The Chi-Squared test measures if there is a significant difference between Expected Frequency (EF) and the Observed Frequency (OF).Р”Р„ survey was conducted on the reading preference of Undergraduate Students in The University of Abuja. The highlighted above shows that 1 unit increase in Income will result to 1.057 increase in Expenditure.Р”В«) ASSOCIATIONS: APPLYING CHI-SQUARE IN SPSSР”В¬hi-Square can be used to test for Association between two or more variables. The Coefficients table provides us with information to predict Expenditure from Income. This implies that there is a Statistically Significant relationship between Income and Expenditure. Column of 0.001 is less than 0.05 (See Figure 1.0 for Decision Rule). To decide whether there is a statistically significant relationship between variables, we will look at the heighted in the Sig. This statistic can be evaluated by comparing the actual value against a critical value found in a Chi-Square distribution. The output is labeled Chi-Square Tests the Chi-Square statistic used in the Test of Independence is labeled Pearson Chi-Square. The ANOVA table is used to check for Fitness of data in the model. The Chi-Square statistic appears as an option when requesting a crosstabulation in SPSS. In other words, 99.1% change in Expenditure can be explained by the Predictor (Income).

R of 0.991 shows the strength of the relationship between the Independent Variable/Predictor (Income) and the Dependent Variable (Expenditure) in Decimal or Percentage form.Р”Р‡rom the above example, Income accounts for 0.991 or 99.1% change in Expenditure. In this example, the value of the chi square statistic is 6.718. The chi square statistic appears in the Value column immediately to the right of Pearson Chi-Square. WeРІР‚в„ўre interested in the Pearson Chi-Square measure. The model Summary shows the relationship between the variables. As you can see below, SPSS calculates a number of different measures of association. The following data shows the Income and Expenditure pattern of Mr. The example below illustrates how this can be applied using SPSS. To measure relationship(s) between two or more variables linearly, we can use the Linear Regression.

*This is dependent on a significance level of 0.05.Р”Р„) Relationships: Applying Linear Regression in SPSS The table below represents the Decision Rule:

On the other hand, when p is greater than 0.05, we should reject the hypothesis (i.e. This simply implies that when p (the Significance level) is less than or equal to 0.05, we should accept (i.e. Using a 95% Confidence Level, the decision is pegged at 0.05. These four areas are:Р”В«efore we go deep into these four major areas mentioned above, it is necessary to know the Decision Rule when Using the Statistical Package for Social Sciences (SPSS) for judgements. To make this tutorial simple and straight forward for a beginner, I will stick with these areas where Inferential Statistics could be applied in research. A hypothesis may be needed in a study for users to decide appropriately on whether an assumption is true or false. One way to make inferences is by formulating a Hypothesis. When decisions or judgements are to be made from a study or a descriptive data, it is imperative to adopt various techniques for inferences.

Chi square in spss