# Bivariate analysis using spss (data analysis part-10)

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Bivariate analysis using spss is very simple procedure of finding the association between two variables. Spss is a very simple data analytics tool by which we can perform our analysis very easily.  Bivariate Analysis

### What is bivariate analysis?

Bivariate analysis is the analysis of two random variable and find their association. For bivariate analysis we mainly use crosstabs and to show the association we use chi-square test. in chi-square table, we interpret the p-values. P-values interpretation is following-
• A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so we reject the null hypothesis.
• A largep-value (> 0.05) indicates weak evidence against the null hypothesis, so we fail to reject the null hypothesis.
• p-values very close to the cutoff (0.05) are considered to be marginal.

### Bivariate analysis using spss

For bivariate analysis in spss we use the following data set and conduct bivariate analysis for the two highlighted variable (gender and result) and we also fond their association.  Data set 1.First we have to go to–Analyze →→→Descriptive statistics →→→→Crosstabs  Crosstabs 2.Now we can see two box named Row and Column and we have to enter one variable into Row box and another into column box and click on statistics.  Row and column box

3. In statistics we have to select chi-square and click on continue.  Chi-square

4. Finally we have to click on OK and then we the see the output window.  click on OK

OUTPUT

### Interpretation:

To interpret the chi square value we have build a hypothesis for association test. Let,
H0: There is no association between gender and result, vs
H1: H0 is not true.
Here we have a p-value 0.895 which is greater than 0.05. we we can not reject our null hypothesis. That means our null hypothesis is accepted and there is no association between gender and result.
• A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so we reject the null hypothesis.
• A largep-value (> 0.05) indicates weak evidence against the null hypothesis, so we fail to reject the null hypothesis.
• p-values very close to the cutoff (0.05) are considered to be marginal.

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