Now we need to weight the cases with respect to Freq. After entering the data, your Data View window should look like this: When we go to enter our data in SPSS, we will need to create three new variables: ClassRank, PickedAMajor, and a frequency variable (let's name it "Freq"). So there are 3*2 = 6 unique factor combinations. In this situation, we have two variables: ClassRank (which has three levels) and PickedAMajor (which has two levels). Whenever you want to re-create a frequency table or crosstab, you first need to figure out how many unique combinations of the factors there are, and how many observations there were for each factor combination. You get the feeling that they may have used the Chi-square test of independence, but want to verify this for yourself. You do not see any test statistics anywhere, and it is unclear what test he has run.You can never prove anything with a hypothesis test-not even if the p-value is really, really small.You immediately notice several things wrong with this report so far: Suppose you are helping a friend with their statistics homework, and see that they have included the following write-up in their report: To turn off an enabled weighting variable, open Weight Cases window again, and click Do not weight cases.To enable a weighting variable, click Weight cases by, then double-click on the name of the weighting variable in the left-hand column to move it to the Frequency Variable field.To turn on case weights, click Data > Weight Cases. Weighting cases in SPSS works the same way for both situations. Many of these surveys include weighting as a part of the study methodology. The Pew Research Center often makes their raw survey data available online to the public.(This often happens with large surveys: a "weighting" variable is developed to adjust a sample's composition to be reflective of the population's composition, or to control for over- or under-reporting from a certain group.) Your data requires adjustments to correct for over- or under-representation of certain characteristics in your sample.The "weight" is the number of occurrences. Your data is in the form of counts (the number of occurrences) of factors or events.Some situations where this can be useful include: Thus, we can say, “Jews and the non-religious are significantly less likely to believe in an afterlife than are Protestants and Catholics.” Do note that care must be taken in interpreting Chi-Square crosstabs as it is not always perfectly clear where the significant differences between scores lie.In SPSS, weighting cases allows you to assign "importance" or "weight" to the cases in your dataset. In the crosstab table it is clear that Protestants and Catholics are much more likely to report a belief in the afterlife than are Jews or Nones. 05 which shows that there is a relationship between one’s religion and their belief in the afterlife.įinally, we interpret the test in everyday terms, which also means we look more closely at the crosstab table as well. As seen in the table below, the Chi-Square significance value is. 05, there is a relationship between the variables based on the level of confidence we stated in the beginning. (2-sided)” for the Pearson Chi-Square statistic is less than. Select the independent variable (relig) to go in the column and the dependent variable (postlife) to go in the row.Īs we stated in the beginning, our alpha is. Once you click Crosstabs, a window will pop up where you will enter your chosen variables to be tested. To run the test, select: Analyze → Descriptives → Crosstabs. For this example religion is the independent variable and belief in the afterlife is the dependent variable. For this example we will use religion (relig) and belief in the afterlife (postlife). You must have two nominal variables from a single sample that you can use to see if there is a relationship between them. To do a Chi-Square test in SPSS, complete the following steps: The hypothesis we will test in this chapter is whether or not there is a relationship between religious affiliation and belief in the afterlife. To calculate Chi-Square, we use a cross-tabulation, crosstab for short, which shows the frequencies of joint occurrences between two variables. An ordinal variable is similar to a nominal variable, but the categories can be put in an order (e.g., ranked highest to lowest). To reiterate, a nominal variable is one that is only measured by naming categories such as class, quality or kind. The Chi-Square test is used when trying to find a relationship between two nominal or ordinal variables.