To recode these categories as missing data, all you need to do is move over one column to the Missing column. We‘ll include these in our recoding, as they also represent missing data. In addition, there are categories “Item not applicable” (with a value of 4.00) and “Not answered (9)” (with a value of 5.00) listed. In this dialogue box, you can see that “Not answered,” the missing data category, is listed as -9.00. Now you should see a dialogue box that lists all the numerical values of the categories of this variable. When you’ve found s1q62a, move across its row until you find the Values column. This will find the s1q62a row in the dataset.) Just enter s1q62a into the text bar and click Find Next. This will open up a Find and Replace dialogue box. (You can do this easily by clicking to highlight any cell in the Name column on the far left of the Variable View screen, and hitting Ctrl + F. We should code this information as missing data before we run our chi square test, so that we are only performing the test on data relevant to our research question.įirst, find s1q62a in the Variable View window of the SPSS Data Editor. Notice in the frequency output table that along with the answers “Yes,” “No,” and “Not sure,” which we are interested in, there is the category “Not answered.” Because these survey respondents haven’t responded to this question, their answers are missing. Move s1q62a into the Variable(s) box on the right side of the dialogue box. Go to Analyze, Descriptive Statistics, and then Frequencies. Therefore, let’s use s1q62a, which concerns whether or not a young person’s father obtained a degree, in another chi square test with s2q10.īefore we use s1q62a, we should check its frequencies to make sure the data is ready for bivariate analysis. Thinking about other individual characteristics that may influence a young person’s enrolment in full time education after secondary school, we may be interested in the impact parental educational attainment has on a student’s future plans. Let’s run one more chi square test together. Running a chi-square test cannot tell you anything about a causal relationship between truancy and later educational enrolment. It’s worth mentioning now that this test, like all tests of significance, only illuminates that there is a relationship and that that relationship has statistical significance (meaning, it is not due to chance). This means that the relationship between Year 11 truancy and enrolment in full time education after secondary school is significant. The p-value in our chi-square output is p = 0.000. In all tests of significance, if p < 0.05, we can say that there is a statistically significant relationship between the two variables. This value determines the statistical significance of the relationship we’ve just tested. It is the Asymptotic Significance, or p-value, of the chi-square we’ve just run in SPSS. Take a look at the column on the far right of this output table. Your output should look like the table on the right. Find s1truan in the variable list on the left, and move it to the Column(s) box.Ĭlick Continue and then OK to run the analysis. To perform a chi-square exploring the statistical significance of the relationship between s2q10 and s1truan, select Analyze, Descriptive Statistics, and then Crosstabs.įind s2q10 in the variable list on the left, and move it to the Row(s) box. Therefore, a chi-square test is an excellent choice to help us better understand and interpret the relationship between our two categorical variables. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying. Is there a statistically significant relationship between a student’s Year 11 truancy and his or her enrolment in full time education after secondary school?Ī chi-square test is a statistical test used to compare observed results with expected results.
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