# Cross tabulation in SPSS

By Priya Chetty on January 26, 2015

A cross tabulation is a joint frequency distribution of cases based on two or more categorical variables.

Open the SPSS file and CLICK on Analyze. Under that CLICK on Descriptive statistics and then select cross tabulation (See Figure 1).

Once you click on “Cross tabulation”, a new dialogue box would open, (See Figure 2). Here you will see two boxes, Rows, and Columns. You can select one or more than one variable in each of these boxes (Row and Column boxes). This depends on what you have to compare. Then click on “OK”.

If you need more detailed results then Click on “Statistics” (See Figure 2). A new dialogue box would open wherein you can select several statistical tools depending on the requirement of the research query.

Table 1 below shows a cross-tabulation that contains information solely on the number of cases that meet both criteria, but not a % distribution.

## Percentages in cross tabulation

The above information, i.e. the counts in the cell are the basic elements of the table. However, they are usually not the best choice for reporting findings because they cannot be easily compared if there are different totals in the rows and columns of the table. For example, if you know that 17 Males and 24 Females like rap music very much. Then you can conclude little about the relationship between the 2 variables unless you also know the total of men and women in the sample.

In order to calculate the percentages, CLICK on “Analyze”, then “Descriptive Statistics”, then “Crosstabs”, then “Cells” and under “Percentage” select all three options. (As can be seen in Figure 2 above):

Row %: the cell count divided by the number of cases in the row times 100
Column %: the cell count divided by the number of cases in the column times 100
Total %: the cell count divided by the total number of cases in the table times 100

The 3 criteria % stated above convey different information, so be sure to choose the correct one for your problem.  If one of the 2 variables in your table can be considered an independent variable and the other a dependent variable, make sure the % sums up to 100 for each category of the independent variable (see Table 2 below).

Row %: With reference to the above example (Table 2), when we divide the count for Males who “Like very much” (i.e. 17) with the total number of Males (i.e. 615), the result is 2.8% (% within respondent’s sex). (17 and 615 are horizontal, therefore becoming “row%”).

Column %: With reference to the above example (Table 2), when we divide the count for Males who “Like very much” (i.e. 17) by the total number of people who “Like very much” (i.e. 41), the result is 41.5% (% within rap music). (17 and 41 are vertical, therefore becoming “column%”).

Since gender would fall under the realm of an independent variable, you want to calculate the row %. This is because they will tell you what % of women and men fall into each of the attitudinal categories.  This % isn’t affected by unequal numbers of males and females in your sample.  From the row % displayed above, you find that 2.8% of males like rap music very much as do 2.9% of females.  So with regard to strong positive feelings about rap music, you note that there are no visible differences.

The column % depends on the number of men and women in the sample as well as how they feel about rap music.  If men and women have identical attitudes but there are twice as many men in the survey as women. In such a case, the column % for men will be twice as large as the column % for women. You can’t draw any conclusions based on only the column %.

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

• Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
• Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
• Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).