REPORT of NATIONAL FACULTY DEVELOPMENT PROGRAM On
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REPORT OF NATIONAL FACULTY DEVELOPMENT PROGRAM On RESEARCH METHODOLOGY AND TEACHING PEDAGOGY National Faculty Development Programme on ‘Research Methodology and Teaching Pedagogy' organised by Teaching Learning Centre, Ramanujan College, University of Delhi in association with Indian Accounting Association, NCR chapter and Department of Financial Studies, University of Delhi at South Campus, Conference Hall started with an inaugural session on April 30, 2019. The inaugural session marked the presence of Padamshri Prof. Dinesh Singh, chancellor, K.R.Mangalam University, former vice-chancellor, University of Delhi as a Chief Guest. Prof. I. M. Pandey, Director General Vivekanand Institute of Professional Studies as a keynote speaker, Prof. Sanjay Sehgal, Head and Dean, Department of Financial Studies, University of Delhi, C. A. (Dr.) Sanjeev Singhal, Partner, Ernst & Young, Dr. S. P. Aggarwal, Principal, Ramanujan College, University of Delhi, Dr. J. L. Gupta, Member Executive Council, University of Delhi, Prof. C. P. Gupta, Department of Financial Studies, University of Delhi, Dr. Rajesh Giri, Principal, Rajdhani College, University of Delhi, Dr. Rakesh Gupta, Principal, Ram Lal Anand College, University of Delhi and Dr. Gyantosh Jha, Principal, ARSD college. The distinguished personalities enlightened the participants with their knowledge and experience. The FDP Is being attended by 80 participants from all parts of the country like Jammu & Kashmir, Jharkhand, Haryana,U.P., Punjab, Rajasthan to mention few. Prof. Dinesh Singh focused on the rich past of India, which we seemed to have forgotten. Prof. Singh reminded the audience that Calculas, Trignometry and Nautical Miles were invented in India. Much before Newton, the Indian system was well equipped with ships across oceans. Also, knowledge system in India was practical and in tune to the needs of the society. Prof. Singh was of the opinion that there must be an intellectual thrust to identify essential variables and practice-based teaching. Prof. I. M. Pandey, our keynote speaker stressed on the research components and emphasised that there must be knowledge creation. Prof. Pandey felt that teaching should be more unstructured and based on the needs of the industry. Prof. Sanjay Sehgal observed that we must stress upon research which should be turned into papers and published in high impact journals. Prof. Sehgal lamented the fact that we were lagging behind in terms of the international publications. Prof. Sehgal felt the need to look for critical ideas and used tools as means to achieve them. C. A. (Dr.) Sanjeev Singhal was also of the opinion that research work should be idea oriented and application based. The outcome must be shared with the industry. Lastly, Dr. S. P. Aggarwal proposed a vote of thanks and also informed the audience about the successful programmes conducted (26 FDPs, 2 one month Induction Training Programmes, trained 1500 people’s from all over India by 350 resource persons) in the past by Teaching Learning Centre, Ramanujan College, University of Delhi. Dr. Aggarwal thanked the distinguish guests, organisers and participants. Session 2 of Day 1 began with the insightful and motivational words by Prof. C.P. Gupta, Department of Financial Studies, South Campus, University of Delhi. The session centered around basics of research pedagogy. The relevance of idea generation in research was discussed. This was followed by the discussion on various components of research design, viz., idea generation, research design, measuring and scaling data, constructing and pre-testing the questionnaire, sampling process and sample size and then developing a plan of data analysis. Causal research design was also discussed in detail. For any research, background serves as the cause and behaviour describes the effect in any research model. Topics like different levels of data, drawing results from the data collected, hypothesis formulation and testing were also a part of the session. The essence of research is to process Data for delivering necessary inferences about population. The session was followed by the lunch and later on continues with the hands- on practice of Analysis of Variance on SPSS v22. Professor C. P. Gupta, an expert in research and a well-known academician, gave a wonderful lecture on Causal Research in the third session of the FDP. He emphasized on the ANOVA technique of data analysis. This technique is very useful in finding out the cause and effect relationship between different variables. He also told about the assumptions that we make during this test and how can we check that data is true to the assumptions. The session deeply covered the topic. Hands-on training was being given as the participants performed the tests used in research on SPSS, a data analysis software. A lot of case studies were also discussed. Professor explained the uses of the research technique and where can we implement it. He helped the participants to the understand the concept to the core. All the participants were actively learning which is the sole motive of the FDP. Day 2, Session 1 commenced with a prayer song. Before moving on the new topic, a quick recap of Day 1 was done by Prof. C.P. Gupta, Department of Financial Studies, South Campus, University of Delhi. The doubts of day 1 topics were also clarified by Prof. Gupta. The session revolved around two-way analysis of variance. It includes one dependent variable and two categorical independent variables. It helps to understand whether variation due to each factor is significant or not. Cases for the same were taken up in order to gain better understanding of the topic. Next, two-way analysis of variance without replication was discussed. This is used to reduce the impact of a blocking variable. Lastly, hands on SPSS training was done. Post-tea, Prof. Gupta started with the discussion of Two-Way Analysis of Variance with Replication. This technique helps in understanding the impact of the two independent variables on the dependent variable and also the impact of the interactions of both the Independent Variable on the Dependent Variable. First of all, theory was discussed in detail with examples and graphs like boxplot. After that, hands on SPSS training was imparted, where full factorial design was explained. Prof. Gupta explained the difference between Multiple Independent variables and Multi-variate. He also discussed Analysis of co-variance (ANCOVA) in this session. ANCOVA is a combination of the Regression Model and Analysis of Variance Model. This technique removes extraneous variation in the dependent variable that is associated with the covariate. Covariate is a scale level variable that is covariating with the dependent variable. It helps to reduce within-group error variance and elimination of confounds. He also explained the concept of ‘multicollinearity’. A few case studies were reviewed and hands on SPSS training was provided before concluding the session. Professor C.P. Gupta, after lunch, started with another technique of data analysis i.e. Factor Analysis. Its’ motive is to identify the constructs of the problem and to reduce the dimensionality of the problem. He also told that there are two types of factor analysis, i.e. Exploratory factor analysis and Confirmatory factor analysis. He told that there should be correlation between factors in or within a group and there should not be any correlation between the groups. He also focused on the concepts of Factor loading, Eigen values and Communality. He explained the process of factor analysis in detail. Overall, the session was very informational and helpful to the participants. Day 3, Session 1 commenced with motivational music and a video on positive attitude. Professor C.P. Gupta focused on challenges involved in research design and how one can overcome these challenges by concept of regression. It is concerned with study of dependence of one variable on one or more variables with the view of predicting average value of the former. Basic assumptions of the regression model were discussed. He further explained the classical regression model also covering best methods to calculate ‘α’ alpha (intercept) and ‘β’ beta (slope). He also explained how the result of these models can be used to interpret a research problem. Lastly hands on training on Excel was provided before concluding the session. In the session after tea, Professor C.P. Gupta helped us learn a new software 'E-views' which made studying regression analysis rather convenient. We further moved onto discussing the multiple regression models. Multiple linear regression attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data .When we went deeper into regression analysis, Prof. Gupta explained the concept of control variable and its difference from controlled variable (alternatively called independent variable).We have to filter out the effect of such control variable to establish the relation between other variables. Another challenge we faced while studying regression model was with regard to Omitted variable bias which occurs when a statistical model leaves out one or more relevant variables resulting in the model attributing the effect of the missing variables to the estimated effects of the included variables. Furthermore, hands on training was conducted on E- views. After a hearty lunch, the session continued with Prof. C.P Gupta wherein he began with emphasizing on the virtues of patience; a much-needed quality amongst researchers. Reiterating the assumptions for classical regression model that had been covered in the pre-lunch session, he continued the session with detailed explanation of various kinds of violations of assumptions, their detection and their corresponding solutions through the use of data analytical tools such as E-views and SPSS. Prof. Gupta covered multiple examples and case studies entailing a check list where we began with building a model, running univariate statistics, testing assumptions, checking for and resolving data issues (such as the issues of multicollinearity, problems of autocorrelation, etc.) & finally interpreting the results.