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CALL FOR PARTICIPATION

PSYCHOLOGY RESEARCH UNIT

INDIAN STATISTICAL INSTITUTE, KOLKATA

ORIENTATION TRAINING ON ADVANCED APPLIED PSYCHOMETRICS (TAPP23)

(27-28TH DECEMBER, 2023, 11-4 PM, HYBRID MODE)

 

Psychology Research Unit, Indian Statistical Institute, invites application for 'ORIENTATION TRAINING ON ADVANCED APPLIED PSYCHOMETRICS' Classical test theory(CTT) is sample-dependent, as reliability coefficients can differ between groups, impacting the generalizability of findings.Another critical drawback is the failure to address item difficulty and discrimination independently. CTT treats items as interchangeable, neglecting the nuances of each item's contribution to measurement precision. This oversimplification limits the test developer's ability to identify and modify specific problematic items, hindering the refinement of assessments.In conclusion, while Classical Test Theory has played a significant role in the history of psychometrics, its limitations in accounting for measurement error variability and individual item characteristics underscore the need for more sophisticated approaches. Aim of this workshop is to disseminate (a) classical test theory, (b)hierarchical cluster analysis of items, (c) item response theory, and (d) structural equation modelling. This knowledge is relatively new in Indian researches in Psychology. This model is useful in Machine learning for computer adaptive testing. Finally, it will be used for personalized testing. 

Educational Qualification: P.G. in Psychology/Education or related areas.

Eligibility: faculties, researchers and project workers.Knowledge of Statistics and R-studio. 

Application: Please fill out the form (click here)

Certificate: One certificate will be provided after submission of the assignment.

Mode: Online in Zoom. Offline: At the venue of the Indian Statistical Institute. 

TA & DA: Not available.

Recording: No you tube video will be available.

Important Dates: Registration starts: 12th December, 2023

Registration completes: 20th December, 2023.

Declaration of selected trainees: 23 rd December,2023.

Class starts: 27th December,2023

Convener

Dr. Garga Chattopadhyay,

Assistant Professor

Psychology Research Unit

Indian Statistical Institute, Kolkata-700108

 

 

 

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ORIENTATION TRAINING ON ADVANCED APPLIED PSYCHOMETRICS (TAPP23)

(27-28TH DECEMBER, 2023, 11-4 PM, HYBRID MODE)

27.12.2023

11 -00 Inauguration

Dr. Niladri Shekhar Das, Prof-in-charge, SSD.

Dr. Garga Chattopadhyay, Psychology Research Unit.

Dr. Debdulal Dutta Roy, Psychology Research Unit.

12-00 Technical Session 1

Classical Test Theory and Limitations-Dr. Murshida Khatoon,

Department of Psychology, Geetam University, Visakhapattanam, A.P.

13-00    Lunch

14-00    Technical Session 2

Hierarchical cluster analysis of items-Dr. Debdulal Dutta Roy,

Psychology Research Unit.

15-00    Technical Session 3

R-Script- Dr. Debdulal Dutta Roy,

Psychology Research Unit.

16-00    Assignment

 

28.12.2023

11-00   Technical Session 4

Item-Response Theory – Dr. Sumona Datta, Government General Degree College, Singur.

13-00   Lunch

14-00   Technical Session 5

              Structural Equation Modelling - Dr. Sushmita Chatterjee,Assistant Professor Of Economics,Maharaja   

               Manindra Chandra College.

16-00     Evaluation and Distribution of certificates.

 

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Research internship on 'RATING SCALE DESIGN AND ANALYSIS USING R-STUDIO

Psychology Research Unit, Indian Statistical Institute, invites application for Research internship on 'RATING SCALE DESIGN AND ANALYSIS USING R-STUDIO'. The internship includes training and writing paragraphs on following statistics - (a) Statistics of Mean differences, (b) Correlation statistics, (c) Regression analysis, and (d) Statistics for categorical data. This content will be used for teaching students of Psychology and Allied Sciences. This internship will be for two months. There will be 3 -day online classes per week from 2 to 3 PM. One certificate will be provided after submission of the contents. Applicant must have good knowledge in R-studio and relevant packages. Please submit your bio-data with application to the convener, Research internship, Psychology Research Unit, Indian Statistical Institute, Kolkata campus through e-mail: psy@isical.ac.in  before 15th February, 2023. 

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Application received from: 


uditmaheshwari1035@gmail.com Regression Analysis
rashi7019@gmail.com  

Statistics of Mean differences

urbeedutta@gmail.com

Correlation statistics

saswati.barat@gmail.com Regression Analysis
drmichellefernandes@gmail.com

Correlation statistics

moutushibhowmik01@gmail.com

Statistics of Mean differences

drsweta.patel@sxca.edu.in

Statistics of Mean differences

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Sample writing on small sample t-test

1. Definition, 2. Types, 3. Assumptions, 4. Uses, 5. Advantages, 6. Disadvantages, 7. Caes study. 

Definition

Definition

A small sample t-test is a statistical test that is used to compare the means of two groups when the sample sizes are small (typically n<30) and the population standard deviation is unknown. This test is a variant of the Student's t-test, which is used to compare the means of two groups when the sample sizes are large and the population standard deviation is known.

The small sample t-test uses the t-distribution to calculate the probability of obtaining the observed difference in means by chance, assuming that the two groups have equal variances. If the probability is less than a pre-specified level of significance (usually 0.05), then the difference is considered statistically significant.

There are two types of small sample t-tests: the paired-sample t-test, which compares the means of two related groups, and the independent-sample t-test, which compares the means of two unrelated groups. The choice of which test to use depends on the research question and the study design.

Types

There are several types of mean difference statistics, which are used in different contexts and for different research questions. Here are some examples:

  1. Independent samples t-test: This is a statistical test that compares the means of two independent groups on a continuous variable.

  2. Paired samples t-test: This is a statistical test that compares the means of two related groups on a continuous variable.

  3. One-way ANOVA: This is a statistical test that compares the means of three or more independent groups on a continuous variable.

  4. Two-way ANOVA: This is a statistical test that compares the means of two independent variables on a continuous variable.

  5. Repeated measures ANOVA: This is a statistical test that compares the means of two or more related groups on a continuous variable, measured at multiple time points.

  6. MANOVA: This is a statistical test that compares the means of two or more continuous variables across two or more independent groups.

  7. Cohen's d: This is a standardized effect size that quantifies the difference between two means in terms of the standard deviation of the population.

These mean difference statistics are commonly used in different fields of research, including psychology, education, medicine, and social sciences. The choice of which test to use depends on the research question, study design, and data characteristics.

Assumptions

The t-test is a statistical test that is used to compare the means of two groups. There are two types of t-tests: the independent samples t-test and the paired samples t-test. Both tests have certain assumptions that must be met for the test to be valid. Here are some common assumptions of the t-test:

  1. Normality: The distribution of the data should be approximately normal. This means that the data should not be highly skewed or have extreme outliers.

  2. Independence: The data in each group should be independent of each other. This means that there should be no systematic relationship between the observations in one group and the observations in the other group.

  3. Homogeneity of variance: The variances of the two groups should be approximately equal. This assumption is important for the independent samples t-test, but not for the paired samples t-test.

  4. Interval or ratio level of measurement: The t-test assumes that the data are measured on an interval or ratio scale. This means that the data have meaningful units of measurement and zero represents the absence of the variable being measured.

If these assumptions are not met, the results of the t-test may not be reliable or valid. It is important to check the assumptions before conducting a t-test and to use alternative tests if the assumptions are violated.

Uses

Mean difference statistics are commonly used in statistics and research to quantify and compare the differences between the means of two or more groups or conditions. Here are some common uses of mean difference statistics:

  1. Hypothesis testing: Mean difference statistics are often used in hypothesis testing to determine whether the difference between the means of two groups or conditions is statistically significant. For example, a researcher may use a t-test to determine whether there is a significant difference in test scores between two groups of students.

  2. Effect size estimation: Mean difference statistics are often used to estimate the size of an effect or the magnitude of the difference between groups. Effect size estimates can be used to interpret the practical significance of the difference between the means, in addition to the statistical significance.

  3. Treatment evaluation: Mean difference statistics are often used to evaluate the effectiveness of a treatment or intervention. For example, a researcher may use a paired samples t-test to determine whether there is a significant improvement in symptoms before and after a treatment.

  4. Group comparisons: Mean difference statistics can be used to compare the means of two or more groups on a particular variable. This can be useful in understanding differences or similarities between different populations or conditions.

Overall, mean difference statistics are a useful tool in statistics and research for quantifying and comparing differences between groups or conditions, and for evaluating the effectiveness of interventions or treatments.

Advantages

Mean difference statistics have several advantages that make them a popular tool for statistical analysis and research. Here are some advantages of mean difference statistics:

  1. Simple and easy to interpret: Mean difference statistics are simple to compute and easy to interpret, making them accessible to researchers with different levels of statistical expertise.

  2. Quantifies the difference between groups: Mean difference statistics provide a quantitative measure of the difference between groups, which allows researchers to determine the practical and statistical significance of the difference.

  3. Flexible: Mean difference statistics can be used to compare the means of two or more groups or conditions, making them a flexible tool for different research questions and study designs.

  4. Widely used: Mean difference statistics are widely used in different fields of research, such as psychology, medicine, and social sciences. This makes it easier for researchers to compare their results with previous studies and to use established methods.

  5. Can be used with different types of data: Mean difference statistics can be used with continuous, ordinal, or categorical data, making them a versatile tool for different types of research questions.

Overall, mean difference statistics have several advantages that make them a valuable tool for researchers and analysts. By quantifying and comparing the differences between groups or conditions, mean difference statistics provide a valuable tool for understanding differences and evaluating

Disadvantages

While mean difference statistics have many advantages, there are also some disadvantages that should be considered. Here are some disadvantages of mean difference statistics:

  1. Assumes normal distribution: Mean difference statistics, such as the t-test, assume that the data is normally distributed. If the data is not normally distributed, the test results may not be reliable.

  2. Sensitive to outliers: Mean difference statistics can be sensitive to outliers, which can have a large impact on the mean. Outliers can skew the mean and lead to inaccurate conclusions.

  3. Assumes equal variances: The t-test assumes that the variances of the two groups are equal. If the variances are not equal, the test results may not be reliable.

  4. Limited to comparing two groups: Mean difference statistics, such as the t-test, can only compare the means of two groups. If there are more than two groups, other statistical tests may be needed.

  5. Limited to comparing means: Mean difference statistics only compare the means of two or more groups, but they do not provide information about other aspects of the data, such as the shape of the distribution or the presence of outliers.

  6. Type I and Type II errors: Like any statistical test, mean difference statistics are subject to Type I and Type II errors, which can lead to incorrect conclusions.

Overall, mean difference statistics have some disadvantages that should be considered when using them for statistical analysis and research. It is important to carefully consider the assumptions of the statistical test, the characteristics of the data, and the limitations of the test, in order to make valid and reliable conclusions.

Case study

Here is an example of a case study that involves the use of mean difference statistics:

A researcher is interested in whether a new training program improves employee productivity. The researcher randomly assigns 50 employees to either a training group or a control group. The training group receives the new training program, while the control group does not receive any additional training. After one month, the researcher measures the productivity of both groups.

The productivity scores are normally distributed, with a mean of 75 for the control group and a mean of 85 for the training group. The standard deviation for both groups is 5.

To determine whether the training program improves productivity, the researcher uses a two-sample t-test. The null hypothesis is that there is no difference in productivity between the training group and the control group, while the alternative hypothesis is that the training program improves productivity.

The calculated t-value for the mean difference is 10, and the p-value is less than .001. Since the p-value is less than .05, the researcher rejects the null hypothesis and concludes that the training program improves productivity.

In this case, the mean difference statistics, specifically the two-sample t-test, allowed the researcher to compare the means of two groups and determine whether the difference was statistically significant. By using mean difference statistics, the researcher was able to evaluate the effectiveness of the training program and make evidence-based decisions about whether to implement it more widely.

 

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Bivariate Multivariate  
3.1 Mean differences
3.2 Correlation and regression
3.3 Chi-square test

4.1 Analysis of variance
4.2 Multivariate analysis of variance
4.3 Analysis of covariance
4.4 Multivariate analysis of Covariance(MANCOVA)

4.6 Multiple regression
4.7 Discriminant function analysis
4.8 Principal component analysis
4.9 Confirmatory factor analysis
4.10 Cluster analysis
4.11 Structural equation modeling

 
     
     
     

Bivariate
3.1 Mean differences
3.2 Correlation and regression
3.3 Chi-square test
Multivariate

4.1 Analysis of variance
4.2 Multivariate analysis of variance
4.3 Analysis of covariance
4.4 Multivariate analysis of Covariance(MANCOVA)

4.6 Multiple regression
4.7 Discriminant function analysis
4.8 Principal component analysis
4.9 Confirmatory factor analysis
4.10 Cluster analysis
4.11 Structural equation modeling

 

 

 

 

 

CALL FOR PARTICIPATION

PSYCHOLOGY RESEARCH UNIT

INDIAN STATISTICAL INSTITUTE, KOLKATA

WORKSHOP ON RATING SCALE DESIGN AND ANALYSIS USING R-STUDIO (CODE:RSD22)

(21-25TH NOVEMBER, 2022, 11-4 PM, HYBRID MODE)

 

OBJECTIVE:

Aim of this workshop is to disseminate knowledge to the faculties and Ph.D fellows about Rating scale design, analysis of data through R-Script. Recently on 4th November, 2022, Psychology Research Unit of the Indian Statistical Institute completed one day training on 'Norm estimation based on Summated Rating scale'. Ph.D. scholars and teaching faculties of different universities and research institutes participated in this certificate course. It has been felt that multivariate statistics play an important role in analysis of  summated rating scale. Psychology Research Unit trains its scholars about application of multivariate statistics in psychological research. There is a dearth of research on it in the doctoral dissertations of psychology due to inadequate understanding about basic assumptions, tools and the know-how about applications of multivariate statistics. Aim of this workshop is to disseminate knowledge about the tools, their assumptions and applications in analysis of summated rating scale based data.

APPLICATIONhttps://docs.google.com/forms/d/e/1FAIpQLSfFQOwNVsk1ep8uSeAgquXOHlCRsgs_...

SHORT FORM: https://forms.gle/pvHsJ7PyZkzRHUZSA

Registration: Rs. 1330 (One Thousand Three hundred thirty only. including GST).

Payment procedure: A/C NO. 0071050000118, IFSC: PUNB0397700. IN FAVOUR OF: INDIAN STATISTICAL INSTITUTE

 

TENTATIVE SCHEDULE OF THE PROGRAME:

 

DATE & TIME

21ST NOVEMBER

MONDAY

 

EVENT & TIME

RESOURCE PERSON

11:00-12:00PM

  1. INAUGAURATION

 

DR D. DUTTA ROY,  HEAD & ASSOCIAT E PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI

RUCHIRA GANGOPADHYAY

(Student Coordinator)

12:00-12:15PM

  1. TEA BREAK 

-

12:15-1:00PM

  1. INTRODUCTION TO RATING SCALE 

DR D. DUTTA ROY,  HEAD & ASSOCIAT E PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI.

1:00-2:00PM

  1. LUNCH BREAK 

-

2:00-4:00 PM

  1. ITEM RESPONSE THEORY

DR. SUMONA DUTTA, ASSISTANT PROFESSOR OF PSYCHOLOGY AT GOVERNMENT GENERAL DEGREE COLLEGE, SINGUR

DATE & TIME

22ND NOVEMBER

TUESDAY

EVENT 

RESOURCE PERSON

11:00-1:00PM

  1. MULTIVARIATE MODELLING IN ITEM ANALYSIS 

DR D. DUTTA ROY,

HEAD & ASSOCIAT E PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI.

1:00-2:00PM

  1. LUNCH 

-

2:00-3:00 PM

  1. INTRODUCTION TO R-STUDIO 

DR. MURSHIDA KHATOON, ASSISTANT PROFESSOR, DEPARTMENT OF PSYCHOLOGY, GEETAM UNIVERSITY, VISAKHAPATTANAM

3:00-4:00 PM

  1. DATA VISUALIZATION AND ASSIGNMENT GIVEN ON ITEM ANALYSIS

 

DR. D. DUTTA ROY,

HEAD & ASSOCIAT E PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI.

DATE & TIME

23RD NOVEMBER

WEDNESDAY

EVENT 

RESOURCE PERSON

11:00-12:00PM

  1. ANALYSIS OF VARIANCE 

DR. SHIVANI SANTOSH

HEAD, DEPARTMENT OF APPLIED PSYCHOLOGY, NEOTIA UNIVERSITY.

12:00-1:00 PM

  1. MULTIPLE REGRESSION 

DR. ANURUPA KUNDU,ASSISTANT PROFESSOR OF PSYCHOLOGY AT ST. XAVIERS UNIVERSITY, KOLKATA.

1:00-2:00 PM

  1. LUNCH BREAK 

 

2:00-3:00 PM

  1. EXPLORATORY & CONFORMATORY FACTOR ANALYSIS 

DR. SUSHMITA CHATTERJEE,ASSISTANT PROFESSOR OF ECONOMICS AT MAHARAJA MANINDRA CHANDRA COLLEGE

3:00-4:00 PM)

  1. STRUCTURE EQUATIONAL MODELLING 

DR. SUSHMITA CHATTERJEE,ASSISTANT PROFESSOR OF ECONOMICS AT MAHARAJA MANINDRA CHANDRA COLLEGE

DATE & TIME

24th NOVEMBER

THURSDAY

EVENT

RESOURCE PERSON

11:00-1:00 PM

  1.  MULTIVARIATE ANALYSIS OF VARIANCE

 

DR. D. DUTTA ROY

HEAD & ASSOCIATE PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI.

1:00-2:00 PM)

  1. LUNCH BREAK

 

-

2:00-4:00 PM)

  1. CLUSTER ANALYSIS

DR. D. DUTTA ROY

HEAD & ASSOCIATE PROFESSOR, PSYCHOLOGY RESEARCH UNIT, ISI.

DATE & TIME

25th NOVEMBER

THURSDAY

EVENT & TIME

RESOURCE PERSON

11:00-1:00 PM

  1. DISCRIMINANT FUNCTIONAL ANALYSIS

 

DR. SUSHMITA MUKHOPADHYAY

ASSISTANT PROFESSOR, INDIAN INSTITUTE OF TECHNOLOGY, KHARAGPUR

1:00-2:00 PM

  1. LUNCH

 

 

2:00-4:00 PM

  1. PPT PRESENTATION BY PARTICIPANTS

 

 

 

 

ORGANIZING COMMITTEE

Dr. Debdulal Dutta Roy, Head, Psychology Research Unit, Indian Statistical Institute, Kolkata.

Dr. Garga Chattopadhyay, Asst. Professor, Psychology Research Unit, Indian Statistical Institute, Kolkata.

Ms. Sabornee Karmakar, Sr. Research Fellow, Psychology Research Unit, Indian Statistical Institute, Kolkata.

Ms.Ruchira Gangopadhyay, (Student co-ordinator), Psychology Trainee, Psychology Research Unit, Indian Statistical Institute, Kolkata.

 

Technical committee

Dr. Debdulal Dutta Roy, Head, Psychology Research Unit, Indian Statistical Institute, Kolkata.

DR SUSHMITA MUKHOPADHYAY, ASSISTANT PROFESSOR OF MANAGEMENT, IIT KHARAGPUR.

DR. SUMONA DUTTA, ASSISTANT PROFESSOR OF PSYCHOLOGY AT GOVERNMENT GENERAL DEGREE COLLEGE, SINGUR.

Ms. MURSHIDA KHATOON, ASSISTANT PROFESSOR OF PSYCHOLOGY AT GEETAM UNIVERSITY.

DR. ANURUPA KUNDU,ASSISTANT PROFESSOR OF PSYCHOLOGY AT ST. XAVIERS UNIVERSITY, KOLKATA..

DR. SUSHMITA CHATTERJEE,ASSISTANT PROFESSOR OF ECONOMICS AT MAHARAJA MANINDRA CHANDRA COLLEGE.

 

Administrative Committee

Mr. Dipak Sarkar, Section Officer.Psychology Research Unit, Indian Statistical Institute, Kolkata.

Mr. Swarup Ghara, Administrative assistant,Psychology Research Unit, Indian Statistical Institute, Kolkata.

Mr. Shyam Shaw, Administrative assistant,Psychology Research Unit, Indian Statistical Institute, Kolkata.

 

 

 

 

 

Please accept and send me the confirmation by 9th November, 2022.

 

Please write your name, contact details and probable slot on the following table.

 

TOPICS RESOURCE PERSONS Contact number Designation  Affiliated Institute Address  POSSIBLE DATES & TIME SLOT
Analysis of variance and Co-variance            
Multiple regression            
Discriminant function analysis            
Exploratory and Confirmatory factor analysis DR SUSMITA CHATTERJEE  9831813312 ASSISTANT PROFESSOR MAHARAJA MANINDRA CHANDRA COLLEGE 20 RAMKANTO BOSE STREET,SHYAMBAZAR, KOLKATA 700003 23RD NOVEMBER 
2.30 PMTO 3.30 PM
Cluster analysis DR DEBDULAL DUTTA ROY 9830010547 HEAD & ASSOCIATE PROFESSOR INDIAN STATISTICAL INSTITUTE 203, BARACKPORE TRUNK ROAD, DUNLOP, KOLKATA-700108

22ND NOVEMBER

11-1 PM

Structural equation modeling

DR SUSMITA CHATTERJEE  9831813312 ASSISTANT PROFESSOR MAHARAJA MANINDRA CHANDRA COLLEGE 20 RAMKANTO BOSE STREET,SHYAMBAZAR, KOLKATA 700003 23RD NOVEMBER 
3.45 PMTO 4.45 PM
Item-Response Theory DR SUMONA DATTA 9432175390 ASSISTANT PROFESSOR GOVERNMENT GENERAL DEGREE COLLEGE, SINGUR SINGUR WEST BENGAL 712409

21ST NOVEMBER

11 AM- 1 PM / 2 PM-4 PM

             
             

With best wishes,

 

MULTIVARIATE STATISTICS IN ANALYSIS OF SUMMATED RATING SCALE

 

 

  1. Multivariate

4.1 Analysis of variance

4.2 Analysis of covariance

4.3 Multivariate analysis of variance

4.4 Multiple regression

4.5 Discriminant function analysis

4.6 Principal component analysis

4.7 Confirmatory factor analysis

4.7 Cluster analysis

4.8 Structural equation modeling

 

 

 

 

 

 

(PSYNORM22)

ONE DAY ONLINE TRAINING ON NORM ESTIMATION

FOR SUMMATED RATING SCALE  USING R-STUDIO

NOVEMBER 4, 2022

Psychology Research Unit,
INDIAN STATISTICAL INSTITUTE

 

 

Report

Psychology Research Unit has successfully completed one online training on norm estimation for summated rating scale using R-Studio dated 4th November, 2022. Dr Sumona Datta, ex-research fellow of the unit, currently assistant professor of Department of Psychology, Government College, Singur, highlighted importance of Item response theory, norm estimation, and R-studio in the inaugural ceremony of the training. Ms. Ruchira Gangopadhyay of the unit assisted in teaching the course. Ph.D. fellows from renowned institutes and universities joined the program. At the valedictory session, we satisfied their Questions. All of them expressed satisfaction and they assured for the next training program with registration fees.

 

Tentative Programme Schedule

ONLINE REGISTRATION

Status of Registration

Office bearers

BACKGROUND

Summated Rating scale

Summated Rating scale is widely used instrument for classification of people based on norm. Aim of the present training is to disseminate knowledge about norm estimation. Norm estimation in summated rating scale requires few steps - item analysis, standard score computation and conversion to new score like T-score and finally classification of people based on the new classification categories. This training is useful for researchers interested in psychological test construction. Psychology Research Unit has designed many scales in research on educational, clinical, personality, career counselling, cognitive, finance and business psychology.

TOPICS:

1. Item analysis

2. Standard score determination.

3. T-score

4. Classification matrix

 

 

 

 

TENTATIVE PROGRAM SCHEDULE

Venue: Seminar room, Psychology Research Unit,7th floor, P.J.Auditorium,Indian Statistical Institute, 203,B.T.Road, Kolkata-700108.

4.10.22 Event
11-11:30 Inauguration
Invocation,   Speech,    Self-introduction
11:30-1:30 Introduction to Norm
 
1:30-2 Lunch time(self-arranged)
2-3 Basic knowledge about R script -Item frame,    item extraction,    Item transformation,       Outlier detection
-Dr. D. Dutta Roy,Psychology Research Unit, Indian Statistical Institute, Kolkata.
3-4 Introduction to Characteristics of good psychological test
- Ms. Sabornee Karmakar, Senior Research Fellow,Psychology Research Unit, Indian Statistical Institute, Kolkata.
22.11.19(Wednesday) Event
11-1:30 Item analysis - Item difficulty,       Item discrimination
- Dr. Atanu Kumar Dogra, Assistant Professor, Department of Psychology, University of Calcutta.
1:30-2 Lunch time(self-arranged)
2-4 Item shortening,    Item validity
-Ms.Sumona Datta, Assistant Professor, Adamas University, Kolkata

Venue : Psychology Research Unit, Indian Statistical Institute, Kolkata.

Date and time : 21-22 nd November, 2019, 11 - 4PM.

 :20Seats