The researchers are using Chi-square Test in order to analyse the gathered numeric data which are collected from primary source of information. A chi-squared test is written as χ² test, where test statistic is analysed through Chi-square distributed, under the null hypothesis. Mainly the Pearson’s Chi-square test and variants are performed well through SPSS help. SPSS is playing a crucial role in analysing the gathered data and information, where the researchers are trying to analyse Chi-square to test the hypothesis and draw the final conclusion of the research.
The formula of chi square is,
Where,
X2 is chi square
Oi is observed value
Ei is expected value
Chi Square statistic is utilised by the researchers widely in order to analyse the relationship between the categorical variables. The Chi-Square is mainly non symmetric which refers that two non-negative integers are effective to explore the values. For Chi-Square distributors, the degree of freedom is being considered and for example, the population variance is one, n= 1. The researchers mainly test the hypothesis in the research through Chi-Square test. If the alternative hypothesis is accepted, there is strong relationship between the variables. And if the null hypothesis is being accepted after conducting data analysis and evaluation, there is no such relationship between the dependent and independent variables in the data set hence indicating that the variables are independent to each other. The Chi-Square goodness of fit test is hereby helpful for the researchers to test the comparison between the variables and through this test; it is also possible to evaluate distribution of the categorical variables in a specific data set. The p value can be explored through Chi Square test in SPSS data analysis that helps to analyse whether the result is significant or not. The alpha level (α) is being chosen efficiently at 0.05 (5%), which indicates 95% significance. There are mainly three types of Chi-square tests, which are tests of goodness of fit, independence and homogeneity. The Chi Square goodness of fit test is utilised when there is one categorical variable in the data set and that allows the researchers to test whether the frequency distribution of the categorical variable is significantly different from the expectations or not. After testing the significance, the researchers are able to test the hypothesis to choose the alternative hypothesis or null hypothesis as per the result. on the other hand, the Chi Square test of independence is utilised in case of two categorical variables that allows whether the two variables are related to each other or not.
Chi Square test is one of the effective statistical hypothesis test, performed through SPSS, to evaluate the differences between the observed value and the expected values. For quantitative data analysis, it is widely utilised by the researchers where authentic data and valid information are inserted for further critical analysis. It provides a scope to the researchers to calculate the variances in expected and observed values. The significance level indicates the validity of the relationship between the variables through Chi Square test. It is mainly observed through p value of 0.05 or greater for testing the final hypothesis. Hence, in dissertation and thesis papers, the Chi Square test is widely utilised where the researchers try to test the developed hypothesis sin the study and draw the final conclusions. Chi Square test is mainly involved in the expected frequencies, the type of data gathered and the association between the variables. Through comparison of the expected values and observed values, it is possible to test the hypothesis where Chi Square test is performed for making final conclusion.
Chi Square test focuses on determining the differences between the observed data and the expected data in order to identify the relationship between them. Chi Square test is hereby effective for better analysis, where the test of deviations of differences can be evaluated. Goodness of fits and standard variance in the sample through normal distribution can also be measured well. Statistically independence or association between two or more categorical variables are measured through Chi Square test SPSS. The Chi Square test of independence only assesses the associations between the categorical variables and cannot provide any inferences about causation. It is useful in SOSS for two or more categorical variables, two or more categories or groups of population in the data set as well as independence of observations and relatively large samples. Chi Square analysis is mostly used by the researchers, who are studying the survey response data. It is applied for the categorical variables analysis. In the field of demography, consumer, marketing research, as well as economics, political science, the Chi Square test is widely used by the researchers. The regression analysis is hereby possible through Chi Square test with goodness of fit calculations on the training, validation and test data set. Chi Square test is also useful for well-known theoretical probability distribution like the normal or Poisson distribution.
