Statistical analysis

Statistical analysis

Introduction

This assignment is based on the evaluation of statistical; techniques used in the selected article. The article used t-test, chi-square, and multiple regression analysis. These tests are significant as the p value is less than 0.05; hence, the null hypothesis that was tested from multiple regression analysis was accepted.

1. Description of inferential statistics test in the study

There are three types of tested that are sued in the study; Chi-square test, t-test, and Multiple Regression Analysis test. The chi-square test is a non-parametric test and a correlation test. On the other hand, t-test and multiple regression analysis are parametric and inferential tests.

Use of these tests

The chi-square test accomplishes the objective of determining the difference between the observed and expected frequencies. It examines that whether the difference between the expected and observed frequencies is a real difference or it is a sampling error. It helps the researcher to understand that whether the variables have a significant relationship with the testing variable of the study or not. It says that if the chi-square (x2 ) calculated is greater than chi-square tabulated value, reject the null hypothesis, and if the p value is greater than significance level (0.05), accept the null hypothesis and vice versa. Chi-square is commonly known as a test for goodness-of-fit test (Satorra and Bentler, 2001).

T-test for independent sample is used to know the mean difference between the two groups. The t-test includes the Levene test that is used to test the homogeneity of the variances across the groups of independent variables. it says that if the t calculates value is greater than t tabulated value, the null hypothesis is rejected that is there is no mean difference across the groups (Pallant, 2001). On the other hand, the f value of Levene statistics show that if f calculated is greater than f tabulated, the null hypothesis of equal variable is rejected.

Multiple regression analysis tests that whether there is a significant relationship between the independent and dependent variable and whether the dependent variable has an effect on the independent variable or vice-versa (Baltagi, 2011). If the f ratio calculated is greater than f tabulated, it indicates an existence of a linear relationship. Similarly, if p value is less than 0.05, it means there is a linear relationship.

Reason of chosen Chi-square was used to identify the relationship between the characteristics of socio-demographic and categorization of functional groups into ...