![]() Alternatively, a clustered bar chart could be used to illustrate the differences in the ordinal dependent variable, satisfaction level (consisting of five levels to represent how satisfied customers felt: "very satisfied", "somewhat satisfied", "neither satisfied nor dissatisfied", "somewhat dissatisfied" and "very dissatisfied"), based on two nominal independent variables: mobile phone brand (consisting of four groups: "Apple", "Nokia", "Samsung", and "Sony") and a second, nominal independent variable, US mobile carriers (consisting of three groups: "AT&T", "Sprint" and "Verizon Wireless"). Note: If you are using an an independent-samples t-test, paired-samples t-test (dependent t-test), one-way ANOVA or repeated measures ANOVA, you might want to consider a simple bar chart instead.įor example, a clustered bar chart could be used to illustrate the differences in the number of times shoppers preferred one of 5 different brands of ice cream when eating at home compared to eating out (i.e., the statistic being measured could be a "count/frequency", and the two variables, which are both nominal, would be "brand preference" – which has five categories: "ice cream brand A", "ice cream brand B", "ice cream brand C", "ice cream brand D" and "ice cream brand E" – and "place of consumption", which has two categories: "at home" and "eating out").Īlternatively, a clustered bar chart could be used to illustrate the differences in the continuous dependent variable, cholesterol, based on the ordinal independent variable, physical activity level (i.e., consisting of four levels to represent the "sedentary", "low", "moderate" and "high" physical activity groups who participated in a study) and the nominal variable: gender (i.e., consisting of two categories: "males" and "females"). For example, a clustered bar chart can be appropriate if you are analysing your data using a chi-square test for association, a two-way ANOVA, two-way repeated measures ANOVA, and two-way mixed ANOVA. A clustered bar chart can be used when you have either: (a) two nominal or ordinal variables and want to illustrate the differences in the categories of these two variables based on some statistic (e.g., a count/frequency, percentage, mean, median, etc.) or (b) one continuous or ordinal variable and two nominal or ordinal variables and want to illustrate the differences in the continuous variable (which typically acts as a dependent variable) in terms of the categories of the two nominal or ordinal variables (which typically act as independent variables). It will often be used in addition to inferential statistics. Is there something basic I just don't understand about the clustered bar charts? Any help or hints are very appreciated.Creating a Clustered Bar Chart using SPSS Statistics IntroductionĪ clustered bar chart is helpful in graphically describing (visualizing) your data. I mean the very last bar shown in the example should show the mean of Time3 for the last Group, right? That's what I'd figure, but the mean is not correct. While it apparently looks fine and dandy, I can clearly see that the mean values are simply wrong compared to the mean I can calculate myself, or the ones reported when I compare mean using a T-test! Legend shows my three followups (Time1 etc), X-axis shows my two groups (0 or 1). I can easily manually or using SPSS calculate the means for the different follow-ups (Time1, Time2 and Time3) first for Group 0 and then for Group 1.īut then I create a clustered bar chart by choosing 'Clustered bar char', dragging Time1, Time2 and Time3 to the Y-axis, dragging my Group-variable to the X-axis. I'm using SPSS 19 on a dataset structured like this (dummy data): Time1 Time2 Time3 Group I'm pulling my hair out on this one, so hope somebody can help me out. ![]()
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