Speaker 1: Okay, so in the quantitative data analysis, one of the most important factors is to understand the level of your analysis. This is determined by the number of variables that you have in your research. And depending on that, the level of analysis is divided into three different analysis techniques. Univariate analysis, bivariate analysis and multivariate analysis. Let's go through each of these one by one with examples. Univariate analysis is the most basic form of a statistical data analysis technique. So when the data contains only one variable and doesn't deal with a cause or effect relationship with then a univariate analysis technique is used. For instance, in a survey of a classroom, the researcher may be looking to count the number of boys and girls. So in this instance, the data would simply reflect the number, which is only a single variable and the quantity of the boys and girls. So the key objective of univariate analysis is to simply describe the data to find patterns within the data. And that you can do by simply using like mean, median, mode, dispersion, variance. The ways univariate analysis is conducted could be most basic form of descriptive analysis techniques like in a frequency distribution tables, frequency polygons, histograms, using pie charts or bar charts, etc. So bivariate analysis is slightly more analytic than univariate analysis. When the data set contains two variables and the researchers aim to undertake comparison between the two data set, then bivariate analysis is the right type of analysis to undertake. For example, in a survey of a classroom, the researcher may be looking to analyze the ratio of students who scored above 85% corresponding to their genders. So in this case, there are two variables, gender, that is male or female, which is one independent variable and the result, which is the dependent variable. So a bivariate analysis will measure the correlation between the two variables. To undertake bivariate analysis, there are popular statistical techniques like correlation coefficients, regression analysis, and regression analysis could also be like linear regression, simple regression, and there are a whole heap of different other regression pattern or style that could be used to undertake a bivariate analysis. And lastly, the multivariate analysis, which is the most complex form of statistical analysis and used only when there are more than two variables in the data set. So here's an example, a doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects, for example, how many ounces of red meat, fish, dairy products, and chocolates consumed per week. Now she wants to investigate the relationship between the three measures of health and eating habits. So in this instance, a multivariate analysis would be required to understand the relationship of each variable with each other. As you can see, there are multiple variables involved in this research question. So commonly used multivariate analysis techniques include factor analysis, cluster analysis, variance analysis, multidimensional scaling, redundancy analysis, and a whole heap of other statistical techniques. So I hope you have understood the selection of the quantitative data analysis is dependent on a range of different factors, and the number of variables is only one of them. So once you know how many variables have you got in your research question or the study in consideration, then you'll be able to select whether you want to go for a univariate, bivariate, or a multivariate analysis.