Displaying Frequency tables for variables can help you understand how many participants are in each level of a variable and how much missing data of various types you have. For nominal variables, most descriptive statistics are meaningless. Thus, having a frequency table is usually the best way to understand your nominal variables. We created a frequency table for the nominal variable *religion* in Chapter 3 so we will not redo it here.

4.5. Examine the data to get a good understanding of the frequencies of scores for one nominal variable plus one scale/normal, one ordinal, and one dichotomous variable.

Use the following commands:

- Select
**Analyze →****Descriptive Statistics →****Frequencies**. - Click on
**Reset**if any variables are in the**Variable(s)** - Now
*highlight*the**nominal**variable*ethnicity*in the left box*.* - Click on the
**arrow**button pointing right. - Highlight and move over one
**scale**variable (we chose*visualization retest*), one**ordinal**variable (we chose*father’s education*), and one**dichotomous**variable (we used*gender*). - Be sure the
**Display frequency tables box**is checked. - Do not click on
**Statistics**because we do not want to select any this time. - Click on
**OK**

Compare your output to Output 4.5. If it looks the same, you have done the steps correctly.

**Output 4.5 Frequency Tables for Four Variables**

FREQUENCIES VARIABLES=ethnic visual2 faed gend /ORDER= ANALYSIS .

Frequencies

**Interpretation of Output 4.5**

The first table, entitled **Statistics**, provides, in this case, only the number of participants for whom we have **Valid **data and the number with **Missing **data. We did not request any other statistics because almost all of them (e.g., skewness, standard deviation) are not appropriate to use with the nominal and dichotomous data, and we have such statistics for the ordinal and normal/scale variables.

The other four tables are labeled **Frequency Table**; there is one for *ethnicity*, one for *visualization test*, one for *father’s education*, and one for *gender*. The left-hand column shows the **Valid **categories (or levels or values), **Missing **values, and **Total **number of participants. __The Frequency column gives the number of participants who had each value. The Percent __column is the percent who had each value, including missing values. For example, in the ethnicity table, 54.7%

__of all participants__were

*Euro-American,*20.0% were

*African American,*13.3% were

*Latino-American*, and 9.3% were

*Asian American*. There was also a total of 2.7% missing; 1.3% were

*multiethnic*, and 1.3% were left

*blank.*The

**valid percent**shows the percent of those with

*nonmissing*data at each value; for example, 56.2% of the 73 students

__with a single listed ethnic group__were

*Euro-Americans*. Finally,

**Cumulative Percent**is the percent of subjects in a category

*plus*the categories listed above it; however, this is not meaningful for ethnicity unless you want to know the percent of participants who are not Asian American.

As mentioned in Chapter 3, this last column usually is not very useful with nominal data, but can be quite informative for frequency distributions with several ordered categories. For example, in the distribution of father’s education, 74% of the fathers had less than a bachelor’s degree (i.e., they had not graduated from college)*.*

Source: Morgan George A, Leech Nancy L., Gloeckner Gene W., Barrett Karen C.

(2012), *IBM SPSS for Introductory Statistics: Use and Interpretation*, Routledge; 5th edition; download Datasets and Materials.

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