political efficacy and introductory political science course: findings from a survey of a large...

Political Efficacy and Introductory Political Science Course: Findings from a Survey of a Large Public University

Miguel Centellas University of Mississippi [email protected]

Cy Rosenblatt University of Mississippi

[email protected]

Abstract

We conducted a survey of the student population enrolled in undergraduate introductory-level courses in political science at a large public university. We were interested to test whether completing undergraduate introductory-level courses in political science had any effects on political efficacy, using some standard indicators (drawn from the ANES battery), at the individual level. Our findings suggest that earning a high grade (when controlling for various other factors) does seem to positively affect internal political efficacy at the individual level, but that the most important factor affecting external political efficacy is the number of courses completed. However, we found no evidence that completing any undergraduate introductory-level course in political science had any affect on whether students believed that they were capable of understanding politics.

Paper presented at the American Political Science Teaching & Learning Conference, February 16-19, 2012, Washington, DC.

Centellas & Rosenblatt 1

Introduction Among the many potential goals of the undergraduate political science curriculum is civic education. This is reflected in the discussions devoted to fostering or improving students’ civic “engagement” across college and university campuses, both within the academic colleges and departments and in offices of student life. As a discipline, political science has a long history of involvement in civic education, going back to John Dewey (1916) and interest in social reform during the Progressive Era. The most recent phase began in 1996, with the formation of the APSA Task Force on Civic Education (Ostrom 1996). While few of us would describe what we do in the classroom as “teaching civics,” we believe most of us appreciate that a significant part of what we do—particularly in the introductory (or “100-level”) courses in our discipline—serve that function to some extent. This is probably most pronounced in introduction to American government courses, in which significant attention is placed on familiarizing students with the theory and practice of the political system of the United States. Our study approaches the question of civic education by looking at the relationship between political efficacy and completion of an undergraduate-level introductory course in political science. Political efficacy has long been defined as “the feeling that individual political action does have, or can have, an impact upon the political process … the feeling that political and social change is possible, and that the individual citizen can play a part in bringing about this change” (Campbell, Gurin, and Miller 1954, 187). As survey research became prominent in the early days of the behavioral revolution, researchers sought to measure levels of political efficacy. Prominent early behavioralists like David Easton were instrumental in operationalizing the concept of political efficacy, and developing survey tools to measure levels of efficacy. Questions regarding political efficacy have been included in the American National Election Study (ANES) survey since the 1950s. Subsequent decades saw a broad range of studies that either tried to explain how individual acquired (or were “socialized” into) political efficacy values or sought to used existing levels of political efficacy to explain individual political behavior. Our study seeks to explain levels of political efficacy among the undergraduate student population at a large public university. While political efficacy is not synonymous with “civic” education, we believe the two concepts are related. Traditionally, civic education is meant to include a broader appreciation of how the political system works and how individuals can effectively participate in that system. Research in the field of political socialization has long explored the relationship between education and individuals’ internalized political attitudes—including political efficacy (e.g. Easton and Dennis 1967; Luskin 1987; Niemi and Junn 1998). The thrust of findings over time has been that education has a positive impact on raising political efficacy in individuals. In terms of civic education, much of the discussion has focused on what we term “knowledge acquisition.” That is, following the tradition of John Dewey, proponents of civic education argue that increasing “political literacy” (factual knowledge about American politics) significantly improves individuals’ political efficacy, which in turns improves the health of our democracy. In this paper, we seek to assess the impact of education on levels of political efficacy. We do so using a survey instrument administrated in two waves: during the first and last weeks of a single semester. Our survey included the six questions used by the ANES to measure political efficacy, along with a battery of additional questions. We did not assess specific outcomes of individual students (what they may have learned about American politics), but rather whether


there were any systemic effects on political efficacy simply as a product of completing an undergraduate introductory political science course. The Survey: Data and Methodology Our study involved a survey administered in two waves across undergraduate introductory courses in political science at the University of Mississippi, a large public university, during the fall of 2011. The first wave was administered during the first week of the semester; the second wave was administered at the end of the semester, fourteen weeks later. The surveys were not anonymous, allowing us to match individual responses across both waves.1 Students had the option to participate in the survey, and were asked to fill out a consent form, which we retained on file. The surveys were distributed in all three introductory-level courses offered by the department: Introduction to American Politics (POL 101), Introduction to Comparative Politics (POL 102), and Introduction to International Relations (POL 103). Table 1 describes enrollment figures, across the three courses, at the start and end of the semester, as well as the overall participation rate across sections. Table 1. Student enrollment in introductory-level courses and survey participation rates at start and end of the semester. POL 101 POL 102 POL 103 Total Start of Semester

Number of sections 10 5 4 19 Mean section size 91.8 32.6 34.5 64.2 Number of students 918 163 138 1,219 Participation Rate (%) 42.92 76.69 67.39 50.29

End of Semester

Number of sections 10 5 4 19 Mean section size 83.2 29.2 32.25 58.3 Number of students 832 146 129 1,107 Participation Rate (%) 34.01 64.38 51.94 40.11

Note: The two largest sections of POL 101 did not participate in the survey, each had nearly 200 students; this makes the effective means section size for POL 101 sections at the start of the semester 66.3 (61.3 at the end of the semester). Two small sections of POL 103 (both taught at satellite campuses) did not participate; this makes the effective mean section size for POL 103 at the start of the semester 60 (57 at the end of the semester). Our survey included a total of twenty-four questions, in addition to additional questions (birth year and gender) that we asked students to fill in on a Scantron answer sheet we provided. Our survey instrument (see Appendix A) included seven demographic questions (Q1-Q7), eight questions about political opinions and attitudes (Q8-Q15), and nine questions about news and media consumption (Q16-Q24). In addition to the students’ survey responses, we also collected final grade reports (provided by our university’s Office of the Registrar). This allowed us to match up student answers with their individual grades, a key component of our analysis. We should note that although we had relatively high participation rates, particularly in the POL 102 and POL 103 courses, several students did not complete both surveys (completing only one or

1 Our research design was approved by our university’s Institutional Review Board. It is filed as: “Collaborative Assignment and Effects on News Media Consumption and Political Efficacy” (Protocol #12-031). Our original research design included a broader study of political efficacy and news media consumption, as well as the effects of a collaborative assignment involving news media presentations. This paper is the first of a series of reports we intend to produce from this dataset. 2 For most other combinations, Cronbach’s α < 0.3. 3 Pairwise correlation tests for all six pairings comparing first wave (start of semester) and second wave (end of


the other survey). In addition, a number of students (160 total) did not provide their name on the Scantron sheet, making it impossible to match their survey responses. These students were included in aggregate-level analysis, but were not included in our later analysis, which looked at the probability that an individuals’ political efficacy levels changed by the end of the semester. Because we did not have complete participation of all sections, we were concerned that our sample might not be representative of all 100-level students enrolled in our department that semester. Using individual grade data, which was made available to us, we were only able to assess whether our sample was significantly different from the total population using a two-tailed t-test of mean grades between our sampled sections and the total population. Using this very crude test, we found that our surveyed population had a slightly lower mean grade than the total population (Probability T < t = 0.0036), and the un-sampled population had a slightly higher mean grade than the total population (Probability T > t = 0.0010). However, when we tested for differences in section mean grades across surveyed and un-surveyed population, we found no statistically significant difference between the two groups (Probability |T| > |t| = 0.2360 and 0.1428). Individual t-tests by section found that only five of our sampled sections had means that were significantly different from the overall mean (including a small honors section of 11 students) and ten of our sampled sections had means that were not significantly different from the total mean. Still, we should be clear to note that we could not test for any differences between our sample and the general population beyond that single variable. Overall, our sample was relatively diverse (see Appendix B), although with some skewed characteristics we had expected (e.g. our sample included more conservatives than liberals). We were also pleased to find that our samples were remarkably similar across all three courses on most of the main indicators. Not surprisingly, freshmen and sophomores dominated our sample, and freshmen were particularly most pronounced in POL 101. We noted that women and minorities were underrepresented in our sample, relative to the general student population. The singular exception was self-identified Hispanic students, who were slightly over-represented in the first wave of the survey. The second wave survey saw a further decrease in women and minority enrollment, including self-identified Hispanic students. The singular exception in the second wave was that African-American students increased in representation in the second wave survey, but only in POL 102. However, binomial tests showed that there were no statistically significant differences between our sampled population and the university ratios for minorities, even when sorting by course. Female underrepresentation was statistically significant, overall, but it was not significant in the second wave survey (it was also not statistically significantly different in the first wave for the POL 103 subpopulation). In this paper, we focus on political efficacy. Our survey included six questions (Q10-Q15) that sought to measure levels of political efficacy among our student population. All six questions are drawn from the ANES battery. These were:

Q10. Voting is the only way that people like me have any say about how the government runs things.

Q11. People like me have no say about what the government does. Q12. Sometimes politics and government seem so complicated that a person like me

can’t really understand what’s going on. Q13. Public officials don’t care about what people like me think.


Q14. Those we elect to Congress lose touch with the people pretty quickly. Q15. Parties are only interested in peoples’ votes but not their opinion.

The first three questions (Q10-Q12) have been used by ANES to assess subjects’ internal efficacy (belief in the ability to understand and influence politics). The other questions (Q13-Q15) have been used by ANES to assess subjects’ external efficacy (belief that government responds to citizens). In the ANES studies, such questions have frequently been combined to produce internal and external political efficacy indexes. Before simply combining subject responses to the above questions into internal or external (or joint) indexes of political efficacy, we tested their covariance. To our surprise, we found no structural relationship between the questions using principal component analysis. The strongest relationship was between the three questions intended to measure external efficacy, but the relationship was very weak; they did not load on any principal component and had a low internal consistency (Cronbach’s α = 0.6040).2 At first we suspected possible error bias, with a strong probability that respondents did not understand the questions and did not provide meaningful responses. However, as Appendix C illustrates, the aggregate averages on all six indicators were similar across all courses and—more importantly—individual responses were highly consistent (as later predictive analysis will emphasize).3 Thus, we were unable to construct internal or external efficacy indexes. We opted instead to test changes in individual student responses across time on all six questions. Social Determinants of Internal and External Efficacy Before analyzing any potential changes in individual efficacy (as measured across six distinct indicators), we first wanted to establish any relationships between individual responses to our six efficacy indicators and other student characteristics. Because each question was bound by a simple binary response (“Agree” or “Disagree”), we relied on logistic regression analysis. We tested models that included a range of demographic indicators, as well as responses to several news media and social media consumption questions. We included variables related to media consumption because studies have shown that media has a significant effect on political attitudes, particularly on young people (e.g. Pinkleton et al 1998; De Vreese 2002; Pinkleton and Austin 2004). In order to understand and gage any potential impact on political efficacy produced by completing an undergraduate introductory-level course in political science, we believe it is important to first determine—and later control for—any factors that affect a students’ prior political efficacy. Our survey included a range of demographic questions, which were incorporated into our basic regression models. These include: birth year, gender (male/female), race/ethnicity, class standing, parents’ education, and type of hometown (see Appendix B) The tables reported below only include birth year if the variable was significant; in most cases it was not significant and introducing it actually reduced the strength of the predictive model. Birth year and gender were problematic variables. We did not include separate questions for either variable in our

2 For most other combinations, Cronbach’s α < 0.3. 3 Pairwise correlation tests for all six pairings comparing first wave (start of semester) and second wave (end of semester) responses showed positive correlations with statistical significance at the p > 0.001.


questionnaire, relying instead on the Scantron sheet itself, which included places for respondents to indicate their year of birth and gender. A number of students did not complete this portion of the questionnaire, moving instead directly to the survey questions, which were answered on the other side of the Scantron sheet. However, we were able to reconstruct prior gender and/or birth year variables for both surveys if the student responded to the question in at least one of the two survey waves.4

Our race/ethnicity variables were scaled from two different survey questions (Q1 and Q2). The first (Q1) simply asked a student whether he or she self-identified as “Hispanic” or not. The second (Q2) asked the student whether he or she identified with one of the following categories: White, Black or African American, Asian, Native American or Pacific Islander, and Other. These two questions were taken from the most recent US Census questionnaire, though the more detailed race/ethnicity question was truncated to the maximum five categories available on the Scantron sheet. We also created two dummy variables for “White” and “Black”—we did not have enough of data to create dummy variables for other race/ethnicity categories.

Again, our three demographic indicators related to gender and race/ethnicity were relatively stable across our three subpopulations (defined by courses) and were not significantly different from the university population. The exception was gender, but women were only statistically under-represented in the first wave (and then only in the POL 101 and POL 102 subpopulations).

Our question about class standing asked students to identify themselves by the four standard categories employed at our university: freshman, sophomore, junior, and senior. We included a category for “other,” but dropped those from our sample. Not surprisingly, our sample was dominated by freshmen (just over 45 percent in both waves), with most of these found in the POL 101 subpopulation (58.7 percent and 59.4 percent in the first and second waves, respectively). Seniors were the least represented group, comprising less than 8 percent of the total population. We did note that POL 102 had the largest number of seniors (just over 17 percent in both waves), but even here it was the smallest.

Our survey included two questions that asked students to state their parents’ education. The first (Q5) asked about the respondent’s father’s education; the second (Q6) asked about the respondent’s mother’s education. Both questions provided a five-point scale ranging from “Did not complete high school” to “Graduate degree (masters-level or above).” We tested for correlation between the two questions and found that they were significantly correlated, at both first wave (Pearson’s r = 0.5196; p > 0.001) and second wave (Pearson’s r = 0.5006; p > 0.001). This gave us the confidence to construct a “parents’ education” index that summed both variables, creating a nine-point scale. Our sample population leans towards higher education, with fewer than 5 percent of respondents stating that any one of his or her parents had not completed high school; we were also surprised to find that nearly a third of all respondents stated that at least one of his or her parents had a graduate degree.5 Data in Appendix B suggest that

4 We should note that the problem of the double-sided Scantron sheet should not be under-estimated. Because the questions were answered on the other side of the sheet, several respondents also failed to provide their name. This meant that we were unable to match first and second wave surveys, as well as unable to use student grade as a predictive variable in later models. We recommend that future survey efforts take this factor into consideration. 5 We also ran simple logistic regressions on each of our key race/ethnicity dummy variables (Hispanic, Black, and White). We found that students who self-identified as Hispanic and Black or African American were statistically significantly more likely to have less-educated parents than those who self-identified as White; there was no effect for type of hometown. Logistic regression tests of relationship between our dummy variable for gender (Female) showed no relationship to parents’ education or type of hometown.


students in POL 103 were more likely to have higher educated parents, which was confirmed by a t-test (T > t = 0.0104); t-tests showed no statistically significant difference for the mean level of parents’ education for POL 101 and POL 102 subpopulations.

We also asked respondents to tell us how they would describe their hometown (Q7) on a five-point scale that included the options: rural area, small town, small city, suburb of a large city, and large city. Our sample population was neither rural nor urban (though the distribution leaned towards urban). Additional t-tests showed no statistically significant differences between any of the three subpopulations relative to the total population mean.

In addition to our demographic indicators, we also asked questions about attitudes towards politics and media consumption. We had to drop the question that asked students about their level of interest in politics due to a survey construction error (there were two “very interested” responses and no “not very interested” option). We were able to retain the question that asked about political views, defined on a five-point liberal-to-conservative spectrum. As reported in Appendix B, our students leaned conservative, though the mean was at the midpoint between “moderate” and “conservative” (µ = 3.42 at the start of the semester; µ = 3.39 at the end of the semester). There was also no statistically significant difference in course means relative to the total mean, for either survey wave.

Our survey also included five questions (Q17-Q22) about news media consumption in various formats: newspaper, television, magazines, radio, and the Internet. Each of the questions used the same four-point scale: every day, at least once a week, a few times a month, and rarely or never. We tested for covariance between the five indicators, and were surprised to find that (unlike our efficacy questions) responses were significantly inter-correlated for the first wave (Cronbach’s α = 0.7174) and nearly for the second wave (Cronbach’s α = 0.6974). We felt confident in their covariance to construct a composite variable “media” that averaged the five indicators. The mean for our “news media” variable was just above the midpoint of our 1-4 scale for both survey waves (µmedia1 = 2.569 and µmedia2 = 2.575). Overall, our student population moderately consumed news media in various forms and this did not significantly change from the start to the end of the semester.

Our survey also included two questions about social media usage. The first (Q23) asked students to respond whether they had posted a news or current events story through Facebook, Twitter, or other social media in the past week. The second (Q24) asked students to respond whether someone else had sent them a news or current events story through email, Facebook, Twitter, or other social media. Our students are interesting major social media users. Only 29.9 percent of respondents in the first wave reporting posting a news or current events item to a social media service like Facebook or Twitter; that number slightly increased to 34.0 percent in the second wave. However, 65.6 percent of respondents in the first wave claimed that someone else had sent them a news or current events story through a social media service; that number also slightly increased in the second wave to 66.4 percent. Because of this difference, we did not expect any correlation between the two social media indicators. Nevertheless, we found that they were significantly correlated in both the first wave (Pearson’s r = 0.2853; p > 0.001) and second wave (Pearson’s r = 0.3157; p > 0.001). This gave us the confidence to construct a “social media” variable that summed both variables, creating a three-point scale ranging from 0 (no social media use) to 2 (consumers and producers of social media content). Our new indicator showed that 25.7 percent of our first wave participants both consumed and produced social media content, compared to 30.3 percent who did neither; by the second wave those numbers had only slightly changed to 29.6 percent and 29.1 percent, respectively. Overall, our student


population was only moderated engaged with social media and this did not significantly change from the start to the end of the semester.

We tested for any possible determinants of political efficacy at both ends of the study, using logistic regression models. Because these first tests looked at only a single point in time, we were able to include anonymous responses (surveys from students who did not put their names on the Scantron answer sheet) and identifiable surveys from students who later dropped the course (and therefore out of our longitudinal study).

Table 2 shows the results of logistic regression analysis of possible factors predicting responses to our internal efficacy questions in the first wave. The first thing to note is that all of our single-wave logistic regression analyses perform relatively poorly (very low pseudo R-square values and probably > Chi-square values). Still, a few variables are statistically significant and will be used as control variables in later longitudinal models and therefore worth mentioning. In both waves, news media consumption was a significant predictor of responses; in all cases it was also negatively related to efficacy. Heavier news media consumers were more likely to believe voting was the only way they could influence government, to believe they had little say in politics, to claim that government and politics was too complicated. Table 2. Logistic regression analysis of determinants of internal efficacy indicators (at start of semester) Q10. Voting is the

only way that people like me have any say about how the government runs things.

Q11. People like me have no say about what the government does.

Q12. Sometimes politics and government seem so complicated that a person like me can’t really understand what’s going on.

Birth Year ** -0.202 Female 0.140 -0.266 * 0.401 Hispanic -0.895 1.535 -0.510 Black -0.129 0.773 0.614 White (Non-Hispanic) -0.401 0.989 0.596 Class Standing -0.068 -0.218 -0.034 Parents’ Education ** -0.153 0.021 -0.007 Hometown -0.005 0.109 0.098 Political Views * 0.241 -0.010 0.030 News Media Consumption *** 0.592 * 0.464 ** 0.412 Social Media -0.140 0.124 -0.122 Constant -1.033 398.634 -2.003 N (Observations) 444 426 445 Probability > Chi-square 0.0001 0.0265 0.0165 Pseudo R-square 0.0611 0.0517 0.0353

No other variables had consistent impacts. The level of parental education was only significant in predicting responses to questions about voting (students with higher educated parents were less likely to think voting was the only way they could influence politics). Political views were just barely significant in predicting this as well, with more conservative students more likely to think voting was their only way to influence politics. Birth year was only significant in predicting response to questions about whether “people like me” have any say in


what government does—but only in the first wave (this was also the only model in which birth year had any significant impact at all; in most models including the variable also reduced the overall goodness of fit indicators for the model). Younger students were more likely to think they had an impact on government. Finally, in the first wave, gender was slightly significant, with women more likely to claim that politics was too complicated for them to understand.

Table 3 shows the results of logistic regression analysis of possible factors predicting responses to our internal efficacy questions in the second wave. As before, the news media variable was a significant predictor of responses to all three internal efficacy questions, and in the same direction (greater news media consumption decreased efficacy on each indicator). In the second wave of the survey, however, any effects from other variables (parents’ education, birth year, and gender) dropped out Table 3. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q10. Voting is the

only way that people like me have any say about how the government runs things.

Q11. People like me have no say about what the government does.

Q12. Sometimes politics and government seem so complicated that a person like me can’t really understand what’s going on.

Female 0.062 -0.358 0.464 Hispanic 0.686 0.952 0.361 Black 1.956 -0.376 0.181 White (Non-Hispanic) 1.177 0.075 0.565 Class Standing 0.056 0.317 -0.050 Parents’ Education -0.084 -0.010 0.008 Hometown 0.091 0.096 -0.024 Political Views 0.098 0.033 -0.089 News Media Consumption * 0.387 * 0.537 ** 0.564 Social Media 0.180 -0.173 0.025 Individual Course Grade * -0.309 ** -0.437 -0.046 Section Average GPA 0.031 0.225 0.014 Constant -2.163 -3.110 -1.930 N (Observations) 305 305 305 Probability > Chi-square 0.0214 0.1063 0.1408 Pseudo R-square 0.0580 0.0665 0.0412 For the second wave, we included both individual students’ grades and section averages. Our interest here was to see if completing an undergraduate introductory-level course in political science had any positive impact on internal political efficacy. We included both individual students’ grades and section means to control for any potential effects from grade inflation or deflation across sections.6 As expected, grades were significantly correlated with greater internal 6 While most section means were not statistically significant from the total population mean, three were significantly higher and two had means that were significantly lower (based on previously reported t-tests). The sections with significant mean grade deviation from the total mean included one honors section of POL 102 and one section of POL 101 and POL 103; the two sections with section means significantly lower than the total mean were a section of POL 101 and POL 103.


political efficacy—but only along two indicators. Doing well in an introductory-level undergraduate course in political science only seemed to increase the probability that a respondent would think he or she had an impact on politics, and that it was not limited to voting. Somewhat troubling was the finding that doing well in an introductory-level undergraduate course in political science did not decrease the probability that a respondents would say that he or she thought politics was too complicated to understand. Table 4 shows the results of logistic regression analysis of possible factors predicting responses to our external efficacy questions in the first wave. As before, news media consumption had an impact. It significantly increased the probability that respondents would believe that public officials did not care about them and that parties were only interested in peoples’ votes; it had no significant effect on whether respondents believed that “those in Congress lose touch with the people.” Instead, type of hometown and class standing had such an effect: respondents from more urban hometowns and upper-classmen were more likely to believe their elected representatives lost touch with them. Table 4. Logistic regression analysis of determinants of external efficacy indicators (at start of semester) Q13. Public

officials don’t care about what people like me think.

Q14. Those we elect to Congress lose touch with the people pretty quickly.

Q15. Parties are only interested in people’s votes but not their opinion.

Female -0.329 0.002 0.039 Hispanic 0.465 -0.084 0.542 Black 0.178 0.354 1.409 White (Non-Hispanic) 0.682 0.001 0.933 Class Standing -0.002 * 0.255 0.145 Parents’ Education -0.065 -0.070 -0.082 Hometown -0.045 * 0.196 0.042 Political Views -0.149 0.063 -0.188 News Media Consumption ** 0.403 -0.040 * 0.316 Social Media 0.006 -0.004 -0.033 Constant -1.051 -0.169 -0.590 N (Observations) 441 443 442 Probability > Chi-square 0.2017 0.3378 0.1294 Pseudo R-square 0.0267 0.0192 0.0248 Table 5 shows the results of logistic regression analysis of possible factors predicting responses to our external efficacy questions in the second wave. This time, however, our results were particularly intriguing and did not fit any expected outcomes. For responses at the end of completing an undergraduate introductory-level course in political science, news media no longer predicted responses to any of the three questions. For Q13, parents’ education and political views were statistically significant predictors: students whose parents had higher levels of education or who were conservative were least likely to believe that public officials do not care about them. For Q15, parents’ education and class standing were statistically significant predictors, but in opposite directions: upper-classmen were more likely to believe parties only cared about their votes, while students whose parents had higher levels of education were less likely to think so. None of our variables were significant predictors of students’ views about


whether congressional representatives lost touch with the people (this was consistent across any model specification). Table 5. Logistic regression analysis of determinants of external efficacy indicators (at end of semester) Q13. Public

officials don’t care about what people like me think.

Q14. Those we elect to Congress lose touch with the people pretty quickly.

Q15. Parties are only interested in people’s votes but not their opinion.

Female 0.097 -0.439 -0.120 Hispanic -1.222 1.178 -0.269 Black -0.742 1.717 -0.360 White (Non-Hispanic) -0.620 0.907 -0.873 Class Standing 0.161 0.147 * 0.273 Parents’ Education * -0.151 -0.108 * -0.155 Hometown 0.134 0.065 0.106 Political Views * -0.342 -0.074 0.047 News Media Consumption -0.065 0.210 0.188 Social Media -0.028 0.100 0.006 Individual Course Grade -0.190 -0.090 0.010 Section GPA 0.102 0.150 -0.598 Constant 1.903 -0.735 2.165 N (Observations) 305 305 301 Probability > Chi-square 0.2440 0.3627 0.0651 Pseudo R-square 0.0389 0.0312 0.0485 Overall, our single-wave regression analysis suggested some interesting conclusions: Internal efficacy was more consistently affected by news media consumption at the start of the semester, but “successfully” completing (i.e. earning a high grade) an undergraduate introductory-level in political science seemed able to increase internal political efficacy. The story for external efficacy was more complicated: News media consumption was somewhat significant (in two of three indicators) in effecting external political efficacy at the start of the semester. Parents’ education also seemed to have some effect, but only at the end of the semester. Completing an undergraduate introductory-level course seemed to have no effect on external efficacy, as measured by our three indicators. The most troubling finding is the possibility that “successfully” completing an introductory-level course in political science did not seem to improve the probability that students would think they could understand politics. Predicting Changes in Individuals’ Levels of Internal and External Efficacy Because we can match up individual responses from the first and second wave of the survey, we are able to test for possibility of individual-level change in internal and external efficacy after completing an undergraduate introductory-level course in political science. The following tables and discussion test for individual-level changes. Again, we use logistic regression analysis, but this time we introduce dummy variables for all three courses (POL 101, POL 102, POL 103) to also test for any potential course-specific effects. For parsimony, our


models drop all our race/ethnicity variables, which had no effect in any previous models.7 Overall, our logistic regression models confirm most—but not all—of our previous findings. We should note that all of these models perform significantly better, with probability Chi-square values of 0.000 and pseudo R-square values of more than 0.15 (which is a significant improvement relative to the single-wave models). We should also note that our longitudinal analyses have a smaller N than our single-wave studies, since a number of students filled out questionnaires in only one wave (or did not put their name on the answer sheet on one or the other wave). Because of our smaller N, we tested for course-level effects by using dummy variables for each class, rather than running different models across course subpopulations. Table 6 shows the results of logistic regression analysis of possible factors predicting responses to first internal efficacy question (Q10). As with all the following models, a students’ previous response is a highly statistically significant predictor (p > 0.001). Individual course grade is again a statistically significant predictor, and across all courses. We reintroduced birth year in the model, and it was a significant predictor across all courses. Students who earned a higher grade (when controlling for other factors, including section mean grade) were more likely to disagree with the statement that voting was the only way they could influence politics. Additionally, younger students were more likely (even when controlling for class standing, which had no independent effect) in the same direction.8 No other variables were statistically significant predictors. Table 6. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q10. Voting is the only way that people like me have any say

about how the government runs things. Model A Model B Model D Birth Year * -0.359 * -0.358 * -0.358 Female 0.031 0.029 0.031 Class Standing -0.313 -0.315 -0.311 Number of Courses (1-3) -0.434 -0.441 -0.423 Parents’ Education -0.013 -0.013 -0.012 Political Views (at start of semester) -0.037 -0.036 -0.037 News Media Consumption -0.007 -0.004 -0.009 Individual Course Grade * -0.386 * -0.385 * -0.386 Section Average GPA 0.210 0.204 0.204 Previous Response (Q10) *** 2.012 *** 2.014 *** 2.011 POL 101 dummy -0.000 POL 102 dummy 0.042 POL 103 dummy -0.061 Constant 715.774 713.704 714.150 N (Observations) 259 259 259 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.1998 0.1998 0.1999 7 We did test some models that included race/ethnicity variables. But these were not significant and significantly reduced the model’s goodness of fit measures. 8 One would expect birth year (i.e. age) and class standing to be correlated, and this was the case in our dataset. Nevertheless, because of the wide range of reported birth years (low 1952; high 1994), we chose to include both variables to control for independent effects. Logit regression models that dropped birth year did not affect the significance of class standing, but did lower the goodness of fit. The mean birth year was 1990.929, with a standard deviation of 3.31.


Table 7 shows the results of logistic regression analysis of possible factors predicting responses to second internal efficacy question (Q11). Individual course grade is again a statistically significant predictor, and across all courses. Students who earned a higher grade were more likely to disagree with the statement that they had little influence on government. This time, however, individual self-placement on a liberal-conservative political spectrum at the start of the semester was a statistically significant predictor: more conservative students were more likely to believe they had little influence on government. This was consistent with our earlier findings. No other variables were statistically significant predictors. Table 7. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q11. People like me have no say about what the government

does. Model A Model B Model D Birth Year -0.072 -0.077 -0.077 Female -0.664 -0.642 -0.626 Class Standing 0.225 0.266 0.235 Number of Courses (1-3) -0.893 -0.716 -0.909 Parents’ Education 0.047 0.051 0.029 Political Views (at start of semester) * 0.514 * 0.525 * 0.521 News Media Consumption 0.506 0.482 0.493 Individual Course Grade * -0.359 * -0.362 * -0.366 Section Average GPA -0.142 -0.152 -0.033 Previous Response (Q11) *** 2.067 *** 1.963 *** 2.052 POL 101 dummy -0.374 POL 102 dummy -0.113 POL 103 dummy 0.755 Constant 140.076 149.514 149.827 N (Observations) 261 261 261 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.1899 0.1870 0.1948

Table 8 shows the results of logistic regression analysis of possible factors predicting responses to third internal efficacy question (Q12). Birth year was again a statistically significant predict, and across all models. Interestingly, younger students were more likely to disagree with the statement that politics was too complicated for them to understand. As with the earlier models on this question, individual course grade had no significant effect. This was consistent with our earlier findings. This finding is an interesting puzzle: successful completion of an introductory-level undergraduate course in political science seems to have no significant effect (with the possible exception of POL 103; p > 0.052) on convincing students that they could understand politics; but younger students were more likely to believe they could understand politics after completing a course. No other variables were statistically significant predictors.

Table 9 shows the results of logistic regression analysis of possible factors predicting responses to first external efficacy question (Q13). This time, the only statistically significant predictor, across all classes, was the number of courses a student took. Our sample included several students who took more than one introductory-level course that same semester. Of all enrolled students at the end of the semester who answered a survey, 22 were enrolled in two courses (an additional three were enrolled in two courses, but did not participate in our study)


and one was enrolled in all three courses (an additional student was enrolled in all three courses, but did not participate in the study). The only significant factor in predicting whether students believed public officials cared about their opinions seemed to be number of courses taken. No other variables were statistically significant predictors. Table 8. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q12. Sometimes politics and government seem so

complicated that a person like me can’t really understand what’s going on.

Model A Model B Model D Birth Year * -0.292 * -0.302 * -0.303 Female 0.044 0.066 0.039 Class Standing -0.418 -0.383 -0.425 Number of Courses (1-3) -0.537 -0.370 -0.640 Parents’ Education 0.034 0.033 0.018 Political Views (at start of semester) -0.042 -0.053 -0.046 News Media Consumption 0.372 0.341 0.362 Individual Course Grade 0.136 0.133 0.134 Section Average GPA -0.105 -0.030 0.035 Previous Response (Q12) *** 2.148 *** 2.096 *** 2.216 POL 101 dummy -0.221 POL 102 dummy -0.340 POL 103 dummy ~ 0.861 Constant * 580.212 * 600.237 * 602.305 N (Observations) 262 262 262 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.2064 0.2075 0.2159 Table 9. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q13. Public officials don’t care about what people like me

think. Model A Model B Model D Female 0.492 0.482 0.490 Class Standing 0.288 0.290 0.311 Number of Courses (1-3) ** -2.009 * -1.976 * -1.923 Parents’ Education -0.081 -0.075 -0.076 Political Views (at start of semester) -0.055 -0.051 -0.056 News Media Consumption -0.133 -0.132 -0.146 Individual Course Grade -0.086 -0.087 -0.086 Section Average GPA -0.029 -0.067 -0.023 Previous Response (Q13) *** 1.743 *** 1.731 *** 1.717 POL 101 dummy -0.255 POL 102 dummy 0.280 POL 103 dummy 0.071 Constant 1.528 1.310 1.221 N (Observations) 263 263 263 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.1649 0.1648 0.1632


Table 10 shows the results of logistic regression analysis of possible factors predicting responses to second external efficacy question (Q14). This time, no variables had any statistically significant ability to predict (when controlling for their response at the start of the semester) an individual’s response at the end of the semester to a statement about whether congressional representatives lose touch with their constituents. However, it may be worth noting that number of courses was almost statistically significant in predicting a higher propensity for students to disagree with the statement that congressional representatives lose touch with their constituents in POL 102 courses. Table 10. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q14. Those we elect to Congress lose touch with the people

pretty quickly. Model A Model B Model D Female -0.376 -0.379 -0.406 Class Standing 0.109 0.066 0.093 Number of Courses (1-3) -0.576 ~ -0.730 -0.552 Parents’ Education -0.077 -0.086 -0.068 Hometown 0.089 0.083 0.101 Political Views (at start of semester) -0.233 -0.221 -0.232 News Media Consumption 0.111 0.136 0.137 Individual Course Grade -0.060 -0.053 -0.062 Section Average GPA -0.074 -0.105 -0.204 Previous Response (Q14) *** 1.797 *** 1.785 *** 1.785 POL 101 dummy 0.447 POL 102 dummy 0.037 POL 103 dummy * -0.879 Constant 0.599 1.165 1.183 N (Observations) 265 265 265 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.1491 0.1433 0.1555

Table 11 shows the results of logistic regression analysis of possible factors predicting responses to first external efficacy question (Q15). Again, the only variable that could significantly predict how an individual responded to a statement about whether parties were only interested in people’s votes was the number of courses taken. As with Q11, students who took more courses were more likely to disagree with the statement. As with previous models, the effect was consistent across the three introductory-level courses and was not limited to students enrolled in Introduction to American Politics (POL 101). One of our most puzzling findings remained that POL 101 seemed to have no course-specific effect on political efficacy indicators throughout our sample population.


Table 11. Logistic regression analysis of determinants of internal efficacy indicators (at end of semester) Q15. Parties are only interested in people’s votes but not their

opinion. Model A Model B Model D Female -0.077 -0.084 -0.080 Class Standing 0.261 0.265 0.251 Number of Courses (1-3) * -0.884 * -0.811 * -0.913 Parents’ Education -0.141 -0.120 -0.131 Political Views (at start of semester) -0.193 -1.174 -0.172 News Media Consumption 0.010 0.018 0.020 Individual Course Grade 0.202 0.201 0.206 Section Average GPA -0.600 -0.587 -0.543 Previous Response (Q15) *** 1.870 *** 1.843 *** 1.882 POL 101 dummy -0.185 POL 102 dummy -0.059 POL 103 dummy 0.452 Constant 2.058 1.956 1.942 N (Observations) 260 260 260 Probability > Chi-square 0.0000 0.0000 0.0000 Pseudo R-square 0.1724 0.1693 0.1723 Because course grades were significant in at least some of our models, we were concerned that perhaps individual grades reflected some other underlying characteristic. We therefore tested for any correlations between grades and birth year, gender, race/ethnicity, parents’ level of education, class standing, number of courses taken, political attitudes (liberal/conservative), and news media consumption—all while controlling for section mean grade. Simple linear regression across the entire sample found no variables (other that section mean grade) that had any significant effect (R2 = 0.1507). Table 12 reports similar tests, this time using our course dummy variables. We were pleased that the results stood up across all three sections. There was no statistically significant relationship between grades and any demographic indicators (gender, race/ethnicity, or parents’ education). There was also no relationship between grades and class standing or the number of additional introductory-level courses a student took during the same semester. Likewise, there was no statistically significant difference in grades between liberal and conservative students. We were also interested to note that there was no relationship between individual student grades and their levels of news media consumption. Particularly considering that news media consumption had a negative effect on internal political efficacy, we had hoped that news media consumption would at least make students better informed and, as a byproduct, improve their grade performance. At the very least this finding cautions teachers about pushing students to consume more news media—even though we still believe selectively steering students to high quality news media may be important.9

9 This study is part of a broader study that will, in a second phase, study news media consumption patterns in greater detail. Our survey also included questions that asked students to identify news media sources they used “in the past week” (we gave them a list of 14 major print, television, and radio sources; we also provided them with additional space to write in at least three other sources of their choice). We hope to later analyze whether particular types of news media sources had any significant effect on political efficacy and/or overall course performance.


Table 12. Regression analysis of determinants of individual course grade Model A Model B Model D Birth Year 0.065 0.065 0.066 Female -0.141 -0.139 -0.143 Hispanic -0.421 -0.439 -0.392 Black -0.694 -0.715 -0.664 White (Non-Hispanic) -0.366 -0.382 -0.338 Parents’ Education 0.051 0.049 0.050 Class Standing 0.108 0.105 0.102 Number of Courses (1-3) 0.220 0.209 0.195 Political Views (at start of semester) -0.069 -0.069 -0.068 News Media Consumption 0.121 0.122 0.126 Section Average GPA *** 0.778 *** 0.791 *** 0.774 POL 101 dummy 0.075 POL 102 dummy -0.083 POL 103 dummy -0.013 Constant -129.827 -127.993 -130.447 N (Observations) 262 262 262 R-square 0.1518 0.1518 0.1508 Concluding Remarks Overall, our study ends with some troubling findings. While course performance did improve internal efficacy on at least two of our indicators, it did not do so on the one that was perhaps the most crucial to our mission as political science educators: Students did not seem to leave our courses with any more confidence that they could understand politics than they did when they began the semester. We found this to be a particularly disturbing finding. We were also particularly surprised that POL 101 (Introduction to American Politics) did not perform better in terms of raising political efficacy than the other two introductory-level courses, which do not focus exclusively on American government and politics. In fact, our findings suggest that taking any introductory-level course in political science has as good a chance of improving students’ internal political efficacy. Moreover, our findings also suggest that taking any two or more introductory-level course in political science has approximately the same chance of improving students’ external political efficacy. It was also interesting to see that the effects of most other contributing factors—particularly parents’ level of education and news media consumption—disappeared in our end-of-semester longitudinal models. There is some evidence (even if limited) in our data to suggest that taking our courses has some effect, if only in reducing the effects of other variables. However, we were intrigued by our finding regarding political views (student placement on a liberal-conservative spectrum: We were particularly puzzled to note that conservative students were more likely to believe that they had little say in government after taking a course in politics, particularly since the only other significant effect we found for political views was from our aggregate single-wave logistic regression model that found that conservative students were also more likely to believe that public officials cared about what they think. We believe more the question of the relationship between students’ political views and political efficacy—particularly as it interacts with moving through a university setting—merits further attention.


We wish to conclude with a caution about the limitations of our findings. Our findings may not replicate to other contexts. Although we believe our student population is not too dissimilar from that of other public research universities, it is probably not similar to other institutional contexts. Moreover, a majority of our students come from the South. We hope similar studies of other types of institutions and in different regions are necessary to verify our findings. More importantly, however, our study took place in a single semester with a relatively small sample population. Even our strongest models have relatively low R-squared values, suggesting that 80 percent or more of variation is due to other variables. These may include teacher-specific effects (from factors as varied as personality, seniority, teaching style, etc.) that we were unable to test because individual class sizes were too small for more robust tests.

Lastly, we do not know whether the effects of taking a course in political science have any lasting effect. This is particularly important for two reasons: We found evidence that taking more than one introductory-level course had a statistically significant effect on external political efficacy. However, we do not know how many of our students took another introductory-level course previously. Thus, we have no way of knowing about cumulative effects across semesters. We also do not have any evidence about possible cumulative effects from other political science courses or other courses in related disciplines. Secondly, we also do not have any way of knowing whether the effects of completing a political science course are lasting or durable. This is problematic (for those of us who think political efficacy is important for democracy) because we are aware—and our evidence confirms—that news media consumption tends to erode political efficacy over time. Moreover, our data also suggests that political efficacy declines over time (younger students have higher efficacy than older students). If this is the case, then any improvement of political efficacy in the political science classroom may inevitable erode over time. References Campbell, A., G. Gurin, and W. E. Miller. 1954. The Voter Decides. Evanston: Row, Peterson,

and Company. Dewey, John. 1916. Democracy and Education. New York: Mcmillan. De Vreese, Claes H. 2002. “Cynical and Engaged: Strategic Campaign Coverage, Public

Opinion, and Mobilization in a Referendum.” Communication Research 29 (6): 615-641. Dudley, Robert L. and Alan R. Gitelson. 2003. “Civic Education, Civic Engagement, and Youth

Civic Development.” PS: Political Science & Politics 36 (2): 263-267. Easton, David and Jack Dennis. 1965. “The Child’s Acquisition of Regime Norms: Political

Efficacy.” American Political Science Review 61: 25-38. Fox Freyss, Siegun. 2006. “Learning Political Engagement from the Experts: Advocacy Groups,

Neighborhood Councils, and Constituency Service.” PS: Political Science & Politics 39 (1): 137-145.

Jensen, Jennifer M. 2007. “College in the State Capital: Does it Increase the Civic Engagement of Political Science Undergraduate Majors?” PS: Political Science & Politics 40 (3): 563-569.

Kahne, Joseph and Joel Westheimer. 2006. “The Limits of Political Efficacy: Educating Citizens for a Democratic Society.” PS: Political Science & Politics 39 (2): 289-296.

Luskin, Robert C. 1987. “Measuring Political Sophistication.” Political Behavior 12: 331-361.


McIntosh, Hugh, Daniel Hart, and James Youniss. 2007. “The Influence of Family Political Discussions on Youth Civic Development: Which Parent Qualities Matter.” PS: Political Science & Politics 40 (3): 495-499.

Niemi, Richard G. and Jane Junn. 1998. Civic Education. New Haven: Yale University Press. Ostrom, Elinor. 1996. “Civic Education for the Next Century: A Task Force to Initiate

Professional Activity.” PS: Political Science & Politics 29: 755-758. Pinkleton, Bruce E., Erica Weintraub Austin, and Kristine K. J. Fortman. 1998. “Relationship of

Media Use and Political Disaffection to Political Efficacy and Voting Behavior.” Journal of Broadcasting & Electronic Media 42 (1): 34-49.

Pinkleton, Bruce E. and Erica Weintraub Austin. 2004. “Media Perceptions and Public Affairs Apathy in the Politically Inexperienced.” Mass Communications and Society 7 (3): 319-337.


Appendix A: Survey Questions The following is a list of the questions as presented to the students, available responses, and our coding. Demographic Questions Q1. Would you describe yourself as Hispanic or Latino? Yes 1 No 0 Q2. What is your race or ethnicity? White 1 Black 2 Asian 3 Native American or Pacific Islander 4 Other 5 Q3. What is your current class standing? Freshman 1 Sophomore 2 Junior 3 Senior 4 Other 5 Q4. Which of the following choices best describes your current major or field of study? Social Sciences 1 Natural Sciences 2 Humanities & Arts 3 Education 4 Professional Program 5 Q5. What level of education did your father complete? Did not complete high school 1 High school or GED 2 Some college or post-secondary education 3 Bachelor’s degree 4 Graduate degree (masters-level or above) 5 Q6. What level of education did your mother complete? [Same coding as Q5] Q7. How would you describe your hometown? Rural area 1 Small town 2 Small city 3 Suburb of a large city 4 Large city 5


Political Opinions & Attitudes Q8. On the following scale, how would you describe your political views? Very liberal 1 Liberal 2 Moderate 3 Conservative 4 Very conservative 5 Q9. How interested would you say you are in politics? [This question was dropped do to a typo that made the scale unusable] Political Efficacy Questions All these questions had the same coding: 1 = Agree, 0 = Disagree Q10. Voting is the only way that people like me have any say about how government runs things. Q11. People like me have no say about what the government does. Q12. Sometimes politics and government seem so complicated that a person like me can’t really

understand what’s going on. Q13. Public officials don’t care about what people like me think. Q14. Those we elect to Congress lose touch with the people pretty quickly. Q15. Parties are only interested in people’s votes but not their opinions. Media Usage and Exposure Q16. On an average day, about how many hours do you personally watch television (for any purpose)? Less than one hour 1 1-2 hours 2 2-4 hours 3 4-6 hours 4 More than 6 hours 5 Q17. On an average day, about how many hours do you personally spend online (for any purpose)? [Same coding as Q16] News Media Consumption Q18. How often do you read a traditional (print) newspaper? Every day 1 At least once a week 2 A few times a month 3 Rarely or never 4 Q19. How often do you watch news programs on television? [Same coding as Q18]


Q20. How often do you read a news or current events magazine? [Same coding as Q18]

Q21. How often do you listen to news or current events on the radio? [Same coding as Q18] Q22. How often do you read national or world news online? [Same coding as Q18] Social Media Consumption Q23. In the past week, have you posted a news or current events story through Facebook, Twitter, or

other social media? Yes 1 No 0 Q24. In the past week, has someone you know sent you a news or current events story through email,

Facebook, Twitter, or other social media? [Same coding as Q23]


Appendix B: Descriptive Statistics Basic Descriptive Statistics (First Wave) All Sections POL 101 POL 102 POL 103 Gender Male 57.3 57.1 60.2 52.8

Female 42.7 42.9 39.8 47.2 Race Hispanic 3.8 3.1 4.8 5.4

White (Non-Hispanic) 80.7 79.3 81.5 85.2 Black 15.6 17.3 13.5 13.6 Asian 2.0 2.1 2.5 1.1 Native American 0.7 1.1 — — Other 0.7 0.3 2.5 —

Class Standing

Freshman 45.5 58.7 22.0 20.2 Sophomore 31.8 26.5 38.2 46.1 Junior 14.9 9.7 22.0 28.1 Senior 7.8 5.1 17.9 5.6

Major Social Sciences 37.0 24.7 50.8 70.7 Natural Sciences 12.2 15.8 8.9 1.1 Humanities 12.5 13.2 13.7 7.6 Education 7.6 6.6 12.9 4.4 Professional 30.9 39.7 13.7 16.3

Father’s Education

Did not complete HS 3.3 2.8 3.2 5.4 High School or GED 16.0 16.8 16.8 11.8 Some College 19.6 19.6 24.0 14.0 Bachelor’s Degree 34.2 35.1 29.6 36.6 Graduate School 26.8 25.7 26.4 36.3

Mother’s Education


Hometown Rural Area 5.9 5.8 7.2 4.3 Small Town 25.8 24.1 28.8 29.0 Small City 25.0 25.3 24.0 24.7 Suburb of Large City 26.4 27.3 24.8 24.7 Large City 17.0 17.5 15.2 17.2

Political Views

Very Liberal 2.6 1.5 7.2 1.1 Somewhat Liberal 11.8 10.4 15.2 12.9 Moderate 35.3 37.1 28.8 36.6 Somewhat Conservative 41.5 42.1 40.0 40.9 Very Conservative 8.8 8.9 8.8 8.6

N (Responses) 613 445

394 289

125 103

93 53

Enrolled 1,219 918 163 138 Response rate 50.29 42.92 76.69 67.39


Basic Descriptive Statistics (Second Wave) All Sections POL 101 POL 102 POL 103 Gender

Male 55.1 54.0 55.1 58.3 Female 44.9 46.0 44.9 41.7

Race Hispanic 2.5 2.8 2.1 1.5 White (Non-Hispanic) 84.4 84.6 79.4 90.9 Black 13.0 12.9 16.3 9.1 Asian 1.2 1.1 2.2 — Native American 0.7 1.1 — — Other 0.7 0.4 2.2 —

Class Standing

Freshman 45.9 59.4 20.4 20.9 Sophomore 30.8 24.7 36.6 46.3 Junior 15.8 10.6 24.7 23.9 Senior 7.5 5.0 17.2 4.5

Major Social Sciences 36.6 24.1 54.3 64.2 Natural Sciences 9.3 11.4 6.4 4.5 Humanities 13.3 13.5 16.0 9.0 Education 8.4 8.9 8.5 6.0 Professional 32.5 42.2 14.9 16.4

Father’s Education


Mother’s Education


Hometown Rural Area 6.1 6.0 8.5 3.0 Small Town 28.7 27.3 34.0 26.9 Small City 22.4 22.0 21.3 25.4 Suburb of Large City 26.2 27.3 20.2 29.9 Large City 16.7 17.4 16.0 14.9

Political Views

Very Liberal 1.8 0.7 5.3 1.5 Somewhat Liberal 15.5 14.1 17.0 19.4 Moderate 32.0 34.3 26.6 29.9 Somewhat Conservative 42.8 42.8 45.7 38.8 Very Conservative 7.9 8.1 5.3 10.5

N (Responses) 444 243

283 126

94 69

67 48

Enrolled 1,107 832 146 129 Attrition rate (% change from first week) 9.19 9.37 10.43 6.52 Response rate (% of total enrolled) 40.11 34.01 64.38 51.94


Appendix C: Aggregate Levels of Political Efficacy Q10. Voting is the only way that people like me have any say about how the government runs things. Pre-Test All Courses POL 101 POL 102 POL 103 Agree 42.53 48.73 31.20 31.11 Disagree 57.47 51.27 68.80 68.89 N (Respondents) 609 394 125 90 Post-Test All Courses POL 101 POL 102 POL 103 Agree 40.72 42.35 40.43 34.33 Disagree 59.28 57.65 59.57 65.67 N (Respondents) 442 281 94 67 Q11. People like me have no say about what the government does. Pre-Test All Courses POL 101 POL 102 POL 103 Agree 19.31 22.08 14.54 13.98 Disagree 80.69 77.92 85.52 86.02 N (Respondents) 611 394 124 93 Post-Test All Courses POL 101 POL 102 POL 103 Agree 21.04 22.06 19.15 19.40 Disagree 78.96 77.94 80.85 80.60 N (Respondents) 442 281 94 67 Q12. Sometimes politics and government seem so complicated that a person like me can’t really understand what’s going on. Pre-Test All Courses POL 101 POL 102 POL 103 Agree 49.51 56.85 40.00 31.18 Disagree 50.49 43.15 60.00 68.82 N (Respondents) 612 394 125 93 Post-Test All Courses POL 101 POL 102 POL 103 Agree 45.70 48.58 38.71 43.28 Disagree 54.30 51.42 61.29 56.72 N (Respondents) 442 282 93 67


Q13. Public officials don’t care about what people like me think. Start of Semester All Courses POL 101 POL 102 POL 103 Agree 34.66 36.85 32.79 30.77 Disagree 65.34 63.85 67.21 69.23 N (Respondents) 603 390 122 91 End of Semester All Courses POL 101 POL 102 POL 103 Agree 33.17 34.08 37.08 23.81 Disagree 66.83 65.92 62.92 76.19 N (Respondents) 419 267 89 63 Q14. Those we elect to Congress lose touch with the people pretty quickly. Start of Semester All Courses POL 101 POL 102 POL 103 Agree 62.05 62.92 65.85 53.26 Disagree 37.95 37.08 34.15 46.74 N (Respondents) 606 391 123 92 End of Semester All Courses POL 101 POL 102 POL 103 Agree 58.33 61.94 59.55 41.27 Disagree 41.67 38.06 40.45 58.73 N (Respondents) 420 268 89 63 Q15. Parties are only interested in people’s votes but not their opinion. Start of Semester All Courses POL 101 POL 102 POL 103 Agree 55.93 56.81 57.50 50.00 Disagree 44.07 43.19 42.50 50.00 N (Respondents) 599 389 120 90 End of Semester All Courses POL 101 POL 102 POL 103 Agree 54.70 54.72 55.06 54.10 Disagree 45.30 45.28 44.94 45.90 N (Respondents) 415 265 89 61

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