Gender Differences in Cognitive and Noncognitive ... - ACS Publications

Research: Science and Education edited by

Chemical Education Research

Diane M. Bunce The Catholic University of America Washington, D.C. 20064

Gender Differences in Cognitive and Noncognitive Factors Related to Achievement in Organic Chemistry

W

Ronna C. Turner and Harriet A. Lindsay*† Department of Educational Leadership, Counseling, and Foundations, University of Arkansas, Fayetteville, AR 72701; *[email protected]

For many college students in the sciences, organic chemistry poses a difficult challenge. The two-course sequence requires the mastery of a large body of knowledge that is presented in a unique language using a system of representation that is initially unfamiliar to students at this level of science education. The daunting nature of the course has been widely recognized. Indeed, Seymour and Hewitt (1) place it, along with a few calculus and physics courses, on a short list of classes that act as “filters” to the science pipeline. The difficulties of the course and its ensuing reputation provide a challenge to those interested in increasing the numbers of science students in higher education while holding standards constant. To accomplish this goal, educators must first have a clear understanding of factors that contribute to achievement in organic chemistry. Increasing the participation of women in the sciences has been a focus of science educators for decades. In recent years, gains have been made in science disciplines in general, particularly in the numbers of women obtaining bachelor degrees (2). However, as a proportion of the general population, women are still underrepresented in the physical sciences (2). As efforts to increase the numbers of women in the physical sciences continue (3), investigations of factors related to women’s achievement in pivotal courses such as organic chemistry will likely become increasingly important. Achievement in Organic Chemistry

General Assessment Surprisingly few studies have been conducted in the area of organic chemistry achievement. The existing studies have addressed the relationship between organic chemistry achievement and cognitive variables such as performance in general chemistry (4, 5), high school chemistry performance (4), standardized test scores (4–6), and spatial visualization (6, 7). Both Pickering (4) and Sevenair (5) reported that the best predictor of organic chemistry achievement was the general chemistry grade, accounting for up to 45% of the variance associated with organic chemistry grade. These investigations also found standardized test scores to be moderate predictors, with SAT (Scholastic Assessment Test) explaining 15% of the variance (4) and ACT (American College Test) explaining 19% (5). However, both studies were conducted with restricted populations: Pickering’s investigation was conducted † Current address: Department of Chemistry, Eastern Michigan University, Ypsilanti, MI 48197

at an all-male college, while Sevenair’s study took place at an historically African-American university. In a similar investigation, Krylova (6) found a general chemistry pretest to be a moderate-to-strong predictor of achievement on selected topics in organic chemistry, accounting for 23–58% of the variance, while the Group Assessment of Logical Thinking explained 24–34% of the variance. However, the sample sizes used in this study were relatively small compared with those of related investigations (4, 5). Spatial visualization has also been correlated with organic chemistry grades, although the results have varied. Nonsignificant-to-moderate correlation coefficients have been reported (6, 7), with the results varying by curriculum topic, course, and institution. Noncognitive variables may also hold promise as predictors of organic chemistry achievement. Attitude has been linked to behavior based on the premise that people’s actions tend to be reflective of their feelings (8). In a descriptive study, Steiner and Sullivan found evidence of relationships between attitude and perception about organic chemistry and course achievement (9). They reported that higher achieving students were more likely to describe their approach to the course as interested, organized, confident, and enthusiastic, and were more likely to think of chemistry as useful and stimulating. By contrast, lower achieving organic chemistry students were more likely to feel worried, anxious, and disorganized, and were more likely to describe chemistry as strange and puzzling. For the purposes of predicting performance, Garcia demonstrated that noncognitive variables could improve prediction of organic achievement when used in combination with cognitive variables (10). It is worth noting that this investigation is the only incorporation of cognitive and noncognitive predictors of organic chemistry grade. The study revealed that SAT scores, chemistry placement exam scores and high school GPA (grade point average) accounted for 21% of organic chemistry grade variance in a regression analysis. However, adding variables related to motivation, task value, test anxiety, and studying strategies into the regression equation explained an additional 13% beyond that explained by cognitive variables.

Gender Difference Assessment Few studies have addressed gender differences in organic chemistry achievement. Sevenair included gender with cognitive variables for predicting organic chemistry grades of African-American students (5). His results indicated that gender was a weak but significant predictor of organic chemistry grades, but that it did not factor significantly into a multiple

JChemEd.chem.wisc.edu • Vol. 80 No. 5 May 2003 • Journal of Chemical Education

563

Research: Science and Education

regression analysis, presumably as a result of collinearity with other predictor variables. Garcia’s study also reported that gender was not a significant predictor. However, she found that task value and studying strategies were, in general, better predictors of organic chemistry grade for males than for females (10). The need to replicate studies of cognitive factors relating to organic chemistry achievement that used restricted populations (4, 5), the lack of published literature concerning gender differences in cognitive and noncognitive factors, and the focus of national organizations such as the NSF (National Science Foundation; ref 3) to increase the representation of women in the physical sciences attest to the need for further research. The purpose of this investigation was therefore twofold: (1) to examine the relationships between organic chemistry achievement and a number of cognitive and noncognitive variables, and (2) to determine whether gender differences existed for any of those relationships. The results of this investigation could have implications for future research on the implementation and evaluation of cognitive and noncognitive interventions for both women and men in college chemistry courses. Overview of the Study In keeping with the exploratory nature of the investigation, ten predictor variables, six relating to students’ cognitive skills and four relating to their emotional responses and attitudes, were used in this study. The cognitive variables included students’ second-semester general chemistry lecture grade, ACT–math, –English, –reading, and –sciences-reasoning scores, and spatial visualization test scores. Spatial visualization has been defined as the ability to mentally manipulate a representation that involves recognizing, retaining, and recalling configurations in which there is movement of the figure or parts of the figure (11). Noncognitive variables used in this investigation included confidence, anxiety, effectance motivation, and usefulness. Confidence is a measure of students’ trust in their abilities to learn and perform well on tasks in chemistry. Anxiety is defined as students’ feelings of dread, nervousness, and associated bodily symptoms related to doing chemistry. Effectance motivation is students’ desire for challenges in chemistry. Usefulness is students’ perceptions of the relevance of chemistry in their current and future educational or vocational activities (12).

Methods and Procedures

Setting The course under investigation was Organic Chemistry I, the first course in a two-course sequence designed for science majors. The Chemistry Department at the University of Arkansas ordinarily offers two sections of Organic Chemistry I per year, with one section offered in the fall and another in the summer. Only fall sections were used in the study. Target Population and Sample Two sections of Organic Chemistry I were used in the present investigation: Fall 1999 and Fall 2000. Students enrolled in each of two sections were taught by different instructors who constructed and administered their own quizzes and exams. For this reason, each section was treated as a separate sample. Student participation in this project was optional since students’ informed consent was required for data collection; thus, the sample was nonrandom. The 1999 sample initially contained 159 students out of 222 total enrollment. Because of incomplete data (e.g., students withdrawing from the course, absence of standardized testing data), a total of 93 students remained in the 1999 cohort at the end of the semester. The percentage of male and female participants with full data as compared to the total number of males and females enrolled in the course was 41% and 42%, respectively. The 2000 cohort had an initial voluntary sample of 134 out of 270 total enrollment. Incomplete data resulted in a reduction of the initial group of volunteers to 100 participants. The percentage of male and female participants as compared to the total enrollment was 30% and 44%, respectively. An investigation of the differences between the performance levels of the participants and nonparticipants was conducted by comparing the end-of-course point totals for the two groups for each semester. There were no significant differences in total points of the participant and nonparticipant groups in the 1999 cohort [t(187) = .79, p < .001; see Table 1]. However, the participants in the 2000 cohort had significantly higher point totals than the nonparticipants [t(226) = 3.63, p < .001]. The results of this analysis suggest that the 1999 sample was potentially more representative of its total enrollment then the 2000 sample. However, it is also important to note the similarities between participant and nonparticipant groups as

Table 1. t-Test Comparing Total Points in Organic Chemistry I for Students Participating versus Those Not Participating Raw Scores (% correct) Students

N

M

SD

t

Fall 1999 Participant

93

413.38 (68.9)

105.99 (17.2)

Nonparticipant

96

400.92 (66.8)

110.66 (18.4)

0.79a

Fall 2000 Participant

100

3871.5 (70.4)

673.48 (12.2)

Nonparticipant

128

3513.7 (63.9)

784.36 (14.3)

a

564

3.63a

p < .001.

Journal of Chemical Education • Vol. 80 No. 5 May 2003 • JChemEd.chem.wisc.edu


revealed by the grade distributions. For the 1999 cohort, the course averages were 66.8% and 68.9% with standard deviations close to 18 percentage points (Table 1). The 2000 course averages were 63.9% and 70.4% for the nonparticipant and participant groups, respectively, with standard deviations of approximately 12% to 14%. These large standard deviations indicated that both participant and nonparticipant groups contained students whose achievement levels spanned the range of possible letter grades.

Instruments and Data Collection During a regular course meeting at the beginning of the semester, the nature of the investigation was described to the students and their participation was solicited. Those students that consented to participate then completed a spatial visualization exam and a chemistry attitude inventory. For the fall 1999 cohort, these data were obtained during the first week of the course. Due to scheduling issues, the spatial visualization and noncognitive data for the 2000 cohort were collected during the fourth week of the course. The Purdue Visualization of Rotations Test (PVOR; ref 13) was used to measure students’ spatial visualization skills. The PVOR test is a 20-item, multiple-choice exam that is administered with a 10-minute time limit. It has been used to measure spatial visualization skills in a variety of chemistry courses at Purdue University including general chemistry (10) and organic chemistry (6). Recently the PVOR test has been used to assess differences in two samples from a population of organic chemistry students (14). To measure students’ attitudes toward chemistry, a modification of the Fennema–Sherman Mathematics Attitude Scales was used (12). Originally, the mathematics scales were divided into nine Likert-type subscales containing twelve items each. In this investigation, four of the nine subscales were used: anxiety, confidence, usefulness, and effectance motivation. High scores on the confidence, usefulness, and effectance motivation scales indicated high levels of those constructs. Conversely, a high score on the anxiety scale was indicative of a low level of anxiety. Each scale consisted of twelve items except the anxiety scale, which contained eleven items. To assess attitudes specific to the discipline of chemistry, the word “math” was changed to the word “chemistry” for each item in the original instrument.1 Data for the other predictor variables, including ACT–English, –reading, –math, and –science-reasoning scores, and the second-semester general chemistry grade, were obtained from the Office of Institutional Research at the University of Arkansas. Students who did not have ACT scores or general chemistry grades on record were excluded from the study. For each section of Organic Chemistry I under investigation, weekly quiz scores, three hour-exams, and a final exam were used to determine point totals for each student, which were subsequently converted to averages. These averages were used as measures of organic chemistry achievement. All quizzes and exams were constructed by the instructor in charge of the course. At the end of the semester, averages were collected for consenting students. Students that withdrew from the course at any point during the semester were excluded from the study.

Data Analysis Statistical analyses were first computed for all students in each section and then separately for females and males in each section.2 Two sets of analyses were conducted. First, Pearson correlation coefficients were calculated for the relationships between the predictor variables and organic chemistry achievement. Gender-related differences in correlation coefficients were tested for statistical significance using z-tests (15). Second, multiple regression analyses were used to identify the most effective combination of variables for predicting achievement in organic chemistry. A stepwise regression model was used with a significance level of .10. This alpha level was chosen due to the small sample sizes in the study given the number of predictor variables used. In addition, this level was appropriate to an exploratory investigation. The .10 significance level would minimize type II error, thereby maximizing the opportunity to identify potential relationships (15). The stepwise model determined the ordering of independent variables in the regression equation according to each variable’s contribution. The variable that explained the most variance was entered first, followed by the variable that explained the most unique variance after the contribution of the first variable was accounted for. Variables continued to be added in a similar fashion until no other variable contributed unique variance at the 0.10 significance level. Regression analysis of two sections provided a validity assessment of the results obtained from the 1999 section by determining the extent to which the those results were replicated by the 2000 sample. In addition, these analyses allowed for the simultaneous investigation of an effect due to instructor differences. As is often the case with classroom research, the sample sizes in this investigation were limited by the number of students enrolled in each of the sections. The large number of predictor variables used in this investigation given the moderate sample sizes calls for caution when interpreting the results. This further demonstrates the importance of replication studies across different sections. Results

Significant Predictors of Organic Chemistry Achievement For the 1999 section, all independent variables except spatial visualization, effectance motivation, and usefulness showed a significant correlation with organic achievement (Table 2). For the 2000 section, all independent variables were significantly correlated with organic achievement (Table 2). When all independent variables for the 1999 section of Organic Chemistry I were subjected to a stepwise multiple regression analysis, two variables entered into the regression equation using a 90% confidence level entry rate: secondsemester general chemistry grade and ACT–math score (Table 3). Together these two variables accounted for 39% of the variance associated with organic chemistry achievement. The other six predictor variables that produced significant Pearson correlation coefficients did not significantly increase the amount of variance beyond that accounted for by general chemistry grade and ACT–math score. A multiple regression analysis of all predictor variables for the 2000 section of Organic Chemistry I resulted in three


565


Gender Differences in Prediction of Achievement

significant entries at the 90% confidence level: general chemistry grade, ACT–math score, and effectance motivation (Table 3). These three variables combined to explain 55% of the variance associated with course achievement, with effectance motivation accounting for an additional 4% beyond that explained by the cognitive variables. When cognitive variables were removed from the regression equation, confidence appeared to be the most effective predictor of organic chemistry achievement. However, its predictive effectiveness was variable; 9% of variance was explained by confidence for the 1999 section, while 26% was explained for the 2000 section.2

For students in both the 1999 and 2000 sections, gender differences were found in the results of the Pearson correlation analyses. In the 1999 section, the strongest correlate for both males and females was the general chemistry grade, which exhibited similar correlation coefficients for both groups (Table 2). Following the general chemistry grade, ACT subscores were the next strongest correlates for both males and females. For males, all ACT subscores were approximately equally related to organic chemistry achievement. For females, however, science-reasoning and English scores exhibited the

Table 2. Pearson Correlation Coefficients for Independent Variables with Total Points in Organic Chemistr y I 1999 Variable

All Students

2000

Females

Males

z

All Students

Females

Males

z

ACT–English

.39a

.36a

.44a

0.44

.34a

.30a

.48a

1.00

ACT–math

.37a

.28

.45a

0.92

.58a

.58a

.57a

0.05

.18

a

.51

1.78

.24

a

.21

.35a

0.72

a

ACT–reading

.32

ACT–science-reasoning

.40a

.40a

.41a

0.06

.33a

.32a

.35a

0.16

Gen. chem. grade

.57a

.56a

.59a

0.21

.63a

.57a

.73a

1.34

a

1.63

.26

a

.16

a

.35

1.01

0.30

.39a

.26a

.60a

2.03a

a

a

a

Spatial-visualization score

.17

.04

.34

Anxiety

.27a

.24

.30a a

a

Confidence

.30

.28

.34

0.31

.51

.45

.57

0.78

Effectance motivation

.13

.10

.16

0.28

.37a

.36a

.38a

0.11

Usefulness

.12

.07

.17

0.48

.30a

.27a

.37a

0.53

a

p < .05

Table 3. Summar y of Stepwise Multiple Regression Analysis of Organic Chemistr y I Total Points Using Cognitive and Non-cognitive Variables Step

Variable Entered

Partial R-Square

Model R-Square

F

All students (1999) 1

General chemistry grade

.33

.33

44.35

2

ACT–math

.06

.39

9.99

1


.31

.31

21.00

2

ACT–science-reasoning

.06

.37

4.46

1


.35

.35

22.67

2

ACT–reading

.10

.45

7.71

3

Spatial visualization

.04

.49

3.34

1


.39

.39

63.80

2

ACT–math

.12

.51

23.00

3

Effectance motivation

.04

.55

8.58

1

ACT–math

.34

.34

29.45

2


.16

.50

17.43

1


.54

.54

45.03

2

Anxiety

.09

.63

9.37

3

ACT–math

.03

.66

3.16

Females

Males

All students (2000)

Females

Males

NOTE: All entries were significant at p < .10.

566



only statistically significant relationships. Spatial visualization was moderately correlated to the achievement of males in the 1999 cohort, but was not significantly related to the performance of females. For females, none of the noncognitive variables was significantly related to organic chemistry achievement. For males, however, anxiety and confidence were moderately correlated. Multiple regression analyses also revealed differences along gender lines (Table 3). For both female and male students in the 1999 section, the general chemistry grade entered the regression equation first. The full model for females accounted for 37% of the variance associated with organic chemistry achievement and consisted of the general chemistry grade and the ACT–science-reasoning score. The regression model for males explained 49% of the variance and included the general chemistry grade, the ACT–reading score, and the spatial visualization score. However, when only noncognitive variables were used in the regression analyses, the results for males and females were statistically similar.2 Confidence was the sole significant entry for both males and females, accounting for 8% of the variance for females and 11% of the variance for males. For females in the 2000 section, the ACT–math score entered the regression equation first, followed by the general chemistry grade. The two variables together explained 50% of the variance associated with organic chemistry achievement. The regression model for males differed in that general chemistry grade was the first entry into the regression model, followed by the anxiety score and the ACT–math score. Together these variables accounted for 66% of the variance. When only noncognitive variables were subjected to regression analyses, confidence was the sole significant entry for females, explaining 20% of organic chemistry achievement variance. For males, the only entry was anxiety, which accounted for 36% of the variance. Discussion and Conclusions Prediction of Organic Chemistry Achievement Of all the cognitive and noncognitive predictor variables used in this investigation, prior performance in chemistry was found to be the most consistent and best indicator of performance in Organic Chemistry I. This result is consistent with earlier studies that demonstrated that prior knowledge of chemistry as measured on placement exams (6, 10), and previous grades in the general chemistry courses (4, 5) had significant positive relationships with organic chemistry achievement. In this investigation, the general chemistry grade presumably represented more than prior chemistry knowledge as assessed by researchers using chemistry placement exams (6, 10). Other factors such as study time and studying efficiency could have been encompassed by this variable as well. In regression analyses of all students, noncognitive variables were of little or no consequence. For the 1999 section, none of the noncognitive variables accounted for additional variance in the regression analysis. Although effectance motivation did enter the regression equation for the 2000 section, it increased the explained variance by only 4%. While these results are inconsistent with an earlier study that found greater contributions from noncognitive variables in regression equations (10), they are likely due to multicollinearity

between cognitive and noncognitive predictor variables used in this investigation.2 The strong correlation between the general chemistry grade and organic chemistry achievement should catalyze collaborations between instructors of general chemistry and organic chemistry in which concepts central to both courses could be delineated and subsequently emphasized in the classroom. Use of post-tests in general chemistry and pretests in organic chemistry would further ensure students’ competency in the specified central concepts. Improving achievement in the requisite concepts for organic chemistry would presumably lead to improved performance in the course itself. Additionally, variables having strong correlations with organic chemistry achievement, such as ACT subscores and the general chemistry grades, should find use as a means for identifying at-risk organic chemistry students. Advisors could use similar correlation results to counsel students and to direct them to appropriate remediation.

Gender Differences in Prediction of Achievement In studies of introductory college chemistry courses, a greater proportion of total course variance was explained for male students than for females (16). Further, for both introductory chemistry (17) and organic chemistry (10) students, noncognitive variables have been reported to be more important for males than for females in the explanation of course grade. Analogous gender differences were found for the explanations of organic chemistry achievement variance provided by this study, although the cognitive and noncognitive variables used in this investigation differed from those used in the previous reports. With the exception of the ACT–math score for the 2000 section, each predictor variable accounted for more variance among men than among women (Table 2). This trend was also observed in the multiple regression analyses, where the regression models consistently explained greater amounts of variance for men than for women (Table 3). It is reasonable to expect that gender-related differences in factors related to achievement would diminish as students advance through the college science curriculum. However, this investigation has demonstrated that substantial gender differences in cognitive and noncognitve factors related to achievement may be found at this level of college science courses. This result implies that the expected self-selection is not occurring at this level. Perhaps some self-selection may take place following organic chemistry, explaining the course’s reputation as a filter to the science pipeline (1). Although it was anticipated that using noncognitive factors in combination with cognitive predictors might account for some additional variability in the course averages of females, the noncognitive variables only entered the regression equation for male students (Table 3). These results suggest that other, as yet unidentified, constructs may play a role in determining organic chemistry course grades for females. Consequently, the field of research is still open to the investigation of variables that can better identify females potentially at risk for low performance in organic chemistry.

Differences between 1999 and 2000 Sections A greater number of predictor variables were significantly correlated with achievement (Table 2) and a greater amount


567


of total variance related to achievement was explained in the 2000 section than in the 1999 section (Table 3). These varying results could be artifacts of the limited sample sizes, an outcome of the timeframe in which the noncognitive data were collected, or indicative of differences between the instructors. The collection of the noncognitive variables for the 1999 cohort occurred during the first week of the semester, whereas the noncognitive data for the 2000 cohort were collected during the fourth week of class. Responses to the noncognitive scales during the first week of class would likely encompass students’ opinions to chemistry in general. However, after four weeks of classes, it is possible that students’ responses would be more course specific and consequently more highly correlated with their organic chemistry performance. Indeed, when the noncognitive scales were administered during the first week of class, 9% of the variability in final scores was explained by these variables. By contrast, 26% of the variability was explained when the data were collected during the fourth week of class. Future research should determine the optimal timeframe for administration of instruments measuring these types of noncognitive variables. Additionally, the discrepancies in explained variance between the 1999 and 2000 sections could be attributed to differences between the instructors of each class. Because the two sections were taught by different instructors who constructed and administered their own quizzes and exams, some variation in instructional methods and exam construction presumably existed. This variation between instructors may account for some of the observed differences in explained variance for the two sections, although measuring the instructor effect was beyond the scope of this investigation. The reader should nonetheless be wary of the potential for an instructor’s characteristics and assessment measures to influence the outcome of a study of this type and should use caution when applying these results to other organic chemistry courses. We believe that this study can provide a model for similar investigations at other institutions. Because of the potential for variation in results between instructors, more investigations of this type are needed to determine what factors relating to achievement are common for all organic chemistry students. The data from these studies could be used to identify students at risk for low performance and could provide a foundation for the development of instructional aids and innovations. Acknowledgments We thank Lorraine Brewer and Neil Allison for their cooperation in allowing us to collect the data used in this investigation. We also thank George Bodner, Purdue University, for providing a copy of the PVOR test for our use. W

Supplemental Material

Tables reporting means, standard deviations, Pearson correlation coefficients (including intercorrelations between predictor variables), and detailed results of multiple regression analyses are available in this issue of JCE Online.

568

Notes 1. Because of this alteration, the modified instrument was administered to a small (N = 48) pilot group of first semester organic chemistry students in order to establish the reliability of the modified instrument relative to that of the original scales. Coefficient alpha reliability estimates (18) were .90, .93, .88, and .91 for anxiety, confidence, effectance motivation, and usefulness, respectively. These estimates compared favorably with the split-half reliability estimates for the original scales, which were .89, .93, .87, and .88 (11). 2. Tables reporting means, standard deviations, Pearson correlation coefficients (including intercorrelations between predictor variables), and detailed results of multiple regression analyses are included in the Supplemental Material.W

Literature Cited 1. Seymour, E.; Hewitt, N. M. Talking About Leaving: Why Undergraduates Leave the Sciences; Westview Press: Boulder, CO, 1997. 2. National Science Foundation. Women, Minorities, and Persons with Disabilities, Vol. 10, 2000. http://www.nsf.gov/sbe/ srs/nsf00327/start.htm (accessed Feb 2003). 3. Numerous funding agencies are interested in increasing the participation of women and minorities in the sciences and engineering. See, for example, National Science Foundation. Program For Gender Diversity in Science, Technology, Engineering, and Mathematics Education. http://www.nsf.gov/ pubs/2003/nsf03502/nsf03502.htm, 2003. 4. Rixse, J. S.; Pickering, M. J. Chem. Educ. 1985, 62, 313–315. 5. Sevenair, J. P.; Carmichael, J. W.; O’Connor, S. E.; Hunter, J. T. Predictors of Organic Chemistry Grades for Black Americans. Xavier University, 1987, ERIC Document Reproduction Service No. ED 286 974, Washington, D.C., 1987. 6. Krylova, I. Investigation of Causes of Differences in Student Performance on the Topics of Stereochemistry and Reaction Mechanisms in an Undergraduate Organic Chemistry Course. Ph.D. Thesis. Catholic University of America, Washington, DC, 1997. 7. Pribyl, J. R.; Bodner, G. M. J. Res. Sci. Teach. 1987, 24, 229– 240. 8. For a discussion of attitude research in science education, see Koballa, T. R. Sci. Educ. 1988, 72, 115–126. 9. Steiner, R.; Sullivan, J. J. Chem. Educ. 1984, 61, 1072–1074. 10. Garcia, T.; Yu, S. L.; Coppola, B. P. Women and Minorities in Science: Motivational and Cognitive Correlates of Achievement. Presented at the Annual Meeting of the American Educational Research Association, Atlanta, GA, April 1993; ERIC Document Reproduction Service No. ED 359 235. 11. Carter, C. S.; LaRussa, M. A.; Bodner, G. M. J. Res. Sci. Teach. 1987, 24, 645–657. 12. Fennema, E.; Sherman, J. JSAS Catalog of Selected Documents in Psychology 1976, 6, 31 (Ms. No. 1225). 13. Bodner, G. M.; Guay, R. B. Chem. Educator 1997, 2 (4), S1430-4171(97)04138-X. 14. Kurtz, M. J.; Holden, B. E. J. Chem. Educ. 2001, 78, 1122– 1125. 15. Glass, G.; Hopkins, K. Statistical Methods in Education and Psychology, 3rd ed.; Allyn & Bacon: Boston, MA, 1996. 16. House, J. D. Res. Higher Educ. 1995, 36, 473–491. 17. BouJaoude, S. B.; Giuliano, F. School Sci. Math. 1994, 96, 296–302. 18. Cronbach, L. J. Psychometrika 1951, 16, 297–334.


Gender Differences in Cognitive and Noncognitive ... - ACS Publications

Recommend Documents