When doing statistics, it is important to always consider whether there are other factors that may vary systematically with the independent variable. If the other factors vary randomly, they are not likely to confound results, but if they vary systematically with the independent variable, they will confound the findings. As you learned, ANOVA is rooted in experimental tradition, and correlation does not equal causation. We cannot randomly assign these persons to assume the presidential office at given times. (Can you imagine if the president were selected at random or assigned to serve in a randomly selected year?) So, although these results can provide insights, we must also consider their limits. Remember, statistics are important in making causal inferences, but statistical findings alone do not support causal inference. A fuller analysis would require considering and controlling for other factors that may be important.

Cohort->

Confidence in Executive Branch

1978 1988 1998 2008 2018

1 1 3 2 2

2 2 2 2 2

3 1 2 2 1

2 3 2 2 2

2 2 2 1 1

3 2 1 2 2

2 3 2 1 2

1 2 2 1 3

1 2 2 1 1

1 1 1 1 2

2 1 2 1 2

2 2 2 1 1

1 1 1 1 2

2 2 1 1 1

2 3 1 1 1

1 2 1 2 1

2 3 2 1 1

1 3 2 1 3

2 2 2 1 2

2 1 3 2 1

2 1 1 1 1

2 3 1 1 2

3 1 2 2 3

2 3 1 2 2

3 2 2 2 2

What was thepvalue you used to make this determination?

Select a choice:

A p= .004

B p= .04

C p> .05