, removable media or files, when the data stored in them cannot be accessed in a normal way.

DATA RETRIEVAL means obtaining data from a database management system. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data. In order to retrieve the desired data the user present a set of criteria by a query.

DATA CLEANSING : data cleaning, or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty.

DATA MINING :  the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science.

ARTIFICIAL INTELLIGENCE : the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

MACHINE LEARNING:  type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. ... The process of machine learning is similar to that of data mining.

PATTERN RECOGNITION:  a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. ... In machine learning, pattern recognition is the assignment of a label to a given input value.

KNOWLEDGE DISCOVERY IN DATABASES (KDD) is the process of discovering useful knowledge from a collection of data.

CLUSTERING : Grouping the same or similar elements gathered or occuring closely together.

REGRESSION: Statistical measure for predicting the value of a dependent variable Y, based on the value of an independent variable X.

GROUPING: Individual or row wise grouping and variable or column wise grouping provide individual wise and variable wise association respectively. 

ROW AND COLUMN CORRESPONDENCE: The correspondence plot of row and column variables.

PREDICTION: Predicting future trends of data. How well a given predictor can guess the value of predicted attribute for a new data.


FORECASTING: The use of historic data to determine the direction of future trends. 

PSYCHOLOGICAL TESTING IN SCHOOL PSYCHOLOGY

NATIONAL WORKSHOP ON PSYCHOLOGICAL TESTING IN SCHOOL PSYCHOLOGY

Organized by: 

INDIAN INSTITUTE OF PSYCHOMETRY (IIP) 

in collaboration with 

Indian School Psychology Association (InSPA)

Supported by: INDIAN STATISTICAL INSTITUTE (ISI). Kolkata

Venue: NAB 1 and NAB 2, ISI, Kolkata

Date: 24^th and 25^th December, 2016 (9.30 AM to 5.00 PM)

ABOUT IIP

Indian Institute of Psychometry was established as Non Profit Distributing Organization in the late 70’s in  Kolkata to design, devise and promote Psychometric Tests for practical applications like recruitment, selection, counselling and  guidance under the legacy of Founder Director Late Dr.Sibabrata Chatterji and the present Director Dr. Manjula Mukerjee. Once, both were working in the then Psychometric Research and Service Unit (PRSU) of the Indian Statistical Institute, Kolkata under the guidance of Dr. Edwin Harper an eminent American Psychometrician on statistical methods and served the unit as Head.  IIP has grown to its present stature as an “Institute of Considerable Public Utility” certified by the Bureau of Public Enterprises, Ministry of Finance, Govt.of India; registered under section 25 of the Company’s Act. IIP was founded on the 18 th  of July 1978 and it is presently located at the hub of the city at 117, Barrackpore Trunk Road, Kolkata -700108(E-mail: indianinstituteofpsychometry@gmail.com) with a work force strength of over 60. The institute is recognized as a  “Scientific and Industrial Research Organization” under Section 35 (i) (iii) in the year 1982 by the  Department of Scientific and Industrial Research ,Ministry of Science and Technology, Government of India. The Institute broadly deals with the following areas of activities.

OBJECTIVES:
According to National Association of School Psychology (NASP), School psychologists are uniquely qualified members of school teams that support students' ability to learn and teachers' ability to teach. They apply expertise in mental health, learning, and behavior, to help children and youth succeed academically, socially, behaviorally, and emotionally. They create safe, healthy, and supportive learning environments that strengthen connections between home, school, and the community. Their working unit is both micro and macro. Their interventions are based on data collected through observations, interview, experiments, psychological and educational assessment. Their work is more challenging as they can predict unobserved by observed data. Since, there is limited knowledge about description and application of Psychological tests. 

This workshop sets three objectives:

1.      To disseminate knowledge about basics of School Psychology. 

2.      To disseminate knowledge about theory, applications and limitations of selected psychological tests.

3.      To provide platform for interaction between parents and psychologists, special educators and psychiatrists.  

Broad Areas are: 

1. Right to Education and Child Protection Acts

2. NASP guidelines of school psychological assessment 

3. Assessment for children with special needs

4. Assessment of Academic motivation

5. Assessment for counselling and guidance 

Eligible persons are: Special educator, Rehabilitation Psychologist, School Psychologists, School Counsellors, Clinical Psychologist, Child psychiatrist. 

Sessions

1.      Scientific session for Psychologists and Psychiatrists

2.      Parent- Psychologist interaction

Category	Early Bird Registration	Amount	Last Date of Registration with late fees	Amount
Delegates (Professionals) For two days programme	19/12/2016	Rs. 2000/- (Rs. Two thousand only)	22/12/2016	Rs. 2500/- (Rs. Two thousand five hundred only)
Delegates (Parents and Teacher) For 1st session of 24th December, 2016 only	19/12/2016	Rs. 500/- (Rs. Five hundred only)	22/12/2016	Rs. 700/- (Rs. Seven hundred only)

Registration: It includes attending workshop, working lunch, snacks and kits. It doesn't include travel and accommodation. Please submit Rs.2000 (including taxes) through NEFT and fill up the form. Click here Or copy the link given below on your browser

https://docs.google.com/forms/d/e/1FAIpQLSeOvcSis7yFDZ_HwSS_OaGoei1uCW109crfGp2_I-6rYsVOmQ/viewform?c=0&w=1

NEFT Details:

Bank Account number: 056602000000229

Purpose: Workshop registration

Name of the Bank: Indian Overseas Bank

Name of the Bank Branch: Baranagar Dunlop Bridge Branch (0566)

IFSC Code : IOBA0000566

Mobile no: 9903 5426 02

Convener: 

Dr. Rama Manna

Head, Department of Counselling and Guidance

INDIAN INSTITUTE OF PSYCHOMETRY, 

117, B. T. Road, Evergreen Plaza, Kolkata – 700108

Contacts: 9903542602 / 033 2577 2696/ 4393/ 4207 (Extn. 116) from 11.00AM to 5.00PM

E-mail: mannaramaiip@gmail.com / nwschoolpsy2016@gmail.com

PROVISIONAL PROGRAM

DAY 1: 24.12.2016 & DAY 2: 25.12.2016

VENUE: NAB 1 & 2, INDIAN STATISTICAL INSITUTE, KOLKATA

Date	Time	Event	Resource Person
DAY 1: SATURDAY	9-30:10-00	Registration at :NAB 1
	10.00:11.00	Inauguration	Invocation Felicitation Inaugural speech: Prof.Sanghamitra Bandyopadhyay, Director, Indian Statistical Institute. About IIP: Director, IIP About Dept: Rama Manna, Head, Department of the Counselling and Guidance, Indian Institute of psychometry, Kokata. Purpose of workshop: Dr. Debdulal Dutta Roy, Associate Professor, Psychology Research Unit, Indian Institute of psychometry, Kokata.  Keynote address: Dr. Ruchira Goswami, WB National University of Juridical Sciences  Vote of Thanks: Ms. Debanjana Basu, Guest Faculty,Indian Institute of psychometry, Kokata
	11.00-11.15	Tea Break	------------------
	11.15:12.15	Technical session 1	Chairperson: Dr. Chhanda Ray Title: NASP guidelines of Psychological assessment  and Indian model of school psychology ethics Speaker: Dr. Debdulal Dutta Roy, Psychology Research Unit, Indian Statistical Institute, Kolkata-108
	12.15-1.15	Technical session 2	Title: Autism and Assessment Speaker: Prof. Mallika Banerjee, Department of Psychology, University of Calcutta.
	1.15-2.00	Lunch
	2.00-3:00	Technical session 3	Title: P.G.I. battery of brain function. Speaker: Prof. Pritha Mukherjee, Department of Psychology, University of Calcutta.
	3:00-4:00	Technical session 4	Title: Children Apperception test Speaker:  Dr. Manisha Dasgupta, Reader, Department of Psychology, University of Calcutta.
	4:00-5:00	Technical session 5	Title : Assessment of Hyperactivity among children Speaker : Dr. Shivani Santosh,  Head, Asst. Professor,  Department of Applied psychology, Neotia University, Kolkata
DAY 2: SUNDAY	10:00-11:00	Technical session 6	Title: High School Personality Questionnaire  Speaker: Dr. Rama Manna, Head, Department of Counselling and Guidance, Indian Institute of Psychometry, Kolkata.
	11:00-11:30	Technical session 7	Title: NEO -FFI: Implications in School Psychology Speaker: Ms. Anurupa Kundu & Ms. Shravanti Ghosh, Psychology Research Unit, Indian Statistical Institute, Kolkata
	12:00-01:00	Technical session 8	Title: Reading and Writing Motivation test Speaker: Dr. Debdulal Dutta Roy, Psychology Research Unit, Indian Statistical Institute, Kolkata
	1.15-2.00	Lunch
	2.00-3:00	Technical session 9	Title: Verbal Reasoning Test Battery & Memory Assessment  Speaker: Ms. Sumona Datta &Murshida Khatoon , Psychology Research Unit, Indian Statistical Institute, Kolkata
	3:00-4:00	Valedictory Session	Distribution of Certificates

Data Analytics in Psychological Research

Psychological data are very complex to analyze and interpret because of its hidden structure. It is easily affected by random errors of individual differences, differences in situations and by the interaction of individual and environment. Another significance of psychological data is multi-dimensionality. One construct is assessed through multiple sub-constructs. Again, each sub-construct is assessed through multiple consistent attributes. For prediction, psychology follows system approach and big data. Traditional statistical tools sometimes fail to capture the gamut of psychological functions implied in the data. In this connection, data mining from big data warehouse is important. Padma Vibhushan, Professor C.R. Rao, Professor of the University of Buffalo defined big data as massive amounts of very high dimension and unstructured data which are continuously produced and stored with much cheaper cost than they are used to be. The massive samples in big data are typically aggregated from multiple sources at different time points using different technologies. Some of the static big psychological data are psychiatric case history records, answer sheets of Board level examination, selection test, data of disability, health insurance data, crime research data, industrial data. Social networking sites like Facebook, twitter, you tube are dynamic data warehouse. 

Topics

PSYCHOLOGICAL DATA SCIENCE


DATA RESERVOIR

INTRODUCTION TO DATA MINING

DATA DIMENSION REDUCTION ANALYSIS

SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA