Compensation and Benefits continues to be the most researched field with more than thousand academic studies. Given the extensive research on Compensation in academia, there has been evolution in approaches to explore and keep pace with recent trends along with research methodology and technology. As a Research Scholar, I began to realize that while dominant literature on Compensation and Benefits favoured quantitative research to study its impact on organizational outcomes such as performance, turnover, job satisfaction, commitment, etc., both qualitative and quantitative research are needed to be able to study and explore unexplored areas of the said field. The book chapter will elaborate the specific applications of qualitative and quantitative statistical applications in Compensation Research with relevant basic examples. I am hopeful that the book chapter will be of use to academics, researchers and students focusing their studies and research on Compensation and Benefits.
TopPart 1: Relevant Quantitative Data Analysis Techniques And Its Applicability
Before understanding the application of some of the frequent used above mentioned techniques along with its applicability, it is important to understand the meaning of multivariate research methods. Broadly defined, multivariate research methods involve the inclusion of more than one outcome in a singular analysis. The multivariate approach allows the researcher to analyze the data in a way that is most reflective of the actual research context and environment.
In Compensation area, research scenarios involve using multi-dimensional concepts such as pay satisfaction, attitude to money and multiple outcomes such as self-efficacy, attitudes, and behavior, or performance. Hence, multivariate analysis is always called for as it can extend to include models such as those specific to testing structural validity of instruments designed to measure latent constructs such as pay satisfaction or predicting intention to quit. In other words, multivariate research methods are warranted in most situations where multiple-dimensional dependent variables are being considered in the same research scenario. They are developed for analysis of data which has more complex, multi-dimensional, dependence/interdependence structures.
Mostly, ANOVA, Multiple Regression, Correlation Techniques, Factor Analysis, Discriminant Analysis and SEM are the popular and mostly used analytical procedures. Thus, these selected techniques are briefly discussed along with the relevant examples of certain compensation research as illustrations.