Dear Readers,

I am writing this short series of steps that I have formulated to help those who are in a state of confusion regarding the "Analysis" of their research. As a professional from the industry, I encountered several difficulties in understanding the research. I will post that discussion separately. Please bear in mind this information will apply to the "Chapter 4- Analysis" section of your research and the same principle will be applied at the Masters, MPhil, and PhD level. If any expert can help me in further refining the concepts, I would very much appreciate it. I do understand the information might be too much to process at first, so give it a read several times, I guarantee you will find most of the answers. If you are confused about the concept of research i will post another refresher for clarity- I write this because I have been a victim of the same confusion.

P.S. A word of warning, DO NOT confuse "Model" with "Method", I made the same mistake. Model is the equation you formulate in Chapter 3 (X= Alpha + Beta x X1 + error term), Method is what we (some of us) stupidly call models (OLS, Fixed effects, Random effects, etc).

Steps for Analysis (What to do When confused):

1. Identify Data Type (Cross-Sectional, Time Series, Panel)

2. Run Descriptive Statistics

3. Check for Multicollinearity (Tested by correlation among variables or VIF tests) _(Books ridiculously quote application of Heteroscedasticity and Autocorrelation but researches have given little importance and only used these items to defend their methodology so if you use then good, if not that it’s not the end of the world- BUT Multicollinearity is a MUST).

4. Identify if there is any Trend in Data Using Unit Root Tests- This will decide the next steps

a) If there is no Trend in Data- We can Use OLS- Next steps are as follows for point A.

i. Run OLS and check for Heteroscedasticity- If Heteroscedasticity is detected OLS is not applicable- We should move towards the application of Fixed Effects and Random Effects and subsequently decide which is better from Hausman Test. Researchers point out the possible issue of Heterogeneity which is sometimes used as an acid test for choosing between FE and RE. This usually states that if your data is Homogenous (i.e. You are working on a single industry- Fixed Effects is appropriate this is because the only Heterogeneity is a with-in the sample (Firm Size, Capital Structures, etc., i.e. “Beta (Ungeared-CAPM-Industry Risk and NOT the beta we use in research ” is similar/.) If the data is heterogeneous (say an index that includes several companies from different industries this contributed to unidentified Heterogeneity and Random Effects Model is more appropriate). However, you still have to check the "Rho Values" and correlation for presence of endogeneity which can still reject the choice of RE/FE model.

b). If Trend Is detected (Which will obviously be there for Time Series and Panel Data)- than Regression is not applicable- (Do not get confused if you still see researches with this method because the method is just a choice-)In case of Trend, The following steps are to be followed:

a. Use log of variables or perform Differencing (1st observation minus second and so on) - (1st Order, 2nd Order, etc.) and identify the “Level” at which variables become stationary i.e. Trend is removed (Eventually after some levels, data automatically becomes stationary so need to get confused).

If some variables are stationary at “Level” (Initially) - while some become stationary at different levels (1st Order Differencing, 2nd Order...)- then a mixed approach is used. In this instance the following models are applicable: 1. ARDL 2. VAR 3. GMM/LSDV

GMM (Is Applicable when: 1. The panel is Dynamic 2. The panel is Short (Literature states if N