Learn About Using Cross Tabs in Quantitative Research
Cross-tabs or cross tabulation is a quantitative research method appropriate for analyzing the relationship between two or more variables. Data about variables is recorded in a table or matrix. A sample is used to gather information about the variable.
The most common type of data collected in cross tabulation is a count of the occurrences of the variables. This count or number is referred to as frequency. The matrix used to show the frequency of the occurrences of the variables being studied is called. A matrix is used to show and analyze frequencies for a particular group or designation.
Cross Tabulation Provides Structure for Quantitative Data
Raw data is easier to manage and understand when it has structure. Tables permit data about variables to be organized. These tables are often called contingency tables. Contingency refers to the possibility that a relationship exists.
Variables describe an attribute of a person, group, place, thing, or idea. Variables can be either categorical (qualitative) or quantitative. Categorical variables are descriptive, often indicating something about the group from which the data is derived. Examples of categorical variables are attribute labels or names.
Study One Variable with Cross-Tabs
Researchers refer to frequency tables by names that indicate the number or arrangement of variables that are being studied. A univariate frequency table shows data about one variable. Often the data in a univariate table is put into groups that consist of a range of values or designations that have been given value or rank. The ranks are then put into order. An example of univariate data would be the frequency at which students earn grade points and fall into the A, B, C or 4.0, 3.5, 3.0 categories for a college course.
Study Multiple Variables with Cross-Tabs
When a frequency table shows data for more than one variable, it is called joint or bivariate contingency table. Bivariate frequency tables often show data in a two-way arrangement. An example of bivariate data would be the frequency with which people from different regions (north, south, east, west) of the country select crunchy snack bars or chewy snack bars.
A Little More About Variables
Quantitative variables can fall into one of two types: Discrete or continuous. Discrete variables can only be an integer value -- that is, a number between zero and infinity. Continuous variables can be any one of the possible values between the permitted or agreed upon maximum and minimum values in a range of values. As a general rule, variable types -- discrete or continuous -- are not used together in the same frequency distribution.
Cross-Tabs Permit Comparisons of Frequency Distributions
Data from frequency distributions can also be shown in a visual way, as in a graph. Distributions are compared by looking at four features of the data: center, spread, shape, and irregularities. Center refers to the point at which half of the data falls on either side of a central point. Spread refers to the variability of the data, with a widespread indicating greater variability than a narrow spread. Shape refers to the symmetry, skewness, or peaks and valleys of the distribution. Irregularities refer to gaps or outliers in the data pattern.
Cross Tabulation Software
A number of software applications specialize in cross-tabulation research. The Greenbook, the guide for buyers of market research services, lists 14 cross tabulation display software in their directory.