Duncan's Multiple Range Test

Duncan’s Multiple Range test
Duncan’s Multiple Range test (DMRT) is a post hoc test to measure specific differences between pairs of means. Duncan 's new multiple range test (MRT) is a multiple comparison procedure developed by David B. Duncan in 1955.
In this study one way analysis is used to find out the significant difference among respect to factors of HRM based on the demographic characteristics of employees, namely, age, education, destination, religion, community, income and work experience in the organization.
Chi-square test
A Chi-square is a statistical measure used in the context of sampling analysis for comparing a variance to a theoretical variance. he Chi-square statistic is the primary statistic used for testing the statistical

educational qualification, monthly income of the employees and level of job satisfaction. Crosstabs were also done to find out the association between level of job satisfaction and level of organizational commitment.
Friedman test
The Friedman test is a non-parametric test. Similar to the parametric measures like ANOVA, it is used to detect differences in treatments across multiple test attempts. The procedure involves ranking each row (or block) together, then considering the values of ranks by columns. In the present study, Friedman test is used to test the significance difference between mean ranks towards each of the select factors and also towards factors of HRM practice.
Correlation analysis
The degree of relationship between the variables under consideration is measured through the correlation analysis. The measure of correlation or correlation index summarizes in one figure the direction and degree of correlation. The correlation analysis refers to the techniques used in measuring the closeness of the relationship between the variables. Thus, correlation is a statistical device which helps in analyzing the co-variation of two or more variables. The detection and analysis of correlation (i.e., co-variation) between two statistical variables requires relationship of some sort which associated the observation in pairs, one of each pair being

It was also used to find the impact of HRM factors, Job satisfaction and Organizational commitment.
Structural Equation Modelling
Structural Equation Modelling (SEM) is a family of statistical models that seek to explain the relationships among multiple variables. In the process, the structure of interrelationships expressed in a series of equations is examined, similar to a series of multiple regression equations. These equations depict all the relationships among constructs (both the dependent and the independent). Constructs are unobservable or latent factors represented by multiple variables.
A latent construct is a hypothesized and unobserved concept that can be represented by observable variables. It is measured indirectly by examining consistency among multiple measured variables, also referred to as manifest variables or indicators. SEM’s foundation lies in two familiar multivariate techniques: factor analysis and multiple regression