 So today we will be seeing the types of variables in research, there are many types of variables so the basic point commonly what we are seeing is dependent and independent variable, so the same variables will be named differently in different categories based on the context so the common seen examples or some common seen categories are dependent and independent variable so it should be in a very simple manner if we see an article we see the table the variables on the x axis will be dependent variable and variables on the y axis will be independent variables and to explain it variable which is dependent on other variables dependent and they are being as independent independent variable suppose patients general anxiety which in has general anxiety patient we are setting the general anxiety of the factors, so the general anxiety is dependent variable, factors like patients age, gender, social performance, status of the patient patients previous, gender reset, such factors will be independent variable so this general anxiety is dependent on these four independent variables so independent variable or it will be the course and dependent variable will be the effect so this course all the course is written to the effect of it, this is general anxiety so this relationship what we are studying on this is dependent variable is also known as outcome variable, dependent variable is also known as explanatory variables, so also independent variable of course the effects are dependent variable or the result so this is an example that uter is asking 100 students to complete a maths test so this maths test mark will be dependent variable which is depending on the revision time and deadlines, so these two will be independent variable and output that is test mark will be dependent variable, so one example the study of teacher-student classroom interaction in different levels of schooling, the comparative study of professional attitudes of secondary school teachers by gender, so the first one example level of schooling, primary, upper primary, secondary and junior college they will become the independent variable and dependent variable is a score on a classroom observation, similarly second case center that is comparative study of professional attitudes of school teacher by gender, so here independent variable will be gender, dependent variable will be school, so usually this dependent variable always be very much quantity variable, it may have been scores like DTE, sugar level, like pressure level, stress things are in lines IT like status, individual status, gender theory status, stress things will be always dependent variable and the factors which is affecting all these are independent variables, so quantitative and qualitative, the quantitative variables are not exist and on a continuum that runs from low to high, so that will be just like those scores, high weight, weight, sugar level, pressure level, all these are quantitative variables, qualitative we just cannot express in words just like gender, religion, or iconography, quantitative variables, so they are actually qualitative, quantitative, qualitative variables, quantitative are which are in quantity like high weight, sugar level, quantitative and religion, iconography, qualitative variables, so commonly it is required in this, qualitative and quantitative and this classification is very important because it depends on this classification is important for the test of significance, which test needs to be done on additional data, so this qualitative and quantitative data it is important, test of significance you notice depending on normality if it is normally distributed we do certain test and if it is not we do certain test and depending upon the variable also we do classification for test of significance, so qualitative and categorical variables they are nominal and ordinal, quantitative, they are interval and ratio, so we will see what is what, so nominal, ordinal, interval and ratio, nominal is nothing but just names, variable which is names, so variable is nothing but something which changes in a group of people, so if nothing changes between each person in a group of people we cannot say that it is variable, so variable is a quality which changes within a group of people, so nominal scale is just by names, the common example is gender, so it is like male and female, we cannot say that which is better and which is bad, there is no comparison can be done between the categories, what is your political preferences independent democratic within where we live suburbs city or town, we just cannot distinguish the categories, so that is nominal and qualitative variables, the second one is ordinal scale, this is also qualitative but the difference is here you can compare it because it is placed in order, first second three four, the first two will be different from the fifth, you can do comparison between the categories, like unhappy very happy, so happy and very happy are related, this positive side and one is negative side, so ordinal side is just like our like a scale, scale mean should we apply for a question in this like a scale, perfectly happy very happy very happy, so things are ordinal scale, there is a distinct difference between very happy and very unhappy, one is extreme positive and one is extreme negative, we have a nominal scale, we just cannot do that distinction between the categories of variables, so this is another example like zero is education, classification is less than high school, one is high school, then school is college degree college, so you can say that five is always better than four, four is always better than three into one and zero, so this order is zero, so that is why we want to order ordinal variable, other one is nominal variable, both are categorical or qualitative variable, so we are going to see in less of significance this categorical variable, the categorical variable is always calculated or tested by using chi-square test, so the categorical variable nominal ordinal data will be dealt with chi-square test, so the next one is interval scale and ratio scale, so the interval scale and ratio scale are continuous data, so it's just like our high heat weight, blood pressure, temperature, such things and it will be dealt with other test, it's commonly the test that continuous test based on the normality, it is a test, another test, Wilcox syndrome test, so this is nothing but memory scale with order of variable is known with the difference between these variables, so there will be order of variables is known as well as the difference between these variables, so I will explain you one very simple one, so on interval scale the common example is interval and ratio scale, we can take a temperature, temperature is an interval scale, because in ratio scale the thing is the ratio and interval scale, in ratio scale there will be value of true zero, okay, true zero means, if we say weight is zero means that is the absence of weight is no weight, height is zero centimeter means that there is the absence of it, it's the value says that exact zero, okay, so it is calculated as you mean that the variables have an option for zero that is ratio scale, so if you say that 10 centimeter, 5 centimeter and 10 centimeter, so you can say that 10 centimeter is double that of 5 centimeter, because you have an option of zero, if 6 and 3 you can say that 6 is double of 3, so there is a difference value zero, with the problem with interval scale, the problem with interval scale there is no such option of zero, okay, so the value can go to negative, so common example is temperature, the temperature will go to negative, because in Kelvin scale minus, minus values are possible minus 273, that's the value, so the most common example in temperature, if we put a air condition in room and temperature inside 16 and the outside the temperature is 32 degrees Celsius, we cannot say that the temperature outside is double or as the temperature inside, we can just say that temperature outside is 16 degrees more than temperature inside, because there is no option of absolute zero, so zero degree Celsius doesn't mean that absence of heat, okay, so the temperature can go to negative and also minus 10 degree minus 5 degree minus 20 degree, we have seen temperature, so zero degree doesn't mean that absence of heat, so interval scale there is no option of zero, both are continuous scale when numbers will be considered, but in ratio scale there is an option of zero, so we can compare the scores, okay, so in interval scale there is no option of zero, the value can go to negative side, so the application is little different, so both interval and ratio scale are continuous scale or quantitative data, more continuous variable, so it can be used in statistical testing by using detailed analysis based on normality, so this is ratio scale where ratio scale is having absolute zero, so absolute zero, so that is ratio scale, the question poll under, what is your doctor's current, 8, 5 feet, 5 feet 1 inch, 1.6 feet, it is also considered in our categories that is ordinal data, ratio scale and ordinal data, same as here, it is also ratio scale and ordinal data, so some data will fall into both the places, because these categories are there, less than 50 categories, less than 70 categories, so it will become ordinal data because going in an order, at the same time this is ratio scale also, there is an absolute zero option, okay, getting my point, okay, so this is what continuous variable and there is something called as extraneous variable, also extraneous variable or third variable, okay, so suppose we are taking two variables, one is dependent and one is independent, so all the other variables are becoming third variables which are also known as confounding variable, concealing variable, so which can affect both the independent and dependent variable, that it means both outcome and post, so such variables, effects are very important in research, so this confounding or extraneous variable, I have to be dealt with very cautiously, otherwise our results will be totally flawed, so extraneous variables or confounding or third variables are important, so main variable dependent and dependent and variable, because sometimes we study the eating habit and blood pressure of a person, so eating habit is independent and blood pressure is dependent variable, and we are doing a study and we are finding out some oxidation, the oxidation will be quite different than the actual one, because blood pressure is also affected, also influenced by patient's family stress, patient's sedentary lifestyle, patients' systemic diseases and it is also influenced by patient's lifestyle, patient's sedentary lifestyle more than to be eating other gen foods or more than to be on smoking, so some factors which influences both independent and dependent variables, so when we need to obtain a very clear picture we always have to include the extraneous or third variable or the confounding variable, so extraneous variable also known as third variable or confounding variable which is part which will be part of the study, so we need to highlight it, that can be done using regression analysis, it is also known as confounding variable because it actually conceals actual effect, because there will be effect of blood pressure by the patient's eating habit, but there are many factors which affects both eating habit and blood pressure, so that also should be considered, so that is all about types of variable, the commonly seen variables are dependent and independent variables, so dependent which is dependent on other variables, and then we have qualitative and quantitative variables that is continuous and continuous and this one, continuous and categorical, so qualitative will be always categorical, some are not written here, so qualitative is categorical, so normal and normal are two categories, quantitative is continuous, interval and ratio, ratio of absolute zero is possible, the 5 centimeter and tensi-neutral scenario, tensi-n-trims, at least halfs, double halfs, tensi-n-trims, interval, scale is absolute zero, general possibility, select temperature, 32 degree Celsius is not double or past 16 degree Celsius, we can say just 16 degree Celsius mode, negative temperature is possible here in ratio, zero means absence of it, absence of like that, zero option is possible there, so these are the common examples, linear scale and ordinary scale, interval and ratio are important for statistic testing and we will have last one, the extraneous or third variable, the confounding variable which is, which will be part of other, so which will be part of the study most commonly, what will be the written regression analysis, so there are many types of classification or many different names of it, you can always keep in mind that the common thing is dependent and independent variable, okay, this hypothesis testing the results will be delton dependent and independent variable, and variable which is not involved directly to the study of third variable or extraneous of confounding variables, so another classification is whether it is quantitative or qualitative, quantitative means ratio of total scale, qualitative means, ordinal or nominal scale, that is the most common classification, that is better, thank you.