What is frequencies in spss

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. Histograms should only be used for continuous variables; they should not be used for ordinal variables, and should never be used with nominal variables.

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. If you are creating a frequency table using a string variable and notice that the first row has a blank category label, similar to this example:.

This particular issue is specific to frequency tables created from string variables. The blank row represents observations with missing values. SPSS does not automatically recognize blank i. This issue should not be ignored! When missing values are treated as valid values, it causes the "Valid Percent" columns to be calculated incorrectly. Depending on the number of missing values in your sample, the differences could be even more dramatic.

To fix this problem: To get SPSS to recognize blank strings as missing values, you'll need to run the variable through the Automatic Recode procedure. This procedure takes a string variable and converts it to a new, coded numeric variable with value labels attached. During this process, blank string values are recoded to a special missing value code.

To see a worked example, see the Automatic Recode tutorial. Using the sample dataset, let's a create a frequency table and a corresponding bar chart for the class rank variable Rank , and let's also request the Mode statistic for this variable. Two tables appear in the output: Statistics , which reports the number of missing and nonmissing observations in the dataset, plus any requested statistics; and the frequency table for variable Rank.

The Frequencies procedure can also produce histograms with or without a normal distribution overlaid on the graph. A Variable s : The variables to analyze with the Frequencies procedure. To include a variable for analysis, double-click on its name to move it to the Variables box. You can add several variables to this box to obtain statistics for each variable. B Statistics: Opens the Frequencies: Statistics window, which contains various descriptive statistics, most of which are suitable for continuous numeric variables.

Most of the statistics in the Central Tendency , Dispersion , and Distribution groups are valid for continuous variables; the only exception is the Mode , which very rarely has a useful interpretation for situations involving continuous variables. Most of these statistics are identical to the ones that can be obtained with Descriptives, Compare Means, or Explore, so they will not be covered again here.

One noticeable exception to this is the Percentile Values group, which is unique to the Frequencies procedure:. You can select more than one option in the Percentile Values group. If your selections request overlapping information, that information will not be printed twice. Note: The Values are group midpoints check box should only be selected when your data values represent the midpoint of a range.

This situation is more often associated with ordinal categorical variables. C Charts: Opens the Frequencies: Charts window, which contains various graphical options. Options include bar charts, pie charts, and histograms. Histograms are the only appropriate option for continuous variables; bar charts and pie charts should never be used with continuous variables.

If requesting a histogram, the optional Show normal curve on histogram option will overlay a normal curve on top of your histogram, which can be useful when assessing the normality of a variable. Note that the options in the Chart Values area apply only to bar charts. These buttons will be greyed out if the radio button for Histograms is selected.

D Format: Opens the Frequencies: Format window, which contains options for how to sort and organize the table output. The Order by options are not relevant to continuous variables, but the Multiple Variables options allow for customization of output when two or more continuous variables are specified.

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. You will want to uncheck this box if using the Frequencies procedure on a continuous numeric variable. If this box is left checked, a frequency table will be produced where each unique number is treated as its own category.

For variables with skewed distributions, it is often more useful to look at percentiles than it is to look at means. This is because means are more susceptible to outliers: a single strongly outlying value can "pull" the mean up or down from where it would be otherwise. By comparison, percentiles including the median are relatively robust to outliers - that is, percentiles generally do not change much when outliers are present compared to when there aren't outliers present.

When reporting placement or achievement test scores, it's often more useful and more descriptive to report the percentiles than it is to report the means. The sample dataset has placement test scores out of points for four subject areas: English, Reading, Math, and Writing. Let's use the Frequencies procedure to obtain the quintiles i. To run the full analysis, click OK. Executing the above process for the variable Visnhist will produce one frequency table and one bar chart. Both options contain identical information, but will suit different presentational styles.

Figure 3 , 4 and 5 displays SPSS output for our two variables being investigated. SPSS provides four columns of output in Figures 3 and 4. In this example, the Percent and Valid Percent are not equal. This is because we have missing data for a sizeable proportion of the sample across both variables.

We therefore opt to interpret only the Valid Percent column. Figure 3 reveals that, of the observations in the dataset, only have provided a valid response for Vislib visits to a public library.

Figure 4 presents the same information, but this time for visits to the Natural History Museum.