 Hello all, in this video we are going to see about overview of quantitative research. Before going to this quantitative research, most of the health research deals with this quantitative research only. So I am going to ask you five simple questions and through that simple questions I am going to make you understand about what is quantitative research. So my first question is what is the type of variable you are dealing with? So the type of variables can be broadly classified into qualitative or otherwise called as categorical. Most commonly it is categorical, the quantitative or the numerical. So we use here of henceforth we use categorical and numerical. Again we have subsets under the categorical variable and numerical variable which is not required at this point of time for your analysis. So first question number one is over. Number two, you have to ask yourself whether you are going to represent the data or visualize the data. So you are going to deal with two types of statistics that is descriptive statistics and inferential statistics. So under descriptive statistics only we have this representation issue. So under this representation based on the type of the variable from our first question we divide it into categorical variable and numerical variable. The categorical variable can be summarized or represented using tables and visualized using pie charts or bar charts. The basic difference between this representation and visualization is in representation you give all the minute details of the numbers but in visualization you just give the impact of the data whatever you have. So the data so you have to keep in mind that what you are going to do is you have to get some information out of the existing data that you have to keep in your mind always. When we are dealing with the numerical data we have when we are representing it we will represent it in the terms of mean and standard deviation and other measures of central tendency and measures of dispersion but when we visualize it we visualize it with histogram and when it is normally distributed we visualize it with histogram otherwise we use median and interquartile range and box. Now the third question under numerical variable is whether the data or the numerical data follows normal distribution or not. So this is your third question. For this third question if your answer is yes it is normally distributed then all your statistical test which you use will be parametric test and if the data is not normally distributed then you have to go for non-parametric test. So when you are studying association we have outcome variable and exposure variable or we can call it as dependent variable and independent variable. We always do association between one variable and another variable. If that variable is a category versus category then we use chi-square test or vicious exact test based on the needs. If one variable is a category with two outcomes and the other variable is a numerical variable we have t-test. These two are the simple test which we use in our research analysis but there are plenty of test under different circumstances that all have been given in this chart. You can pass and you can see it. Before summing up I want to make you clear when you are writing the results that you should always keep in mind that your data should answer your objectives. You should not deviate from your study objective. Secondly you have to rightly represent the data and rightly visualize the data. Wherever you are using this descriptive statistics wherever inferential statistics necessary you have to apply. Then you have to choose the right choice of test based on the type of variable and the type of parametric nature of the data. Then you need to produce a self-explanatory figures and tables that is nobody should not inquire about the content. Nobody should not inquire about further inquire about the content. Then finally you have to understand there are two types of significance. Statistical significance is not always present. So when you are comparing a number with 100 and 101 if it is a blood pressure it is not a huge difference but if it is a fever temperature then it is a huge difference is there. So always clinical significance is different, statistical significance is different. You have to compare these two and get significance out of the data. To sum up you have to understand five basic concepts to do quantitative research that is number one the type of variables, number two whether you are going to do descriptive statistics or inferential statistics, number three whether you are going to represent the data or visualize the data, then four how the data is distributed whether it is normal or not normal based on that you have to apply parametric and non-parametric test and the first question is your test of significance. Thank you for watching this video. If you would like this video please share it to your friends. Thank you very much.