While my previous posts specifically examined public opinion on federal funding of science, my data analysis project includes some broader variables on the public’s interest in science and the benefits from science. These variables provide a broader picture of whether public interest even exists and the effects of science.

The first new variable is intsci, which asks are you 1 = very interested, moderately interested, or 3 = not at all interested in issues about new scientific discoveries?

The pie chart shows the differences in frequencies for the three possible responses. 85.9% responded either very interested (40%) or moderately interested (45.8%). The median response is moderately interested.

The second variable is bettrlfe, which asked: I’m going to read to you some statements like those you might find in a newspaper or magazine article. For each statement, please tell me if you strongly agree (= 1), agree, disagree, or strongly disagree (= 4). Science and technology are making our lives, healthier, easier, and more comfortable. I used bivariate analysis using sex (1 = male or 2 = female) and degree (0 = lt high school, high school, junior college, bachelor, or 4 = graduate).

From the table, men tend to strongly agree (6.6% difference from women) or strongly disagree (5.2% difference) that science makes our lives better. The research hypothesis would posit a relationship between gender and opinion that science makes our lives better, while the null hypothesis would be no relationship. Pearson’s chi-square has a value of 9.547 with 3 degrees of freedom. SPSS calculates the significance as .023 and gamma at .128 (chosen by identifying bettrlfe as an ordinal variable and sex as a dichotomous nominal variable). The null hypothesis can be rejected, but gamma indicates a very weak positive relationship.

Higher levels of education appear to trend towards slightly higher positive responses (towards strongly agree). The research hypothesis would posit a relationship between level of education and opinion that science makes our lives better, while the null hypothesis would be no relationship. Pearson’s chi-square has a value of 35.214 with 12 degrees of freedom (and 4 cells with values under 5). SPSS calculates the significance as .000 and gamma at -.173 (chosen by identifying bettrlfe and degree as an ordinal variables). The null hypothesis can be rejected, but gamma indicates a very weak negative relationship.

From previous analyses, I’m not surprised that gender has a very weak relationship to variables about science. However, I did expect a stronger relationship to emerge from level of education since it seems to be commonly cited as a measure of scientific literacy. Perhaps the relationship would seem a little stronger with collapsed categories (removing some high school, combining high school with junior college, and combining bachelor’s with graduate).

This is an interesting and timely research question. You’ve got some very low numbers of cases in your second crosstab here. For your paper, I’d suggest collapsing SD with D for the DV, since there are only 19 respondents in this category. This is also true of the degree variable to a lesser extent, but you might want to dichotomize this variable as 4-year college degree and no 4-year college degree. Finally, if you use degree as the row variable in the second table , you will need only row percentages. Including the column percentages also is confusing. Finally, be sure to discuss the joint percentage distribution for all crosstabs. Saying that a relationship is negative is not helpful to a reader, unless you tell them how the variable is coded .