Joinsubscribers and get a daily digest of news, geek trivia, and our feature articles. Pie charts are popular in Excel, but they are limited. Pie charts can only show one series of values. So if you have multiple series, and you want to present data with pie charts, you need multiple pie charts. The image below shows the contribution to total revenues of five products across three different cities.

We have a pie chart for each city with the data ranges shown above them. This enables us to compare the sales of products across different cities. But there are complications when we want to change them all consistently, or view them as a single figure.

It makes sense to show one pie chart instead of three. The easiest and quickest way to combine the data from the three pie charts is to use the Consolidate tool in Excel. Click a cell on the sheet where you the consolidated data to be placed.

Next, we have to gather all of the references which we want to consolidate. This pie chart makes it easier to see the contribution of each product type to the total revenue, but we lose the comparison between each city that we had with three different charts. Another reason that you may want to combine the pie charts is so that you can move and resize them as one. Click on the first chart and then hold the Ctrl key as you click on each of the other charts to select them all.

Although this article is about combining pie charts, another option would be to opt for a different chart type. Pie charts are not the only way to visualize parts of a whole. Take the example data below. This is the data used in this article but now combined into one table. There are two types of Stacked Column to choose from.

The first one will present your data as below.Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below:. The Frequencies procedure can produce summary measures for categorical variables in the form of frequency tables, bar charts, or pie charts.

A Variable s : The variables to produce Frequencies output for. To include a variable for analysis, double-click on its name to move it to the Variables box.

Moving several variables to this box will create several frequency tables at once.

**Chi-square test in SPSS + interpretation**

B Statistics: Opens the Frequencies: Statistics window, which contains various descriptive statistics. The vast majority of the descriptive statistics available in the Frequencies: Statistics window are never appropriate for nominal variables, and are rarely appropriate for ordinal variables in most situations. There are two exceptions to this:. If your categorical variables are coded numerically, it is very easy to mis-use measures like the mean and standard deviation.

SPSS will compute those statistics if they are requested, regardless of whether or not they are meaningful. It is up to the researcher to determine if these measures are appropriate for their data. In general, you should never use any of these statistics for dichotomous variables or nominal variables, and should only use these statistics with caution for ordinal variables. C Charts: Opens the Frequencies: Charts window, which contains various graphical options.

Options include bar charts, pie charts, and histograms. For categorical variables, bar charts and pie charts are appropriate. Note that the options in the Chart Values area apply only to bar charts and pie charts. In particular, these options affect whether the labeling for the pie slices or the y-axis of the bar chart uses counts or percentages.

This setting will greyed out if Histograms is selected. D Format: Opens the Frequencies: Format window, which contains options for how to sort and organize the table output. When working with two or more categorical variables, the Multiple Variables options only affects the order of the output. If Compare variables is selected, then the frequency tables for all of the variables will appear first, and all of the graphs for the variables will appear after.

If Organize output by variables is selected, then the frequency table and graph for the first variable will appear together; then the frequency table and graph for the second variable will appear together; etc. E Display frequency tables : When checked, frequency tables will be printed.

This box is checked by default. If this check box is not checked, no frequency tables will be produced, and the only output will come from supplementary options from Statistics or Charts.

For categorical variables, you will usually want to leave this box checked. Using the sample dataset, let's a create a frequency table and a corresponding bar chart for the class rank variable Rankand let's also request the Mode statistic for this variable.

Two tables appear in the output: Statisticswhich reports the number of missing and nonmissing observations in the dataset, plus any requested statistics; and the frequency table for variable Rank. The table title for the frequency table is determined by the variable's label or the variable name, if a label is not assigned.

Here, the Statistics table shows that there are valid and 29 missing values.A clustered bar chart is helpful in graphically describing visualizing your data. It will often be used in addition to inferential statistics. 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. For 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.

Alternatively, a clustered bar chart could be used to illustrate the differences in the continuous dependent variable, cholesterolbased on the ordinal independent variable, physical activity level i. First, we introduce the example we have used in this guide. A researcher was interested in whether an individual's interest in politics was influenced by their level of education and their gender.

The researcher recruited a random sample of participants and asked them about their interest in politics, which they scored from 0 - with higher scores indicating a greater interest. This guide will also show you how to add error bars in this case, using confidence intervals. If you want to analyse your data using a two-way ANOVA, our introductory guide will help get you started.

The 11 steps that follow show you how to create a clustered bar chart in SPSS Statistics version 25 and above which includes the subscription version of SPSS Statistics using the example above. Note: You can ignore the " Filter? Note 1: The chart preview pane does not accurately plot the variable data that you have dragged across in its preview pane, even though it might appear that it does due to the bar chart's bars changing when you add your variables.

Therefore, do not get confused and think that you have done something wrong. You will only see your true data when you actually generate the clustered bar chart.

## SPSS Tutorials: Frequency Tables

Note 2: You could easily swap the two independent variables around without any problems. Note 1: You do not have to select error barsbut it is common in academia to do so.

We illustrate a clustered bar chart with and without error bars at the end of the guide. Note 2: You can use this area to select other types of error bars, such as multiples of either the standard error or standard deviation.

These values might be different for your variables, so you should adjust them as you see fit. If you are not sure at first what these values should be, don't change the values; see what the clustered bar chart looks like and then re-run the clustered bar chart with new axes values if necessary.

You can also re-edit the clustered bar chart later on. Note: If the type of clustered bar chart that you want to create is different from the example above or there are specific options you want to include in your clustered bar chart that we have not covered, please contact us.Charts and graphs are a way of organizing data so it can be read and interpreted more easily.

In order to choose which type of chart or graph to use you must first decide the level of measurement, i. Next, consider the objective behind creating a chart and the target audience. For example, if your audience is the general public, you want your graph to be colorful, uncluttered, and include an overview of the statistics presented. If your audience is more technical, you may want to use more tables.

As stated before, choosing which type of graph to create requires that you first determine the level of measurement. In statistics, the basic rules are as follows:. Let's now utilize the Genetic Counselors data set to create and interpret different types of graphs. This classic chart type is particularly useful for conveying the sense of each constituent's share of a total. Remember that pie charts are based on a nominal or ordinal variable, so we choose one, "relig", the self-reported religious affiliation of our survey participants.

Click it and and move it to the Variable s box by clicking the blue arrow:. Next you want to click on the Charts Click on Pie charts. You can also determine if you would like frequencies or percentages to appear with your pie chart:.

For this example, considering the data as percentages of a total will be most useful, so click Percentages and Continue. Click OK and the Output Window will pop up and display the requested pie chart, with the source data above it:. By referencing the box above the pie chart we can also determine exact percentages and frequencies, for example, that the Jewish percentage of Genetic Counselors is 9. Bar charts are useful for projecting a sense of competition among categories.

Let's create a bar chart of our genetic counselor's religiosity, as measured by religious service attendance, to learn the category that "wins the competition". Finally, we click Continue and OK. The requested bar chart will be displayed in the Output Window:. Here, just as in the pie chart output, you can see the frequencies and percentages in the box located above the bar chart.

You can also see that the answers are weighted to the left and with this we can more or less instantly conclude that the genetic counselors surveyed do not, as a group, attend religious services as often as they might.

Histograms, also known as frequency histograms, are similar to bar charts except that the columns of a histogram touch and are of equal interval. For this example we will use our "age" variable and so see how age varies across genetic counselors. Next, just like before, move your chosen variable age over using the blue arrow and click on Charts The Output Window will pop up, displaying first the frequency table:.

If you were to look at the frequency chart you would see that 57 people answered 28 years old the "Valid" bar and 56 people answer 29 years old. This explains why the bar abutting 30 is highest.Login or Register Log in with. Forums FAQ. Search in titles only. Posts Latest Activity. Page of 1. Filtered by:. Antonio Ccopa. How do you make pie chart by combining two variables in stata? How do you make pie chart by combining two variables in stata?. For example, make pie charts by sex male, female and studies yes, no.

Tags: None. Nick Cox. Surely there is a better display unless your audience is small children? But I guess that something like Code:. Comment Post Cancel. William Lisowski. If I understand graph pie correctly, Nick's answer will produce two pies - for studies yes and no - each with two pieces - for sex male and female. If instead you wanted a single pie with four pieces - for the four combinations of sex and studies - you will want to create a single variable taking four distinct values corresponding to the four combinations perhaps using egen with its group function and use that as the argument to the over option, dropping the by option.

Myself, I would likely produce a two-by-two tabulation and trust that my readers can comprehend it and draw their own conclusions about how the pieces compare.

Marcos Almeida. Pie charts would surely raise eyebrows from those with much expertise, and that for good reason. Considered as " evil " by some authorthe worst type of graph by other, it has been so despised, to the point that R, when we search the term "pie", gives this note:.

Last edited by Marcos Almeida ; 04 Mar Previous Next. Yes No. OK Cancel.A multiple-response set is much like a new variable made of other variables you already have. But it does show up among the items you can choose from when defining graphs and tables. The following steps explain how you can define a multiple-response set, but not how you can use one — that comes later when you generate a table or a graph.

Also, there are two Multiple Response menus: The one in the Data menu is for tables and graphs; the one in the Analyze menu is for using special menus that you see in this example. Note four dichotomous variables that have 1 for Yes and 0 for No as their possible answers, as shown here.

Your variables appear in the Set Definition area. If you previously defined any multiple datasets, they appear in the list on the right. In the Set Definition list, select each variable you want to include in your new multiple dataset, and then click the arrow to move the selections to the Variables in Set list. The dollar sign in the filename identifies the variable as a multiple-response set. The new name will appear in two special menus in the Analyze menu.

There are other applications of multiple response as well, notably in the menus of the Custom Tables module, but you have to define those multiple-response sets in the Data menu. The new special Frequencies report appears in the output window, as shown here. Ten people bought 24 pieces of fruit. Nine pieces of fruit were apples — So, the difference is the denominator.

What makes this table special is that what you usually care about is the people with multiple responses. In other words, how many people shopping at the store are going to buy apples along with other things that they might buy?

This table is the only one that easily displays them both ways. If multiple-response sets are a common variable type for you, you should consider trying to get the Custom Tables module because it offers lots of options for this kind of variable. The window showing the complete definition. The Multiple Response Frequencies dialog box. The Multiple Response Frequencies table.One of the best known charts is a simple bar chart containing frequencies or percentages. This tutorial walks you through some options.

We'll use freelancers. Its syntax -shown below- is so simple that just typing it is probably faster than messing around with the menu. Unsurprisingly, we created the desired bar charts but -like most SPSS charts- they look awful. A great way to fix that is using an SPSS chart template.

### Variables and Chart Types

Just one template is sufficient for having pretty bar charts for once and for all. One of the charts that resulted from the template we built is shown below.

A nice -albeit little known- option is sorting the categories. Alternatively, set the desired chart titles as new variable labels. Since its syntax is a bit more difficult, we'll generate it from the menu as shown below. However, we can remove the line breaks from the syntax and copy-paste-edit it a couple of times for a handful of variables. One issue with all SPSS charts is that their sizes are fixed in pixels.

However, a bar chart for many categories needs more space than a chart for few categories. In fact, categories may disappear altogether if they don't fit into the chart anymore. With respect to the layout of reports, we prefer having the heights rather than the widths of our charts depend on the amount of content they contain.

This is yet another good reason for always transposing our bar charts. As with most charts, G raphs C hart Builder is better avoided since it's way more complicated and results in the exact same chart as the aforementioned options. The table below quickly summarizes the differences between the two options we discussed in this tutorial. First of, why are you using the chart builder? In the vast majority of cases, the legacy dialogs are to be prefered. I'm not sure what you mean by "lines running through bars".

Did you mean gridlines? Are you on SPSS version 25? If so, try running. Also see SPSS chart templates. I have a quick problem that needs solving. I'm unsure as to why the spss bar chart via the chart builder keeps appearing with lines running through the bars. I am unaware if this is causing a change in the results of the chart but i need to find a way to create the car without any lines cutting through them!

Is there anyway you can help solve this?

But unfortunately, no invitation from IBM thus far Let me know what you think! Your comment will show up after approval from a moderator.

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## How to make a pie chart with multiple variables in spss