FACULTY DEVELOPMENT PROGRAMMES (FDP)

We at IARDO believe in constant upgradation and renewal of knowledge and skills to make one well acquainted with the dynamic environment. The following are certain sample programs that give a glimpse of what all we can offer in an FDP to our prospective audience:

SAMPLE PROGRAM SCHEDULE- PROPOSAL FOR TWO DAYS

Day

Topics to be Covered

Day 1

SPSS Module

Inauguration followed by participants’ interaction
Identification of research problem, Formulating Hypothesis
Research and Sampling design
Hypothesis testing and p value, Point and interval estimation
Data cleaning and imputation
Univariate data analysis- central tendency, dispersion and distribution analysis, spatial statistics
One sample t test, independent sample t test, repeated samples t test
One way ANOVA
Day 2

SPSS Module

Correlation analysis, partial correlation, non-parametric correlation
Bivariate regression models
Multiple Regression Models, R, R-Square, Adjusted RSquare, Explained Variance and Unexplained Variance, FRatio, Unstandardized and Standardized Beta
Assumptions of Multiple Regression Model: Heteroscedasticity, Autocorrelation and Multicollinearity
Dummy Regression Models, ANOVA, ANCOVA
Logistic Regression

 

SAMPLE PROGRAM SCHEDULE- PROPOSAL FOR THREE DAYS

Day

Topics to be Covered

Day 1

SPSS

Inauguration followed by participants’ interaction
Identification of research problem, Formulating Hypothesis
Research and Sampling design
Hypothesis testing and p value, Point and interval estimation
Data cleaning and imputation
Univariate data analysis- central tendency, dispersion and distribution analysis, spatial statistics
One sample t test, independent sample t test, repeated samples t test
One way ANOVA
Day 2

SPSS

Correlation analysis, partial correlation, non-parametric correlation
Bivariate regression models
Multiple Regression Models, R, R-Square, Adjusted RSquare, Explained Variance and Unexplained Variance, FRatio, Unstandardized and Standardized Beta
Assumptions of Multiple Regression Model: Heteroscedasticity, Autocorrelation and Multicollinearity
Dummy Regression Models, ANOVA, ANCOVA
Logistic Regression
 

 

Day 3

R-Analytics

Introduction to R
Open and Save data in R
Simple Graphs in R
Manipulating Data in R
Statistical tests in R
Packages in R, Worked example analysis in R Analytics

 

SAMPLE PROGRAM SCHEDULE- PROPOSAL FOR ONE WEEK (FIVE DAYS)

Day

Topics to be Covered

Day 1

SPSS

Inauguration followed by participants’ interaction
Identification of research problem, Formulating Hypothesis
Research and Sampling design
Hypothesis testing and p value, Point and interval estimation
Data cleaning and imputation
Univariate data analysis- central tendency, dispersion and distribution analysis, spatial statistics
One sample t test, independent sample t test, repeated samples t test
One way ANOVA
Day 2

SPSS

Correlation analysis, partial correlation, non-parametric correlation
Bivariate regression models
Multiple Regression Models, R, R-Square, Adjusted RSquare, Explained Variance and Unexplained Variance, FRatio, Unstandardized and Standardized Beta
Assumptions of Multiple Regression Model: Heteroscedasticity, Autocorrelation and Multicollinearity
Dummy Regression Models, ANOVA, ANCOVA
Logistic Regression
Exploratory Factor Analysis
Cluster Analysis
MDS, MDS with Discriminant Analysis
 

 

 

 

Day 3

Structural Equation Modeling

Introduction to AMOS, Practical Sessions on AMOS
Validity & Reliability, Model-Fit
Exploratory Factor Analysis (EFA), Practicalsessions on EFA
Basics Confirmatory Factor Analysis, Practical Sessions on CFA (in AMOS), various statistics of CFA
Zero-order, First-order, Second-order CFA, Pre-requisites of SEM: Invariance test, Common Method Biasness, Impute
Higher order effects: Moderation, Mediation, Interaction & Control variables
SEM and Path Analysis
 

Day 4

AHP

Introduction to AHP
Consistency Ratio
Eigenvalues & Practical Session on AHP
Uses & Applications of AHP
 

 

Day 5

R Analytics

Introduction to R
Open and Save data in R
Simple Graphs in R
Manipulating Data in R
Statistical tests in R
Packages in R, Worked example analysis in R Analytics

 Please Note:

We also have professionals with knowledge on unique research techniques like

Interpretive Structural Modeling

  • The course modules are flexible and can be modified as per requirements.
  • The practical exercises will provide a hands-on experience to the participants who will be able to use these techniques on their own.
  • The participation fee structure is very nominal and these workshops are very affordable for one and all.

We will conduct these workshops in collaboration with the research partners of IARDO at your campus and the participants will be certified for the same.

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