Methods of Communication Research and Statistics

Enter the following data into SPSS:

- Age: age measured in years;
- Income: income measured in number of euros per unknown unit of time;
- TV: number of hours spend watching TV per week;
- Radio: number of hours spend listening to the radio per week;
- Trust: trust in the media, measured in points on a scale of 1 to 10;
- Female: 0 = no, 1 = yes.

You might expect that the average amount of time spent watching television on a work day (v5hour, v5min) is related to the average amount of time spent watching television on weekend days (v6hour v6min). Database: Mcodata2.sav.

a. On the basis of (v5hour, v5min) and (v6hour, v6min), create two new variables. One variable for average time spent watching television on workdays, and another for the average number of minutes spent watching television on weekend days. What is the level of measurement of both variables? What is the mean of both variables?

ratio, 143.43, 180.69

The level of measurement of both variables is ratio.

On average 143.43 minutes are spend watching television on workdays.

On average the respondents spend 180.69 minutes watching television at weekend days.

in the population there is no association between average time spent watching television on workdays and average time spent watching television at weekend days.

c. Generate a scatter plot showing both variables mentioned under 1a. Using the scatter plot, give your initial impression of the strength and the direction of the association between both variables.

strong association

The correlation coefficient (r) is .59.

From the correlation coefficient, you can infer that there is a strong positive association. The longer someone watches television on workdays, the more someone watches it on weekend days, and the other way around.

yes, there is a significant strong association between amount of time spend watching television on work days and amount of time spend watching television on weekend days: r = .59, p < .001.

SPSS exercise 6.1