Univariate and Multivariate Analyses

Biostatistics
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Univariate and Multivariate Analyses
Univariate and multivariate are two statistical analysis approaches. Univariate analysis is the simplest form of statistical analysis technique that involves the analysis of a single variable (National Science Digital Library's Computation Science Education Research Desk, 2016). It does not deal with causes or relationships because it contains only one variable. The main purpose of this approach is to describe the data and find the existing patterns. In this case, univariate analysis is used at the initial stages by analyzing the existing data. Examples of a variable in univariate analysis are age and height (LaMorte & Sullivan, 2016). A univariate analysis neither examines these two variables simultaneously nor looks at their relationship. Some ways researchers can describe univariate data patterns include calculating mean, mode, median, variance, range, quartiles, and standard deviation. Additional ways include frequency distribution tables, pie charts, histograms, bar graphs, and frequency polygons.
In contrast, a multivariate is the complex form of statistical analysis that involves the analysis of more than two variables in the data set. The technique is used across numerous dimensions while considering the effects of all variables on the responses of interest. It is especially useful when working with correlated variables (Barrat & Kirwan, 2009). Unlike a univariate analysis, multivariate is a far more complex method because it takes into account relationships, interdependencies and correlations. This highlights and explains relationships between variables (Barrat & Kirwan, 2009). It is used for inferential study since multiple variables can be indefinite or estimated. Some multivariate analysis methods include additive tree, cluster analysis, redundancy analysis, multiple regression analysis, partial least square regression, Multivariate analysis of variance (MANOVA), multidimensional scanning, generalized procrustean analysis, and canonical correlation analysis (MarinStatsLectures, 2018). Whereas a univariate analysis aims to describe data, multivariate analysis is aimed towards hypothesis testing and explanations.
Dependent versus Independent Variables
A dependent variable is a variable that relies on other factors being measured, usually the independent variable (National Library of Medicine, n.d). It is the variable being measured and tested in a scientific study. This variable is subject to changes due to experimental manipulation of an independent variable. That is to say, a change in an independent variable causes a change in the dependent variable. It is the presumed effect. An independent variable is a stable variable that is unaffected by other factors being measured (Wunsch, 2007). Its variation does not depend on another variable. It is the presumed cause.
Key differences between the two variables are as follows. First, an independent variable does not rely on another variable, whereas a dependent variable depends on another variable in the scope of the experiment (LaMorte & Sullivan, 2016). In reality, the variables depend on other factors. The difference is the factor that determines their reliance. For instance, a dependent variable depends on an independent variable, whereas an independent variable relies on external manipulation.
Second, independent variables do not need any intricate observation and mathematical procedures. In contrast, researchers obtain the dependent variables from longitudinal studies, observations, scientific experiments, and solving comp...