CHI-SQUARE TEST

Chi-Square Test with SPSS

Chi-Square Test with SPSS

Introduction

The study is related to the Chi-Square Goodness of fit which is statistical test that is used to find out whether a sample of data belongs to a population with a specific distribution. Chi-Squared Goodness of Fit Tests with provides a complete and thorough context for the theoretical implementation and basis of Pearson's monumental contribution and its wide applicability for chi-squared goodness of fit tests.

Statistical Assumptions

The Chi-Square Goodness of fit is based on certain assumptions which are mentioned below:

There should be one categorical variable that can be comprised of two or more categories;

No more than twenty percent of the expected frequencies have counts below 5;

A hypothesized proportion which can be unequal or equal.

The Chi-square goodness of fit test is applied in order to check whether the observed data values matches to any specific distribution. Moreover, calculating the Chi-square test is an assessment of the data which is observed with the expected data supported by the distribution data values (Greenwood and Nikulin, 1996). When applying the goodness of fit test, it is imperative and essential that the chi-square test is applied with the intension to find out, whether the statistical significance is in existence for the dissimilarity in the ratios for dissimilar groups; to attain this, it breaks all results in the groups. Furthermore, the chi-square test determines the amount of imperfections, for case in point the expected values, in each group engaged; it does this by assuming that all members have the identical defect rate (Munro, 2005).

The basic principle of this method is to compare proportions that is, possible differences between expected and observed frequencies for a certain event. Of course, it may be said that two groups behave similarly if the differences between expected and observed frequencies in each category are very small, near zero (Gravetter, Wallnau and Gravetter, 2009). Therefore, the test is used to verify whether the frequency with which a particular event observed in a sample deviates significantly or not the frequency with which it is expected. Moreover, Chi-square goodness of fit is also used to compare the distribution of various events in different samples in order to assess whether the observed proportions of these events or not show differences significant or if the samples differ significantly in the proportions of these events (Gravetter, Wallnau and Gravetter, 2009).

Furthermore, the necessary conditions to apply the test, the following propositions must be ...