Local Independence - Example

Example

Local independence can be explained by an example of Lazarsfeld and Henry (1968). Suppose that a sample of 1000 people asked whether they read journals A and B. Their responses were as follows:

Read A

Did not read A

Total

Read B

260

140

400

Did not read B

240

360

600

Total

500

500

1000

One can easily see that the two variables (reading A and reading B) are strongly related, and thus dependent on each other. Readers of A tend to read B more often (52%) than non-readers of A (28%). If reading A and B were independent, then the formula P(A&B) = P(A)×P(B) would hold. But 260/1000 isn't 400/1000 × 500/1000. Thus, reading A and B are statistically dependent on each other.

If the analysis is extended to also look at the education level of these people, the following tables are found.

High education

Read A

Did not read A

Total

Low education

Read A

Did not read A

Total

Read B

240

60

300

Read B

20

80

100

Did not read B

160

40

200

Did not read B

80

320

400

Total

400

100

500

Total

100

400

500

Again, if reading A and B were independent, then P(A&B) = P(A)×P(B) would hold separately for each education level. And, in fact, 240/500 = 300/500×400/500 and 20/500 = 100/500×100/500. Thus if a separation is made between people with high and low education backgrounds, there is no dependence between readership of the two journals. That is, reading A and B are independent once educational level is taken into consideration. The educational level 'explains' the difference in reading of A and B. If educational level is never actually observed or known, it may still appear as a latent variable in the model.

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