Spss chisquare
Assumption #3: The groups of the categorical variable must be mutually exclusive.Assumption #2: You should have independence of observations, which means that there is no relationship between any of the cases (e.g., participants).Examples of ordinal variables include Likert scales (e.g., a 7-point scale from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 5-point scale for measuring job satisfaction, ranging from "most satisfied" to "least satisfied" a 4-point scale determining how easy it was to navigate a new website, ranging from "very easy" to "very difficult or a 3-point scale explaining how much a customer liked a product, ranging from "Not very much", to "It is OK", to "Yes, a lot"), and physical activity level (e.g., 4 groups: sedentary, low, moderate and high). Examples of nominal variables include ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), and profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist). Examples of dichotomous variables include gender (2 groups: male or female), treatment type (2 groups: medication or no medication), educational level (2 groups: undergraduate or postgraduate) and religious (2 groups: yes or no). Assumption #1: One categorical variable (i.e., the variable can be dichotomous, nominal or ordinal).Let’s take a look at these four assumptions: In practice, checking for these assumptions is a relatively simple process, only requiring you to use SPSS Statistics. You need to do this because it is only appropriate to use a chi-square goodness-of-fit test if your data meets four assumptions that are required for a chi-square goodness-of-fit test to give you a valid result. When you choose to analyse your data using the chi-square goodness-of-fit test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a chi-square goodness-of-fit test. Therefore, assuming that you would like to know the SPSS Statistics procedure and interpretation of the chi-square goodness-of-fit test when you have equal expected proportions, you first need to understand the different assumptions that your data must meet in order for a chi-square goodness-of-fit to give you a valid result.
#Spss chisquare how to
However, if you have "unequal" expected proportions (e.g., you anticipated 70% of those voting for the Republican Party being male and only 30% female), we show you how to do this in our enhanced chi-square goodness-of-fit guide (N.B., you can learn about our enhanced content on our Features: Overview page). In addition, we explain how to interpret the results from this test. In this "quick start" guide, we show you how to carry out a chi-square goodness-of-fit test using SPSS Statistics when you have "equal" expected proportions (e.g., you anticipated an "equal" proportion of males and females voting for the Republican Party). Not only is it an important aspect of your research design, but from a practical perspective, it will determine how you carry out the chi-square goodness-of-fit test in SPSS Statistics, as well as how you interpret and write up your results. When you carry out a chi-square goodness-of-fit test, "hypothesising" whether you expect the proportion of cases in each group of your categorical variable to be "equal" or "unequal" is critical. The proportion of cases expected in each group of the categorical variable can be equal or unequal (e.g., we may anticipate an "equal" proportion of males and females voting for the Republican Party, or an "unequal" proportion, with 70% of those voting for the Republican Party being male and only 30% female). It is used to determine whether the distribution of cases (e.g., participants) in a single categorical variable (e.g., "gender", consisting of two groups: "males" and "females") follows a known or hypothesised distribution (e.g., a distribution that is "known", such as the proportion of males and females in a country or a distribution that is "hypothesised", such as the proportion of males versus females that we anticipate voting for a particular political party in the next elections). The chi-square goodness-of-fit test is a single-sample nonparametric test, also referred to as the one-sample goodness-of-fit test or Pearson's chi-square goodness-of-fit test. Chi-Square Goodness-of-Fit Test in SPSS Statistics Introduction