Frequency tables are created in statistics if you want to display the absolute and relative frequencies of the characteristics of your variables. A frequency table thus provides you with a table that shows you how often each value occurs in your data. A frequency table for the variable gender, for example, shows how often the male and female values occur in the sample.
Absolute frequencies are those values that indicate how often the respective category of a variable occurs.
Relative frequencies, on the other hand, indicate how often the respective category occurs in relation to all cases and are therefore usually given as a percentage.
Depending on the subject area and question, the categories or characteristics can be, for example, persons, companies, locations or households.
Frequency tables are often used to get a first overview of the data. The result can then be displayed graphically in a bar chart.
It is particularly important to pay attention to missing or invalid values when creating and interpreting frequency tables. In the field of survey research, missing values are usually found where people have answered with "no answer", "Can't say" or "Don't know". So that the statistics are not distorted by the missing values, you should enter both the percentage and the valid percentage in the frequency table.
How to calculate valid percent?
To calculate the valid percent, the absolute frequencies of a characteristic must be divided by the total number of valid cases. If you have asked 30 people in a survey what their favourite car brand is and 7 have said "Don't know", then there are 23 valid cases. If 5 people have answerd Ford, then the valid percentages are 5/23 or 21.7 %
Frequency table in statistics
Frequency tables usually consist of the following columns
- Absolute frequency
- Percent (=relative frequency)
- Valid percent
The column of valid percentages is now the one that shows the relative frequencies of a characteristic in percent, but only considers those cases that have valid bets. Since missing values can always occur, it is advisable to also use this form of percentages.
Example for the valid percent:
For example, if a Sunday poll is taken and the question asked "Which party would you vote for if there were an election next Sunday?" it could turn out that there are still some undecided people. In this case, both the percentages and the valid percentages would be interesting. The percentage based on all values would therefore show how much support there is for a party in relation to all respondents, including the undecided. The percentage of valid values, on the other hand, indicates the level of agreement among those who have already decided.
An extension of frequency tables are crosstabs. In crosstabs, not only one but two variables are considered.
Example frequency table
In the frequency table calculator on DATAtab you can easily create frequency tables for your data. The procedure will now be illustrated with an example:
In a statistics course the participants were asked which brand of car they drive.
That's how it works with DATAtab: Simply copy the table into the descriptive statistics calculator and select the variable Car Brand. Now you can choose which values you want to calculate. The result of the frequency table now looks like this:
Finally, DATAtab also automatically gives you a graphical visualization of the frequency distribution of car brand, here in the form of a bar chart:
If the variable is metrically scaled, a histogram is used to display the frequencies.
Frequency table APA style
If you want to create a frequency table in APA format, you have to take the following into account:
|Font and spacing||Times new roman, size 12 with double spacing|
|Caption||All tables must be numbered in APA format|
|Borders||As few borders as possible should be used.|
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