The bivariate statistical test is a common technique that nurse researchers deem prolific in scrutinizing data. Bivariate analysis is a form of quantifiable statistical analysis used to establish the relationship between two variables. The analysis process begins with data collection about the variables and storing it in a two-column data table, with one variable being independent and the other dependent (Stephanie, 2020). The bivariate analysis process involves using the data to construct scatter plots to necessitate regression analysis that paves the way to establishing the correlation coefficients between the subject data sets.
The application/conducting of bivariate analysis involves the following significant steps. The first step is defining the nature of the relationships and identifying the type and the direction of the relationship. In analyzing medical data for research, it is vital first to understand the nature of the relationship between the independent and dependent variables in terms of how their values relate. Besides, understanding the type and the direction of the relationship where applicable is vital. Therefore, to work with data, the relationship between data sets should be understood.
The next step for conducting bivariate analysis involves determining the statistical significance of the relationship between the variables alongside identifying the strengths of the relationships. Nurse researchers mainly use Chi-square to determine the statistical significance of these relationships. Besides, establishing the strength of the relationship between the variables is vital. The strength of the relationship is best measured using Cramer’s V. It is noteworthy that a relationship can be weak but statistically significant.
Multivariate statistics entails a collection of methods designed for simultaneous observation and analysis of multivariate data. Multivariate data is data that its observation score has more than two random variables (Johnson & Wichern, 2015). The primary concern of multivariate statistics is unveiling the aim and background of the different forms of multivariate analysis and their relationships. It is noteworthy that in multivariate statistics, understanding variables’ relationships and their relevance may involve the application of univariate and multivariate analysis.
The process of multivariate analysis is critical and involves several techniques. In nursing research, the choice of the technique to use is dependent on the type of data one intents to analyze. However, it is noteworthy that establishing relationships among variables is paramount; this necessitates the correlation analysis process. The correlation procedure summarizes two or more numeric data columns and calculates summary statistics for each variable and its correlations.
The multivariate analysis process also involves factor analysis. Factor analysis is vital in deriving linear combinations of multiple variables to unveil the largest percentage of variations among the variables. Factor analysis is an essential process in multivariate analysis as it reduces problem dimensionality, making it easy to uncover factors affecting the variables. In nursing research, the factor analysis process of multivariate analysis is essential for easy understanding of nursing problems.