Let's begin with the simplest possible crosstabulation, the 2 by 2 table. Suppose we were interested in the relationship between age and the graying of people's hair. We took a sample of 100 subjects, and determined who does and does not have gray hair. We also recorded the approximate age of the subjects. The results of this study may be summarized as follows:

Gray Hair |
Age |
Total | |

Below 40 |
40 or older |
||

No |
40 |
5 |
45 |

Yes |
20 |
35 |
55 |

Total |
60 |
40 |
100 |

While interpreting the results of our little study, let's introduce the terminology that will enable us to generalize to complex tables more easily.

Design variables and response variables. In Multiple Regression or analysis of variance (ANOVA/MANOVA), we customarily distinguish between independent and dependent variables. Dependent variables are those that we are trying to explain, that is, that we hypothesize to depend on the independent variables. We could classify the factors in the 2 by 2 table accordingly: we may think of hair color (gray, not gray) as the dependent variable, and age as the independent variable. Alternative terms that are often used in the context of frequency tables are response variables and design variables, respectively. Response variables are those that vary in response to the design variables. Thus, in the example table above, hair color can be considered to be the response variable, and age the design variable.

Fitting marginal frequencies. Let's now turn to the analysis of our example table. We could ask ourselves what the frequencies would look like if there were no relationship between variables (the null hypothesis). Without going into details, intuitively we could expect that the frequencies in each cell would proportionately reflect the marginal frequencies (Totals). For example, consider the following table:

Gray Hair |
Age |
Total | |

Below 40 |
40 or older |
||

No |
27 |
18 |
45 |

Yes |
33 |
22 |
55 |

Total |
60 |
40 |
100 |

In this table, the proportions of the marginal frequencies are reflected in the individual cells. Thus, 27/33=18/22=45/55 and 27/18=33/22=60/40. Given the marginal frequencies, these are the cell frequencies that we would expect if there were no relationship between age and graying. If we compare this table with the previous one, we will see that the previous table does reflect a relationship between the two variables: There are more than expected (under the null hypothesis) cases below age 40 without gray hair, and more cases above age 40 with gray hair.

This example illustrates the general principle on which the log-linear analysis is based: Given the marginal totals for two (or more) factors, we can compute the cell frequencies that would be expected if the two (or more) factors are unrelated. Significant deviations of the observed frequencies from those expected frequencies reflect a relationship between the two (or more) variables.

Model fitting approach. Let's now rephrase our discussion of the 2 by 2 table so far. We can say that fitting the model of two variables that are not related (age and hair color) amounts to computing the cell frequencies in the table based on the respective marginal frequencies (totals). Significant deviations of the observed table from those fitted frequencies reflect the lack of fit of the independence (between two variables) model. In that case we would reject that model for our data, and instead accept the model that allows for a relationship or association between age and hair color.

See also, Exploratory Data Analysis and Data Mining Techniques.